daily update
This commit is contained in:
David Brazda
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research/strats/WVAP_DIVERGENCE/vwap_div_v0.1.ipynb
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research/strats/WVAP_DIVERGENCE/vwap_div_v0.1.ipynb
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research/strats/strat1/strat1_v1_MULTI.ipynb
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research/strats/strat1/strat1_v1_MULTI.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from v2realbot.tools.loadbatch import load_batch\n",
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"from v2realbot.utils.utils import zoneNY\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import vectorbtpro as vbt\n",
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"from itables import init_notebook_mode, show\n",
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"import datetime\n",
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"from itertools import product\n",
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"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
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"\n",
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"init_notebook_mode(all_interactive=True)\n",
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"\n",
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"vbt.settings.set_theme(\"dark\")\n",
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"vbt.settings['plotting']['layout']['width'] = 1280\n",
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"vbt.settings.plotting.auto_rangebreaks = True\n",
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"# Set the option to display with pagination\n",
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"pd.set_option('display.notebook_repr_html', True)\n",
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"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
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"\n",
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"# Define the market open and close times\n",
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"market_open = datetime.time(9, 30)\n",
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"market_close = datetime.time(16, 0)\n",
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"entry_window_opens = 1\n",
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"entry_window_closes = 370\n",
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"\n",
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"forced_exit_start = 380\n",
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"forced_exit_end = 390\n",
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"\n",
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"#LOAD FROM BATCH\n",
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"# res, df = load_batch(batch_id=\"0fb5043a\", #0fb5043a bde6d0be\n",
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"# space_resolution_evenly=False,\n",
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"# indicators_columns=[\"Rsi14\"],\n",
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"# main_session_only=True,\n",
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"# verbose = False)\n",
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"# if res < 0:\n",
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"# print(\"Error\" + str(res) + str(df))\n",
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"# df = df[\"bars\"]\n",
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"\n",
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"# #df\n",
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"\n",
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"# basic_data = vbt.Data.from_data(vbt.symbol_dict({\"BAC\": df}), tz_convert=zoneNY)\n",
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"# #m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
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"# basic_data = basic_data.transform(lambda df: df.between_time('09:30', '16:00'))\n",
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"\n",
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"#LOAD FROM PARQUET\n",
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"#LOAD FROM PARQUET\n",
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"#list all files is dir directory with parquet extension\n",
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"dir = DATA_DIR + \"/notebooks/\"\n",
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"import os\n",
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"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
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"#print('\\n'.join(map(str, files)))\n",
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"file_name = \"ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\"\n",
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"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
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"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"SPY\": ohlcv_df}), tz_convert=zoneNY)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#parameters (primary y line, secondary y line, close)\n",
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"def plot_2y_close(priminds, secinds, close):\n",
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" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
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" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
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" for ind in priminds:\n",
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" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
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" for ind in secinds:\n",
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" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
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" return fig\n",
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"\n",
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"# close = basic_data.xloc[\"09:30\":\"10:00\"].close"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#PIPELINE - FOR - LOOP\n",
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"\n",
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"#indicator parameters\n",
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"mom_timeperiod = list(range(2, 12))\n",
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"\n",
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"#uzavreni okna od 1 do 200\n",
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"#entry_window_closes = list(range(2, 50, 3))\n",
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"entry_window_closes = [5, 10, 30, 45]\n",
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"#entry_window_closes = 30\n",
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"#threshold entries parameters\n",
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"#long\n",
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"mom_th = np.round(np.arange(0.01, 0.5 + 0.02, 0.02),4).tolist()#-0.02\n",
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"# short\n",
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"#mom_th = np.round(np.arange(-0.01, -0.3 - 0.02, -0.02),4).tolist()#-0.02\n",
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"roc_th = np.round(np.arange(-0.2, -0.8 - 0.05, -0.05),4).tolist()#-0.2\n",
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"#print(mom_th, roc_th)\n",
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"\n",
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"#portfolio simulation parameters\n",
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"sl_stop =np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
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"tp_stop = np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
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"\n",
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"combs = list(product(mom_timeperiod, mom_th, roc_th, sl_stop, tp_stop))\n",
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"\n",
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"@vbt.parameterized(merge_func = \"concat\", random_subset = 2000, show_progress=True) \n",
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"def test_strat(entry_window_closes=60,\n",
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" mom_timeperiod=2,\n",
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" mom_th=-0.04,\n",
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" #roc_th=-0.2,\n",
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" sl_stop=0.19/100,\n",
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" tp_stop=0.19/100):\n",
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" # mom_timeperiod=2\n",
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" # mom_th=-0.06\n",
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" # roc_th=-0.2\n",
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" # sl_stop=0.04/100\n",
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" # tp_stop=0.04/100\n",
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"\n",
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" momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
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" rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
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" #rate of change + momentum\n",
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"\n",
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" #momshort.plot rocp.real_crossed_below(roc_th) & \n",
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" #short_signal = momshort.real_crossed_below(mom_th)\n",
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" long_signal = momshort.real_crossed_above(mom_th)\n",
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" # print(\"short signal\")\n",
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" # print(short_signal.value_counts())\n",
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"\n",
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" #forced_exit = pd.Series(False, index=close.index)\n",
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" forced_exit = basic_data.symbol_wrapper.fill(False)\n",
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" #entry_window_open = pd.Series(False, index=close.index)\n",
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" entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
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"\n",
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" #print(entry_window_closes, \"entry window closes\")\n",
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" # Calculate the time difference in minutes from market open for each timestamp\n",
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" elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
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"\n",
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" entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
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"\n",
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" #print(entry_window_open.value_counts())\n",
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"\n",
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" forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
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" #short_entries = (short_signal & entry_window_open)\n",
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" #short_exits = forced_exit\n",
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" entries = (long_signal & entry_window_open)\n",
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" exits = forced_exit\n",
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" #long_entries.info()\n",
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" #number of trues and falses in long_entries\n",
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" #print(short_exits.value_counts())\n",
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" #print(short_entries.value_counts())\n",
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"\n",
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" #fig = plot_2y_close([],[momshort, rocp], close)\n",
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" #short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
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" #print(sl_stop)\n",
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" #tsl_th=sl_stop, \n",
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" #short_entries=short_entries, short_exits=short_exits,\n",
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" pf = vbt.Portfolio.from_signals(close=basic_data.close, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\", price=\"close\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
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" \n",
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" return pf.stats([\n",
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" 'total_return',\n",
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" 'max_dd', \n",
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" 'total_trades', \n",
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" 'win_rate', \n",
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" 'expectancy'\n",
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" ])\n",
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"\n",
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"pf_results = test_strat(vbt.Param(entry_window_closes),\n",
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" vbt.Param(mom_timeperiod),\n",
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" vbt.Param(mom_th),\n",
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" #vbt.Param(roc_th)\n",
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" vbt.Param(sl_stop),\n",
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" vbt.Param(tp_stop, condition=\"tp_stop > sl_stop\"))\n",
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"pf_results = pf_results.unstack(level=-1)\n",
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"pf_results.sort_values(by=[\"Total Return [%]\", \"Max Drawdown [%]\"], ascending=[False, True])\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#pf_results.load(\"10tiscomb.pickle\")\n",
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"#pf_results.info()\n",
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"\n",
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"vbt.save(pf_results, \"8tiscomb_tsl.pickle\")\n",
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"\n",
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"# pf_results = vbt.load(\"8tiscomb_tsl.pickle\")\n",
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"# pf_results\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# parallel_coordinates method¶\n",
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"\n",
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"# attach_px_methods.<locals>.plot_func(\n",
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"# *args,\n",
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"# layout=None,\n",
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"# **kwargs\n",
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"# )\n",
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"\n",
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"# pf_results.vbt.px.parallel_coordinates() #ocdf\n",
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"\n",
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"res = pf_results.reset_index()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pf_results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"from sklearn.decomposition import PCA\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Assuming pf_results is your DataFrame\n",
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"# Convert columns to numeric, assuming NaNs where conversion fails\n",
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"metrics = ['Total Return [%]', 'Max Drawdown [%]', 'Total Trades']\n",
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"for metric in metrics:\n",
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" pf_results[metric] = pd.to_numeric(pf_results[metric], errors='coerce')\n",
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"\n",
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"# Handle missing values, for example filling with the median\n",
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"pf_results['Max Drawdown [%]'].fillna(pf_results['Max Drawdown [%]'].median(), inplace=True)\n",
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"\n",
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"# Extract the metrics into a new DataFrame\n",
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"data_for_pca = pf_results[metrics]\n",
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"\n",
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"# Standardize the data before applying PCA\n",
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"scaler = StandardScaler()\n",
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"data_scaled = scaler.fit_transform(data_for_pca)\n",
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"\n",
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"# Apply PCA\n",
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"pca = PCA(n_components=2) # Adjust components as needed\n",
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"principal_components = pca.fit_transform(data_scaled)\n",
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"\n",
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"# Create a DataFrame with the principal components\n",
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"pca_results = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])\n",
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"\n",
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"# Visualize the results\n",
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"plt.figure(figsize=(8,6))\n",
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"plt.scatter(pca_results['PC1'], pca_results['PC2'], alpha=0.5)\n",
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"plt.xlabel('Principal Component 1')\n",
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"plt.ylabel('Principal Component 2')\n",
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"plt.title('PCA of Strategy Optimization Results')\n",
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"plt.grid(True)\n",
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"plt.savefig(\"ddd.png\")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Check if there is any unnamed level and rename it\n",
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"if None in df.index.names:\n",
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" # Generate new names list replacing None with 'stat'\n",
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" new_names = ['stat' if name is None else name for name in df.index.names]\n",
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" df.index.set_names(new_names, inplace=True)\n",
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"\n",
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"rs= df\n",
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"\n",
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"rs.info()\n",
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"\n",
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"\n",
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"# # Now, 'stat' is the name of the previously unnamed level\n",
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"\n",
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"# # Filter for 'Total Return' assuming it is a correct identifier in the 'stat' level\n",
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"# total_return_series = df.xs('Total Return [%]', level='stat')\n",
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"\n",
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"# # Sort the Series to get the largest 'Total Return' values\n",
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"# sorted_series = total_return_series.sort_values(ascending=False)\n",
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"\n",
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"# # Print the sorted filtered data\n",
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"# sorted_series.head(20)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sorted_series.vbt.save()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#df.info()\n",
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"total_return_series = df.xs('Total Return [%]')\n",
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"sorted_series = total_return_series.sort_values(ascending=False)\n",
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"\n",
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"# Display the top N entries, e.g., top 5\n",
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"sorted_series.head(5)"
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": null,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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"df"
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"comb_stats_df.nlargest(10, 'Total Return [%]')\n",
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"#stats_df.info()\n",
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"\n",
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"\n",
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"8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
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"4 -0.02 -0.25 0.0028 0.0048 0.84433\n",
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"3 -0.02 -0.25 0.0033 0.0023 Total Return [%] 0.846753\n",
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"#2\t-0.04\t-0.2\t0.0019\t0.0019\n",
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"# 2\t-0.04\t-0.2\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
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"# 2\t-0.04\t-0.25\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
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"# 2\t-0.04\t-0.3\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
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"# 2\t-0.04\t-0.35\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
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"# 2\t-0.04\t-0.4\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
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"# 2\t-0.04\t-0.2\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
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"# 2\t-0.04\t-0.25\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
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"# 2\t-0.04\t-0.3\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
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"# 2\t-0.04\t-0.35\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"basic_data.symbols"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
">>> def apply_func(ts, entries, exits, fastw, sloww, minp=None):\n",
|
||||
"... fast_ma = vbt.nb.rolling_mean_nb(ts, fastw, minp=minp)\n",
|
||||
"... slow_ma = vbt.nb.rolling_mean_nb(ts, sloww, minp=minp)\n",
|
||||
"... entries[:] = vbt.nb.crossed_above_nb(fast_ma, slow_ma) \n",
|
||||
"... exits[:] = vbt.nb.crossed_above_nb(slow_ma, fast_ma)\n",
|
||||
"... return (fast_ma, slow_ma) \n",
|
||||
"\n",
|
||||
">>> CrossSig = vbt.IF(\n",
|
||||
"... class_name=\"CrossSig\",\n",
|
||||
"... input_names=['ts'],\n",
|
||||
"... in_output_names=['entries', 'exits'],\n",
|
||||
"... param_names=['fastw', 'sloww'],\n",
|
||||
"... output_names=['fast_ma', 'slow_ma']\n",
|
||||
"... ).with_apply_func(\n",
|
||||
"... apply_func,\n",
|
||||
"... in_output_settings=dict(\n",
|
||||
"... entries=dict(dtype=np.bool_), #initialize output with bool\n",
|
||||
"... exits=dict(dtype=np.bool_)\n",
|
||||
"... )\n",
|
||||
"... )\n",
|
||||
">>> cross_sig = CrossSig.run(ts2, 2, 4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - parameters in one go\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#TOTO prepsat do FOR-LOOPu\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 6))\n",
|
||||
"\n",
|
||||
"#threshold entries parameters\n",
|
||||
"mom_th = np.round(np.arange(-0.02, -0.1 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.4 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"#jejich product\n",
|
||||
"# mom_th_prod, roc_th_prod = zip(*product(mom_th, roc_th))\n",
|
||||
"\n",
|
||||
"# #convert threshold to vbt param\n",
|
||||
"# mom_th_index = vbt.Param(mom_th_prod, name='mom_th_th') \n",
|
||||
"# roc_th_index = vbt.Param(roc_th_prod, name='roc_th_th')\n",
|
||||
"\n",
|
||||
"mom_th = vbt.Param(mom_th, name='mom_th')\n",
|
||||
"roc_th = vbt.Param(roc_th, name='roc_th')\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"tp_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"tp_stop = [round(val, 4) for val in tp_stop]\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"tp_stop = vbt.Param(tp_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#def test_mom(window=14, mom_th=0.2, roc_th=0.2, sl_stop=0.03/100, tp_stop=0.03/100):\n",
|
||||
"#close = basic_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(basic_data.get(\"Close\"), timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
"\n",
|
||||
"#ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(basic_data.get(\"Close\"), short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"\n",
|
||||
"rocp_signal = rocp.real_crossed_below(mom_th)\n",
|
||||
"mom_signal = momshort.real_crossed_below(roc_th)\n",
|
||||
"\n",
|
||||
"#mom_signal\n",
|
||||
"print(rocp_signal.info())\n",
|
||||
"print(mom_signal.info())\n",
|
||||
"#print(rocp.real)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"short_signal = (mom_signal.vbt & rocp_signal)\n",
|
||||
"\n",
|
||||
"# #short_signal = (rocp.real_crossed_below(roc_th_index) & momshort.real_crossed_below(mom_th_index))\n",
|
||||
"# forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"# entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"# elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"# entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"# forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"# short_entries = (short_signal & entry_window_open)\n",
|
||||
"# short_exits = forced_exit\n",
|
||||
"# #long_entries.info()\n",
|
||||
"# #number of trues and falses in long_entries\n",
|
||||
"# #short_exits.value_counts()\n",
|
||||
"# #short_entries.value_counts()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# filter dates"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#filter na dny\n",
|
||||
"dates_of_interest = pd.to_datetime(['2024-04-22']).tz_localize('US/Eastern')\n",
|
||||
"filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
|
||||
"\n",
|
||||
"df = filtered_df\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import plotly.io as pio\n",
|
||||
"# pio.renderers.default = 'notebook'\n",
|
||||
"\n",
|
||||
"#naloadujeme do vbt symbol as column\n",
|
||||
"basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
|
||||
"\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"#basic_data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"\n",
|
||||
"m1_data.data[\"BAC\"]\n",
|
||||
"#m5_data = m1_data.resample(\"5T\")\n",
|
||||
"\n",
|
||||
"#m5_data.data[\"BAC\"].head(10)\n",
|
||||
"\n",
|
||||
"# m15_data = m1_data.resample(\"15T\")\n",
|
||||
"\n",
|
||||
"# m15 = m15_data.data[\"BAC\"]\n",
|
||||
"\n",
|
||||
"# m15.vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"# m1_data.wrapper.index\n",
|
||||
"\n",
|
||||
"# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
|
||||
"# m1_resampler.index_difference(reverse=True)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# m5_resampler.prettify()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MOM indicator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.indicator(\"talib:ROCP\").run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"vyuzití rychleho klesani na sekundove urovni behem open rush\n",
|
||||
"- MOM + ROC during open rush\n",
|
||||
"- short signal\n",
|
||||
"- pipeline kombinace thresholdu pro vstup mom_th, roc_th + hodnota sl_stop a tp_stop (pripadne trailing) - nalezeni optimalni kombinace atributu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"# short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
"\n",
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"close = m1_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(close, timeperiod=3, short_name = \"slope_short\")\n",
|
||||
"ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(close, short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"short_signal = (rocp.real_crossed_below(-0.2) & momshort.real_crossed_below(-0.02))\n",
|
||||
"#indlong = vbt.indicator(\"talib:MOM\").run(close, timeperiod=10, short_name = \"slope_long\")\n",
|
||||
"fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"#vbt.phelp(vbt.OLS.run)\n",
|
||||
"\n",
|
||||
"#oer steepmnes of regression line\n",
|
||||
"#talib.LINEARREG_SLOPE(close, timeperiod=timeperiod)\n",
|
||||
"#a také ON BALANCE VOLUME - http://5.161.179.223:8000/static/js/vbt/api/indicators/custom/obv/index.html\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mom_ind = vbt.indicator(\"talib:MOM\") \n",
|
||||
"#vbt.phelp(mom_ind.run)\n",
|
||||
"\n",
|
||||
"mom = mom_ind.run(close, timeperiod=10)\n",
