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vectorized aggregator, minor changes (#198)

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David Brazda
2024-05-17 14:09:42 +02:00
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parent 031b2427b9
commit 63c2f7e748
17 changed files with 36066 additions and 41746 deletions
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{
"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",
"\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",
"res, df = load_batch(batch_id=\"0fb5043a\", #46 days 1.3 - 6.5.\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"
]
},
{
"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', '2024-04-23']).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",
"start_date = pd.Timestamp('2024-03-12 09:30', tz=zoneNY)\n",
"end_date = pd.Timestamp('2024-03-13 16:00', tz=zoneNY)\n",
"\n",
"#basic_data = basic_data.transform(lambda df: df[df.index.date == start_date.date()])\n",
"#basic_data = basic_data.transform(lambda df: df[(df.index >= start_date) & (df.index <= end_date)])\n",
"#basic_data.data[\"BAC\"].info()\n",
"\n",
"# fig = basic_data.plot(plot_volume=False)\n",
"# pivot_info = basic_data.run(