|
||||
"\n",
|
||||
"plot_2y_close(mom, close)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# defining ENTRY WINDOW and forced EXIT window"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#m1_data.data[\"BAC\"].info()\n",
|
||||
"import datetime\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 2\n",
|
||||
"entry_window_closes = 30\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"\n",
|
||||
"#entry_window_open.info()\n",
|
||||
"# forced_exit.tail(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"\n",
|
||||
"#rsi = vbt.RSI.run(close, window=14)\n",
|
||||
"\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"#short_exits.value_counts()\n",
|
||||
"short_entries.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_rsi(close, entries, exits):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" #rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" entries.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_rsi(close, short_entries, short_exits)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.Portfolio.from_signals)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"print(sl_stop)\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=0.03/100, tp_stop = 0.03/100, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"#pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#list of orders\n",
|
||||
"#pf.orders.records_readable\n",
|
||||
"#pf.orders.plots()\n",
|
||||
"#pf.stats()\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0015,0.0013)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[0.03].plot_trade_signals()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# pristup k pf jako multi index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf[0.03].plot()\n",
|
||||
"#pf.order_records\n",
|
||||
"pf[(0.03)].stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#zgrupovane statistiky\n",
|
||||
"stats_df = pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'total_trades',\n",
|
||||
" 'win_rate',\n",
|
||||
" 'expectancy'\n",
|
||||
"], agg_func=None)\n",
|
||||
"stats_df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0011,0.0013000000000000002)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pandas.tseries.offsets import DateOffset\n",
|
||||
"\n",
|
||||
"temp_data = basic_data['2024-4-22']\n",
|
||||
"temp_data\n",
|
||||
"res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
|
||||
"\n",
|
||||
"# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
|
||||
"custom_offset = DateOffset(hours=4, minutes=30)\n",
|
||||
"\n",
|
||||
"# res1m = res1m.get().resample(\"4H\").agg({ \n",
|
||||
"# \"Open\": \"first\",\n",
|
||||
"# \"High\": \"max\",\n",
|
||||
"# \"Low\": \"min\",\n",
|
||||
"# \"Close\": \"last\",\n",
|
||||
"# \"Volume\": \"sum\"\n",
|
||||
"# })\n",
|
||||
"\n",
|
||||
"res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res4h.data\n",
|
||||
"\n",
|
||||
"res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res15m.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@vbt.njit\n",
|
||||
"def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
|
||||
" market_open_minutes = 570 # 9 hours * 60 minutes + 30 minutes\n",
|
||||
"\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
"\n",
|
||||
" current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
|
||||
" #print(\"current_minutes\", current_minutes)\n",
|
||||
" # Calculate elapsed minutes since market open at 9:30 AM\n",
|
||||
" elapsed_from_open = current_minutes - market_open_minutes\n",
|
||||
" elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
|
||||
" #print( \"elapsed_from_open\", elapsed_from_open)\n",
|
||||
"\n",
|
||||
" #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
|
||||
" in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
|
||||
" #print(\"in_window\", in_window)\n",
|
||||
" # if in_window:\n",
|
||||
" # print(\"in window\")\n",
|
||||
"\n",
|
||||
" if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9: # (4)!\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"@vbt.njit\n",
|
||||
"def long_exit_place_func_nb(c, high, close, time_index, tp, sl): # (5)!\n",
|
||||
" entry_i = c.from_i - c.wait\n",
|
||||
" entry_price = close[entry_i, c.col]\n",
|
||||
" hit_price = entry_price * (1 + tp)\n",
|
||||
" stop_price = entry_price * (1 - sl)\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
" last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
|
||||
"\n",
|
||||
" #print(next_day)\n",
|
||||
" if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
|
||||
" print(\"ted\",out_i)\n",
|
||||
" return out_i\n",
|
||||
" if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
|
||||
"\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.sum()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
265
research/strats/strat1/strat1_v1_SINGLE.ipynb
Normal file
265
research/strats/strat1/strat1_v1_SINGLE.ipynb
Normal file
@ -0,0 +1,265 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"celkovy optimalizacni backtest na vetsim oknu 1 - 300\n",
|
||||
"a možná take to udělat jako parametr\n",
|
||||
"zkusit CV\n",
|
||||
"zobrazit nejak robustnost parametru"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"\n",
|
||||
"init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"vbt.settings.returns.year_freq = \"252 days\" \n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM BATCH\n",
|
||||
"# res, df = load_batch(batch_id=\"f1ac6651\", #138170bc 0fb5043a bde6d0be f1ac6651\n",
|
||||
"# space_resolution_evenly=False,\n",
|
||||
"# indicators_columns=[\"Rsi14\"],\n",
|
||||
"# main_session_only=True,\n",
|
||||
"# verbose = False)\n",
|
||||
"# if res < 0:\n",
|
||||
"# print(\"Error\" + str(res) + str(df))\n",
|
||||
"# df = df[\"bars\"]\n",
|
||||
"\n",
|
||||
"# basic_data = vbt.Data.from_data(vbt.symbol_dict({\"BAC\": df}), tz_convert=zoneNY)\n",
|
||||
"# #m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"# basic_data = basic_data.transform(lambda df: df.between_time('09:30', '16:00'))\n",
|
||||
"# #basic_data.info()\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"SPY\": ohlcv_df}), tz_convert=zoneNY)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# basic_data.stats()\n",
|
||||
"\n",
|
||||
"basic_data.data[\"SPY\"].info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.open_api_ref(vbt.base)\n",
|
||||
"\n",
|
||||
"vbt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"##na toto udelat crosssvalidationu nebo alespon na testovacim ci jinem obdobi\n",
|
||||
"#take udelat long leg - tato je shortovaci\n",
|
||||
"\n",
|
||||
"#8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
|
||||
"\n",
|
||||
"#short combination ok for train(4)/test(0.1) (window 1-90, fe 95-100)\n",
|
||||
"#2,\t-0.02,\t-0.25,\t0.0018,\t0.0068\n",
|
||||
"\n",
|
||||
"#dalsi ok hodnota shortu for train/test 4/1\n",
|
||||
"#70,\t8,\t-0.06,\t-0.2,\t0.0013,\t0.0053\t\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#kombinace bez roc_th, train/test 7/-1.5\n",
|
||||
"#70\t7\t-0.07\t0.0033\t0.0063\n",
|
||||
"\n",
|
||||
"#opet bez roc_th, train(5.77)/test 0.9 - spolus tsl_stop + tsl_th\n",
|
||||
"#29\t7\t-0.09\t0.0033\t0.0068\n",
|
||||
"\n",
|
||||
"#bez roc_th a s trailing sl train/test 8.1/-0.8 \n",
|
||||
"#70\t2\t-0.05\t0.0018\t0.0068\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#SPY - short\n",
|
||||
"# entry_window_closes\tmom_timeperiod\tmom_th\tsl_stop\ttp_stop\t\t\t\t\t\n",
|
||||
"# 10\t6\t-0.13\t0.0022\t0.0057\n",
|
||||
"\n",
|
||||
"#5\t7\t-0.19\t0.0037\t0.0042\n",
|
||||
"\n",
|
||||
"#SPY - long\n",
|
||||
"#45\t4\t0.27\t0.0047\t0.0067\n",
|
||||
"\n",
|
||||
"# TODO:\n",
|
||||
"#- vyzkouset zda nejvyhodnejsi kombinace krom train/testu funguje i na nasledujicich dnech po trainu\n",
|
||||
"# -zkusit najit v short datasetu neco vyhodneho co funguji i na testu\n",
|
||||
"# - dodelat kombinace pro long signaly\n",
|
||||
"# - zkusit walk forward\n",
|
||||
"# - vytvorit vysledkove totoznou na v2realbot\n",
|
||||
"# - podivat se jak detailne funguji tsl_stop a tsl_th\n",
|
||||
"\n",
|
||||
"#70,\t4,\t-0.07,\t0.0048,\t0.0068\t\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"entry_window_closes, mom_timeperiod, mom_th, sl_stop, tp_stop = 8,\t3,\t0.07,\t0.0028,\t0.0033\t\n",
|
||||
"roc_th = 0\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"\n",
|
||||
"#momshort.plot rocp.real_crossed_below(roc_th) & \n",
|
||||
"short_signal = momshort.real_crossed_below(mom_th)\n",
|
||||
"\n",
|
||||
"long_signal = momshort.real_crossed_above(mom_th)\n",
|
||||
"\n",
|
||||
"# print(\"short signal\")\n",
|
||||
"# print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
"#forced_exit = pd.Series(False, index=close.index)\n",
|
||||
"forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
"#entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
"entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
"#print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"\n",
|
||||
"entries = (long_signal & entry_window_open)\n",
|
||||
"exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"# print(short_exits.value_counts())\n",
|
||||
"# print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
"#fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
"#short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
"#print(sl_stop)\n",
|
||||
"#short_entries=short_entries, short_exits=short_exits,\n",
|
||||
"pf = vbt.Portfolio.from_signals(close=basic_data, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf_B = pf.resample(\"30T\")\n",
|
||||
"\n",
|
||||
"pf_B.stats()\n",
|
||||
"pf_B.orders.records_readable\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.get_drawdowns().records_readable"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.orders.records_readable"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.value.plot().show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
932
research/strats/strat_CANDLEGAPS/CANDLEGAPS_v1_MULTI.ipynb
Normal file
932
research/strats/strat_CANDLEGAPS/CANDLEGAPS_v1_MULTI.ipynb
Normal file
@ -0,0 +1,932 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"\n",
|
||||
"init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"#print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"SPY\": ohlcv_df}), tz_convert=zoneNY)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"# close = basic_data.xloc[\"09:30\":\"10:00\"].close"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - FOR - LOOP\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 12))\n",
|
||||
"\n",
|
||||
"#uzavreni okna od 1 do 200\n",
|
||||
"#entry_window_closes = list(range(2, 50, 3))\n",
|
||||
"entry_window_closes = [5, 10, 30, 45]\n",
|
||||
"#entry_window_closes = 30\n",
|
||||
"#threshold entries parameters\n",
|
||||
"#long\n",
|
||||
"mom_th = np.round(np.arange(0.01, 0.5 + 0.02, 0.02),4).tolist()#-0.02\n",
|
||||
"# short\n",
|
||||
"#mom_th = np.round(np.arange(-0.01, -0.3 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.8 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop =np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"tp_stop = np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"\n",
|
||||
"combs = list(product(mom_timeperiod, mom_th, roc_th, sl_stop, tp_stop))\n",
|
||||
"\n",
|
||||
"@vbt.parameterized(merge_func = \"concat\", random_subset = 2000, show_progress=True) \n",
|
||||
"def test_strat(entry_window_closes=60,\n",
|
||||
" mom_timeperiod=2,\n",
|
||||
" mom_th=-0.04,\n",
|
||||
" #roc_th=-0.2,\n",
|
||||
" sl_stop=0.19/100,\n",
|
||||
" tp_stop=0.19/100):\n",
|
||||
" # mom_timeperiod=2\n",
|
||||
" # mom_th=-0.06\n",
|
||||
" # roc_th=-0.2\n",
|
||||
" # sl_stop=0.04/100\n",
|
||||
" # tp_stop=0.04/100\n",
|
||||
"\n",
|
||||
" momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
" rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
|
||||
" #rate of change + momentum\n",
|
||||
"\n",
|
||||
" #momshort.plot rocp.real_crossed_below(roc_th) & \n",
|
||||
" #short_signal = momshort.real_crossed_below(mom_th)\n",
|
||||
" long_signal = momshort.real_crossed_above(mom_th)\n",
|
||||
" # print(\"short signal\")\n",
|
||||
" # print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
" #forced_exit = pd.Series(False, index=close.index)\n",
|
||||
" forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
" #entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
" entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
" #print(entry_window_closes, \"entry window closes\")\n",
|
||||
" # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
" elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
" entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
" #print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
" forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
" #short_entries = (short_signal & entry_window_open)\n",
|
||||
" #short_exits = forced_exit\n",
|
||||
" entries = (long_signal & entry_window_open)\n",
|
||||
" exits = forced_exit\n",
|
||||
" #long_entries.info()\n",
|
||||
" #number of trues and falses in long_entries\n",
|
||||
" #print(short_exits.value_counts())\n",
|
||||
" #print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
" #fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
" #short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" #print(sl_stop)\n",
|
||||
" #tsl_th=sl_stop, \n",
|
||||
" #short_entries=short_entries, short_exits=short_exits,\n",
|
||||
" pf = vbt.Portfolio.from_signals(close=basic_data.close, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\", price=\"close\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
" \n",
|
||||
" return pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'max_dd', \n",
|
||||
" 'total_trades', \n",
|
||||
" 'win_rate', \n",
|
||||
" 'expectancy'\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"pf_results = test_strat(vbt.Param(entry_window_closes),\n",
|
||||
" vbt.Param(mom_timeperiod),\n",
|
||||
" vbt.Param(mom_th),\n",
|
||||
" #vbt.Param(roc_th)\n",
|
||||
" vbt.Param(sl_stop),\n",
|
||||
" vbt.Param(tp_stop, condition=\"tp_stop > sl_stop\"))\n",
|
||||
"pf_results = pf_results.unstack(level=-1)\n",
|
||||
"pf_results.sort_values(by=[\"Total Return [%]\", \"Max Drawdown [%]\"], ascending=[False, True])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf_results.load(\"10tiscomb.pickle\")\n",
|
||||
"#pf_results.info()\n",
|
||||
"\n",
|
||||
"vbt.save(pf_results, \"8tiscomb_tsl.pickle\")\n",
|
||||
"\n",
|
||||
"# pf_results = vbt.load(\"8tiscomb_tsl.pickle\")\n",
|
||||
"# pf_results\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# parallel_coordinates method¶\n",
|
||||
"\n",
|
||||
"# attach_px_methods.<locals>.plot_func(\n",
|
||||
"# *args,\n",
|
||||
"# layout=None,\n",
|
||||
"# **kwargs\n",
|
||||
"# )\n",
|
||||
"\n",
|
||||
"# pf_results.vbt.px.parallel_coordinates() #ocdf\n",
|
||||
"\n",
|
||||
"res = pf_results.reset_index()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf_results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# Assuming pf_results is your DataFrame\n",
|
||||
"# Convert columns to numeric, assuming NaNs where conversion fails\n",
|
||||
"metrics = ['Total Return [%]', 'Max Drawdown [%]', 'Total Trades']\n",
|
||||
"for metric in metrics:\n",
|
||||
" pf_results[metric] = pd.to_numeric(pf_results[metric], errors='coerce')\n",
|
||||
"\n",
|
||||
"# Handle missing values, for example filling with the median\n",
|
||||
"pf_results['Max Drawdown [%]'].fillna(pf_results['Max Drawdown [%]'].median(), inplace=True)\n",
|
||||
"\n",
|
||||
"# Extract the metrics into a new DataFrame\n",
|
||||
"data_for_pca = pf_results[metrics]\n",
|
||||
"\n",
|
||||
"# Standardize the data before applying PCA\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"data_scaled = scaler.fit_transform(data_for_pca)\n",
|
||||
"\n",
|
||||
"# Apply PCA\n",
|
||||
"pca = PCA(n_components=2) # Adjust components as needed\n",
|
||||
"principal_components = pca.fit_transform(data_scaled)\n",
|
||||
"\n",
|
||||
"# Create a DataFrame with the principal components\n",
|
||||
"pca_results = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])\n",
|
||||
"\n",
|
||||
"# Visualize the results\n",
|
||||
"plt.figure(figsize=(8,6))\n",
|
||||
"plt.scatter(pca_results['PC1'], pca_results['PC2'], alpha=0.5)\n",
|
||||
"plt.xlabel('Principal Component 1')\n",
|
||||
"plt.ylabel('Principal Component 2')\n",
|
||||
"plt.title('PCA of Strategy Optimization Results')\n",
|
||||
"plt.grid(True)\n",
|
||||
"plt.savefig(\"ddd.png\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check if there is any unnamed level and rename it\n",
|
||||
"if None in df.index.names:\n",
|
||||
" # Generate new names list replacing None with 'stat'\n",
|
||||
" new_names = ['stat' if name is None else name for name in df.index.names]\n",
|
||||
" df.index.set_names(new_names, inplace=True)\n",
|
||||
"\n",
|
||||
"rs= df\n",
|
||||
"\n",
|
||||
"rs.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Now, 'stat' is the name of the previously unnamed level\n",
|
||||
"\n",
|
||||
"# # Filter for 'Total Return' assuming it is a correct identifier in the 'stat' level\n",
|
||||
"# total_return_series = df.xs('Total Return [%]', level='stat')\n",
|
||||
"\n",
|
||||
"# # Sort the Series to get the largest 'Total Return' values\n",
|
||||
"# sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# # Print the sorted filtered data\n",
|
||||
"# sorted_series.head(20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sorted_series.vbt.save()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#df.info()\n",
|
||||
"total_return_series = df.xs('Total Return [%]')\n",
|
||||
"sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# Display the top N entries, e.g., top 5\n",
|
||||
"sorted_series.head(5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"comb_stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
|
||||
"4 -0.02 -0.25 0.0028 0.0048 0.84433\n",
|
||||
"3 -0.02 -0.25 0.0033 0.0023 Total Return [%] 0.846753\n",
|
||||
"#2\t-0.04\t-0.2\t0.0019\t0.0019\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"basic_data.symbols"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
">>> def apply_func(ts, entries, exits, fastw, sloww, minp=None):\n",
|
||||
"... fast_ma = vbt.nb.rolling_mean_nb(ts, fastw, minp=minp)\n",
|
||||
"... slow_ma = vbt.nb.rolling_mean_nb(ts, sloww, minp=minp)\n",
|
||||
"... entries[:] = vbt.nb.crossed_above_nb(fast_ma, slow_ma) \n",
|
||||
"... exits[:] = vbt.nb.crossed_above_nb(slow_ma, fast_ma)\n",
|
||||
"... return (fast_ma, slow_ma) \n",
|
||||
"\n",
|
||||
">>> CrossSig = vbt.IF(\n",
|
||||
"... class_name=\"CrossSig\",\n",
|
||||
"... input_names=['ts'],\n",
|
||||
"... in_output_names=['entries', 'exits'],\n",
|
||||
"... param_names=['fastw', 'sloww'],\n",
|
||||
"... output_names=['fast_ma', 'slow_ma']\n",
|
||||
"... ).with_apply_func(\n",
|
||||
"... apply_func,\n",
|
||||
"... in_output_settings=dict(\n",
|
||||
"... entries=dict(dtype=np.bool_), #initialize output with bool\n",
|
||||
"... exits=dict(dtype=np.bool_)\n",
|
||||
"... )\n",
|
||||
"... )\n",
|
||||
">>> cross_sig = CrossSig.run(ts2, 2, 4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - parameters in one go\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#TOTO prepsat do FOR-LOOPu\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 6))\n",
|
||||
"\n",
|
||||
"#threshold entries parameters\n",
|
||||
"mom_th = np.round(np.arange(-0.02, -0.1 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.4 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"#jejich product\n",
|
||||
"# mom_th_prod, roc_th_prod = zip(*product(mom_th, roc_th))\n",
|
||||
"\n",
|
||||
"# #convert threshold to vbt param\n",
|
||||
"# mom_th_index = vbt.Param(mom_th_prod, name='mom_th_th') \n",
|
||||
"# roc_th_index = vbt.Param(roc_th_prod, name='roc_th_th')\n",
|
||||
"\n",
|
||||
"mom_th = vbt.Param(mom_th, name='mom_th')\n",
|
||||
"roc_th = vbt.Param(roc_th, name='roc_th')\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"tp_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"tp_stop = [round(val, 4) for val in tp_stop]\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"tp_stop = vbt.Param(tp_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#def test_mom(window=14, mom_th=0.2, roc_th=0.2, sl_stop=0.03/100, tp_stop=0.03/100):\n",
|
||||
"#close = basic_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(basic_data.get(\"Close\"), timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
"\n",
|
||||
"#ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(basic_data.get(\"Close\"), short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"\n",
|
||||
"rocp_signal = rocp.real_crossed_below(mom_th)\n",
|
||||
"mom_signal = momshort.real_crossed_below(roc_th)\n",
|
||||
"\n",
|
||||
"#mom_signal\n",
|
||||
"print(rocp_signal.info())\n",
|
||||
"print(mom_signal.info())\n",
|
||||
"#print(rocp.real)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"short_signal = (mom_signal.vbt & rocp_signal)\n",
|
||||
"\n",
|
||||
"# #short_signal = (rocp.real_crossed_below(roc_th_index) & momshort.real_crossed_below(mom_th_index))\n",
|
||||
"# forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"# entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"# elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"# entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"# forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"# short_entries = (short_signal & entry_window_open)\n",
|
||||
"# short_exits = forced_exit\n",
|
||||
"# #long_entries.info()\n",
|
||||
"# #number of trues and falses in long_entries\n",
|
||||
"# #short_exits.value_counts()\n",
|
||||
"# #short_entries.value_counts()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# filter dates"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#filter na dny\n",
|
||||
"dates_of_interest = pd.to_datetime(['2024-04-22']).tz_localize('US/Eastern')\n",
|
||||
"filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
|
||||
"\n",
|
||||
"df = filtered_df\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import plotly.io as pio\n",
|
||||
"# pio.renderers.default = 'notebook'\n",
|
||||
"\n",
|
||||
"#naloadujeme do vbt symbol as column\n",
|
||||
"basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
|
||||
"\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"#basic_data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"\n",
|
||||
"m1_data.data[\"BAC\"]\n",
|
||||
"#m5_data = m1_data.resample(\"5T\")\n",
|
||||
"\n",
|
||||
"#m5_data.data[\"BAC\"].head(10)\n",
|
||||
"\n",
|
||||
"# m15_data = m1_data.resample(\"15T\")\n",
|
||||
"\n",
|
||||
"# m15 = m15_data.data[\"BAC\"]\n",
|
||||
"\n",
|
||||
"# m15.vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"# m1_data.wrapper.index\n",
|
||||
"\n",
|
||||
"# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
|
||||
"# m1_resampler.index_difference(reverse=True)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# m5_resampler.prettify()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MOM indicator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.indicator(\"talib:ROCP\").run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"vyuzití rychleho klesani na sekundove urovni behem open rush\n",
|
||||
"- MOM + ROC during open rush\n",
|
||||
"- short signal\n",
|
||||
"- pipeline kombinace thresholdu pro vstup mom_th, roc_th + hodnota sl_stop a tp_stop (pripadne trailing) - nalezeni optimalni kombinace atributu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"# short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
"\n",
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"close = m1_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(close, timeperiod=3, short_name = \"slope_short\")\n",
|
||||
"ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(close, short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"short_signal = (rocp.real_crossed_below(-0.2) & momshort.real_crossed_below(-0.02))\n",
|
||||
"#indlong = vbt.indicator(\"talib:MOM\").run(close, timeperiod=10, short_name = \"slope_long\")\n",
|
||||
"fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"#vbt.phelp(vbt.OLS.run)\n",
|
||||
"\n",
|
||||
"#oer steepmnes of regression line\n",
|
||||
"#talib.LINEARREG_SLOPE(close, timeperiod=timeperiod)\n",
|
||||
"#a také ON BALANCE VOLUME - http://5.161.179.223:8000/static/js/vbt/api/indicators/custom/obv/index.html\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mom_ind = vbt.indicator(\"talib:MOM\") \n",
|
||||
"#vbt.phelp(mom_ind.run)\n",
|
||||
"\n",
|
||||
"mom = mom_ind.run(close, timeperiod=10)\n",
|
||||
"\n",
|
||||
"plot_2y_close(mom, close)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# defining ENTRY WINDOW and forced EXIT window"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#m1_data.data[\"BAC\"].info()\n",
|
||||
"import datetime\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 2\n",
|
||||
"entry_window_closes = 30\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"\n",
|
||||
"#entry_window_open.info()\n",
|
||||
"# forced_exit.tail(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"\n",
|
||||
"#rsi = vbt.RSI.run(close, window=14)\n",
|
||||
"\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"#short_exits.value_counts()\n",
|
||||
"short_entries.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_rsi(close, entries, exits):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" #rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" entries.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_rsi(close, short_entries, short_exits)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.Portfolio.from_signals)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"print(sl_stop)\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=0.03/100, tp_stop = 0.03/100, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"#pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#list of orders\n",
|
||||
"#pf.orders.records_readable\n",
|
||||
"#pf.orders.plots()\n",
|
||||
"#pf.stats()\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0015,0.0013)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[0.03].plot_trade_signals()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# pristup k pf jako multi index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf[0.03].plot()\n",
|
||||
"#pf.order_records\n",
|
||||
"pf[(0.03)].stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#zgrupovane statistiky\n",
|
||||
"stats_df = pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'total_trades',\n",
|
||||
" 'win_rate',\n",
|
||||
" 'expectancy'\n",
|
||||
"], agg_func=None)\n",
|
||||
"stats_df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0011,0.0013000000000000002)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pandas.tseries.offsets import DateOffset\n",
|
||||
"\n",
|
||||
"temp_data = basic_data['2024-4-22']\n",
|
||||
"temp_data\n",
|
||||
"res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
|
||||
"\n",
|
||||
"# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
|
||||
"custom_offset = DateOffset(hours=4, minutes=30)\n",
|
||||
"\n",
|
||||
"# res1m = res1m.get().resample(\"4H\").agg({ \n",
|
||||
"# \"Open\": \"first\",\n",
|
||||
"# \"High\": \"max\",\n",
|
||||
"# \"Low\": \"min\",\n",
|
||||
"# \"Close\": \"last\",\n",
|
||||
"# \"Volume\": \"sum\"\n",
|
||||
"# })\n",
|
||||
"\n",
|
||||
"res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res4h.data\n",
|
||||
"\n",
|
||||
"res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res15m.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@vbt.njit\n",
|
||||
"def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
|
||||
" market_open_minutes = 570 # 9 hours * 60 minutes + 30 minutes\n",
|
||||
"\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
"\n",
|
||||
" current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
|
||||
" #print(\"current_minutes\", current_minutes)\n",
|
||||
" # Calculate elapsed minutes since market open at 9:30 AM\n",
|
||||
" elapsed_from_open = current_minutes - market_open_minutes\n",
|
||||
" elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
|
||||
" #print( \"elapsed_from_open\", elapsed_from_open)\n",
|
||||
"\n",
|
||||
" #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
|
||||
" in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
|
||||
" #print(\"in_window\", in_window)\n",
|
||||
" # if in_window:\n",
|
||||
" # print(\"in window\")\n",
|
||||
"\n",
|
||||
" if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9: # (4)!\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"@vbt.njit\n",
|
||||
"def long_exit_place_func_nb(c, high, close, time_index, tp, sl): # (5)!\n",
|
||||
" entry_i = c.from_i - c.wait\n",
|
||||
" entry_price = close[entry_i, c.col]\n",
|
||||
" hit_price = entry_price * (1 + tp)\n",
|
||||
" stop_price = entry_price * (1 - sl)\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
" last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
|
||||
"\n",
|
||||
" #print(next_day)\n",
|
||||
" if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
|
||||
" print(\"ted\",out_i)\n",
|
||||
" return out_i\n",
|
||||
" if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
|
||||
"\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.sum()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
931
research/strats/strat_CANDLEGAPS/CANDLEGAPS_v1_SINGLE.ipynb
Normal file
931
research/strats/strat_CANDLEGAPS/CANDLEGAPS_v1_SINGLE.ipynb
Normal file
File diff suppressed because one or more lines are too long
949
research/strats/strat_LINREG_MULTI/v1_MULTI.ipynb
Normal file
949
research/strats/strat_LINREG_MULTI/v1_MULTI.ipynb
Normal file
@ -0,0 +1,949 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"\n",
|
||||
"init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM BATCH\n",
|
||||
"# res, df = load_batch(batch_id=\"0fb5043a\", #0fb5043a bde6d0be\n",
|
||||
"# space_resolution_evenly=False,\n",
|
||||
"# indicators_columns=[\"Rsi14\"],\n",
|
||||
"# main_session_only=True,\n",
|
||||
"# verbose = False)\n",
|
||||
"# if res < 0:\n",
|
||||
"# print(\"Error\" + str(res) + str(df))\n",
|
||||
"# df = df[\"bars\"]\n",
|
||||
"\n",
|
||||
"# #df\n",
|
||||
"\n",
|
||||
"# basic_data = vbt.Data.from_data(vbt.symbol_dict({\"BAC\": df}), tz_convert=zoneNY)\n",
|
||||
"# #m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"# basic_data = basic_data.transform(lambda df: df.between_time('09:30', '16:00'))\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"#print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"SPY\": ohlcv_df}), tz_convert=zoneNY)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"# close = basic_data.xloc[\"09:30\":\"10:00\"].close"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - FOR - LOOP\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 12))\n",
|
||||
"\n",
|
||||
"#uzavreni okna od 1 do 200\n",
|
||||
"#entry_window_closes = list(range(2, 50, 3))\n",
|
||||
"entry_window_closes = [5, 10, 30, 45]\n",
|
||||
"#entry_window_closes = 30\n",
|
||||
"#threshold entries parameters\n",
|
||||
"#long\n",
|
||||
"mom_th = np.round(np.arange(0.01, 0.5 + 0.02, 0.02),4).tolist()#-0.02\n",
|
||||
"# short\n",
|
||||
"#mom_th = np.round(np.arange(-0.01, -0.3 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.8 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop =np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"tp_stop = np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"\n",
|
||||
"combs = list(product(mom_timeperiod, mom_th, roc_th, sl_stop, tp_stop))\n",
|
||||
"\n",
|
||||
"@vbt.parameterized(merge_func = \"concat\", random_subset = 2000, show_progress=True) \n",
|
||||
"def test_strat(entry_window_closes=60,\n",
|
||||
" mom_timeperiod=2,\n",
|
||||
" mom_th=-0.04,\n",
|
||||
" #roc_th=-0.2,\n",
|
||||
" sl_stop=0.19/100,\n",
|
||||
" tp_stop=0.19/100):\n",
|
||||
" # mom_timeperiod=2\n",
|
||||
" # mom_th=-0.06\n",
|
||||
" # roc_th=-0.2\n",
|
||||
" # sl_stop=0.04/100\n",
|
||||
" # tp_stop=0.04/100\n",
|
||||
"\n",
|
||||
" momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
" rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
|
||||
" #rate of change + momentum\n",
|
||||
"\n",
|
||||
" #momshort.plot rocp.real_crossed_below(roc_th) & \n",
|
||||
" #short_signal = momshort.real_crossed_below(mom_th)\n",
|
||||
" long_signal = momshort.real_crossed_above(mom_th)\n",
|
||||
" # print(\"short signal\")\n",
|
||||
" # print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
" #forced_exit = pd.Series(False, index=close.index)\n",
|
||||
" forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
" #entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
" entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
" #print(entry_window_closes, \"entry window closes\")\n",
|
||||
" # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
" elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
" entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
" #print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
" forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
" #short_entries = (short_signal & entry_window_open)\n",
|
||||
" #short_exits = forced_exit\n",
|
||||
" entries = (long_signal & entry_window_open)\n",
|
||||
" exits = forced_exit\n",
|
||||
" #long_entries.info()\n",
|
||||
" #number of trues and falses in long_entries\n",
|
||||
" #print(short_exits.value_counts())\n",
|
||||
" #print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
" #fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
" #short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" #print(sl_stop)\n",
|
||||
" #tsl_th=sl_stop, \n",
|
||||
" #short_entries=short_entries, short_exits=short_exits,\n",
|
||||
" pf = vbt.Portfolio.from_signals(close=basic_data.close, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\", price=\"close\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
" \n",
|
||||
" return pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'max_dd', \n",
|
||||
" 'total_trades', \n",
|
||||
" 'win_rate', \n",
|
||||
" 'expectancy'\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"pf_results = test_strat(vbt.Param(entry_window_closes),\n",
|
||||
" vbt.Param(mom_timeperiod),\n",
|
||||
" vbt.Param(mom_th),\n",
|
||||
" #vbt.Param(roc_th)\n",
|
||||
" vbt.Param(sl_stop),\n",
|
||||
" vbt.Param(tp_stop, condition=\"tp_stop > sl_stop\"))\n",
|
||||
"pf_results = pf_results.unstack(level=-1)\n",
|
||||
"pf_results.sort_values(by=[\"Total Return [%]\", \"Max Drawdown [%]\"], ascending=[False, True])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf_results.load(\"10tiscomb.pickle\")\n",
|
||||
"#pf_results.info()\n",
|
||||
"\n",
|
||||
"vbt.save(pf_results, \"8tiscomb_tsl.pickle\")\n",
|
||||
"\n",
|
||||
"# pf_results = vbt.load(\"8tiscomb_tsl.pickle\")\n",
|
||||
"# pf_results\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# parallel_coordinates method¶\n",
|
||||
"\n",
|
||||
"# attach_px_methods.<locals>.plot_func(\n",
|
||||
"# *args,\n",
|
||||
"# layout=None,\n",
|
||||
"# **kwargs\n",
|
||||
"# )\n",
|
||||
"\n",
|
||||
"# pf_results.vbt.px.parallel_coordinates() #ocdf\n",
|
||||
"\n",
|
||||
"res = pf_results.reset_index()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf_results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# Assuming pf_results is your DataFrame\n",
|
||||
"# Convert columns to numeric, assuming NaNs where conversion fails\n",
|
||||
"metrics = ['Total Return [%]', 'Max Drawdown [%]', 'Total Trades']\n",
|
||||
"for metric in metrics:\n",
|
||||
" pf_results[metric] = pd.to_numeric(pf_results[metric], errors='coerce')\n",
|
||||
"\n",
|
||||
"# Handle missing values, for example filling with the median\n",
|
||||
"pf_results['Max Drawdown [%]'].fillna(pf_results['Max Drawdown [%]'].median(), inplace=True)\n",
|
||||
"\n",
|
||||
"# Extract the metrics into a new DataFrame\n",
|
||||
"data_for_pca = pf_results[metrics]\n",
|
||||
"\n",
|
||||
"# Standardize the data before applying PCA\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"data_scaled = scaler.fit_transform(data_for_pca)\n",
|
||||
"\n",
|
||||
"# Apply PCA\n",
|
||||
"pca = PCA(n_components=2) # Adjust components as needed\n",
|
||||
"principal_components = pca.fit_transform(data_scaled)\n",
|
||||
"\n",
|
||||
"# Create a DataFrame with the principal components\n",
|
||||
"pca_results = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])\n",
|
||||
"\n",
|
||||
"# Visualize the results\n",
|
||||
"plt.figure(figsize=(8,6))\n",
|
||||
"plt.scatter(pca_results['PC1'], pca_results['PC2'], alpha=0.5)\n",
|
||||
"plt.xlabel('Principal Component 1')\n",
|
||||
"plt.ylabel('Principal Component 2')\n",
|
||||
"plt.title('PCA of Strategy Optimization Results')\n",
|
||||
"plt.grid(True)\n",
|
||||
"plt.savefig(\"ddd.png\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check if there is any unnamed level and rename it\n",
|
||||
"if None in df.index.names:\n",
|
||||
" # Generate new names list replacing None with 'stat'\n",
|
||||
" new_names = ['stat' if name is None else name for name in df.index.names]\n",
|
||||
" df.index.set_names(new_names, inplace=True)\n",
|
||||
"\n",
|
||||
"rs= df\n",
|
||||
"\n",
|
||||
"rs.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Now, 'stat' is the name of the previously unnamed level\n",
|
||||
"\n",
|
||||
"# # Filter for 'Total Return' assuming it is a correct identifier in the 'stat' level\n",
|
||||
"# total_return_series = df.xs('Total Return [%]', level='stat')\n",
|
||||
"\n",
|
||||
"# # Sort the Series to get the largest 'Total Return' values\n",
|
||||
"# sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# # Print the sorted filtered data\n",
|
||||
"# sorted_series.head(20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sorted_series.vbt.save()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#df.info()\n",
|
||||
"total_return_series = df.xs('Total Return [%]')\n",
|
||||
"sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# Display the top N entries, e.g., top 5\n",
|
||||
"sorted_series.head(5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"comb_stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
|
||||
"4 -0.02 -0.25 0.0028 0.0048 0.84433\n",
|
||||
"3 -0.02 -0.25 0.0033 0.0023 Total Return [%] 0.846753\n",
|
||||
"#2\t-0.04\t-0.2\t0.0019\t0.0019\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"basic_data.symbols"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
">>> def apply_func(ts, entries, exits, fastw, sloww, minp=None):\n",
|
||||
"... fast_ma = vbt.nb.rolling_mean_nb(ts, fastw, minp=minp)\n",
|
||||
"... slow_ma = vbt.nb.rolling_mean_nb(ts, sloww, minp=minp)\n",
|
||||
"... entries[:] = vbt.nb.crossed_above_nb(fast_ma, slow_ma) \n",
|
||||
"... exits[:] = vbt.nb.crossed_above_nb(slow_ma, fast_ma)\n",
|
||||
"... return (fast_ma, slow_ma) \n",
|
||||
"\n",
|
||||
">>> CrossSig = vbt.IF(\n",
|
||||
"... class_name=\"CrossSig\",\n",
|
||||
"... input_names=['ts'],\n",
|
||||
"... in_output_names=['entries', 'exits'],\n",
|
||||
"... param_names=['fastw', 'sloww'],\n",
|
||||
"... output_names=['fast_ma', 'slow_ma']\n",
|
||||
"... ).with_apply_func(\n",
|
||||
"... apply_func,\n",
|
||||
"... in_output_settings=dict(\n",
|
||||
"... entries=dict(dtype=np.bool_), #initialize output with bool\n",
|
||||
"... exits=dict(dtype=np.bool_)\n",
|
||||
"... )\n",
|
||||
"... )\n",
|
||||
">>> cross_sig = CrossSig.run(ts2, 2, 4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - parameters in one go\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#TOTO prepsat do FOR-LOOPu\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 6))\n",
|
||||
"\n",
|
||||
"#threshold entries parameters\n",
|
||||
"mom_th = np.round(np.arange(-0.02, -0.1 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.4 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"#jejich product\n",
|
||||
"# mom_th_prod, roc_th_prod = zip(*product(mom_th, roc_th))\n",
|
||||
"\n",
|
||||
"# #convert threshold to vbt param\n",
|
||||
"# mom_th_index = vbt.Param(mom_th_prod, name='mom_th_th') \n",
|
||||
"# roc_th_index = vbt.Param(roc_th_prod, name='roc_th_th')\n",
|
||||
"\n",
|
||||
"mom_th = vbt.Param(mom_th, name='mom_th')\n",
|
||||
"roc_th = vbt.Param(roc_th, name='roc_th')\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"tp_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"tp_stop = [round(val, 4) for val in tp_stop]\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"tp_stop = vbt.Param(tp_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#def test_mom(window=14, mom_th=0.2, roc_th=0.2, sl_stop=0.03/100, tp_stop=0.03/100):\n",
|
||||
"#close = basic_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(basic_data.get(\"Close\"), timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
"\n",
|
||||
"#ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(basic_data.get(\"Close\"), short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"\n",
|
||||
"rocp_signal = rocp.real_crossed_below(mom_th)\n",
|
||||
"mom_signal = momshort.real_crossed_below(roc_th)\n",
|
||||
"\n",
|
||||
"#mom_signal\n",
|
||||
"print(rocp_signal.info())\n",
|
||||
"print(mom_signal.info())\n",
|
||||
"#print(rocp.real)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"short_signal = (mom_signal.vbt & rocp_signal)\n",
|
||||
"\n",
|
||||
"# #short_signal = (rocp.real_crossed_below(roc_th_index) & momshort.real_crossed_below(mom_th_index))\n",
|
||||
"# forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"# entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"# elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"# entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"# forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"# short_entries = (short_signal & entry_window_open)\n",
|
||||
"# short_exits = forced_exit\n",
|
||||
"# #long_entries.info()\n",
|
||||
"# #number of trues and falses in long_entries\n",
|
||||
"# #short_exits.value_counts()\n",
|
||||
"# #short_entries.value_counts()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# filter dates"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#filter na dny\n",
|
||||
"dates_of_interest = pd.to_datetime(['2024-04-22']).tz_localize('US/Eastern')\n",
|
||||
"filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
|
||||
"\n",
|
||||
"df = filtered_df\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import plotly.io as pio\n",
|
||||
"# pio.renderers.default = 'notebook'\n",
|
||||
"\n",
|
||||
"#naloadujeme do vbt symbol as column\n",
|
||||
"basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
|
||||
"\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"#basic_data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"\n",
|
||||
"m1_data.data[\"BAC\"]\n",
|
||||
"#m5_data = m1_data.resample(\"5T\")\n",
|
||||
"\n",
|
||||
"#m5_data.data[\"BAC\"].head(10)\n",
|
||||
"\n",
|
||||
"# m15_data = m1_data.resample(\"15T\")\n",
|
||||
"\n",
|
||||
"# m15 = m15_data.data[\"BAC\"]\n",
|
||||
"\n",
|
||||
"# m15.vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"# m1_data.wrapper.index\n",
|
||||
"\n",
|
||||
"# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
|
||||
"# m1_resampler.index_difference(reverse=True)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# m5_resampler.prettify()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MOM indicator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.indicator(\"talib:ROCP\").run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"vyuzití rychleho klesani na sekundove urovni behem open rush\n",
|
||||
"- MOM + ROC during open rush\n",
|
||||
"- short signal\n",
|
||||
"- pipeline kombinace thresholdu pro vstup mom_th, roc_th + hodnota sl_stop a tp_stop (pripadne trailing) - nalezeni optimalni kombinace atributu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"# short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
"\n",
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"close = m1_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(close, timeperiod=3, short_name = \"slope_short\")\n",
|
||||
"ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(close, short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"short_signal = (rocp.real_crossed_below(-0.2) & momshort.real_crossed_below(-0.02))\n",
|
||||
"#indlong = vbt.indicator(\"talib:MOM\").run(close, timeperiod=10, short_name = \"slope_long\")\n",
|
||||
"fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"#vbt.phelp(vbt.OLS.run)\n",
|
||||
"\n",
|
||||
"#oer steepmnes of regression line\n",
|
||||
"#talib.LINEARREG_SLOPE(close, timeperiod=timeperiod)\n",
|
||||
"#a také ON BALANCE VOLUME - http://5.161.179.223:8000/static/js/vbt/api/indicators/custom/obv/index.html\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mom_ind = vbt.indicator(\"talib:MOM\") \n",
|
||||
"#vbt.phelp(mom_ind.run)\n",
|
||||
"\n",
|
||||
"mom = mom_ind.run(close, timeperiod=10)\n",
|
||||
"\n",
|
||||
"plot_2y_close(mom, close)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# defining ENTRY WINDOW and forced EXIT window"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#m1_data.data[\"BAC\"].info()\n",
|
||||
"import datetime\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 2\n",
|
||||
"entry_window_closes = 30\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"\n",
|
||||
"#entry_window_open.info()\n",
|
||||
"# forced_exit.tail(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"\n",
|
||||
"#rsi = vbt.RSI.run(close, window=14)\n",
|
||||
"\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"#short_exits.value_counts()\n",
|
||||
"short_entries.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_rsi(close, entries, exits):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" #rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" entries.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_rsi(close, short_entries, short_exits)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.Portfolio.from_signals)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"print(sl_stop)\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=0.03/100, tp_stop = 0.03/100, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"#pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#list of orders\n",
|
||||
"#pf.orders.records_readable\n",
|
||||
"#pf.orders.plots()\n",
|
||||
"#pf.stats()\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0015,0.0013)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[0.03].plot_trade_signals()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# pristup k pf jako multi index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf[0.03].plot()\n",
|
||||
"#pf.order_records\n",
|
||||
"pf[(0.03)].stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#zgrupovane statistiky\n",
|
||||
"stats_df = pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'total_trades',\n",
|
||||
" 'win_rate',\n",
|
||||
" 'expectancy'\n",
|
||||
"], agg_func=None)\n",
|
||||
"stats_df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0011,0.0013000000000000002)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pandas.tseries.offsets import DateOffset\n",
|
||||
"\n",
|
||||
"temp_data = basic_data['2024-4-22']\n",
|
||||
"temp_data\n",
|
||||
"res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
|
||||
"\n",
|
||||
"# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
|
||||
"custom_offset = DateOffset(hours=4, minutes=30)\n",
|
||||
"\n",
|
||||
"# res1m = res1m.get().resample(\"4H\").agg({ \n",
|
||||
"# \"Open\": \"first\",\n",
|
||||
"# \"High\": \"max\",\n",
|
||||
"# \"Low\": \"min\",\n",
|
||||
"# \"Close\": \"last\",\n",
|
||||
"# \"Volume\": \"sum\"\n",
|
||||
"# })\n",
|
||||
"\n",
|
||||
"res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res4h.data\n",
|
||||
"\n",
|
||||
"res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res15m.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@vbt.njit\n",
|
||||
"def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
|
||||
" market_open_minutes = 570 # 9 hours * 60 minutes + 30 minutes\n",
|
||||
"\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
"\n",
|
||||
" current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
|
||||
" #print(\"current_minutes\", current_minutes)\n",
|
||||
" # Calculate elapsed minutes since market open at 9:30 AM\n",
|
||||
" elapsed_from_open = current_minutes - market_open_minutes\n",
|
||||
" elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
|
||||
" #print( \"elapsed_from_open\", elapsed_from_open)\n",
|
||||
"\n",
|
||||
" #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
|
||||
" in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
|
||||
" #print(\"in_window\", in_window)\n",
|
||||
" # if in_window:\n",
|
||||
" # print(\"in window\")\n",
|
||||
"\n",
|
||||
" if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9: # (4)!\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"@vbt.njit\n",
|
||||
"def long_exit_place_func_nb(c, high, close, time_index, tp, sl): # (5)!\n",
|
||||
" entry_i = c.from_i - c.wait\n",
|
||||
" entry_price = close[entry_i, c.col]\n",
|
||||
" hit_price = entry_price * (1 + tp)\n",
|
||||
" stop_price = entry_price * (1 - sl)\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
" last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
|
||||
"\n",
|
||||
" #print(next_day)\n",
|
||||
" if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
|
||||
" print(\"ted\",out_i)\n",
|
||||
" return out_i\n",
|
||||
" if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
|
||||
"\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.sum()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
584
research/strats/strat_LINREG_MULTI/v1_SINGLE.ipynb
Normal file
584
research/strats/strat_LINREG_MULTI/v1_SINGLE.ipynb
Normal file
@ -0,0 +1,584 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Multi timeframe momentum\n",
|
||||
"Cílen je nalézt kombinaci trendu, kdy je velmi pravdě+podobné, že trend bude o určitou hodnootu ještě pokračovat.\n",
|
||||
"\n",
|
||||
"jsou počítány linregression úhly pro více timeframů a délku oken\n",
|
||||
"\n",
|
||||
"Pro každou kombinaci je daný parametr nad kterým musí být. Pokud je nad všemi pak je entry (short/long).\n",
|
||||
"\n",
|
||||
"Zvážit i nějaký kumulativní počítadlo anglů - něco jako trend kummulátor."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"\n",
|
||||
"init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM BATCH\n",
|
||||
"# res, df = load_batch(batch_id=\"f1ac6651\", #138170bc 0fb5043a bde6d0be f1ac6651\n",
|
||||
"# space_resolution_evenly=False,\n",
|
||||
"# indicators_columns=[\"Rsi14\"],\n",
|
||||
"# main_session_only=True,\n",
|
||||
"# verbose = False)\n",
|
||||
"# if res < 0:\n",
|
||||
"# print(\"Error\" + str(res) + str(df))\n",
|
||||
"# df = df[\"bars\"]\n",
|
||||
"\n",
|
||||
"# basic_data = vbt.Data.from_data(vbt.symbol_dict({\"BAC\": df}), tz_convert=zoneNY)\n",
|
||||
"# #m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"# basic_data = basic_data.transform(lambda df: df.between_time('09:30', '16:00'))\n",
|
||||
"# #basic_data.info()\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T15_30_00-47BCFOPUVWZ-100.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"#filter ohlcv_df to certain date range (assuming datetime index)\n",
|
||||
"ohlcv_df = ohlcv_df.loc[\"2024-02-12 10:30\":\"2024-02-14 12:00\"]\n",
|
||||
"\n",
|
||||
"#add vwap column to ohlcv_df\n",
|
||||
"#ohlcv_df[\"hlcc4\"] = (ohlcv_df[\"close\"] + ohlcv_df[\"high\"] + ohlcv_df[\"low\"] + ohlcv_df[\"close\"]) / 4\n",
|
||||
"\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"BAC\": ohlcv_df}), tz_convert=zoneNY)\n",
|
||||
"ohlcv_df= None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Add resample function to custom columns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig\n",
|
||||
"from vectorbtpro import _typing as tp\n",
|
||||
"from vectorbtpro.generic import nb as generic_nb\n",
|
||||
"\n",
|
||||
"_feature_config: tp.ClassVar[Config] = HybridConfig(\n",
|
||||
" {\n",
|
||||
" \"buyvolume\": dict(\n",
|
||||
" resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
|
||||
" resampler,\n",
|
||||
" generic_nb.sum_reduce_nb,\n",
|
||||
" )\n",
|
||||
" ),\n",
|
||||
" \"sellvolume\": dict(\n",
|
||||
" resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
|
||||
" resampler,\n",
|
||||
" generic_nb.sum_reduce_nb,\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" }\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"basic_data._feature_config = _feature_config"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#asic_data.stats()\n",
|
||||
"basic_data.wrapper.index.normalize().nunique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"1T\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t1data.data[\"BAC\"].buyvolume"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t1data.data[\"BAC\"].sellvolume"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"buyvolume = t1data.data[\"BAC\"].buyvolume\n",
|
||||
"sellvolume = t1data.data[\"BAC\"].sellvolume\n",
|
||||
"totalvolume = buyvolume + sellvolume\n",
|
||||
"\n",
|
||||
"#adjust to minimal value to avoid division by zero\n",
|
||||
"sellvolume_adjusted = sellvolume.replace(0, 1e-10)\n",
|
||||
"oibratio = buyvolume / sellvolume\n",
|
||||
"\n",
|
||||
"#cumulative order flow (net difference)\n",
|
||||
"cof = buyvolume - sellvolume\n",
|
||||
"\n",
|
||||
"# Calculate the order imbalance (net differene) normalize the order imbalance by calculating the difference between buy and sell volumes and then scaling it by the total volume.\n",
|
||||
"order_imbalance = cof / totalvolume\n",
|
||||
"\n",
|
||||
"order_imbalance_allvolume = cof / t1data.data[\"BAC\"].volume"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"order_imbalance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#priminds list (cena), secinds list (napr. rsi), close, voluminds (volume based)\n",
|
||||
"def plot_2y_close(priminds, secinds, close, volume):\n",
|
||||
" fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, \n",
|
||||
" specs=[[{\"secondary_y\": True}], [{\"secondary_y\": False}]], \n",
|
||||
" vertical_spacing=0.02, subplot_titles=(\"Price and Indicators\", \"Volume\"))\n",
|
||||
"\n",
|
||||
" # Plotting the close price\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False,row=1, col=1), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" \n",
|
||||
" # Plotting primary indicators on the first row\n",
|
||||
" for ind in priminds:\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
|
||||
" \n",
|
||||
" # Plotting secondary indicators on the first row\n",
|
||||
" for ind in secinds:\n",
|
||||
" #ind = ind.rename(str(ind.name))\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))\n",
|
||||
" \n",
|
||||
" for indvolume in volume:\n",
|
||||
" # Plotting the volume on the second row\n",
|
||||
" indvolume.rename(str(indvolume.name)).vbt.barplot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))\n",
|
||||
" #vbt.Bar(indvolume, fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))\n",
|
||||
" \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_2y_close([], [cof,oibratio], t1data.close, [t1data.data[\"BAC\"].buyvolume, t1data.data[\"BAC\"].sellvolume, t1data.volume])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"t0data = basic_data\n",
|
||||
"t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"1T\")\n",
|
||||
"t2data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"15T\")\n",
|
||||
"t3data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"30T\")\n",
|
||||
"t4data = basic_data[['open', 'high', 'low', 'close', 'volume', 'vwap']].resample(\"D\").dropna()\n",
|
||||
"\n",
|
||||
"t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"t2data = t2data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"t3data = t3data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"\n",
|
||||
"#30min data to daily\n",
|
||||
"# t4data = t3data.resample(\"D\").dropna()\n",
|
||||
"\n",
|
||||
"#t4data = t4data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"#m1data.data[\"SPY\"].info()\n",
|
||||
"\n",
|
||||
"#m1data.data[\"SPY\"].vbt.ohlcv.plot()\n",
|
||||
"#h2data.data[\"SPY\"].vbt.ohlcv.plot()\n",
|
||||
"#ddata.data[\"SPY\"]\n",
|
||||
"t2data.data[\"BAC\"].vbt.ohlcv.plot().show()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#t4data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t2data.close\n",
|
||||
"\n",
|
||||
"#in df remove rows with nan\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#realign na 1T = t1data + oriznout main session\n",
|
||||
"t2data_vwap = t2data.vwap.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"t3data_vwap = t3data.vwap.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"t4data_vwap = t4data.vwap.vbt.realign_closing(\"1T\").dropna()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t2data_vwap"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t4data.clos.vbt \n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"obvind = vbt.indicator.obv.run(t1data.close, t1data.volume)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t1_lengtgh = 15\n",
|
||||
"t2_length = 15\n",
|
||||
"t3_length = 15\n",
|
||||
"t4_length = 5\n",
|
||||
"t1_th = 0.1\n",
|
||||
"t2_th = 0.1\n",
|
||||
"t3_th = 0.1\n",
|
||||
"t4_th = 0.1\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#minute\n",
|
||||
"t1slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t1data.close, timeperiod=t1_lengtgh) # -0.09, 0.09\n",
|
||||
"t2slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t2data.vwap, timeperiod=t2_length) # -0.08 , 0.079\n",
|
||||
"t3slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t3data.vwap, timeperiod=t3_length) # -0.08, 0.08\n",
|
||||
"#daily\n",
|
||||
"t4slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t4data.vwap, timeperiod=t4_length) # -0.1, 0.09\n",
|
||||
"\n",
|
||||
"plot_2y_close(priminds=[], secinds=[t1slope, t2slope, t3slope, t4slope], close=t1data.close).show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#thirtymin_slope = thirtymin_slope.real.rename(\"30min\") #timto se prejmenuje real na 30min\n",
|
||||
"t3slope = t3slope.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"##filter daily_slope_to_compare to only monday to friday\n",
|
||||
"t3slope = t3slope[t3slope.index.dayofweek < 5]\n",
|
||||
"\n",
|
||||
"#t3slope.info()\n",
|
||||
"\n",
|
||||
"t2slope = t2slope.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"##filter daily_slope_to_compare to only monday to friday\n",
|
||||
"t2slope = t2slope[t2slope.index.dayofweek < 5]\n",
|
||||
"\n",
|
||||
"t2slope.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"oibratio"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"#\n",
|
||||
"short_entries = order_imbalance.vbt < 0.0002\n",
|
||||
"#short_entries = oibratio.vbt < 0.01\n",
|
||||
"short_entries.value_counts()\n",
|
||||
"\n",
|
||||
"entries = order_imbalance.vbt > 0.7\n",
|
||||
"#entries = oibratio.vbt > 10\n",
|
||||
"entries.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = vbt.make_subplots(rows=3, cols=1, shared_xaxes=True, \n",
|
||||
" specs=[[{\"secondary_y\": True}], [{\"secondary_y\": True}], [{\"secondary_y\": False}]], \n",
|
||||
" vertical_spacing=0.02, subplot_titles=(\"Price and Indicators\", \"Volume\"))\n",
|
||||
"t1data.data[\"BAC\"].vbt.ohlcv.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
|
||||
"#oibratio.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))\n",
|
||||
"order_imbalance.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))\n",
|
||||
"entries.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"LONGS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"limegreen\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ), add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
|
||||
"\n",
|
||||
"short_entries.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"SHORTS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"red\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ), add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# thirtymin_slope_to_compare.vbt.xloc[\"04-16-2024\"].get()\n",
|
||||
"thirty_down_signal.vbt.xloc[\"04-16-2024\"].get()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#short_signal = t1slope.real_below(t1_th) & t2slope.real_below(t2_th) & t3slope.real_below(t3_th) & t4slope.real_below(t4_th)\n",
|
||||
"#long_signal = t1slope.real_above(t1_th) & t2slope.real_above(t2_th) & t3slope.real_above(t3_th) & t4slope.real_above(t4_th)\n",
|
||||
"\n",
|
||||
"#test na daily s reversem crossed 0\n",
|
||||
"short_signal = t2slope.vbt < -0.01 & t3slope.vbt < -0.01 #min value of threshold\n",
|
||||
"long_signal = t2slope.vbt > 0.01 & t3slope.vbt > 0.01 #min\n",
|
||||
"\n",
|
||||
"# thirty_up_signal = t3slope.vbt.crossed_above(0.01)\n",
|
||||
"# thirty_down_signal = t3slope.vbt.crossed_below(-0.01)\n",
|
||||
"\n",
|
||||
"fig = plot_2y_close(priminds=[], secinds=[t3slope], close=t1data.close)\n",
|
||||
"#short_signal.vbt.signals.plot_as_entries(basic_data.close, fig=fig)\n",
|
||||
"\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"SHORTS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"red\", symbol=\"triangle-down\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ))\n",
|
||||
"long_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"LONGS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"limegreen\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ))\n",
|
||||
"\n",
|
||||
"# thirty_down_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"DOWN30\",\n",
|
||||
"# line=dict(color=\"#ffe476\"),\n",
|
||||
"# marker=dict(color=\"yellow\", symbol=\"triangle-down\"),\n",
|
||||
"# fill=None,\n",
|
||||
"# connectgaps=True,\n",
|
||||
"# ))\n",
|
||||
"# thirty_up_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"UP30\",\n",
|
||||
"# line=dict(color=\"#ffe476\"),\n",
|
||||
"# marker=dict(color=\"grey\"),\n",
|
||||
"# fill=None,\n",
|
||||
"# connectgaps=True,\n",
|
||||
"# ))\n",
|
||||
"\n",
|
||||
"# thirtymin_slope_to_compare.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True), trace_kwargs=dict(name=\"30min slope\",\n",
|
||||
"# line=dict(color=\"yellow\"), \n",
|
||||
"# fill=None,\n",
|
||||
"# connectgaps=True,\n",
|
||||
"# ))\n",
|
||||
"\n",
|
||||
"fig.show()\n",
|
||||
"# print(\"short signal\")\n",
|
||||
"# print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
"#forced_exit = pd.Series(False, index=close.index)\n",
|
||||
"forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
"#entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
"entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
"#print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"\n",
|
||||
"entries = (long_signal & entry_window_open)\n",
|
||||
"exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"# print(short_exits.value_counts())\n",
|
||||
"# print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
"#fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
"#short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
"#print(sl_stop)\n",
|
||||
"#short_entries=short_entries, short_exits=short_exits,\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=basic_data, entries=short_entries, exits=exits, tsl_stop=0.005, tp_stop = 0.05, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"# pf.stats()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.get_drawdowns().records_readable"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.orders.records_readable"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
932
research/strats/strat_MARKOV_REGIME/v1_MULTI.ipynb
Normal file
932
research/strats/strat_MARKOV_REGIME/v1_MULTI.ipynb
Normal file
@ -0,0 +1,932 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"\n",
|
||||
"init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"#print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"SPY\": ohlcv_df}), tz_convert=zoneNY)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"# close = basic_data.xloc[\"09:30\":\"10:00\"].close"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - FOR - LOOP\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 12))\n",
|
||||
"\n",
|
||||
"#uzavreni okna od 1 do 200\n",
|
||||
"#entry_window_closes = list(range(2, 50, 3))\n",
|
||||
"entry_window_closes = [5, 10, 30, 45]\n",
|
||||
"#entry_window_closes = 30\n",
|
||||
"#threshold entries parameters\n",
|
||||
"#long\n",
|
||||
"mom_th = np.round(np.arange(0.01, 0.5 + 0.02, 0.02),4).tolist()#-0.02\n",
|
||||
"# short\n",
|
||||
"#mom_th = np.round(np.arange(-0.01, -0.3 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.8 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop =np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"tp_stop = np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"\n",
|
||||
"combs = list(product(mom_timeperiod, mom_th, roc_th, sl_stop, tp_stop))\n",
|
||||
"\n",
|
||||
"@vbt.parameterized(merge_func = \"concat\", random_subset = 2000, show_progress=True) \n",
|
||||
"def test_strat(entry_window_closes=60,\n",
|
||||
" mom_timeperiod=2,\n",
|
||||
" mom_th=-0.04,\n",
|
||||
" #roc_th=-0.2,\n",
|
||||
" sl_stop=0.19/100,\n",
|
||||
" tp_stop=0.19/100):\n",
|
||||
" # mom_timeperiod=2\n",
|
||||
" # mom_th=-0.06\n",
|
||||
" # roc_th=-0.2\n",
|
||||
" # sl_stop=0.04/100\n",
|
||||
" # tp_stop=0.04/100\n",
|
||||
"\n",
|
||||
" momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
" rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
|
||||
" #rate of change + momentum\n",
|
||||
"\n",
|
||||
" #momshort.plot rocp.real_crossed_below(roc_th) & \n",
|
||||
" #short_signal = momshort.real_crossed_below(mom_th)\n",
|
||||
" long_signal = momshort.real_crossed_above(mom_th)\n",
|
||||
" # print(\"short signal\")\n",
|
||||
" # print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
" #forced_exit = pd.Series(False, index=close.index)\n",
|
||||
" forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
" #entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
" entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
" #print(entry_window_closes, \"entry window closes\")\n",
|
||||
" # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
" elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
" entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
" #print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
" forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
" #short_entries = (short_signal & entry_window_open)\n",
|
||||
" #short_exits = forced_exit\n",
|
||||
" entries = (long_signal & entry_window_open)\n",
|
||||
" exits = forced_exit\n",
|
||||
" #long_entries.info()\n",
|
||||
" #number of trues and falses in long_entries\n",
|
||||
" #print(short_exits.value_counts())\n",
|
||||
" #print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
" #fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
" #short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" #print(sl_stop)\n",
|
||||
" #tsl_th=sl_stop, \n",
|
||||
" #short_entries=short_entries, short_exits=short_exits,\n",
|
||||
" pf = vbt.Portfolio.from_signals(close=basic_data.close, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\", price=\"close\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
" \n",
|
||||
" return pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'max_dd', \n",
|
||||
" 'total_trades', \n",
|
||||
" 'win_rate', \n",
|
||||
" 'expectancy'\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"pf_results = test_strat(vbt.Param(entry_window_closes),\n",
|
||||
" vbt.Param(mom_timeperiod),\n",
|
||||
" vbt.Param(mom_th),\n",
|
||||
" #vbt.Param(roc_th)\n",
|
||||
" vbt.Param(sl_stop),\n",
|
||||
" vbt.Param(tp_stop, condition=\"tp_stop > sl_stop\"))\n",
|
||||
"pf_results = pf_results.unstack(level=-1)\n",
|
||||
"pf_results.sort_values(by=[\"Total Return [%]\", \"Max Drawdown [%]\"], ascending=[False, True])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf_results.load(\"10tiscomb.pickle\")\n",
|
||||
"#pf_results.info()\n",
|
||||
"\n",
|
||||
"vbt.save(pf_results, \"8tiscomb_tsl.pickle\")\n",
|
||||
"\n",
|
||||
"# pf_results = vbt.load(\"8tiscomb_tsl.pickle\")\n",
|
||||
"# pf_results\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# parallel_coordinates method¶\n",
|
||||
"\n",
|
||||
"# attach_px_methods.<locals>.plot_func(\n",
|
||||
"# *args,\n",
|
||||
"# layout=None,\n",
|
||||
"# **kwargs\n",
|
||||
"# )\n",
|
||||
"\n",
|
||||
"# pf_results.vbt.px.parallel_coordinates() #ocdf\n",
|
||||
"\n",
|
||||
"res = pf_results.reset_index()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf_results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# Assuming pf_results is your DataFrame\n",
|
||||
"# Convert columns to numeric, assuming NaNs where conversion fails\n",
|
||||
"metrics = ['Total Return [%]', 'Max Drawdown [%]', 'Total Trades']\n",
|
||||
"for metric in metrics:\n",
|
||||
" pf_results[metric] = pd.to_numeric(pf_results[metric], errors='coerce')\n",
|
||||
"\n",
|
||||
"# Handle missing values, for example filling with the median\n",
|
||||
"pf_results['Max Drawdown [%]'].fillna(pf_results['Max Drawdown [%]'].median(), inplace=True)\n",
|
||||
"\n",
|
||||
"# Extract the metrics into a new DataFrame\n",
|
||||
"data_for_pca = pf_results[metrics]\n",
|
||||
"\n",
|
||||
"# Standardize the data before applying PCA\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"data_scaled = scaler.fit_transform(data_for_pca)\n",
|
||||
"\n",
|
||||
"# Apply PCA\n",
|
||||
"pca = PCA(n_components=2) # Adjust components as needed\n",
|
||||
"principal_components = pca.fit_transform(data_scaled)\n",
|
||||
"\n",
|
||||
"# Create a DataFrame with the principal components\n",
|
||||
"pca_results = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])\n",
|
||||
"\n",
|
||||
"# Visualize the results\n",
|
||||
"plt.figure(figsize=(8,6))\n",
|
||||
"plt.scatter(pca_results['PC1'], pca_results['PC2'], alpha=0.5)\n",
|
||||
"plt.xlabel('Principal Component 1')\n",
|
||||
"plt.ylabel('Principal Component 2')\n",
|
||||
"plt.title('PCA of Strategy Optimization Results')\n",
|
||||
"plt.grid(True)\n",
|
||||
"plt.savefig(\"ddd.png\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check if there is any unnamed level and rename it\n",
|
||||
"if None in df.index.names:\n",
|
||||
" # Generate new names list replacing None with 'stat'\n",
|
||||
" new_names = ['stat' if name is None else name for name in df.index.names]\n",
|
||||
" df.index.set_names(new_names, inplace=True)\n",
|
||||
"\n",
|
||||
"rs= df\n",
|
||||
"\n",
|
||||
"rs.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Now, 'stat' is the name of the previously unnamed level\n",
|
||||
"\n",
|
||||
"# # Filter for 'Total Return' assuming it is a correct identifier in the 'stat' level\n",
|
||||
"# total_return_series = df.xs('Total Return [%]', level='stat')\n",
|
||||
"\n",
|
||||
"# # Sort the Series to get the largest 'Total Return' values\n",
|
||||
"# sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# # Print the sorted filtered data\n",
|
||||
"# sorted_series.head(20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sorted_series.vbt.save()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#df.info()\n",
|
||||
"total_return_series = df.xs('Total Return [%]')\n",
|
||||
"sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# Display the top N entries, e.g., top 5\n",
|
||||
"sorted_series.head(5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"comb_stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
|
||||
"4 -0.02 -0.25 0.0028 0.0048 0.84433\n",
|
||||
"3 -0.02 -0.25 0.0033 0.0023 Total Return [%] 0.846753\n",
|
||||
"#2\t-0.04\t-0.2\t0.0019\t0.0019\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"basic_data.symbols"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
">>> def apply_func(ts, entries, exits, fastw, sloww, minp=None):\n",
|
||||
"... fast_ma = vbt.nb.rolling_mean_nb(ts, fastw, minp=minp)\n",
|
||||
"... slow_ma = vbt.nb.rolling_mean_nb(ts, sloww, minp=minp)\n",
|
||||
"... entries[:] = vbt.nb.crossed_above_nb(fast_ma, slow_ma) \n",
|
||||
"... exits[:] = vbt.nb.crossed_above_nb(slow_ma, fast_ma)\n",
|
||||
"... return (fast_ma, slow_ma) \n",
|
||||
"\n",
|
||||
">>> CrossSig = vbt.IF(\n",
|
||||
"... class_name=\"CrossSig\",\n",
|
||||
"... input_names=['ts'],\n",
|
||||
"... in_output_names=['entries', 'exits'],\n",
|
||||
"... param_names=['fastw', 'sloww'],\n",
|
||||
"... output_names=['fast_ma', 'slow_ma']\n",
|
||||
"... ).with_apply_func(\n",
|
||||
"... apply_func,\n",
|
||||
"... in_output_settings=dict(\n",
|
||||
"... entries=dict(dtype=np.bool_), #initialize output with bool\n",
|
||||
"... exits=dict(dtype=np.bool_)\n",
|
||||
"... )\n",
|
||||
"... )\n",
|
||||
">>> cross_sig = CrossSig.run(ts2, 2, 4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - parameters in one go\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#TOTO prepsat do FOR-LOOPu\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 6))\n",
|
||||
"\n",
|
||||
"#threshold entries parameters\n",
|
||||
"mom_th = np.round(np.arange(-0.02, -0.1 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.4 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"#jejich product\n",
|
||||
"# mom_th_prod, roc_th_prod = zip(*product(mom_th, roc_th))\n",
|
||||
"\n",
|
||||
"# #convert threshold to vbt param\n",
|
||||
"# mom_th_index = vbt.Param(mom_th_prod, name='mom_th_th') \n",
|
||||
"# roc_th_index = vbt.Param(roc_th_prod, name='roc_th_th')\n",
|
||||
"\n",
|
||||
"mom_th = vbt.Param(mom_th, name='mom_th')\n",
|
||||
"roc_th = vbt.Param(roc_th, name='roc_th')\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"tp_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"tp_stop = [round(val, 4) for val in tp_stop]\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"tp_stop = vbt.Param(tp_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#def test_mom(window=14, mom_th=0.2, roc_th=0.2, sl_stop=0.03/100, tp_stop=0.03/100):\n",
|
||||
"#close = basic_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(basic_data.get(\"Close\"), timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
"\n",
|
||||
"#ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(basic_data.get(\"Close\"), short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"\n",
|
||||
"rocp_signal = rocp.real_crossed_below(mom_th)\n",
|
||||
"mom_signal = momshort.real_crossed_below(roc_th)\n",
|
||||
"\n",
|
||||
"#mom_signal\n",
|
||||
"print(rocp_signal.info())\n",
|
||||
"print(mom_signal.info())\n",
|
||||
"#print(rocp.real)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"short_signal = (mom_signal.vbt & rocp_signal)\n",
|
||||
"\n",
|
||||
"# #short_signal = (rocp.real_crossed_below(roc_th_index) & momshort.real_crossed_below(mom_th_index))\n",
|
||||
"# forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"# entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"# elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"# entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"# forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"# short_entries = (short_signal & entry_window_open)\n",
|
||||
"# short_exits = forced_exit\n",
|
||||
"# #long_entries.info()\n",
|
||||
"# #number of trues and falses in long_entries\n",
|
||||
"# #short_exits.value_counts()\n",
|
||||
"# #short_entries.value_counts()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# filter dates"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#filter na dny\n",
|
||||
"dates_of_interest = pd.to_datetime(['2024-04-22']).tz_localize('US/Eastern')\n",
|
||||
"filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
|
||||
"\n",
|
||||
"df = filtered_df\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import plotly.io as pio\n",
|
||||
"# pio.renderers.default = 'notebook'\n",
|
||||
"\n",
|
||||
"#naloadujeme do vbt symbol as column\n",
|
||||
"basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
|
||||
"\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"#basic_data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"\n",
|
||||
"m1_data.data[\"BAC\"]\n",
|
||||
"#m5_data = m1_data.resample(\"5T\")\n",
|
||||
"\n",
|
||||
"#m5_data.data[\"BAC\"].head(10)\n",
|
||||
"\n",
|
||||
"# m15_data = m1_data.resample(\"15T\")\n",
|
||||
"\n",
|
||||
"# m15 = m15_data.data[\"BAC\"]\n",
|
||||
"\n",
|
||||
"# m15.vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"# m1_data.wrapper.index\n",
|
||||
"\n",
|
||||
"# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
|
||||
"# m1_resampler.index_difference(reverse=True)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# m5_resampler.prettify()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MOM indicator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.indicator(\"talib:ROCP\").run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"vyuzití rychleho klesani na sekundove urovni behem open rush\n",
|
||||
"- MOM + ROC during open rush\n",
|
||||
"- short signal\n",
|
||||
"- pipeline kombinace thresholdu pro vstup mom_th, roc_th + hodnota sl_stop a tp_stop (pripadne trailing) - nalezeni optimalni kombinace atributu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"# short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
"\n",
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"close = m1_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(close, timeperiod=3, short_name = \"slope_short\")\n",
|
||||
"ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(close, short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"short_signal = (rocp.real_crossed_below(-0.2) & momshort.real_crossed_below(-0.02))\n",
|
||||
"#indlong = vbt.indicator(\"talib:MOM\").run(close, timeperiod=10, short_name = \"slope_long\")\n",
|
||||
"fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"#vbt.phelp(vbt.OLS.run)\n",
|
||||
"\n",
|
||||
"#oer steepmnes of regression line\n",
|
||||
"#talib.LINEARREG_SLOPE(close, timeperiod=timeperiod)\n",
|
||||
"#a také ON BALANCE VOLUME - http://5.161.179.223:8000/static/js/vbt/api/indicators/custom/obv/index.html\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mom_ind = vbt.indicator(\"talib:MOM\") \n",
|
||||
"#vbt.phelp(mom_ind.run)\n",
|
||||
"\n",
|
||||
"mom = mom_ind.run(close, timeperiod=10)\n",
|
||||
"\n",
|
||||
"plot_2y_close(mom, close)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# defining ENTRY WINDOW and forced EXIT window"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#m1_data.data[\"BAC\"].info()\n",
|
||||
"import datetime\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 2\n",
|
||||
"entry_window_closes = 30\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"\n",
|
||||
"#entry_window_open.info()\n",
|
||||
"# forced_exit.tail(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"\n",
|
||||
"#rsi = vbt.RSI.run(close, window=14)\n",
|
||||
"\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"#short_exits.value_counts()\n",
|
||||
"short_entries.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_rsi(close, entries, exits):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" #rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" entries.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_rsi(close, short_entries, short_exits)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.Portfolio.from_signals)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"print(sl_stop)\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=0.03/100, tp_stop = 0.03/100, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"#pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#list of orders\n",
|
||||
"#pf.orders.records_readable\n",
|
||||
"#pf.orders.plots()\n",
|
||||
"#pf.stats()\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0015,0.0013)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[0.03].plot_trade_signals()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# pristup k pf jako multi index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf[0.03].plot()\n",
|
||||
"#pf.order_records\n",
|
||||
"pf[(0.03)].stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#zgrupovane statistiky\n",
|
||||
"stats_df = pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'total_trades',\n",
|
||||
" 'win_rate',\n",
|
||||
" 'expectancy'\n",
|
||||
"], agg_func=None)\n",
|
||||
"stats_df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0011,0.0013000000000000002)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pandas.tseries.offsets import DateOffset\n",
|
||||
"\n",
|
||||
"temp_data = basic_data['2024-4-22']\n",
|
||||
"temp_data\n",
|
||||
"res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
|
||||
"\n",
|
||||
"# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
|
||||
"custom_offset = DateOffset(hours=4, minutes=30)\n",
|
||||
"\n",
|
||||
"# res1m = res1m.get().resample(\"4H\").agg({ \n",
|
||||
"# \"Open\": \"first\",\n",
|
||||
"# \"High\": \"max\",\n",
|
||||
"# \"Low\": \"min\",\n",
|
||||
"# \"Close\": \"last\",\n",
|
||||
"# \"Volume\": \"sum\"\n",
|
||||
"# })\n",
|
||||
"\n",
|
||||
"res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res4h.data\n",
|
||||
"\n",
|
||||
"res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res15m.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@vbt.njit\n",
|
||||
"def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
|
||||
" market_open_minutes = 570 # 9 hours * 60 minutes + 30 minutes\n",
|
||||
"\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
"\n",
|
||||
" current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
|
||||
" #print(\"current_minutes\", current_minutes)\n",
|
||||
" # Calculate elapsed minutes since market open at 9:30 AM\n",
|
||||
" elapsed_from_open = current_minutes - market_open_minutes\n",
|
||||
" elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
|
||||
" #print( \"elapsed_from_open\", elapsed_from_open)\n",
|
||||
"\n",
|
||||
" #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
|
||||
" in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
|
||||
" #print(\"in_window\", in_window)\n",
|
||||
" # if in_window:\n",
|
||||
" # print(\"in window\")\n",
|
||||
"\n",
|
||||
" if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9: # (4)!\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"@vbt.njit\n",
|
||||
"def long_exit_place_func_nb(c, high, close, time_index, tp, sl): # (5)!\n",
|
||||
" entry_i = c.from_i - c.wait\n",
|
||||
" entry_price = close[entry_i, c.col]\n",
|
||||
" hit_price = entry_price * (1 + tp)\n",
|
||||
" stop_price = entry_price * (1 - sl)\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
" last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
|
||||
"\n",
|
||||
" #print(next_day)\n",
|
||||
" if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
|
||||
" print(\"ted\",out_i)\n",
|
||||
" return out_i\n",
|
||||
" if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
|
||||
"\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.sum()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
1899
research/strats/strat_MARKOV_REGIME/v2_SINGLE.ipynb
Normal file
1899
research/strats/strat_MARKOV_REGIME/v2_SINGLE.ipynb
Normal file
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
932
research/strats/strat_ORDER_IMBALANCE/v1_MULTI.ipynb
Normal file
932
research/strats/strat_ORDER_IMBALANCE/v1_MULTI.ipynb
Normal file
@ -0,0 +1,932 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"\n",
|
||||
"init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"#print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"SPY\": ohlcv_df}), tz_convert=zoneNY)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"# close = basic_data.xloc[\"09:30\":\"10:00\"].close"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - FOR - LOOP\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 12))\n",
|
||||
"\n",
|
||||
"#uzavreni okna od 1 do 200\n",
|
||||
"#entry_window_closes = list(range(2, 50, 3))\n",
|
||||
"entry_window_closes = [5, 10, 30, 45]\n",
|
||||
"#entry_window_closes = 30\n",
|
||||
"#threshold entries parameters\n",
|
||||
"#long\n",
|
||||
"mom_th = np.round(np.arange(0.01, 0.5 + 0.02, 0.02),4).tolist()#-0.02\n",
|
||||
"# short\n",
|
||||
"#mom_th = np.round(np.arange(-0.01, -0.3 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.8 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop =np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"tp_stop = np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"\n",
|
||||
"combs = list(product(mom_timeperiod, mom_th, roc_th, sl_stop, tp_stop))\n",
|
||||
"\n",
|
||||
"@vbt.parameterized(merge_func = \"concat\", random_subset = 2000, show_progress=True) \n",
|
||||
"def test_strat(entry_window_closes=60,\n",
|
||||
" mom_timeperiod=2,\n",
|
||||
" mom_th=-0.04,\n",
|
||||
" #roc_th=-0.2,\n",
|
||||
" sl_stop=0.19/100,\n",
|
||||
" tp_stop=0.19/100):\n",
|
||||
" # mom_timeperiod=2\n",
|
||||
" # mom_th=-0.06\n",
|
||||
" # roc_th=-0.2\n",
|
||||
" # sl_stop=0.04/100\n",
|
||||
" # tp_stop=0.04/100\n",
|
||||
"\n",
|
||||
" momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
" rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
|
||||
" #rate of change + momentum\n",
|
||||
"\n",
|
||||
" #momshort.plot rocp.real_crossed_below(roc_th) & \n",
|
||||
" #short_signal = momshort.real_crossed_below(mom_th)\n",
|
||||
" long_signal = momshort.real_crossed_above(mom_th)\n",
|
||||
" # print(\"short signal\")\n",
|
||||
" # print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
" #forced_exit = pd.Series(False, index=close.index)\n",
|
||||
" forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
" #entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
" entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
" #print(entry_window_closes, \"entry window closes\")\n",
|
||||
" # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
" elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
" entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
" #print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
" forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
" #short_entries = (short_signal & entry_window_open)\n",
|
||||
" #short_exits = forced_exit\n",
|
||||
" entries = (long_signal & entry_window_open)\n",
|
||||
" exits = forced_exit\n",
|
||||
" #long_entries.info()\n",
|
||||
" #number of trues and falses in long_entries\n",
|
||||
" #print(short_exits.value_counts())\n",
|
||||
" #print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
" #fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
" #short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" #print(sl_stop)\n",
|
||||
" #tsl_th=sl_stop, \n",
|
||||
" #short_entries=short_entries, short_exits=short_exits,\n",
|
||||
" pf = vbt.Portfolio.from_signals(close=basic_data.close, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\", price=\"close\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
" \n",
|
||||
" return pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'max_dd', \n",
|
||||
" 'total_trades', \n",
|
||||
" 'win_rate', \n",
|
||||
" 'expectancy'\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"pf_results = test_strat(vbt.Param(entry_window_closes),\n",
|
||||
" vbt.Param(mom_timeperiod),\n",
|
||||
" vbt.Param(mom_th),\n",
|
||||
" #vbt.Param(roc_th)\n",
|
||||
" vbt.Param(sl_stop),\n",
|
||||
" vbt.Param(tp_stop, condition=\"tp_stop > sl_stop\"))\n",
|
||||
"pf_results = pf_results.unstack(level=-1)\n",
|
||||
"pf_results.sort_values(by=[\"Total Return [%]\", \"Max Drawdown [%]\"], ascending=[False, True])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf_results.load(\"10tiscomb.pickle\")\n",
|
||||
"#pf_results.info()\n",
|
||||
"\n",
|
||||
"vbt.save(pf_results, \"8tiscomb_tsl.pickle\")\n",
|
||||
"\n",
|
||||
"# pf_results = vbt.load(\"8tiscomb_tsl.pickle\")\n",
|
||||
"# pf_results\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# parallel_coordinates method¶\n",
|
||||
"\n",
|
||||
"# attach_px_methods.<locals>.plot_func(\n",
|
||||
"# *args,\n",
|
||||
"# layout=None,\n",
|
||||
"# **kwargs\n",
|
||||
"# )\n",
|
||||
"\n",
|
||||
"# pf_results.vbt.px.parallel_coordinates() #ocdf\n",
|
||||
"\n",
|
||||
"res = pf_results.reset_index()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf_results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# Assuming pf_results is your DataFrame\n",
|
||||
"# Convert columns to numeric, assuming NaNs where conversion fails\n",
|
||||
"metrics = ['Total Return [%]', 'Max Drawdown [%]', 'Total Trades']\n",
|
||||
"for metric in metrics:\n",
|
||||
" pf_results[metric] = pd.to_numeric(pf_results[metric], errors='coerce')\n",
|
||||
"\n",
|
||||
"# Handle missing values, for example filling with the median\n",
|
||||
"pf_results['Max Drawdown [%]'].fillna(pf_results['Max Drawdown [%]'].median(), inplace=True)\n",
|
||||
"\n",
|
||||
"# Extract the metrics into a new DataFrame\n",
|
||||
"data_for_pca = pf_results[metrics]\n",
|
||||
"\n",
|
||||
"# Standardize the data before applying PCA\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"data_scaled = scaler.fit_transform(data_for_pca)\n",
|
||||
"\n",
|
||||
"# Apply PCA\n",
|
||||
"pca = PCA(n_components=2) # Adjust components as needed\n",
|
||||
"principal_components = pca.fit_transform(data_scaled)\n",
|
||||
"\n",
|
||||
"# Create a DataFrame with the principal components\n",
|
||||
"pca_results = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])\n",
|
||||
"\n",
|
||||
"# Visualize the results\n",
|
||||
"plt.figure(figsize=(8,6))\n",
|
||||
"plt.scatter(pca_results['PC1'], pca_results['PC2'], alpha=0.5)\n",
|
||||
"plt.xlabel('Principal Component 1')\n",
|
||||
"plt.ylabel('Principal Component 2')\n",
|
||||
"plt.title('PCA of Strategy Optimization Results')\n",
|
||||
"plt.grid(True)\n",
|
||||
"plt.savefig(\"ddd.png\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check if there is any unnamed level and rename it\n",
|
||||
"if None in df.index.names:\n",
|
||||
" # Generate new names list replacing None with 'stat'\n",
|
||||
" new_names = ['stat' if name is None else name for name in df.index.names]\n",
|
||||
" df.index.set_names(new_names, inplace=True)\n",
|
||||
"\n",
|
||||
"rs= df\n",
|
||||
"\n",
|
||||
"rs.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Now, 'stat' is the name of the previously unnamed level\n",
|
||||
"\n",
|
||||
"# # Filter for 'Total Return' assuming it is a correct identifier in the 'stat' level\n",
|
||||
"# total_return_series = df.xs('Total Return [%]', level='stat')\n",
|
||||
"\n",
|
||||
"# # Sort the Series to get the largest 'Total Return' values\n",
|
||||
"# sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# # Print the sorted filtered data\n",
|
||||
"# sorted_series.head(20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sorted_series.vbt.save()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#df.info()\n",
|
||||
"total_return_series = df.xs('Total Return [%]')\n",
|
||||
"sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# Display the top N entries, e.g., top 5\n",
|
||||
"sorted_series.head(5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"comb_stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
|
||||
"4 -0.02 -0.25 0.0028 0.0048 0.84433\n",
|
||||
"3 -0.02 -0.25 0.0033 0.0023 Total Return [%] 0.846753\n",
|
||||
"#2\t-0.04\t-0.2\t0.0019\t0.0019\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"basic_data.symbols"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
">>> def apply_func(ts, entries, exits, fastw, sloww, minp=None):\n",
|
||||
"... fast_ma = vbt.nb.rolling_mean_nb(ts, fastw, minp=minp)\n",
|
||||
"... slow_ma = vbt.nb.rolling_mean_nb(ts, sloww, minp=minp)\n",
|
||||
"... entries[:] = vbt.nb.crossed_above_nb(fast_ma, slow_ma) \n",
|
||||
"... exits[:] = vbt.nb.crossed_above_nb(slow_ma, fast_ma)\n",
|
||||
"... return (fast_ma, slow_ma) \n",
|
||||
"\n",
|
||||
">>> CrossSig = vbt.IF(\n",
|
||||
"... class_name=\"CrossSig\",\n",
|
||||
"... input_names=['ts'],\n",
|
||||
"... in_output_names=['entries', 'exits'],\n",
|
||||
"... param_names=['fastw', 'sloww'],\n",
|
||||
"... output_names=['fast_ma', 'slow_ma']\n",
|
||||
"... ).with_apply_func(\n",
|
||||
"... apply_func,\n",
|
||||
"... in_output_settings=dict(\n",
|
||||
"... entries=dict(dtype=np.bool_), #initialize output with bool\n",
|
||||
"... exits=dict(dtype=np.bool_)\n",
|
||||
"... )\n",
|
||||
"... )\n",
|
||||
">>> cross_sig = CrossSig.run(ts2, 2, 4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - parameters in one go\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#TOTO prepsat do FOR-LOOPu\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 6))\n",
|
||||
"\n",
|
||||
"#threshold entries parameters\n",
|
||||
"mom_th = np.round(np.arange(-0.02, -0.1 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.4 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"#jejich product\n",
|
||||
"# mom_th_prod, roc_th_prod = zip(*product(mom_th, roc_th))\n",
|
||||
"\n",
|
||||
"# #convert threshold to vbt param\n",
|
||||
"# mom_th_index = vbt.Param(mom_th_prod, name='mom_th_th') \n",
|
||||
"# roc_th_index = vbt.Param(roc_th_prod, name='roc_th_th')\n",
|
||||
"\n",
|
||||
"mom_th = vbt.Param(mom_th, name='mom_th')\n",
|
||||
"roc_th = vbt.Param(roc_th, name='roc_th')\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"tp_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"tp_stop = [round(val, 4) for val in tp_stop]\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"tp_stop = vbt.Param(tp_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#def test_mom(window=14, mom_th=0.2, roc_th=0.2, sl_stop=0.03/100, tp_stop=0.03/100):\n",
|
||||
"#close = basic_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(basic_data.get(\"Close\"), timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
"\n",
|
||||
"#ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(basic_data.get(\"Close\"), short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"\n",
|
||||
"rocp_signal = rocp.real_crossed_below(mom_th)\n",
|
||||
"mom_signal = momshort.real_crossed_below(roc_th)\n",
|
||||
"\n",
|
||||
"#mom_signal\n",
|
||||
"print(rocp_signal.info())\n",
|
||||
"print(mom_signal.info())\n",
|
||||
"#print(rocp.real)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"short_signal = (mom_signal.vbt & rocp_signal)\n",
|
||||
"\n",
|
||||
"# #short_signal = (rocp.real_crossed_below(roc_th_index) & momshort.real_crossed_below(mom_th_index))\n",
|
||||
"# forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"# entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"# elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"# entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"# forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"# short_entries = (short_signal & entry_window_open)\n",
|
||||
"# short_exits = forced_exit\n",
|
||||
"# #long_entries.info()\n",
|
||||
"# #number of trues and falses in long_entries\n",
|
||||
"# #short_exits.value_counts()\n",
|
||||
"# #short_entries.value_counts()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# filter dates"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#filter na dny\n",
|
||||
"dates_of_interest = pd.to_datetime(['2024-04-22']).tz_localize('US/Eastern')\n",
|
||||
"filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
|
||||
"\n",
|
||||
"df = filtered_df\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import plotly.io as pio\n",
|
||||
"# pio.renderers.default = 'notebook'\n",
|
||||
"\n",
|
||||
"#naloadujeme do vbt symbol as column\n",
|
||||
"basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
|
||||
"\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"#basic_data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"\n",
|
||||
"m1_data.data[\"BAC\"]\n",
|
||||
"#m5_data = m1_data.resample(\"5T\")\n",
|
||||
"\n",
|
||||
"#m5_data.data[\"BAC\"].head(10)\n",
|
||||
"\n",
|
||||
"# m15_data = m1_data.resample(\"15T\")\n",
|
||||
"\n",
|
||||
"# m15 = m15_data.data[\"BAC\"]\n",
|
||||
"\n",
|
||||
"# m15.vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"# m1_data.wrapper.index\n",
|
||||
"\n",
|
||||
"# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
|
||||
"# m1_resampler.index_difference(reverse=True)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# m5_resampler.prettify()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MOM indicator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.indicator(\"talib:ROCP\").run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"vyuzití rychleho klesani na sekundove urovni behem open rush\n",
|
||||
"- MOM + ROC during open rush\n",
|
||||
"- short signal\n",
|
||||
"- pipeline kombinace thresholdu pro vstup mom_th, roc_th + hodnota sl_stop a tp_stop (pripadne trailing) - nalezeni optimalni kombinace atributu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"# short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
"\n",
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"close = m1_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(close, timeperiod=3, short_name = \"slope_short\")\n",
|
||||
"ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(close, short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"short_signal = (rocp.real_crossed_below(-0.2) & momshort.real_crossed_below(-0.02))\n",
|
||||
"#indlong = vbt.indicator(\"talib:MOM\").run(close, timeperiod=10, short_name = \"slope_long\")\n",
|
||||
"fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"#vbt.phelp(vbt.OLS.run)\n",
|
||||
"\n",
|
||||
"#oer steepmnes of regression line\n",
|
||||
"#talib.LINEARREG_SLOPE(close, timeperiod=timeperiod)\n",
|
||||
"#a také ON BALANCE VOLUME - http://5.161.179.223:8000/static/js/vbt/api/indicators/custom/obv/index.html\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mom_ind = vbt.indicator(\"talib:MOM\") \n",
|
||||
"#vbt.phelp(mom_ind.run)\n",
|
||||
"\n",
|
||||
"mom = mom_ind.run(close, timeperiod=10)\n",
|
||||
"\n",
|
||||
"plot_2y_close(mom, close)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# defining ENTRY WINDOW and forced EXIT window"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#m1_data.data[\"BAC\"].info()\n",
|
||||
"import datetime\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 2\n",
|
||||
"entry_window_closes = 30\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"\n",
|
||||
"#entry_window_open.info()\n",
|
||||
"# forced_exit.tail(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"\n",
|
||||
"#rsi = vbt.RSI.run(close, window=14)\n",
|
||||
"\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"#short_exits.value_counts()\n",
|
||||
"short_entries.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_rsi(close, entries, exits):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" #rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" entries.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_rsi(close, short_entries, short_exits)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.Portfolio.from_signals)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"print(sl_stop)\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=0.03/100, tp_stop = 0.03/100, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"#pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#list of orders\n",
|
||||
"#pf.orders.records_readable\n",
|
||||
"#pf.orders.plots()\n",
|
||||
"#pf.stats()\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0015,0.0013)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[0.03].plot_trade_signals()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# pristup k pf jako multi index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf[0.03].plot()\n",
|
||||
"#pf.order_records\n",
|
||||
"pf[(0.03)].stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#zgrupovane statistiky\n",
|
||||
"stats_df = pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'total_trades',\n",
|
||||
" 'win_rate',\n",
|
||||
" 'expectancy'\n",
|
||||
"], agg_func=None)\n",
|
||||
"stats_df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0011,0.0013000000000000002)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pandas.tseries.offsets import DateOffset\n",
|
||||
"\n",
|
||||
"temp_data = basic_data['2024-4-22']\n",
|
||||
"temp_data\n",
|
||||
"res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
|
||||
"\n",
|
||||
"# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
|
||||
"custom_offset = DateOffset(hours=4, minutes=30)\n",
|
||||
"\n",
|
||||
"# res1m = res1m.get().resample(\"4H\").agg({ \n",
|
||||
"# \"Open\": \"first\",\n",
|
||||
"# \"High\": \"max\",\n",
|
||||
"# \"Low\": \"min\",\n",
|
||||
"# \"Close\": \"last\",\n",
|
||||
"# \"Volume\": \"sum\"\n",
|
||||
"# })\n",
|
||||
"\n",
|
||||
"res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res4h.data\n",
|
||||
"\n",
|
||||
"res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res15m.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@vbt.njit\n",
|
||||
"def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
|
||||
" market_open_minutes = 570 # 9 hours * 60 minutes + 30 minutes\n",
|
||||
"\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
"\n",
|
||||
" current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
|
||||
" #print(\"current_minutes\", current_minutes)\n",
|
||||
" # Calculate elapsed minutes since market open at 9:30 AM\n",
|
||||
" elapsed_from_open = current_minutes - market_open_minutes\n",
|
||||
" elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
|
||||
" #print( \"elapsed_from_open\", elapsed_from_open)\n",
|
||||
"\n",
|
||||
" #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
|
||||
" in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
|
||||
" #print(\"in_window\", in_window)\n",
|
||||
" # if in_window:\n",
|
||||
" # print(\"in window\")\n",
|
||||
"\n",
|
||||
" if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9: # (4)!\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"@vbt.njit\n",
|
||||
"def long_exit_place_func_nb(c, high, close, time_index, tp, sl): # (5)!\n",
|
||||
" entry_i = c.from_i - c.wait\n",
|
||||
" entry_price = close[entry_i, c.col]\n",
|
||||
" hit_price = entry_price * (1 + tp)\n",
|
||||
" stop_price = entry_price * (1 - sl)\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
" last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
|
||||
"\n",
|
||||
" #print(next_day)\n",
|
||||
" if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
|
||||
" print(\"ted\",out_i)\n",
|
||||
" return out_i\n",
|
||||
" if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
|
||||
"\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.sum()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
964
research/strats/strat_ORDER_IMBALANCE/v1_SINGLE.ipynb
Normal file
964
research/strats/strat_ORDER_IMBALANCE/v1_SINGLE.ipynb
Normal file
@ -0,0 +1,964 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# ORDER Imbalance\n",
|
||||
"\n",
|
||||
"* introduced buyvolume and sellvolume on bar level.\n",
|
||||
"* calculated order imbalance ratio (buyvolume-sellvolume/totalvolume)\n",
|
||||
"* calculated on multiple timeframes\n",
|
||||
"* entry based on confluences imbalances\n",
|
||||
"\n",
|
||||
"## Note\n",
|
||||
"\n",
|
||||
"Order disbalance nepodminuje zmenu ceny (tzn. muze byt order disbalance na buy stranu, ale cena nemusi jit nahoru a naopak)\n",
|
||||
"Nicmene pokud je disbalance delsi a nedochazi ke zmene ceny - může to něco indikovat. \n",
|
||||
"Vytvořit si kumulativní disbalance - kumulátory, které se budou načítat, když se budou silné disbalance, bez změny ceny. Tento akumulátor se bude nabíjet disbalancí a vybíjet příslušnou změnou ceny."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"# from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"from lightweight_charts import JupyterChart, chart as chartp, Panel\n",
|
||||
"from IPython.display import display\n",
|
||||
"\n",
|
||||
"# init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T15_30_00-47BCFOPUVWZ-100.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"#filter ohlcv_df to certain date range (assuming datetime index)\n",
|
||||
"ohlcv_df = ohlcv_df.loc[\"2024-02-12 9:30\":\"2024-03-14 16:00\"]\n",
|
||||
"\n",
|
||||
"#add vwap column to ohlcv_df\n",
|
||||
"#ohlcv_df[\"hlcc4\"] = (ohlcv_df[\"close\"] + ohlcv_df[\"high\"] + ohlcv_df[\"low\"] + ohlcv_df[\"close\"]) / 4\n",
|
||||
"\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"BAC\": ohlcv_df}), tz_convert=zoneNY)\n",
|
||||
"ohlcv_df= None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Add resample function to custom columns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig\n",
|
||||
"from vectorbtpro import _typing as tp\n",
|
||||
"from vectorbtpro.generic import nb as generic_nb\n",
|
||||
"\n",
|
||||
"_feature_config: tp.ClassVar[Config] = HybridConfig(\n",
|
||||
" {\n",
|
||||
" \"buyvolume\": dict(\n",
|
||||
" resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
|
||||
" resampler,\n",
|
||||
" generic_nb.sum_reduce_nb,\n",
|
||||
" )\n",
|
||||
" ),\n",
|
||||
" \"sellvolume\": dict(\n",
|
||||
" resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
|
||||
" resampler,\n",
|
||||
" generic_nb.sum_reduce_nb,\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" }\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"basic_data._feature_config = _feature_config"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#asic_data.stats()\n",
|
||||
"basic_data.wrapper.index.normalize().nunique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#basic_data.ohlcv.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"second_data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']]\n",
|
||||
"t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"1T\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"#t1data = t1data.xloc[\"2024-02-12 9:30\":\"2024-02-14 16:00\"]\n",
|
||||
"basic_data = t1data"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"buyvolume = t1data.data[\"BAC\"].buyvolume\n",
|
||||
"sellvolume = t1data.data[\"BAC\"].sellvolume\n",
|
||||
"totalvolume = buyvolume + sellvolume\n",
|
||||
"\n",
|
||||
"#adjust to minimal value to avoid division by zero\n",
|
||||
"sellvolume_adjusted = sellvolume.replace(0, 1e-10)\n",
|
||||
"oibratio = buyvolume / sellvolume\n",
|
||||
"\n",
|
||||
"#cumulative order flow (net difference)\n",
|
||||
"cof = buyvolume - sellvolume\n",
|
||||
"\n",
|
||||
"# Calculate the order imbalance (net differene) normalize the order imbalance by calculating the difference between buy and sell volumes and then scaling it by the total volume.\n",
|
||||
"order_imbalance = cof / totalvolume\n",
|
||||
"order_imbalance = order_imbalance.fillna(0) #nan nahradime 0\n",
|
||||
"\n",
|
||||
"order_imbalance_allvolume = cof / t1data.data[\"BAC\"].volume\n",
|
||||
"\n",
|
||||
"order_imbalance_sma = vbt.indicator(\"talib:EMA\").run(order_imbalance, timeperiod=5)\n",
|
||||
"short_signals = order_imbalance.vbt < -0.5\n",
|
||||
"#short_entries = oibratio.vbt < 0.01\n",
|
||||
"short_signals.value_counts()\n",
|
||||
"short_signals.name = \"short_entries\"\n",
|
||||
"#.fillna(False)\n",
|
||||
"short_exits = short_signals.shift(-2).fillna(False).astype(bool)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"##z tohoto si udelat plot funkci (i pro entries,exits)\n",
|
||||
"#t1data = t1data[[\"open\", \"high\", \"low\", \"close\", \"volume\"]]\n",
|
||||
"chart = JupyterChart(width=1000, height=600, inner_width=1, inner_height=0.5, leftScale=True)\n",
|
||||
"#set resolution\n",
|
||||
"t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"1T\")\n",
|
||||
"t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"chart.set(t1data.data[\"BAC\"])\n",
|
||||
"line_vwap = chart.create_line(name=\"vwap\")#, color=\"blue\")\n",
|
||||
"line_vwap.set(t1data.vwap)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"chart.topbar.textbox(\"title\",\"Nadpis\")\n",
|
||||
"chart2 = chart.create_subchart(position='right', width=1, height=0.5, sync=True, leftScale=True)\n",
|
||||
"t2data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"5T\")\n",
|
||||
"t2data = t2data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#5min close realigned to 1T\n",
|
||||
"close_realigned = t2data.close.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
|
||||
"#close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
|
||||
"line1 = chart.create_line(name=\"5minclose\")#, color=\"green\")\n",
|
||||
"line1.set(close_realigned)\n",
|
||||
"\n",
|
||||
"#sma z realigned dat\n",
|
||||
"sma_tp = 20\n",
|
||||
"sma_t2 = vbt.indicator(\"talib:EMA\").run(close_realigned, timeperiod=sma_tp)\n",
|
||||
"smaline = chart.create_line(name=f\"sma{sma_tp}\")#, color=\"blue\")\n",
|
||||
"smaline.set(sma_t2)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#sma z puvodnich resamplovanych dat plus navic realign, melo by byt stejne \n",
|
||||
"sma_real = vbt.indicator(\"talib:EMA\").run(t2data.close, timeperiod=sma_tp)\n",
|
||||
"sma_real_value = sma_real.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"sma_real_value = sma_real_value[sma_real_value.index.dayofweek < 5]\n",
|
||||
"smaline_real = chart.create_line(name=f\"smareal{sma_tp}\", color=\"yellow\")\n",
|
||||
"smaline_real.set(sma_real_value)\n",
|
||||
"\n",
|
||||
"#resample 15T\n",
|
||||
"t15data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"15T\")\n",
|
||||
"t15data = t15data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"\n",
|
||||
"#5min close realigned to 1T\n",
|
||||
"close_15realigned = t15data.close.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"close_15realigned = close_15realigned[close_15realigned.index.dayofweek < 5]\n",
|
||||
"#close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
|
||||
"line2 = chart.create_line(name=\"15minclose\")#, color=\"pink\")\n",
|
||||
"line2.set(close_15realigned)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"chart.legend(True)\n",
|
||||
"hst = chart2.create_histogram(name=\"buyvolume\", color=\"rgba(53, 94, 59, 0.6)\") #green transparent\n",
|
||||
"hst1 = chart2.create_histogram(name=\"sellvolume\", color=\"rgba(165, 42, 42, 0.6)\") #red transparent\n",
|
||||
"hst.set(t1data.data[\"BAC\"])\n",
|
||||
"hst1.set(t1data.data[\"BAC\"])\n",
|
||||
"line2 = chart2.create_line(name=\"5minclose\")#, color=\"green\")\n",
|
||||
"line2.set(close_realigned)\n",
|
||||
"\n",
|
||||
"lineoib = chart2.create_line(name=\"oib\", priceScaleId=\"left\") #color=\"violet\", \n",
|
||||
"#lineoib.scale(0.7,0)\n",
|
||||
"lineoib.set(order_imbalance_allvolume)\n",
|
||||
"\n",
|
||||
"lineoib_sma = chart2.create_line(name=\"oibsma5\", priceScaleId=\"left\") #, color=\"blue\", \n",
|
||||
"lineoib_sma.set(order_imbalance_sma)\n",
|
||||
"\n",
|
||||
"chart.fit()\n",
|
||||
"chart2.legend(True)\n",
|
||||
"#\n",
|
||||
"line2.markers_set(short_signals, \"entries\")\n",
|
||||
"# TODO jelikoz se davaji do jednoho pole je treba zajistit spravne sortovani\n",
|
||||
"# domyslet jak to pojmout iterativni doplnovani markeru\n",
|
||||
"line2.markers_set(short_exits, \"exits\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"chart2.fit()\n",
|
||||
"chart.load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"short_signals.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#vbt.IF.list_indicators(\"*ma\")\n",
|
||||
"vbt.phelp(vbt.indicator(\"talib:EMA\").run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sma = vbt.indicator(\"talib:EMA\").run(t1data.close, timeperiod=20)\n",
|
||||
"sma.real.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rr = vbt.RSI.run(t1data.close)\n",
|
||||
"type(rr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"buyvolume = t1data.data[\"BAC\"].buyvolume\n",
|
||||
"sellvolume = t1data.data[\"BAC\"].sellvolume\n",
|
||||
"totalvolume = buyvolume + sellvolume\n",
|
||||
"\n",
|
||||
"#adjust to minimal value to avoid division by zero\n",
|
||||
"sellvolume_adjusted = sellvolume.replace(0, 1e-10)\n",
|
||||
"oibratio = buyvolume / sellvolume\n",
|
||||
"\n",
|
||||
"#cumulative order flow (net difference)\n",
|
||||
"cof = buyvolume - sellvolume\n",
|
||||
"\n",
|
||||
"# Calculate the order imbalance (net differene) normalize the order imbalance by calculating the difference between buy and sell volumes and then scaling it by the total volume.\n",
|
||||
"order_imbalance = cof / totalvolume\n",
|
||||
"order_imbalance = order_imbalance.fillna(0) #nan nahradime 0\n",
|
||||
"\n",
|
||||
"order_imbalance_allvolume = cof / t1data.data[\"BAC\"].volume"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"order_imbalance_sma = vbt.indicator(\"talib:EMA\").run(order_imbalance, timeperiod=5)\n",
|
||||
"short_signals = order_imbalance.vbt < -0.5\n",
|
||||
"#short_entries = oibratio.vbt < 0.01\n",
|
||||
"short_signals.value_counts()\n",
|
||||
"short_signals.name = \"short_entries\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"short_signals.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"short_signals"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"order_imbalance.fillna(0)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"order_imbalance.vbt.plot()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"order_imbalance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"chartN = JupyterChart(width=500, height=300, inner_width=1, inner_height=0.3, leftScale=True)\n",
|
||||
"t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"1T\")\n",
|
||||
"t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"chartN.set(t1data.data[\"BAC\"])\n",
|
||||
"line_sma = chartN.create_line(name=\"sma\", priceScaleId=\"right\")#, color=\"blue\")\n",
|
||||
"line_sma.set(sma)\n",
|
||||
"# line_sma.markers_set(short_signals, \"entries\")\n",
|
||||
"# line_sma.markers_set(short_exits, \"exits\")\n",
|
||||
"# hst = chartN.create_histogram(name=\"oivol\")\n",
|
||||
"# hst.set(order_imbalance_allvolume)\n",
|
||||
"# chartN.legend(True)\n",
|
||||
"# chartN.fit()\n",
|
||||
"\n",
|
||||
"# subchart = chartN.create_subchart(position='right', width=1, height=0.5, sync=False, leftScale=True)\n",
|
||||
"# # subchart.set(t1data.data[\"BAC\"])\n",
|
||||
"# line_sma1 = subchart.create_line(name=\"smao\", priceScaleId=\"left\")#, color=\"blue\")\n",
|
||||
"# line_sma1.set(sma)\n",
|
||||
"# # line_sma1.markers_set(short_signals, \"entries\")\n",
|
||||
"# # line_sma1.markers_set(short_exits, \"exits\")\n",
|
||||
"# hsto = subchart.create_histogram(name=\"oivolo\")\n",
|
||||
"# hsto.set(order_imbalance_sma)\n",
|
||||
"\n",
|
||||
"chart2 = chartN.create_subchart(position='left', width=1, height=0.5, sync=True, leftScale=True, toolbox=True)\n",
|
||||
"# hst = chart2.create_histogram(name=\"buyvolume\", color=\"rgba(53, 94, 59, 0.6)\") #green transparent\n",
|
||||
"# hst1 = chart2.create_histogram(name=\"sellvolume\", color=\"rgba(165, 42, 42, 0.6)\") #red transparent\n",
|
||||
"# hst.set(t1data.data[\"BAC\"])\n",
|
||||
"# hst1.set(t1data.data[\"BAC\"])\n",
|
||||
"line2 = chart2.create_line(name=\"sma\")#, color=\"green\")\n",
|
||||
"line2.set(sma)\n",
|
||||
"chart2.topbar.textbox(\"title\",\"Nadpis\")\n",
|
||||
"# chartN.topbar.textbox(\"title\",\"NadpisT\")\n",
|
||||
"\n",
|
||||
"# subchart.legend(True)\n",
|
||||
"# subchart.fit()\n",
|
||||
"chartN.load()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"##z tohoto si udelat plot funkci (i pro entries,exits)\n",
|
||||
"#t1data = t1data[[\"open\", \"high\", \"low\", \"close\", \"volume\"]]\n",
|
||||
"chart = JupyterChart(width=1000, height=600, inner_width=1, inner_height=0.5, leftScale=True)\n",
|
||||
"#set resolution\n",
|
||||
"t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"1T\")\n",
|
||||
"t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"chart.set(t1data.data[\"BAC\"])\n",
|
||||
"line_vwap = chart.create_line(name=\"vwap\")#, color=\"blue\")\n",
|
||||
"line_vwap.set(t1data.vwap)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"chart.topbar.textbox(\"title\",\"Nadpis\")\n",
|
||||
"chart2 = chart.create_subchart(position='right', width=1, height=0.5, sync=True, leftScale=True)\n",
|
||||
"t2data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"5T\")\n",
|
||||
"t2data = t2data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#5min close realigned to 1T\n",
|
||||
"close_realigned = t2data.close.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
|
||||
"#close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
|
||||
"line1 = chart.create_line(name=\"5minclose\")#, color=\"green\")\n",
|
||||
"line1.set(close_realigned)\n",
|
||||
"\n",
|
||||
"#sma z realigned dat\n",
|
||||
"sma_tp = 20\n",
|
||||
"sma_t2 = vbt.indicator(\"talib:EMA\").run(close_realigned, timeperiod=sma_tp)\n",
|
||||
"smaline = chart.create_line(name=f\"sma{sma_tp}\")#, color=\"blue\")\n",
|
||||
"smaline.set(sma_t2)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#sma z puvodnich resamplovanych dat plus navic realign, melo by byt stejne \n",
|
||||
"sma_real = vbt.indicator(\"talib:EMA\").run(t2data.close, timeperiod=sma_tp)\n",
|
||||
"sma_real_value = sma_real.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"sma_real_value = sma_real_value[sma_real_value.index.dayofweek < 5]\n",
|
||||
"smaline_real = chart.create_line(name=f\"smareal{sma_tp}\", color=\"yellow\")\n",
|
||||
"smaline_real.set(sma_real_value)\n",
|
||||
"\n",
|
||||
"#resample 15T\n",
|
||||
"t15data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"15T\")\n",
|
||||
"t15data = t15data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"\n",
|
||||
"#5min close realigned to 1T\n",
|
||||
"close_15realigned = t15data.close.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"close_15realigned = close_15realigned[close_15realigned.index.dayofweek < 5]\n",
|
||||
"#close_realigned = close_realigned[close_realigned.index.dayofweek < 5]\n",
|
||||
"line2 = chart.create_line(name=\"15minclose\")#, color=\"pink\")\n",
|
||||
"line2.set(close_15realigned)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"chart.legend(True)\n",
|
||||
"hst = chart2.create_histogram(name=\"buyvolume\", color=\"rgba(53, 94, 59, 0.6)\") #green transparent\n",
|
||||
"hst1 = chart2.create_histogram(name=\"sellvolume\", color=\"rgba(165, 42, 42, 0.6)\") #red transparent\n",
|
||||
"hst.set(t1data.data[\"BAC\"])\n",
|
||||
"hst1.set(t1data.data[\"BAC\"])\n",
|
||||
"line2 = chart2.create_line(name=\"5minclose\")#, color=\"green\")\n",
|
||||
"line2.set(close_realigned)\n",
|
||||
"\n",
|
||||
"lineoib = chart2.create_line(name=\"oib\", priceScaleId=\"left\") #color=\"violet\", \n",
|
||||
"#lineoib.scale(0.7,0)\n",
|
||||
"lineoib.set(order_imbalance_allvolume)\n",
|
||||
"\n",
|
||||
"lineoib_sma = chart2.create_line(name=\"oibsma5\", priceScaleId=\"left\") #, color=\"blue\", \n",
|
||||
"lineoib_sma.set(order_imbalance_sma)\n",
|
||||
"\n",
|
||||
"chart.fit()\n",
|
||||
"chart2.legend(True)\n",
|
||||
"#\n",
|
||||
"line2.markers_set(short_signals, \"entries\")\n",
|
||||
"# TODO jelikoz se davaji do jednoho pole je treba zajistit spravne sortovani\n",
|
||||
"# domyslet jak to pojmout iterativni doplnovani markeru\n",
|
||||
"line2.markers_set(short_exits, \"exits\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"chart2.fit()\n",
|
||||
"chart.load()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sma.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#priminds list (same Y as price), secinds list (secondary Y napr. rsi), close, voluminds (volume based) list\n",
|
||||
"def plot_2y_close(priminds, secinds, close, volumeinds, ohlcv=None):\n",
|
||||
" fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, \n",
|
||||
" specs=[[{\"secondary_y\": True}], [{\"secondary_y\": False}]], \n",
|
||||
" vertical_spacing=0.02, subplot_titles=(\"Price and Indicators\", \"Volume\"))\n",
|
||||
"\n",
|
||||
" if ohlcv is not None:\n",
|
||||
" ohlcv.vbt.ohlcv.plot(fig=fig, add_trace_kwargs=dict(row=1, col=1))\n",
|
||||
"\n",
|
||||
" # Plotting the close price\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False,row=1, col=1), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" \n",
|
||||
" # Plotting primary indicators on the first row\n",
|
||||
" for ind in priminds:\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" #if series has no name, make the name same as the variable name\n",
|
||||
" \n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
|
||||
" \n",
|
||||
" # Plotting secondary indicators on the first row\n",
|
||||
" for ind in secinds:\n",
|
||||
" #ind = ind.rename(str(ind.name))\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1), trace_kwargs=dict(line=dict(color=\"rgba(255, 0, 0, 0.4)\")))\n",
|
||||
" \n",
|
||||
" for indvolume in volumeinds:\n",
|
||||
" # Plotting the volume on the second row\n",
|
||||
" indvolume.rename(str(indvolume.name)).vbt.barplot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))\n",
|
||||
" #vbt.Bar(indvolume, fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))\n",
|
||||
" \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"fig = plot_2y_close([sma], [order_imbalance.rename(\"order_imbalance_norm\"),order_imbalance_sma.real.rename(\"oib_sma\")], t1data.close, [t1data.data[\"BAC\"].buyvolume, t1data.data[\"BAC\"].sellvolume, t1data.volume], t1data.data[\"BAC\"])\n",
|
||||
"fig.update_yaxes(range=[33,34], secondary_y=False, row=1, col=1) #update y axis range\n",
|
||||
"fig.update_yaxes(range=[-1,1], secondary_y=True, row=1, col=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"t0data = basic_data\n",
|
||||
"t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"1T\")\n",
|
||||
"t2data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"15T\")\n",
|
||||
"t3data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"30T\")\n",
|
||||
"t4data = basic_data[['open', 'high', 'low', 'close', 'volume', 'vwap']].resample(\"D\").dropna()\n",
|
||||
"\n",
|
||||
"t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"t2data = t2data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"t3data = t3data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"\n",
|
||||
"#30min data to daily\n",
|
||||
"# t4data = t3data.resample(\"D\").dropna()\n",
|
||||
"\n",
|
||||
"#t4data = t4data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"#m1data.data[\"SPY\"].info()\n",
|
||||
"\n",
|
||||
"#m1data.data[\"SPY\"].vbt.ohlcv.plot()\n",
|
||||
"#h2data.data[\"SPY\"].vbt.ohlcv.plot()\n",
|
||||
"#ddata.data[\"SPY\"]\n",
|
||||
"t2data.data[\"BAC\"].vbt.ohlcv.plot().show()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#t4data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t2data.close\n",
|
||||
"\n",
|
||||
"#in df remove rows with nan\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#realign na 1T = t1data + oriznout main session\n",
|
||||
"t2data_vwap = t2data.vwap.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"t3data_vwap = t3data.vwap.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"t4data_vwap = t4data.vwap.vbt.realign_closing(\"1T\").dropna()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t2data_vwap"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t4data.clos.vbt \n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"obvind = vbt.indicator.obv.run(t1data.close, t1data.volume)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t1_lengtgh = 15\n",
|
||||
"t2_length = 15\n",
|
||||
"t3_length = 15\n",
|
||||
"t4_length = 5\n",
|
||||
"t1_th = 0.1\n",
|
||||
"t2_th = 0.1\n",
|
||||
"t3_th = 0.1\n",
|
||||
"t4_th = 0.1\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#minute\n",
|
||||
"t1slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t1data.close, timeperiod=t1_lengtgh) # -0.09, 0.09\n",
|
||||
"t2slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t2data.vwap, timeperiod=t2_length) # -0.08 , 0.079\n",
|
||||
"t3slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t3data.vwap, timeperiod=t3_length) # -0.08, 0.08\n",
|
||||
"#daily\n",
|
||||
"t4slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t4data.vwap, timeperiod=t4_length) # -0.1, 0.09\n",
|
||||
"\n",
|
||||
"plot_2y_close(priminds=[], secinds=[t1slope, t2slope, t3slope, t4slope], close=t1data.close).show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#thirtymin_slope = thirtymin_slope.real.rename(\"30min\") #timto se prejmenuje real na 30min\n",
|
||||
"t3slope = t3slope.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"##filter daily_slope_to_compare to only monday to friday\n",
|
||||
"t3slope = t3slope[t3slope.index.dayofweek < 5]\n",
|
||||
"\n",
|
||||
"#t3slope.info()\n",
|
||||
"\n",
|
||||
"t2slope = t2slope.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"##filter daily_slope_to_compare to only monday to friday\n",
|
||||
"t2slope = t2slope[t2slope.index.dayofweek < 5]\n",
|
||||
"\n",
|
||||
"t2slope.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"oibratio"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"#\n",
|
||||
"short_signals = order_imbalance.vbt < -0.3\n",
|
||||
"#short_entries = oibratio.vbt < 0.01\n",
|
||||
"short_signals.value_counts()\n",
|
||||
"\n",
|
||||
"long_signals = order_imbalance.vbt > 0.3\n",
|
||||
"#entries = oibratio.vbt > 10\n",
|
||||
"long_signals.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = vbt.make_subplots(rows=3, cols=1, shared_xaxes=True, \n",
|
||||
" specs=[[{\"secondary_y\": True}], [{\"secondary_y\": True}], [{\"secondary_y\": False}]], \n",
|
||||
" vertical_spacing=0.02, subplot_titles=(\"Price and Indicators\", \"Volume\"))\n",
|
||||
"t1data.data[\"BAC\"].vbt.ohlcv.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
|
||||
"#oibratio.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))\n",
|
||||
"order_imbalance.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))\n",
|
||||
"long_signals.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"LONGS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"limegreen\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ), add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
|
||||
"\n",
|
||||
"short_signals.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"SHORTS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"red\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ), add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# thirtymin_slope_to_compare.vbt.xloc[\"04-16-2024\"].get()\n",
|
||||
"thirty_down_signal.vbt.xloc[\"04-16-2024\"].get()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#short_signal = t1slope.real_below(t1_th) & t2slope.real_below(t2_th) & t3slope.real_below(t3_th) & t4slope.real_below(t4_th)\n",
|
||||
"#long_signal = t1slope.real_above(t1_th) & t2slope.real_above(t2_th) & t3slope.real_above(t3_th) & t4slope.real_above(t4_th)\n",
|
||||
"\n",
|
||||
"#test na daily s reversem crossed 0\n",
|
||||
"short_signal = t2slope.vbt < -0.01 & t3slope.vbt < -0.01 #min value of threshold\n",
|
||||
"long_signal = t2slope.vbt > 0.01 & t3slope.vbt > 0.01 #min\n",
|
||||
"\n",
|
||||
"# thirty_up_signal = t3slope.vbt.crossed_above(0.01)\n",
|
||||
"# thirty_down_signal = t3slope.vbt.crossed_below(-0.01)\n",
|
||||
"\n",
|
||||
"fig = plot_2y_close(priminds=[], secinds=[t3slope], close=t1data.close)\n",
|
||||
"#short_signal.vbt.signals.plot_as_entries(basic_data.close, fig=fig)\n",
|
||||
"\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"SHORTS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"red\", symbol=\"triangle-down\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ))\n",
|
||||
"long_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"LONGS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"limegreen\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ))\n",
|
||||
"\n",
|
||||
"# thirty_down_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"DOWN30\",\n",
|
||||
"# line=dict(color=\"#ffe476\"),\n",
|
||||
"# marker=dict(color=\"yellow\", symbol=\"triangle-down\"),\n",
|
||||
"# fill=None,\n",
|
||||
"# connectgaps=True,\n",
|
||||
"# ))\n",
|
||||
"# thirty_up_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"UP30\",\n",
|
||||
"# line=dict(color=\"#ffe476\"),\n",
|
||||
"# marker=dict(color=\"grey\"),\n",
|
||||
"# fill=None,\n",
|
||||
"# connectgaps=True,\n",
|
||||
"# ))\n",
|
||||
"\n",
|
||||
"# thirtymin_slope_to_compare.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True), trace_kwargs=dict(name=\"30min slope\",\n",
|
||||
"# line=dict(color=\"yellow\"), \n",
|
||||
"# fill=None,\n",
|
||||
"# connectgaps=True,\n",
|
||||
"# ))\n",
|
||||
"\n",
|
||||
"fig.show()\n",
|
||||
"# print(\"short signal\")\n",
|
||||
"# print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
"#forced_exit = pd.Series(False, index=close.index)\n",
|
||||
"forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
"#entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
"entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
"#print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"\n",
|
||||
"entries = (long_signal & entry_window_open)\n",
|
||||
"exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"# print(short_exits.value_counts())\n",
|
||||
"# print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
"#fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
"#short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
"#print(sl_stop)\n",
|
||||
"#short_entries=short_entries, short_exits=short_exits,\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=basic_data, entries=short_entries, exits=exits, tsl_stop=0.005, tp_stop = 0.05, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"# pf.stats()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"forced_exit = t1data.symbol_wrapper.fill(False)\n",
|
||||
"#entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
"entry_window_open= t1data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
"#print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"short_entries = (short_signals & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"\n",
|
||||
"entries = (long_signals & entry_window_open)\n",
|
||||
"exits = forced_exit\n",
|
||||
"\n",
|
||||
"pf = vbt.Portfolio.from_signals(close=t1data, entries=entries, exits=exits, short_entries=short_entries, short_exits=exits,\n",
|
||||
"td_stop=2, time_delta_format=\"rows\",\n",
|
||||
"tsl_stop=0.005, tp_stop = 0.005, fees=0.0167/100)#, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.get_drawdowns().records_readable"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.orders.records_readable"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
1166
research/strats/strat_ORDER_IMBALANCE/v2_SINGLE.ipynb
Normal file
1166
research/strats/strat_ORDER_IMBALANCE/v2_SINGLE.ipynb
Normal file
File diff suppressed because it is too large
Load Diff
94524
research/strats/strat_STUMPY/STUMPY_OHLCV.ipynb
Normal file
94524
research/strats/strat_STUMPY/STUMPY_OHLCV.ipynb
Normal file
File diff suppressed because one or more lines are too long
445200
research/strats/strat_STUMPY/STUMPY_OHLCV_animated.ipynb
Normal file
445200
research/strats/strat_STUMPY/STUMPY_OHLCV_animated.ipynb
Normal file
File diff suppressed because one or more lines are too long
2038
research/strats/strat_STUMPY/USREC.csv
Normal file
2038
research/strats/strat_STUMPY/USREC.csv
Normal file
File diff suppressed because it is too large
Load Diff
623912
research/strats/strat_STUMPY/stumpy_test_shittles.ipynb
Normal file
623912
research/strats/strat_STUMPY/stumpy_test_shittles.ipynb
Normal file
File diff suppressed because one or more lines are too long
932
research/strats/strat_STUMPY/v1_MULTI.ipynb
Normal file
932
research/strats/strat_STUMPY/v1_MULTI.ipynb
Normal file
@ -0,0 +1,932 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"\n",
|
||||
"init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"#print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"SPY\": ohlcv_df}), tz_convert=zoneNY)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"# close = basic_data.xloc[\"09:30\":\"10:00\"].close"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - FOR - LOOP\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 12))\n",
|
||||
"\n",
|
||||
"#uzavreni okna od 1 do 200\n",
|
||||
"#entry_window_closes = list(range(2, 50, 3))\n",
|
||||
"entry_window_closes = [5, 10, 30, 45]\n",
|
||||
"#entry_window_closes = 30\n",
|
||||
"#threshold entries parameters\n",
|
||||
"#long\n",
|
||||
"mom_th = np.round(np.arange(0.01, 0.5 + 0.02, 0.02),4).tolist()#-0.02\n",
|
||||
"# short\n",
|
||||
"#mom_th = np.round(np.arange(-0.01, -0.3 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.8 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop =np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"tp_stop = np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"\n",
|
||||
"combs = list(product(mom_timeperiod, mom_th, roc_th, sl_stop, tp_stop))\n",
|
||||
"\n",
|
||||
"@vbt.parameterized(merge_func = \"concat\", random_subset = 2000, show_progress=True) \n",
|
||||
"def test_strat(entry_window_closes=60,\n",
|
||||
" mom_timeperiod=2,\n",
|
||||
" mom_th=-0.04,\n",
|
||||
" #roc_th=-0.2,\n",
|
||||
" sl_stop=0.19/100,\n",
|
||||
" tp_stop=0.19/100):\n",
|
||||
" # mom_timeperiod=2\n",
|
||||
" # mom_th=-0.06\n",
|
||||
" # roc_th=-0.2\n",
|
||||
" # sl_stop=0.04/100\n",
|
||||
" # tp_stop=0.04/100\n",
|
||||
"\n",
|
||||
" momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
" rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
|
||||
" #rate of change + momentum\n",
|
||||
"\n",
|
||||
" #momshort.plot rocp.real_crossed_below(roc_th) & \n",
|
||||
" #short_signal = momshort.real_crossed_below(mom_th)\n",
|
||||
" long_signal = momshort.real_crossed_above(mom_th)\n",
|
||||
" # print(\"short signal\")\n",
|
||||
" # print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
" #forced_exit = pd.Series(False, index=close.index)\n",
|
||||
" forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
" #entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
" entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
" #print(entry_window_closes, \"entry window closes\")\n",
|
||||
" # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
" elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
" entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
" #print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
" forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
" #short_entries = (short_signal & entry_window_open)\n",
|
||||
" #short_exits = forced_exit\n",
|
||||
" entries = (long_signal & entry_window_open)\n",
|
||||
" exits = forced_exit\n",
|
||||
" #long_entries.info()\n",
|
||||
" #number of trues and falses in long_entries\n",
|
||||
" #print(short_exits.value_counts())\n",
|
||||
" #print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
" #fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
" #short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" #print(sl_stop)\n",
|
||||
" #tsl_th=sl_stop, \n",
|
||||
" #short_entries=short_entries, short_exits=short_exits,\n",
|
||||
" pf = vbt.Portfolio.from_signals(close=basic_data.close, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\", price=\"close\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
" \n",
|
||||
" return pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'max_dd', \n",
|
||||
" 'total_trades', \n",
|
||||
" 'win_rate', \n",
|
||||
" 'expectancy'\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"pf_results = test_strat(vbt.Param(entry_window_closes),\n",
|
||||
" vbt.Param(mom_timeperiod),\n",
|
||||
" vbt.Param(mom_th),\n",
|
||||
" #vbt.Param(roc_th)\n",
|
||||
" vbt.Param(sl_stop),\n",
|
||||
" vbt.Param(tp_stop, condition=\"tp_stop > sl_stop\"))\n",
|
||||
"pf_results = pf_results.unstack(level=-1)\n",
|
||||
"pf_results.sort_values(by=[\"Total Return [%]\", \"Max Drawdown [%]\"], ascending=[False, True])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf_results.load(\"10tiscomb.pickle\")\n",
|
||||
"#pf_results.info()\n",
|
||||
"\n",
|
||||
"vbt.save(pf_results, \"8tiscomb_tsl.pickle\")\n",
|
||||
"\n",
|
||||
"# pf_results = vbt.load(\"8tiscomb_tsl.pickle\")\n",
|
||||
"# pf_results\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# parallel_coordinates method¶\n",
|
||||
"\n",
|
||||
"# attach_px_methods.<locals>.plot_func(\n",
|
||||
"# *args,\n",
|
||||
"# layout=None,\n",
|
||||
"# **kwargs\n",
|
||||
"# )\n",
|
||||
"\n",
|
||||
"# pf_results.vbt.px.parallel_coordinates() #ocdf\n",
|
||||
"\n",
|
||||
"res = pf_results.reset_index()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf_results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# Assuming pf_results is your DataFrame\n",
|
||||
"# Convert columns to numeric, assuming NaNs where conversion fails\n",
|
||||
"metrics = ['Total Return [%]', 'Max Drawdown [%]', 'Total Trades']\n",
|
||||
"for metric in metrics:\n",
|
||||
" pf_results[metric] = pd.to_numeric(pf_results[metric], errors='coerce')\n",
|
||||
"\n",
|
||||
"# Handle missing values, for example filling with the median\n",
|
||||
"pf_results['Max Drawdown [%]'].fillna(pf_results['Max Drawdown [%]'].median(), inplace=True)\n",
|
||||
"\n",
|
||||
"# Extract the metrics into a new DataFrame\n",
|
||||
"data_for_pca = pf_results[metrics]\n",
|
||||
"\n",
|
||||
"# Standardize the data before applying PCA\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"data_scaled = scaler.fit_transform(data_for_pca)\n",
|
||||
"\n",
|
||||
"# Apply PCA\n",
|
||||
"pca = PCA(n_components=2) # Adjust components as needed\n",
|
||||
"principal_components = pca.fit_transform(data_scaled)\n",
|
||||
"\n",
|
||||
"# Create a DataFrame with the principal components\n",
|
||||
"pca_results = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])\n",
|
||||
"\n",
|
||||
"# Visualize the results\n",
|
||||
"plt.figure(figsize=(8,6))\n",
|
||||
"plt.scatter(pca_results['PC1'], pca_results['PC2'], alpha=0.5)\n",
|
||||
"plt.xlabel('Principal Component 1')\n",
|
||||
"plt.ylabel('Principal Component 2')\n",
|
||||
"plt.title('PCA of Strategy Optimization Results')\n",
|
||||
"plt.grid(True)\n",
|
||||
"plt.savefig(\"ddd.png\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check if there is any unnamed level and rename it\n",
|
||||
"if None in df.index.names:\n",
|
||||
" # Generate new names list replacing None with 'stat'\n",
|
||||
" new_names = ['stat' if name is None else name for name in df.index.names]\n",
|
||||
" df.index.set_names(new_names, inplace=True)\n",
|
||||
"\n",
|
||||
"rs= df\n",
|
||||
"\n",
|
||||
"rs.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Now, 'stat' is the name of the previously unnamed level\n",
|
||||
"\n",
|
||||
"# # Filter for 'Total Return' assuming it is a correct identifier in the 'stat' level\n",
|
||||
"# total_return_series = df.xs('Total Return [%]', level='stat')\n",
|
||||
"\n",
|
||||
"# # Sort the Series to get the largest 'Total Return' values\n",
|
||||
"# sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# # Print the sorted filtered data\n",
|
||||
"# sorted_series.head(20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sorted_series.vbt.save()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#df.info()\n",
|
||||
"total_return_series = df.xs('Total Return [%]')\n",
|
||||
"sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# Display the top N entries, e.g., top 5\n",
|
||||
"sorted_series.head(5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"comb_stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
|
||||
"4 -0.02 -0.25 0.0028 0.0048 0.84433\n",
|
||||
"3 -0.02 -0.25 0.0033 0.0023 Total Return [%] 0.846753\n",
|
||||
"#2\t-0.04\t-0.2\t0.0019\t0.0019\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"basic_data.symbols"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
">>> def apply_func(ts, entries, exits, fastw, sloww, minp=None):\n",
|
||||
"... fast_ma = vbt.nb.rolling_mean_nb(ts, fastw, minp=minp)\n",
|
||||
"... slow_ma = vbt.nb.rolling_mean_nb(ts, sloww, minp=minp)\n",
|
||||
"... entries[:] = vbt.nb.crossed_above_nb(fast_ma, slow_ma) \n",
|
||||
"... exits[:] = vbt.nb.crossed_above_nb(slow_ma, fast_ma)\n",
|
||||
"... return (fast_ma, slow_ma) \n",
|
||||
"\n",
|
||||
">>> CrossSig = vbt.IF(\n",
|
||||
"... class_name=\"CrossSig\",\n",
|
||||
"... input_names=['ts'],\n",
|
||||
"... in_output_names=['entries', 'exits'],\n",
|
||||
"... param_names=['fastw', 'sloww'],\n",
|
||||
"... output_names=['fast_ma', 'slow_ma']\n",
|
||||
"... ).with_apply_func(\n",
|
||||
"... apply_func,\n",
|
||||
"... in_output_settings=dict(\n",
|
||||
"... entries=dict(dtype=np.bool_), #initialize output with bool\n",
|
||||
"... exits=dict(dtype=np.bool_)\n",
|
||||
"... )\n",
|
||||
"... )\n",
|
||||
">>> cross_sig = CrossSig.run(ts2, 2, 4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - parameters in one go\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#TOTO prepsat do FOR-LOOPu\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 6))\n",
|
||||
"\n",
|
||||
"#threshold entries parameters\n",
|
||||
"mom_th = np.round(np.arange(-0.02, -0.1 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.4 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"#jejich product\n",
|
||||
"# mom_th_prod, roc_th_prod = zip(*product(mom_th, roc_th))\n",
|
||||
"\n",
|
||||
"# #convert threshold to vbt param\n",
|
||||
"# mom_th_index = vbt.Param(mom_th_prod, name='mom_th_th') \n",
|
||||
"# roc_th_index = vbt.Param(roc_th_prod, name='roc_th_th')\n",
|
||||
"\n",
|
||||
"mom_th = vbt.Param(mom_th, name='mom_th')\n",
|
||||
"roc_th = vbt.Param(roc_th, name='roc_th')\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"tp_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"tp_stop = [round(val, 4) for val in tp_stop]\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"tp_stop = vbt.Param(tp_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#def test_mom(window=14, mom_th=0.2, roc_th=0.2, sl_stop=0.03/100, tp_stop=0.03/100):\n",
|
||||
"#close = basic_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(basic_data.get(\"Close\"), timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
"\n",
|
||||
"#ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(basic_data.get(\"Close\"), short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"\n",
|
||||
"rocp_signal = rocp.real_crossed_below(mom_th)\n",
|
||||
"mom_signal = momshort.real_crossed_below(roc_th)\n",
|
||||
"\n",
|
||||
"#mom_signal\n",
|
||||
"print(rocp_signal.info())\n",
|
||||
"print(mom_signal.info())\n",
|
||||
"#print(rocp.real)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"short_signal = (mom_signal.vbt & rocp_signal)\n",
|
||||
"\n",
|
||||
"# #short_signal = (rocp.real_crossed_below(roc_th_index) & momshort.real_crossed_below(mom_th_index))\n",
|
||||
"# forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"# entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"# elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"# entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"# forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"# short_entries = (short_signal & entry_window_open)\n",
|
||||
"# short_exits = forced_exit\n",
|
||||
"# #long_entries.info()\n",
|
||||
"# #number of trues and falses in long_entries\n",
|
||||
"# #short_exits.value_counts()\n",
|
||||
"# #short_entries.value_counts()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# filter dates"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#filter na dny\n",
|
||||
"dates_of_interest = pd.to_datetime(['2024-04-22']).tz_localize('US/Eastern')\n",
|
||||
"filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
|
||||
"\n",
|
||||
"df = filtered_df\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import plotly.io as pio\n",
|
||||
"# pio.renderers.default = 'notebook'\n",
|
||||
"\n",
|
||||
"#naloadujeme do vbt symbol as column\n",
|
||||
"basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
|
||||
"\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"#basic_data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"\n",
|
||||
"m1_data.data[\"BAC\"]\n",
|
||||
"#m5_data = m1_data.resample(\"5T\")\n",
|
||||
"\n",
|
||||
"#m5_data.data[\"BAC\"].head(10)\n",
|
||||
"\n",
|
||||
"# m15_data = m1_data.resample(\"15T\")\n",
|
||||
"\n",
|
||||
"# m15 = m15_data.data[\"BAC\"]\n",
|
||||
"\n",
|
||||
"# m15.vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"# m1_data.wrapper.index\n",
|
||||
"\n",
|
||||
"# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
|
||||
"# m1_resampler.index_difference(reverse=True)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# m5_resampler.prettify()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MOM indicator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.indicator(\"talib:ROCP\").run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"vyuzití rychleho klesani na sekundove urovni behem open rush\n",
|
||||
"- MOM + ROC during open rush\n",
|
||||
"- short signal\n",
|
||||
"- pipeline kombinace thresholdu pro vstup mom_th, roc_th + hodnota sl_stop a tp_stop (pripadne trailing) - nalezeni optimalni kombinace atributu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"# short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
"\n",
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"close = m1_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(close, timeperiod=3, short_name = \"slope_short\")\n",
|
||||
"ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(close, short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"short_signal = (rocp.real_crossed_below(-0.2) & momshort.real_crossed_below(-0.02))\n",
|
||||
"#indlong = vbt.indicator(\"talib:MOM\").run(close, timeperiod=10, short_name = \"slope_long\")\n",
|
||||
"fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"#vbt.phelp(vbt.OLS.run)\n",
|
||||
"\n",
|
||||
"#oer steepmnes of regression line\n",
|
||||
"#talib.LINEARREG_SLOPE(close, timeperiod=timeperiod)\n",
|
||||
"#a také ON BALANCE VOLUME - http://5.161.179.223:8000/static/js/vbt/api/indicators/custom/obv/index.html\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mom_ind = vbt.indicator(\"talib:MOM\") \n",
|
||||
"#vbt.phelp(mom_ind.run)\n",
|
||||
"\n",
|
||||
"mom = mom_ind.run(close, timeperiod=10)\n",
|
||||
"\n",
|
||||
"plot_2y_close(mom, close)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# defining ENTRY WINDOW and forced EXIT window"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#m1_data.data[\"BAC\"].info()\n",
|
||||
"import datetime\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 2\n",
|
||||
"entry_window_closes = 30\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"\n",
|
||||
"#entry_window_open.info()\n",
|
||||
"# forced_exit.tail(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"\n",
|
||||
"#rsi = vbt.RSI.run(close, window=14)\n",
|
||||
"\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"#short_exits.value_counts()\n",
|
||||
"short_entries.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_rsi(close, entries, exits):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" #rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" entries.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_rsi(close, short_entries, short_exits)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.Portfolio.from_signals)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"print(sl_stop)\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=0.03/100, tp_stop = 0.03/100, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"#pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#list of orders\n",
|
||||
"#pf.orders.records_readable\n",
|
||||
"#pf.orders.plots()\n",
|
||||
"#pf.stats()\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0015,0.0013)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[0.03].plot_trade_signals()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# pristup k pf jako multi index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf[0.03].plot()\n",
|
||||
"#pf.order_records\n",
|
||||
"pf[(0.03)].stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#zgrupovane statistiky\n",
|
||||
"stats_df = pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'total_trades',\n",
|
||||
" 'win_rate',\n",
|
||||
" 'expectancy'\n",
|
||||
"], agg_func=None)\n",
|
||||
"stats_df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0011,0.0013000000000000002)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pandas.tseries.offsets import DateOffset\n",
|
||||
"\n",
|
||||
"temp_data = basic_data['2024-4-22']\n",
|
||||
"temp_data\n",
|
||||
"res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
|
||||
"\n",
|
||||
"# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
|
||||
"custom_offset = DateOffset(hours=4, minutes=30)\n",
|
||||
"\n",
|
||||
"# res1m = res1m.get().resample(\"4H\").agg({ \n",
|
||||
"# \"Open\": \"first\",\n",
|
||||
"# \"High\": \"max\",\n",
|
||||
"# \"Low\": \"min\",\n",
|
||||
"# \"Close\": \"last\",\n",
|
||||
"# \"Volume\": \"sum\"\n",
|
||||
"# })\n",
|
||||
"\n",
|
||||
"res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res4h.data\n",
|
||||
"\n",
|
||||
"res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res15m.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@vbt.njit\n",
|
||||
"def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
|
||||
" market_open_minutes = 570 # 9 hours * 60 minutes + 30 minutes\n",
|
||||
"\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
"\n",
|
||||
" current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
|
||||
" #print(\"current_minutes\", current_minutes)\n",
|
||||
" # Calculate elapsed minutes since market open at 9:30 AM\n",
|
||||
" elapsed_from_open = current_minutes - market_open_minutes\n",
|
||||
" elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
|
||||
" #print( \"elapsed_from_open\", elapsed_from_open)\n",
|
||||
"\n",
|
||||
" #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
|
||||
" in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
|
||||
" #print(\"in_window\", in_window)\n",
|
||||
" # if in_window:\n",
|
||||
" # print(\"in window\")\n",
|
||||
"\n",
|
||||
" if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9: # (4)!\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"@vbt.njit\n",
|
||||
"def long_exit_place_func_nb(c, high, close, time_index, tp, sl): # (5)!\n",
|
||||
" entry_i = c.from_i - c.wait\n",
|
||||
" entry_price = close[entry_i, c.col]\n",
|
||||
" hit_price = entry_price * (1 + tp)\n",
|
||||
" stop_price = entry_price * (1 - sl)\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
" last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
|
||||
"\n",
|
||||
" #print(next_day)\n",
|
||||
" if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
|
||||
" print(\"ted\",out_i)\n",
|
||||
" return out_i\n",
|
||||
" if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
|
||||
"\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.sum()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
932
research/strats/strat_SUPERTREND/SUPERTREND_v1_MULTI.ipynb
Normal file
932
research/strats/strat_SUPERTREND/SUPERTREND_v1_MULTI.ipynb
Normal file
@ -0,0 +1,932 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"\n",
|
||||
"init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"#print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"SPY\": ohlcv_df}), tz_convert=zoneNY)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"# close = basic_data.xloc[\"09:30\":\"10:00\"].close"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - FOR - LOOP\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 12))\n",
|
||||
"\n",
|
||||
"#uzavreni okna od 1 do 200\n",
|
||||
"#entry_window_closes = list(range(2, 50, 3))\n",
|
||||
"entry_window_closes = [5, 10, 30, 45]\n",
|
||||
"#entry_window_closes = 30\n",
|
||||
"#threshold entries parameters\n",
|
||||
"#long\n",
|
||||
"mom_th = np.round(np.arange(0.01, 0.5 + 0.02, 0.02),4).tolist()#-0.02\n",
|
||||
"# short\n",
|
||||
"#mom_th = np.round(np.arange(-0.01, -0.3 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.8 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop =np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"tp_stop = np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"\n",
|
||||
"combs = list(product(mom_timeperiod, mom_th, roc_th, sl_stop, tp_stop))\n",
|
||||
"\n",
|
||||
"@vbt.parameterized(merge_func = \"concat\", random_subset = 2000, show_progress=True) \n",
|
||||
"def test_strat(entry_window_closes=60,\n",
|
||||
" mom_timeperiod=2,\n",
|
||||
" mom_th=-0.04,\n",
|
||||
" #roc_th=-0.2,\n",
|
||||
" sl_stop=0.19/100,\n",
|
||||
" tp_stop=0.19/100):\n",
|
||||
" # mom_timeperiod=2\n",
|
||||
" # mom_th=-0.06\n",
|
||||
" # roc_th=-0.2\n",
|
||||
" # sl_stop=0.04/100\n",
|
||||
" # tp_stop=0.04/100\n",
|
||||
"\n",
|
||||
" momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
" rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
|
||||
" #rate of change + momentum\n",
|
||||
"\n",
|
||||
" #momshort.plot rocp.real_crossed_below(roc_th) & \n",
|
||||
" #short_signal = momshort.real_crossed_below(mom_th)\n",
|
||||
" long_signal = momshort.real_crossed_above(mom_th)\n",
|
||||
" # print(\"short signal\")\n",
|
||||
" # print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
" #forced_exit = pd.Series(False, index=close.index)\n",
|
||||
" forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
" #entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
" entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
" #print(entry_window_closes, \"entry window closes\")\n",
|
||||
" # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
" elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
" entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
" #print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
" forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
" #short_entries = (short_signal & entry_window_open)\n",
|
||||
" #short_exits = forced_exit\n",
|
||||
" entries = (long_signal & entry_window_open)\n",
|
||||
" exits = forced_exit\n",
|
||||
" #long_entries.info()\n",
|
||||
" #number of trues and falses in long_entries\n",
|
||||
" #print(short_exits.value_counts())\n",
|
||||
" #print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
" #fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
" #short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" #print(sl_stop)\n",
|
||||
" #tsl_th=sl_stop, \n",
|
||||
" #short_entries=short_entries, short_exits=short_exits,\n",
|
||||
" pf = vbt.Portfolio.from_signals(close=basic_data.close, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\", price=\"close\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
" \n",
|
||||
" return pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'max_dd', \n",
|
||||
" 'total_trades', \n",
|
||||
" 'win_rate', \n",
|
||||
" 'expectancy'\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"pf_results = test_strat(vbt.Param(entry_window_closes),\n",
|
||||
" vbt.Param(mom_timeperiod),\n",
|
||||
" vbt.Param(mom_th),\n",
|
||||
" #vbt.Param(roc_th)\n",
|
||||
" vbt.Param(sl_stop),\n",
|
||||
" vbt.Param(tp_stop, condition=\"tp_stop > sl_stop\"))\n",
|
||||
"pf_results = pf_results.unstack(level=-1)\n",
|
||||
"pf_results.sort_values(by=[\"Total Return [%]\", \"Max Drawdown [%]\"], ascending=[False, True])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf_results.load(\"10tiscomb.pickle\")\n",
|
||||
"#pf_results.info()\n",
|
||||
"\n",
|
||||
"vbt.save(pf_results, \"8tiscomb_tsl.pickle\")\n",
|
||||
"\n",
|
||||
"# pf_results = vbt.load(\"8tiscomb_tsl.pickle\")\n",
|
||||
"# pf_results\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# parallel_coordinates method¶\n",
|
||||
"\n",
|
||||
"# attach_px_methods.<locals>.plot_func(\n",
|
||||
"# *args,\n",
|
||||
"# layout=None,\n",
|
||||
"# **kwargs\n",
|
||||
"# )\n",
|
||||
"\n",
|
||||
"# pf_results.vbt.px.parallel_coordinates() #ocdf\n",
|
||||
"\n",
|
||||
"res = pf_results.reset_index()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf_results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# Assuming pf_results is your DataFrame\n",
|
||||
"# Convert columns to numeric, assuming NaNs where conversion fails\n",
|
||||
"metrics = ['Total Return [%]', 'Max Drawdown [%]', 'Total Trades']\n",
|
||||
"for metric in metrics:\n",
|
||||
" pf_results[metric] = pd.to_numeric(pf_results[metric], errors='coerce')\n",
|
||||
"\n",
|
||||
"# Handle missing values, for example filling with the median\n",
|
||||
"pf_results['Max Drawdown [%]'].fillna(pf_results['Max Drawdown [%]'].median(), inplace=True)\n",
|
||||
"\n",
|
||||
"# Extract the metrics into a new DataFrame\n",
|
||||
"data_for_pca = pf_results[metrics]\n",
|
||||
"\n",
|
||||
"# Standardize the data before applying PCA\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"data_scaled = scaler.fit_transform(data_for_pca)\n",
|
||||
"\n",
|
||||
"# Apply PCA\n",
|
||||
"pca = PCA(n_components=2) # Adjust components as needed\n",
|
||||
"principal_components = pca.fit_transform(data_scaled)\n",
|
||||
"\n",
|
||||
"# Create a DataFrame with the principal components\n",
|
||||
"pca_results = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])\n",
|
||||
"\n",
|
||||
"# Visualize the results\n",
|
||||
"plt.figure(figsize=(8,6))\n",
|
||||
"plt.scatter(pca_results['PC1'], pca_results['PC2'], alpha=0.5)\n",
|
||||
"plt.xlabel('Principal Component 1')\n",
|
||||
"plt.ylabel('Principal Component 2')\n",
|
||||
"plt.title('PCA of Strategy Optimization Results')\n",
|
||||
"plt.grid(True)\n",
|
||||
"plt.savefig(\"ddd.png\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check if there is any unnamed level and rename it\n",
|
||||
"if None in df.index.names:\n",
|
||||
" # Generate new names list replacing None with 'stat'\n",
|
||||
" new_names = ['stat' if name is None else name for name in df.index.names]\n",
|
||||
" df.index.set_names(new_names, inplace=True)\n",
|
||||
"\n",
|
||||
"rs= df\n",
|
||||
"\n",
|
||||
"rs.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Now, 'stat' is the name of the previously unnamed level\n",
|
||||
"\n",
|
||||
"# # Filter for 'Total Return' assuming it is a correct identifier in the 'stat' level\n",
|
||||
"# total_return_series = df.xs('Total Return [%]', level='stat')\n",
|
||||
"\n",
|
||||
"# # Sort the Series to get the largest 'Total Return' values\n",
|
||||
"# sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# # Print the sorted filtered data\n",
|
||||
"# sorted_series.head(20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sorted_series.vbt.save()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#df.info()\n",
|
||||
"total_return_series = df.xs('Total Return [%]')\n",
|
||||
"sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# Display the top N entries, e.g., top 5\n",
|
||||
"sorted_series.head(5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"comb_stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
|
||||
"4 -0.02 -0.25 0.0028 0.0048 0.84433\n",
|
||||
"3 -0.02 -0.25 0.0033 0.0023 Total Return [%] 0.846753\n",
|
||||
"#2\t-0.04\t-0.2\t0.0019\t0.0019\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"basic_data.symbols"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
">>> def apply_func(ts, entries, exits, fastw, sloww, minp=None):\n",
|
||||
"... fast_ma = vbt.nb.rolling_mean_nb(ts, fastw, minp=minp)\n",
|
||||
"... slow_ma = vbt.nb.rolling_mean_nb(ts, sloww, minp=minp)\n",
|
||||
"... entries[:] = vbt.nb.crossed_above_nb(fast_ma, slow_ma) \n",
|
||||
"... exits[:] = vbt.nb.crossed_above_nb(slow_ma, fast_ma)\n",
|
||||
"... return (fast_ma, slow_ma) \n",
|
||||
"\n",
|
||||
">>> CrossSig = vbt.IF(\n",
|
||||
"... class_name=\"CrossSig\",\n",
|
||||
"... input_names=['ts'],\n",
|
||||
"... in_output_names=['entries', 'exits'],\n",
|
||||
"... param_names=['fastw', 'sloww'],\n",
|
||||
"... output_names=['fast_ma', 'slow_ma']\n",
|
||||
"... ).with_apply_func(\n",
|
||||
"... apply_func,\n",
|
||||
"... in_output_settings=dict(\n",
|
||||
"... entries=dict(dtype=np.bool_), #initialize output with bool\n",
|
||||
"... exits=dict(dtype=np.bool_)\n",
|
||||
"... )\n",
|
||||
"... )\n",
|
||||
">>> cross_sig = CrossSig.run(ts2, 2, 4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - parameters in one go\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#TOTO prepsat do FOR-LOOPu\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 6))\n",
|
||||
"\n",
|
||||
"#threshold entries parameters\n",
|
||||
"mom_th = np.round(np.arange(-0.02, -0.1 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.4 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"#jejich product\n",
|
||||
"# mom_th_prod, roc_th_prod = zip(*product(mom_th, roc_th))\n",
|
||||
"\n",
|
||||
"# #convert threshold to vbt param\n",
|
||||
"# mom_th_index = vbt.Param(mom_th_prod, name='mom_th_th') \n",
|
||||
"# roc_th_index = vbt.Param(roc_th_prod, name='roc_th_th')\n",
|
||||
"\n",
|
||||
"mom_th = vbt.Param(mom_th, name='mom_th')\n",
|
||||
"roc_th = vbt.Param(roc_th, name='roc_th')\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"tp_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"tp_stop = [round(val, 4) for val in tp_stop]\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"tp_stop = vbt.Param(tp_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#def test_mom(window=14, mom_th=0.2, roc_th=0.2, sl_stop=0.03/100, tp_stop=0.03/100):\n",
|
||||
"#close = basic_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(basic_data.get(\"Close\"), timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
"\n",
|
||||
"#ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(basic_data.get(\"Close\"), short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"\n",
|
||||
"rocp_signal = rocp.real_crossed_below(mom_th)\n",
|
||||
"mom_signal = momshort.real_crossed_below(roc_th)\n",
|
||||
"\n",
|
||||
"#mom_signal\n",
|
||||
"print(rocp_signal.info())\n",
|
||||
"print(mom_signal.info())\n",
|
||||
"#print(rocp.real)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"short_signal = (mom_signal.vbt & rocp_signal)\n",
|
||||
"\n",
|
||||
"# #short_signal = (rocp.real_crossed_below(roc_th_index) & momshort.real_crossed_below(mom_th_index))\n",
|
||||
"# forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"# entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"# elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"# entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"# forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"# short_entries = (short_signal & entry_window_open)\n",
|
||||
"# short_exits = forced_exit\n",
|
||||
"# #long_entries.info()\n",
|
||||
"# #number of trues and falses in long_entries\n",
|
||||
"# #short_exits.value_counts()\n",
|
||||
"# #short_entries.value_counts()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# filter dates"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#filter na dny\n",
|
||||
"dates_of_interest = pd.to_datetime(['2024-04-22']).tz_localize('US/Eastern')\n",
|
||||
"filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
|
||||
"\n",
|
||||
"df = filtered_df\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import plotly.io as pio\n",
|
||||
"# pio.renderers.default = 'notebook'\n",
|
||||
"\n",
|
||||
"#naloadujeme do vbt symbol as column\n",
|
||||
"basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
|
||||
"\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"#basic_data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"\n",
|
||||
"m1_data.data[\"BAC\"]\n",
|
||||
"#m5_data = m1_data.resample(\"5T\")\n",
|
||||
"\n",
|
||||
"#m5_data.data[\"BAC\"].head(10)\n",
|
||||
"\n",
|
||||
"# m15_data = m1_data.resample(\"15T\")\n",
|
||||
"\n",
|
||||
"# m15 = m15_data.data[\"BAC\"]\n",
|
||||
"\n",
|
||||
"# m15.vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"# m1_data.wrapper.index\n",
|
||||
"\n",
|
||||
"# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
|
||||
"# m1_resampler.index_difference(reverse=True)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# m5_resampler.prettify()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MOM indicator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.indicator(\"talib:ROCP\").run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"vyuzití rychleho klesani na sekundove urovni behem open rush\n",
|
||||
"- MOM + ROC during open rush\n",
|
||||
"- short signal\n",
|
||||
"- pipeline kombinace thresholdu pro vstup mom_th, roc_th + hodnota sl_stop a tp_stop (pripadne trailing) - nalezeni optimalni kombinace atributu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"# short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
"\n",
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"close = m1_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(close, timeperiod=3, short_name = \"slope_short\")\n",
|
||||
"ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(close, short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"short_signal = (rocp.real_crossed_below(-0.2) & momshort.real_crossed_below(-0.02))\n",
|
||||
"#indlong = vbt.indicator(\"talib:MOM\").run(close, timeperiod=10, short_name = \"slope_long\")\n",
|
||||
"fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"#vbt.phelp(vbt.OLS.run)\n",
|
||||
"\n",
|
||||
"#oer steepmnes of regression line\n",
|
||||
"#talib.LINEARREG_SLOPE(close, timeperiod=timeperiod)\n",
|
||||
"#a také ON BALANCE VOLUME - http://5.161.179.223:8000/static/js/vbt/api/indicators/custom/obv/index.html\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mom_ind = vbt.indicator(\"talib:MOM\") \n",
|
||||
"#vbt.phelp(mom_ind.run)\n",
|
||||
"\n",
|
||||
"mom = mom_ind.run(close, timeperiod=10)\n",
|
||||
"\n",
|
||||
"plot_2y_close(mom, close)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# defining ENTRY WINDOW and forced EXIT window"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#m1_data.data[\"BAC\"].info()\n",
|
||||
"import datetime\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 2\n",
|
||||
"entry_window_closes = 30\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"\n",
|
||||
"#entry_window_open.info()\n",
|
||||
"# forced_exit.tail(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"\n",
|
||||
"#rsi = vbt.RSI.run(close, window=14)\n",
|
||||
"\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"#short_exits.value_counts()\n",
|
||||
"short_entries.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_rsi(close, entries, exits):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" #rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" entries.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_rsi(close, short_entries, short_exits)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.Portfolio.from_signals)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"print(sl_stop)\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=0.03/100, tp_stop = 0.03/100, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"#pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#list of orders\n",
|
||||
"#pf.orders.records_readable\n",
|
||||
"#pf.orders.plots()\n",
|
||||
"#pf.stats()\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0015,0.0013)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[0.03].plot_trade_signals()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# pristup k pf jako multi index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf[0.03].plot()\n",
|
||||
"#pf.order_records\n",
|
||||
"pf[(0.03)].stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#zgrupovane statistiky\n",
|
||||
"stats_df = pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'total_trades',\n",
|
||||
" 'win_rate',\n",
|
||||
" 'expectancy'\n",
|
||||
"], agg_func=None)\n",
|
||||
"stats_df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0011,0.0013000000000000002)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pandas.tseries.offsets import DateOffset\n",
|
||||
"\n",
|
||||
"temp_data = basic_data['2024-4-22']\n",
|
||||
"temp_data\n",
|
||||
"res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
|
||||
"\n",
|
||||
"# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
|
||||
"custom_offset = DateOffset(hours=4, minutes=30)\n",
|
||||
"\n",
|
||||
"# res1m = res1m.get().resample(\"4H\").agg({ \n",
|
||||
"# \"Open\": \"first\",\n",
|
||||
"# \"High\": \"max\",\n",
|
||||
"# \"Low\": \"min\",\n",
|
||||
"# \"Close\": \"last\",\n",
|
||||
"# \"Volume\": \"sum\"\n",
|
||||
"# })\n",
|
||||
"\n",
|
||||
"res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res4h.data\n",
|
||||
"\n",
|
||||
"res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res15m.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@vbt.njit\n",
|
||||
"def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
|
||||
" market_open_minutes = 570 # 9 hours * 60 minutes + 30 minutes\n",
|
||||
"\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
"\n",
|
||||
" current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
|
||||
" #print(\"current_minutes\", current_minutes)\n",
|
||||
" # Calculate elapsed minutes since market open at 9:30 AM\n",
|
||||
" elapsed_from_open = current_minutes - market_open_minutes\n",
|
||||
" elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
|
||||
" #print( \"elapsed_from_open\", elapsed_from_open)\n",
|
||||
"\n",
|
||||
" #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
|
||||
" in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
|
||||
" #print(\"in_window\", in_window)\n",
|
||||
" # if in_window:\n",
|
||||
" # print(\"in window\")\n",
|
||||
"\n",
|
||||
" if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9: # (4)!\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"@vbt.njit\n",
|
||||
"def long_exit_place_func_nb(c, high, close, time_index, tp, sl): # (5)!\n",
|
||||
" entry_i = c.from_i - c.wait\n",
|
||||
" entry_price = close[entry_i, c.col]\n",
|
||||
" hit_price = entry_price * (1 + tp)\n",
|
||||
" stop_price = entry_price * (1 - sl)\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
" last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
|
||||
"\n",
|
||||
" #print(next_day)\n",
|
||||
" if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
|
||||
" print(\"ted\",out_i)\n",
|
||||
" return out_i\n",
|
||||
" if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
|
||||
"\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.sum()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
1376
research/strats/strat_SUPERTREND/SUPERTREND_v1_SINGLE.ipynb
Normal file
1376
research/strats/strat_SUPERTREND/SUPERTREND_v1_SINGLE.ipynb
Normal file
File diff suppressed because one or more lines are too long
932
research/strats/strat_TIME_ENTRIES copy/v1_MULTI.ipynb
Normal file
932
research/strats/strat_TIME_ENTRIES copy/v1_MULTI.ipynb
Normal file
@ -0,0 +1,932 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"\n",
|
||||
"init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"#print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-SPY-2024-01-01T09:30:00-2024-05-14T16:00:00.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"SPY\": ohlcv_df}), tz_convert=zoneNY)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"# close = basic_data.xloc[\"09:30\":\"10:00\"].close"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - FOR - LOOP\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 12))\n",
|
||||
"\n",
|
||||
"#uzavreni okna od 1 do 200\n",
|
||||
"#entry_window_closes = list(range(2, 50, 3))\n",
|
||||
"entry_window_closes = [5, 10, 30, 45]\n",
|
||||
"#entry_window_closes = 30\n",
|
||||
"#threshold entries parameters\n",
|
||||
"#long\n",
|
||||
"mom_th = np.round(np.arange(0.01, 0.5 + 0.02, 0.02),4).tolist()#-0.02\n",
|
||||
"# short\n",
|
||||
"#mom_th = np.round(np.arange(-0.01, -0.3 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.8 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop =np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"tp_stop = np.round(np.arange(0.02/100, 0.7/100, 0.05/100),4).tolist()\n",
|
||||
"\n",
|
||||
"combs = list(product(mom_timeperiod, mom_th, roc_th, sl_stop, tp_stop))\n",
|
||||
"\n",
|
||||
"@vbt.parameterized(merge_func = \"concat\", random_subset = 2000, show_progress=True) \n",
|
||||
"def test_strat(entry_window_closes=60,\n",
|
||||
" mom_timeperiod=2,\n",
|
||||
" mom_th=-0.04,\n",
|
||||
" #roc_th=-0.2,\n",
|
||||
" sl_stop=0.19/100,\n",
|
||||
" tp_stop=0.19/100):\n",
|
||||
" # mom_timeperiod=2\n",
|
||||
" # mom_th=-0.06\n",
|
||||
" # roc_th=-0.2\n",
|
||||
" # sl_stop=0.04/100\n",
|
||||
" # tp_stop=0.04/100\n",
|
||||
"\n",
|
||||
" momshort = vbt.indicator(\"talib:MOM\").run(basic_data.close, timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
" rocp = vbt.indicator(\"talib:ROC\").run(basic_data.close, short_name = \"rocp\")\n",
|
||||
" #rate of change + momentum\n",
|
||||
"\n",
|
||||
" #momshort.plot rocp.real_crossed_below(roc_th) & \n",
|
||||
" #short_signal = momshort.real_crossed_below(mom_th)\n",
|
||||
" long_signal = momshort.real_crossed_above(mom_th)\n",
|
||||
" # print(\"short signal\")\n",
|
||||
" # print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
" #forced_exit = pd.Series(False, index=close.index)\n",
|
||||
" forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
" #entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
" entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
" #print(entry_window_closes, \"entry window closes\")\n",
|
||||
" # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
" elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
" entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
" #print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
" forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
" #short_entries = (short_signal & entry_window_open)\n",
|
||||
" #short_exits = forced_exit\n",
|
||||
" entries = (long_signal & entry_window_open)\n",
|
||||
" exits = forced_exit\n",
|
||||
" #long_entries.info()\n",
|
||||
" #number of trues and falses in long_entries\n",
|
||||
" #print(short_exits.value_counts())\n",
|
||||
" #print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
" #fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
" #short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" #print(sl_stop)\n",
|
||||
" #tsl_th=sl_stop, \n",
|
||||
" #short_entries=short_entries, short_exits=short_exits,\n",
|
||||
" pf = vbt.Portfolio.from_signals(close=basic_data.close, entries=entries, exits=exits, tsl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\", price=\"close\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
" \n",
|
||||
" return pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'max_dd', \n",
|
||||
" 'total_trades', \n",
|
||||
" 'win_rate', \n",
|
||||
" 'expectancy'\n",
|
||||
" ])\n",
|
||||
"\n",
|
||||
"pf_results = test_strat(vbt.Param(entry_window_closes),\n",
|
||||
" vbt.Param(mom_timeperiod),\n",
|
||||
" vbt.Param(mom_th),\n",
|
||||
" #vbt.Param(roc_th)\n",
|
||||
" vbt.Param(sl_stop),\n",
|
||||
" vbt.Param(tp_stop, condition=\"tp_stop > sl_stop\"))\n",
|
||||
"pf_results = pf_results.unstack(level=-1)\n",
|
||||
"pf_results.sort_values(by=[\"Total Return [%]\", \"Max Drawdown [%]\"], ascending=[False, True])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf_results.load(\"10tiscomb.pickle\")\n",
|
||||
"#pf_results.info()\n",
|
||||
"\n",
|
||||
"vbt.save(pf_results, \"8tiscomb_tsl.pickle\")\n",
|
||||
"\n",
|
||||
"# pf_results = vbt.load(\"8tiscomb_tsl.pickle\")\n",
|
||||
"# pf_results\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# parallel_coordinates method¶\n",
|
||||
"\n",
|
||||
"# attach_px_methods.<locals>.plot_func(\n",
|
||||
"# *args,\n",
|
||||
"# layout=None,\n",
|
||||
"# **kwargs\n",
|
||||
"# )\n",
|
||||
"\n",
|
||||
"# pf_results.vbt.px.parallel_coordinates() #ocdf\n",
|
||||
"\n",
|
||||
"res = pf_results.reset_index()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf_results"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"from sklearn.decomposition import PCA\n",
|
||||
"from sklearn.preprocessing import StandardScaler\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"# Assuming pf_results is your DataFrame\n",
|
||||
"# Convert columns to numeric, assuming NaNs where conversion fails\n",
|
||||
"metrics = ['Total Return [%]', 'Max Drawdown [%]', 'Total Trades']\n",
|
||||
"for metric in metrics:\n",
|
||||
" pf_results[metric] = pd.to_numeric(pf_results[metric], errors='coerce')\n",
|
||||
"\n",
|
||||
"# Handle missing values, for example filling with the median\n",
|
||||
"pf_results['Max Drawdown [%]'].fillna(pf_results['Max Drawdown [%]'].median(), inplace=True)\n",
|
||||
"\n",
|
||||
"# Extract the metrics into a new DataFrame\n",
|
||||
"data_for_pca = pf_results[metrics]\n",
|
||||
"\n",
|
||||
"# Standardize the data before applying PCA\n",
|
||||
"scaler = StandardScaler()\n",
|
||||
"data_scaled = scaler.fit_transform(data_for_pca)\n",
|
||||
"\n",
|
||||
"# Apply PCA\n",
|
||||
"pca = PCA(n_components=2) # Adjust components as needed\n",
|
||||
"principal_components = pca.fit_transform(data_scaled)\n",
|
||||
"\n",
|
||||
"# Create a DataFrame with the principal components\n",
|
||||
"pca_results = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])\n",
|
||||
"\n",
|
||||
"# Visualize the results\n",
|
||||
"plt.figure(figsize=(8,6))\n",
|
||||
"plt.scatter(pca_results['PC1'], pca_results['PC2'], alpha=0.5)\n",
|
||||
"plt.xlabel('Principal Component 1')\n",
|
||||
"plt.ylabel('Principal Component 2')\n",
|
||||
"plt.title('PCA of Strategy Optimization Results')\n",
|
||||
"plt.grid(True)\n",
|
||||
"plt.savefig(\"ddd.png\")\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Check if there is any unnamed level and rename it\n",
|
||||
"if None in df.index.names:\n",
|
||||
" # Generate new names list replacing None with 'stat'\n",
|
||||
" new_names = ['stat' if name is None else name for name in df.index.names]\n",
|
||||
" df.index.set_names(new_names, inplace=True)\n",
|
||||
"\n",
|
||||
"rs= df\n",
|
||||
"\n",
|
||||
"rs.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Now, 'stat' is the name of the previously unnamed level\n",
|
||||
"\n",
|
||||
"# # Filter for 'Total Return' assuming it is a correct identifier in the 'stat' level\n",
|
||||
"# total_return_series = df.xs('Total Return [%]', level='stat')\n",
|
||||
"\n",
|
||||
"# # Sort the Series to get the largest 'Total Return' values\n",
|
||||
"# sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# # Print the sorted filtered data\n",
|
||||
"# sorted_series.head(20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sorted_series.vbt.save()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#df.info()\n",
|
||||
"total_return_series = df.xs('Total Return [%]')\n",
|
||||
"sorted_series = total_return_series.sort_values(ascending=False)\n",
|
||||
"\n",
|
||||
"# Display the top N entries, e.g., top 5\n",
|
||||
"sorted_series.head(5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"comb_stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"8\t-0.06\t-0.2\t0.0028\t0.0048\t4.156254\n",
|
||||
"4 -0.02 -0.25 0.0028 0.0048 0.84433\n",
|
||||
"3 -0.02 -0.25 0.0033 0.0023 Total Return [%] 0.846753\n",
|
||||
"#2\t-0.04\t-0.2\t0.0019\t0.0019\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0019\t0.556919\t91\t60.43956\t0.00612\n",
|
||||
"# 2\t-0.04\t-0.2\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.25\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.3\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.35\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853\n",
|
||||
"# 2\t-0.04\t-0.4\t0.0019\t0.0017\t0.451338\t93\t63.44086\t0.004853"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"basic_data.symbols"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
">>> def apply_func(ts, entries, exits, fastw, sloww, minp=None):\n",
|
||||
"... fast_ma = vbt.nb.rolling_mean_nb(ts, fastw, minp=minp)\n",
|
||||
"... slow_ma = vbt.nb.rolling_mean_nb(ts, sloww, minp=minp)\n",
|
||||
"... entries[:] = vbt.nb.crossed_above_nb(fast_ma, slow_ma) \n",
|
||||
"... exits[:] = vbt.nb.crossed_above_nb(slow_ma, fast_ma)\n",
|
||||
"... return (fast_ma, slow_ma) \n",
|
||||
"\n",
|
||||
">>> CrossSig = vbt.IF(\n",
|
||||
"... class_name=\"CrossSig\",\n",
|
||||
"... input_names=['ts'],\n",
|
||||
"... in_output_names=['entries', 'exits'],\n",
|
||||
"... param_names=['fastw', 'sloww'],\n",
|
||||
"... output_names=['fast_ma', 'slow_ma']\n",
|
||||
"... ).with_apply_func(\n",
|
||||
"... apply_func,\n",
|
||||
"... in_output_settings=dict(\n",
|
||||
"... entries=dict(dtype=np.bool_), #initialize output with bool\n",
|
||||
"... exits=dict(dtype=np.bool_)\n",
|
||||
"... )\n",
|
||||
"... )\n",
|
||||
">>> cross_sig = CrossSig.run(ts2, 2, 4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#PIPELINE - parameters in one go\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#TOTO prepsat do FOR-LOOPu\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#indicator parameters\n",
|
||||
"mom_timeperiod = list(range(2, 6))\n",
|
||||
"\n",
|
||||
"#threshold entries parameters\n",
|
||||
"mom_th = np.round(np.arange(-0.02, -0.1 - 0.02, -0.02),4).tolist()#-0.02\n",
|
||||
"roc_th = np.round(np.arange(-0.2, -0.4 - 0.05, -0.05),4).tolist()#-0.2\n",
|
||||
"#print(mom_th, roc_th)\n",
|
||||
"#jejich product\n",
|
||||
"# mom_th_prod, roc_th_prod = zip(*product(mom_th, roc_th))\n",
|
||||
"\n",
|
||||
"# #convert threshold to vbt param\n",
|
||||
"# mom_th_index = vbt.Param(mom_th_prod, name='mom_th_th') \n",
|
||||
"# roc_th_index = vbt.Param(roc_th_prod, name='roc_th_th')\n",
|
||||
"\n",
|
||||
"mom_th = vbt.Param(mom_th, name='mom_th')\n",
|
||||
"roc_th = vbt.Param(roc_th, name='roc_th')\n",
|
||||
"\n",
|
||||
"#portfolio simulation parameters\n",
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"tp_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"tp_stop = [round(val, 4) for val in tp_stop]\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"tp_stop = vbt.Param(tp_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#def test_mom(window=14, mom_th=0.2, roc_th=0.2, sl_stop=0.03/100, tp_stop=0.03/100):\n",
|
||||
"#close = basic_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(basic_data.get(\"Close\"), timeperiod=mom_timeperiod, short_name = \"slope_short\")\n",
|
||||
"\n",
|
||||
"#ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(basic_data.get(\"Close\"), short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"\n",
|
||||
"rocp_signal = rocp.real_crossed_below(mom_th)\n",
|
||||
"mom_signal = momshort.real_crossed_below(roc_th)\n",
|
||||
"\n",
|
||||
"#mom_signal\n",
|
||||
"print(rocp_signal.info())\n",
|
||||
"print(mom_signal.info())\n",
|
||||
"#print(rocp.real)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"short_signal = (mom_signal.vbt & rocp_signal)\n",
|
||||
"\n",
|
||||
"# #short_signal = (rocp.real_crossed_below(roc_th_index) & momshort.real_crossed_below(mom_th_index))\n",
|
||||
"# forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"# entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# # Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"# elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"# entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"# forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"# short_entries = (short_signal & entry_window_open)\n",
|
||||
"# short_exits = forced_exit\n",
|
||||
"# #long_entries.info()\n",
|
||||
"# #number of trues and falses in long_entries\n",
|
||||
"# #short_exits.value_counts()\n",
|
||||
"# #short_entries.value_counts()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=sl_stop, tp_stop = tp_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# filter dates"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#filter na dny\n",
|
||||
"dates_of_interest = pd.to_datetime(['2024-04-22']).tz_localize('US/Eastern')\n",
|
||||
"filtered_df = df.loc[df.index.normalize().isin(dates_of_interest)]\n",
|
||||
"\n",
|
||||
"df = filtered_df\n",
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# import plotly.io as pio\n",
|
||||
"# pio.renderers.default = 'notebook'\n",
|
||||
"\n",
|
||||
"#naloadujeme do vbt symbol as column\n",
|
||||
"basic_data = vbt.Data.from_data({\"BAC\": df}, tz_convert=zoneNY)\n",
|
||||
"\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"#basic_data.data[\"BAC\"].vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"#basic_data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"m1_data = basic_data[['Open', 'High', 'Low', 'Close', 'Volume']]\n",
|
||||
"\n",
|
||||
"m1_data.data[\"BAC\"]\n",
|
||||
"#m5_data = m1_data.resample(\"5T\")\n",
|
||||
"\n",
|
||||
"#m5_data.data[\"BAC\"].head(10)\n",
|
||||
"\n",
|
||||
"# m15_data = m1_data.resample(\"15T\")\n",
|
||||
"\n",
|
||||
"# m15 = m15_data.data[\"BAC\"]\n",
|
||||
"\n",
|
||||
"# m15.vbt.ohlcv.plot()\n",
|
||||
"\n",
|
||||
"# m1_data.wrapper.index\n",
|
||||
"\n",
|
||||
"# m1_resampler = m1_data.wrapper.get_resampler(\"1T\")\n",
|
||||
"# m1_resampler.index_difference(reverse=True)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# m5_resampler.prettify()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# MOM indicator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.indicator(\"talib:ROCP\").run)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"vyuzití rychleho klesani na sekundove urovni behem open rush\n",
|
||||
"- MOM + ROC during open rush\n",
|
||||
"- short signal\n",
|
||||
"- pipeline kombinace thresholdu pro vstup mom_th, roc_th + hodnota sl_stop a tp_stop (pripadne trailing) - nalezeni optimalni kombinace atributu"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"# short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
"\n",
|
||||
"#parameters (primary y line, secondary y line, close)\n",
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"close = m1_data.xloc[\"09:30\":\"10:00\"].close\n",
|
||||
"momshort = vbt.indicator(\"talib:MOM\").run(close, timeperiod=3, short_name = \"slope_short\")\n",
|
||||
"ht_trendline = vbt.indicator(\"talib:HT_TRENDLINE\").run(close, short_name = \"httrendline\")\n",
|
||||
"rocp = vbt.indicator(\"talib:ROC\").run(close, short_name = \"rocp\")\n",
|
||||
"#rate of change + momentum\n",
|
||||
"short_signal = (rocp.real_crossed_below(-0.2) & momshort.real_crossed_below(-0.02))\n",
|
||||
"#indlong = vbt.indicator(\"talib:MOM\").run(close, timeperiod=10, short_name = \"slope_long\")\n",
|
||||
"fig = plot_2y_close([ht_trendline],[momshort, rocp], close)\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"#vbt.phelp(vbt.OLS.run)\n",
|
||||
"\n",
|
||||
"#oer steepmnes of regression line\n",
|
||||
"#talib.LINEARREG_SLOPE(close, timeperiod=timeperiod)\n",
|
||||
"#a také ON BALANCE VOLUME - http://5.161.179.223:8000/static/js/vbt/api/indicators/custom/obv/index.html\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"mom_ind = vbt.indicator(\"talib:MOM\") \n",
|
||||
"#vbt.phelp(mom_ind.run)\n",
|
||||
"\n",
|
||||
"mom = mom_ind.run(close, timeperiod=10)\n",
|
||||
"\n",
|
||||
"plot_2y_close(mom, close)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# defining ENTRY WINDOW and forced EXIT window"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#m1_data.data[\"BAC\"].info()\n",
|
||||
"import datetime\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 2\n",
|
||||
"entry_window_closes = 30\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"forced_exit = m1_data.symbol_wrapper.fill(False)\n",
|
||||
"entry_window_open= m1_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"\n",
|
||||
"#entry_window_open.info()\n",
|
||||
"# forced_exit.tail(100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"close = m1_data.close\n",
|
||||
"\n",
|
||||
"#rsi = vbt.RSI.run(close, window=14)\n",
|
||||
"\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"#short_exits.value_counts()\n",
|
||||
"short_entries.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_rsi(close, entries, exits):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"RSI\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" #rsi.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" entries.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_rsi(close, short_entries, short_exits)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vbt.phelp(vbt.Portfolio.from_signals)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sl_stop = np.arange(0.03/100, 0.2/100, 0.02/100).tolist()\n",
|
||||
"# Using the round function\n",
|
||||
"sl_stop = [round(val, 4) for val in sl_stop]\n",
|
||||
"print(sl_stop)\n",
|
||||
"sl_stop = vbt.Param(sl_stop) #np.nan mean s no stoploss\n",
|
||||
"\n",
|
||||
"pf = vbt.Portfolio.from_signals(close=close, short_entries=short_entries, short_exits=short_exits, sl_stop=0.03/100, tp_stop = 0.03/100, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"#pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#list of orders\n",
|
||||
"#pf.orders.records_readable\n",
|
||||
"#pf.orders.plots()\n",
|
||||
"#pf.stats()\n",
|
||||
"pf.stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0015,0.0013)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[0.03].plot_trade_signals()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# pristup k pf jako multi index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#pf[0.03].plot()\n",
|
||||
"#pf.order_records\n",
|
||||
"pf[(0.03)].stats()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#zgrupovane statistiky\n",
|
||||
"stats_df = pf.stats([\n",
|
||||
" 'total_return',\n",
|
||||
" 'total_trades',\n",
|
||||
" 'win_rate',\n",
|
||||
" 'expectancy'\n",
|
||||
"], agg_func=None)\n",
|
||||
"stats_df\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"stats_df.nlargest(10, 'Total Return [%]')\n",
|
||||
"#stats_df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf[(0.0011,0.0013000000000000002)].plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pandas.tseries.offsets import DateOffset\n",
|
||||
"\n",
|
||||
"temp_data = basic_data['2024-4-22']\n",
|
||||
"temp_data\n",
|
||||
"res1m = temp_data[[\"Open\", \"High\", \"Low\", \"Close\", \"Volume\"]]\n",
|
||||
"\n",
|
||||
"# Define a custom date offset that starts at 9:30 AM and spans 4 hours\n",
|
||||
"custom_offset = DateOffset(hours=4, minutes=30)\n",
|
||||
"\n",
|
||||
"# res1m = res1m.get().resample(\"4H\").agg({ \n",
|
||||
"# \"Open\": \"first\",\n",
|
||||
"# \"High\": \"max\",\n",
|
||||
"# \"Low\": \"min\",\n",
|
||||
"# \"Close\": \"last\",\n",
|
||||
"# \"Volume\": \"sum\"\n",
|
||||
"# })\n",
|
||||
"\n",
|
||||
"res4h = res1m.resample(\"1h\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res4h.data\n",
|
||||
"\n",
|
||||
"res15m = res1m.resample(\"15T\", resample_kwargs=dict(origin=\"start\"))\n",
|
||||
"\n",
|
||||
"res15m.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"@vbt.njit\n",
|
||||
"def long_entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
|
||||
" market_open_minutes = 570 # 9 hours * 60 minutes + 30 minutes\n",
|
||||
"\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
"\n",
|
||||
" current_minutes = vbt.dt_nb.hour_nb(time_in_ns[i]) * 60 + vbt.dt_nb.minute_nb(time_in_ns[i])\n",
|
||||
" #print(\"current_minutes\", current_minutes)\n",
|
||||
" # Calculate elapsed minutes since market open at 9:30 AM\n",
|
||||
" elapsed_from_open = current_minutes - market_open_minutes\n",
|
||||
" elapsed_from_open = elapsed_from_open if elapsed_from_open >= 0 else 0\n",
|
||||
" #print( \"elapsed_from_open\", elapsed_from_open)\n",
|
||||
"\n",
|
||||
" #elapsed_from_open = elapsed_minutes_from_open_nb(time_in_ns) \n",
|
||||
" in_window = elapsed_from_open > window_open and elapsed_from_open < window_close\n",
|
||||
" #print(\"in_window\", in_window)\n",
|
||||
" # if in_window:\n",
|
||||
" # print(\"in window\")\n",
|
||||
"\n",
|
||||
" if in_window and rsi14[i] > 60: # and low[i, c.col] <= hit_price: # and hour == 9: # (4)!\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"@vbt.njit\n",
|
||||
"def long_exit_place_func_nb(c, high, close, time_index, tp, sl): # (5)!\n",
|
||||
" entry_i = c.from_i - c.wait\n",
|
||||
" entry_price = close[entry_i, c.col]\n",
|
||||
" hit_price = entry_price * (1 + tp)\n",
|
||||
" stop_price = entry_price * (1 - sl)\n",
|
||||
" for out_i in range(len(c.out)):\n",
|
||||
" i = c.from_i + out_i\n",
|
||||
" last_bar_of_day = vbt.dt_nb.day_changed_nb(time_index[i], time_index[i + 1])\n",
|
||||
"\n",
|
||||
" #print(next_day)\n",
|
||||
" if last_bar_of_day: #pokud je dalsi next day, tak zavirame posledni\n",
|
||||
" print(\"ted\",out_i)\n",
|
||||
" return out_i\n",
|
||||
" if close[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
|
||||
" return out_i\n",
|
||||
" return -1\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(np.random.random(size=(5, 10)), columns=list('abcdefghij'))\n",
|
||||
"\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.sum()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
595
research/strats/strat_TIME_ENTRIES copy/v1_SINGLE.ipynb
Normal file
595
research/strats/strat_TIME_ENTRIES copy/v1_SINGLE.ipynb
Normal file
@ -0,0 +1,595 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# TIME based entries, exits\n",
|
||||
"\n",
|
||||
"Recurring time bases entries and exits"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from v2realbot.tools.loadbatch import load_batch\n",
|
||||
"from v2realbot.utils.utils import zoneNY\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import vectorbtpro as vbt\n",
|
||||
"from itables import init_notebook_mode, show\n",
|
||||
"import datetime\n",
|
||||
"from itertools import product\n",
|
||||
"from v2realbot.config import ACCOUNT1_PAPER_API_KEY, ACCOUNT1_PAPER_SECRET_KEY, DATA_DIR\n",
|
||||
"\n",
|
||||
"init_notebook_mode(all_interactive=True)\n",
|
||||
"\n",
|
||||
"vbt.settings.set_theme(\"dark\")\n",
|
||||
"vbt.settings['plotting']['layout']['width'] = 1280\n",
|
||||
"vbt.settings.plotting.auto_rangebreaks = True\n",
|
||||
"# Set the option to display with pagination\n",
|
||||
"pd.set_option('display.notebook_repr_html', True)\n",
|
||||
"pd.set_option('display.max_rows', 10) # Number of rows per page\n",
|
||||
"\n",
|
||||
"# Define the market open and close times\n",
|
||||
"market_open = datetime.time(9, 30)\n",
|
||||
"market_close = datetime.time(16, 0)\n",
|
||||
"entry_window_opens = 1\n",
|
||||
"entry_window_closes = 370\n",
|
||||
"\n",
|
||||
"forced_exit_start = 380\n",
|
||||
"forced_exit_end = 390\n",
|
||||
"\n",
|
||||
"#LOAD FROM PARQUET\n",
|
||||
"#list all files is dir directory with parquet extension\n",
|
||||
"dir = DATA_DIR + \"/notebooks/\"\n",
|
||||
"import os\n",
|
||||
"files = [f for f in os.listdir(dir) if f.endswith(\".parquet\")]\n",
|
||||
"print('\\n'.join(map(str, files)))\n",
|
||||
"file_name = \"ohlcv_df-BAC-2023-01-01T09_30_00-2024-05-25T15_30_00-47BCFOPUVWZ-100.parquet\"\n",
|
||||
"ohlcv_df = pd.read_parquet(dir+file_name,engine='pyarrow')\n",
|
||||
"#filter ohlcv_df to certain date range (assuming datetime index)\n",
|
||||
"ohlcv_df = ohlcv_df.loc[\"2024-02-12 9:30\":\"2024-02-14 16:00\"]\n",
|
||||
"\n",
|
||||
"#add vwap column to ohlcv_df\n",
|
||||
"#ohlcv_df[\"hlcc4\"] = (ohlcv_df[\"close\"] + ohlcv_df[\"high\"] + ohlcv_df[\"low\"] + ohlcv_df[\"close\"]) / 4\n",
|
||||
"\n",
|
||||
"basic_data = vbt.Data.from_data(vbt.symbol_dict({\"BAC\": ohlcv_df}), tz_convert=zoneNY)\n",
|
||||
"ohlcv_df= None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Add resample function to custom columns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from vectorbtpro.utils.config import merge_dicts, Config, HybridConfig\n",
|
||||
"from vectorbtpro import _typing as tp\n",
|
||||
"from vectorbtpro.generic import nb as generic_nb\n",
|
||||
"\n",
|
||||
"_feature_config: tp.ClassVar[Config] = HybridConfig(\n",
|
||||
" {\n",
|
||||
" \"buyvolume\": dict(\n",
|
||||
" resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
|
||||
" resampler,\n",
|
||||
" generic_nb.sum_reduce_nb,\n",
|
||||
" )\n",
|
||||
" ),\n",
|
||||
" \"sellvolume\": dict(\n",
|
||||
" resample_func=lambda self, obj, resampler: obj.vbt.resample_apply(\n",
|
||||
" resampler,\n",
|
||||
" generic_nb.sum_reduce_nb,\n",
|
||||
" )\n",
|
||||
" )\n",
|
||||
" }\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"basic_data._feature_config = _feature_config"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#asic_data.stats()\n",
|
||||
"basic_data.wrapper.index.normalize().nunique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"basic_data.ohlcv.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"1T\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t1data = t1data.xloc[\"2024-02-12 9:30\":\"2024-02-12 10:20\"]\n",
|
||||
"#t1data = t1data.transform(lambda df: df.between_time('09:30', '10:00').dropna())\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"buyvolume = t1data.data[\"BAC\"].buyvolume\n",
|
||||
"sellvolume = t1data.data[\"BAC\"].sellvolume\n",
|
||||
"totalvolume = buyvolume + sellvolume\n",
|
||||
"\n",
|
||||
"#adjust to minimal value to avoid division by zero\n",
|
||||
"sellvolume_adjusted = sellvolume.replace(0, 1e-10)\n",
|
||||
"oibratio = buyvolume / sellvolume\n",
|
||||
"\n",
|
||||
"#cumulative order flow (net difference)\n",
|
||||
"cof = buyvolume - sellvolume\n",
|
||||
"\n",
|
||||
"# Calculate the order imbalance (net differene) normalize the order imbalance by calculating the difference between buy and sell volumes and then scaling it by the total volume.\n",
|
||||
"order_imbalance = cof / totalvolume\n",
|
||||
"order_imbalance.fillna(0) #nan nahradime 0\n",
|
||||
"\n",
|
||||
"order_imbalance_allvolume = cof / t1data.data[\"BAC\"].volume"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"cof\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"order_imbalance.vbt.plot()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"order_imbalance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#priminds list (same Y as price), secinds list (secondary Y napr. rsi), close, voluminds (volume based) list\n",
|
||||
"def plot_2y_close(priminds, secinds, close, volumeinds):\n",
|
||||
" fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, \n",
|
||||
" specs=[[{\"secondary_y\": True}], [{\"secondary_y\": False}]], \n",
|
||||
" vertical_spacing=0.02, subplot_titles=(\"Price and Indicators\", \"Volume\"))\n",
|
||||
"\n",
|
||||
" # Plotting the close price\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False,row=1, col=1), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" \n",
|
||||
" # Plotting primary indicators on the first row\n",
|
||||
" for ind in priminds:\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" #if series has no name, make the name same as the variable name\n",
|
||||
" \n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
|
||||
" \n",
|
||||
" # Plotting secondary indicators on the first row\n",
|
||||
" for ind in secinds:\n",
|
||||
" #ind = ind.rename(str(ind.name))\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1), trace_kwargs=dict(line=dict(color=\"rgba(255, 0, 0, 0.4)\")))\n",
|
||||
" \n",
|
||||
" for indvolume in volumeinds:\n",
|
||||
" # Plotting the volume on the second row\n",
|
||||
" indvolume.rename(str(indvolume.name)).vbt.barplot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))\n",
|
||||
" #vbt.Bar(indvolume, fig=fig, add_trace_kwargs=dict(secondary_y=False, row=2, col=1))\n",
|
||||
" \n",
|
||||
" return fig\n",
|
||||
"\n",
|
||||
"plot_2y_close([], [order_imbalance.rename(\"order_imbalance_norm\")], t1data.close, [t1data.data[\"BAC\"].buyvolume, t1data.data[\"BAC\"].sellvolume, t1data.volume])\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%matplotlib inline\n",
|
||||
"t0data = basic_data\n",
|
||||
"t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"1T\")\n",
|
||||
"t2data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"15T\")\n",
|
||||
"t3data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap']].resample(\"30T\")\n",
|
||||
"t4data = basic_data[['open', 'high', 'low', 'close', 'volume', 'vwap']].resample(\"D\").dropna()\n",
|
||||
"\n",
|
||||
"t1data = t1data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"t2data = t2data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"t3data = t3data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"\n",
|
||||
"#30min data to daily\n",
|
||||
"# t4data = t3data.resample(\"D\").dropna()\n",
|
||||
"\n",
|
||||
"#t4data = t4data.transform(lambda df: df.between_time('09:30', '16:00').dropna())\n",
|
||||
"#m1data.data[\"SPY\"].info()\n",
|
||||
"\n",
|
||||
"#m1data.data[\"SPY\"].vbt.ohlcv.plot()\n",
|
||||
"#h2data.data[\"SPY\"].vbt.ohlcv.plot()\n",
|
||||
"#ddata.data[\"SPY\"]\n",
|
||||
"t2data.data[\"BAC\"].vbt.ohlcv.plot().show()\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#t4data.data[\"BAC\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t2data.close\n",
|
||||
"\n",
|
||||
"#in df remove rows with nan\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#realign na 1T = t1data + oriznout main session\n",
|
||||
"t2data_vwap = t2data.vwap.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"t3data_vwap = t3data.vwap.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"t4data_vwap = t4data.vwap.vbt.realign_closing(\"1T\").dropna()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t2data_vwap"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def plot_2y_close(priminds, secinds, close):\n",
|
||||
" fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}]], vertical_spacing=0.02, subplot_titles=(\"MOM\", \"Price\" ))\n",
|
||||
" close.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False), trace_kwargs=dict(line=dict(color=\"blue\")))\n",
|
||||
" for ind in priminds:\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
" for ind in secinds:\n",
|
||||
" if isinstance(ind, pd.Series):\n",
|
||||
" ind = ind.vbt\n",
|
||||
" ind.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True))\n",
|
||||
" return fig"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t4data.clos.vbt \n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"obvind = vbt.indicator.obv.run(t1data.close, t1data.volume)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"t1_lengtgh = 15\n",
|
||||
"t2_length = 15\n",
|
||||
"t3_length = 15\n",
|
||||
"t4_length = 5\n",
|
||||
"t1_th = 0.1\n",
|
||||
"t2_th = 0.1\n",
|
||||
"t3_th = 0.1\n",
|
||||
"t4_th = 0.1\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"#minute\n",
|
||||
"t1slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t1data.close, timeperiod=t1_lengtgh) # -0.09, 0.09\n",
|
||||
"t2slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t2data.vwap, timeperiod=t2_length) # -0.08 , 0.079\n",
|
||||
"t3slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t3data.vwap, timeperiod=t3_length) # -0.08, 0.08\n",
|
||||
"#daily\n",
|
||||
"t4slope = vbt.indicator(\"talib:LINEARREG_SLOPE \").run(t4data.vwap, timeperiod=t4_length) # -0.1, 0.09\n",
|
||||
"\n",
|
||||
"plot_2y_close(priminds=[], secinds=[t1slope, t2slope, t3slope, t4slope], close=t1data.close).show()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#thirtymin_slope = thirtymin_slope.real.rename(\"30min\") #timto se prejmenuje real na 30min\n",
|
||||
"t3slope = t3slope.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"##filter daily_slope_to_compare to only monday to friday\n",
|
||||
"t3slope = t3slope[t3slope.index.dayofweek < 5]\n",
|
||||
"\n",
|
||||
"#t3slope.info()\n",
|
||||
"\n",
|
||||
"t2slope = t2slope.real.vbt.realign_closing(\"1T\").between_time('09:30', '16:00').dropna()\n",
|
||||
"##filter daily_slope_to_compare to only monday to friday\n",
|
||||
"t2slope = t2slope[t2slope.index.dayofweek < 5]\n",
|
||||
"\n",
|
||||
"t2slope.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"oibratio"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"#\n",
|
||||
"short_entries = order_imbalance.vbt < 0.0002\n",
|
||||
"#short_entries = oibratio.vbt < 0.01\n",
|
||||
"short_entries.value_counts()\n",
|
||||
"\n",
|
||||
"entries = order_imbalance.vbt > 0.7\n",
|
||||
"#entries = oibratio.vbt > 10\n",
|
||||
"entries.value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"fig = vbt.make_subplots(rows=3, cols=1, shared_xaxes=True, \n",
|
||||
" specs=[[{\"secondary_y\": True}], [{\"secondary_y\": True}], [{\"secondary_y\": False}]], \n",
|
||||
" vertical_spacing=0.02, subplot_titles=(\"Price and Indicators\", \"Volume\"))\n",
|
||||
"t1data.data[\"BAC\"].vbt.ohlcv.plot(fig=fig, add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
|
||||
"#oibratio.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))\n",
|
||||
"order_imbalance.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True, row=1, col=1))\n",
|
||||
"entries.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"LONGS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"limegreen\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ), add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n",
|
||||
"\n",
|
||||
"short_entries.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"SHORTS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"red\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ), add_trace_kwargs=dict(secondary_y=False, row=1, col=1))\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# thirtymin_slope_to_compare.vbt.xloc[\"04-16-2024\"].get()\n",
|
||||
"thirty_down_signal.vbt.xloc[\"04-16-2024\"].get()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#short_signal = t1slope.real_below(t1_th) & t2slope.real_below(t2_th) & t3slope.real_below(t3_th) & t4slope.real_below(t4_th)\n",
|
||||
"#long_signal = t1slope.real_above(t1_th) & t2slope.real_above(t2_th) & t3slope.real_above(t3_th) & t4slope.real_above(t4_th)\n",
|
||||
"\n",
|
||||
"#test na daily s reversem crossed 0\n",
|
||||
"short_signal = t2slope.vbt < -0.01 & t3slope.vbt < -0.01 #min value of threshold\n",
|
||||
"long_signal = t2slope.vbt > 0.01 & t3slope.vbt > 0.01 #min\n",
|
||||
"\n",
|
||||
"# thirty_up_signal = t3slope.vbt.crossed_above(0.01)\n",
|
||||
"# thirty_down_signal = t3slope.vbt.crossed_below(-0.01)\n",
|
||||
"\n",
|
||||
"fig = plot_2y_close(priminds=[], secinds=[t3slope], close=t1data.close)\n",
|
||||
"#short_signal.vbt.signals.plot_as_entries(basic_data.close, fig=fig)\n",
|
||||
"\n",
|
||||
"short_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"SHORTS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"red\", symbol=\"triangle-down\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ))\n",
|
||||
"long_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"LONGS\",\n",
|
||||
" line=dict(color=\"#ffe476\"),\n",
|
||||
" marker=dict(color=\"limegreen\"),\n",
|
||||
" fill=None,\n",
|
||||
" connectgaps=True,\n",
|
||||
" ))\n",
|
||||
"\n",
|
||||
"# thirty_down_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"DOWN30\",\n",
|
||||
"# line=dict(color=\"#ffe476\"),\n",
|
||||
"# marker=dict(color=\"yellow\", symbol=\"triangle-down\"),\n",
|
||||
"# fill=None,\n",
|
||||
"# connectgaps=True,\n",
|
||||
"# ))\n",
|
||||
"# thirty_up_signal.vbt.signals.plot_as_entries(t1data.close, fig=fig, trace_kwargs=dict(name=\"UP30\",\n",
|
||||
"# line=dict(color=\"#ffe476\"),\n",
|
||||
"# marker=dict(color=\"grey\"),\n",
|
||||
"# fill=None,\n",
|
||||
"# connectgaps=True,\n",
|
||||
"# ))\n",
|
||||
"\n",
|
||||
"# thirtymin_slope_to_compare.vbt.plot(fig=fig, add_trace_kwargs=dict(secondary_y=True), trace_kwargs=dict(name=\"30min slope\",\n",
|
||||
"# line=dict(color=\"yellow\"), \n",
|
||||
"# fill=None,\n",
|
||||
"# connectgaps=True,\n",
|
||||
"# ))\n",
|
||||
"\n",
|
||||
"fig.show()\n",
|
||||
"# print(\"short signal\")\n",
|
||||
"# print(short_signal.value_counts())\n",
|
||||
"\n",
|
||||
"#forced_exit = pd.Series(False, index=close.index)\n",
|
||||
"forced_exit = basic_data.symbol_wrapper.fill(False)\n",
|
||||
"#entry_window_open = pd.Series(False, index=close.index)\n",
|
||||
"entry_window_open= basic_data.symbol_wrapper.fill(False)\n",
|
||||
"\n",
|
||||
"# Calculate the time difference in minutes from market open for each timestamp\n",
|
||||
"elapsed_min_from_open = (forced_exit.index.hour - market_open.hour) * 60 + (forced_exit.index.minute - market_open.minute)\n",
|
||||
"\n",
|
||||
"entry_window_open[(elapsed_min_from_open >= entry_window_opens) & (elapsed_min_from_open < entry_window_closes)] = True\n",
|
||||
"\n",
|
||||
"#print(entry_window_open.value_counts())\n",
|
||||
"\n",
|
||||
"forced_exit[(elapsed_min_from_open >= forced_exit_start) & (elapsed_min_from_open < forced_exit_end)] = True\n",
|
||||
"short_entries = (short_signal & entry_window_open)\n",
|
||||
"short_exits = forced_exit\n",
|
||||
"\n",
|
||||
"entries = (long_signal & entry_window_open)\n",
|
||||
"exits = forced_exit\n",
|
||||
"#long_entries.info()\n",
|
||||
"#number of trues and falses in long_entries\n",
|
||||
"# print(short_exits.value_counts())\n",
|
||||
"# print(short_entries.value_counts())\n",
|
||||
"\n",
|
||||
"#fig = plot_2y_close([],[momshort, rocp], close)\n",
|
||||
"#short_signal.vbt.signals.plot_as_entries(close, fig=fig, add_trace_kwargs=dict(secondary_y=False))\n",
|
||||
"#print(sl_stop)\n",
|
||||
"#short_entries=short_entries, short_exits=short_exits,\n",
|
||||
"# pf = vbt.Portfolio.from_signals(close=basic_data, entries=short_entries, exits=exits, tsl_stop=0.005, tp_stop = 0.05, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n",
|
||||
"\n",
|
||||
"# pf.stats()\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# pf.plot()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.get_drawdowns().records_readable"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pf.orders.records_readable"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user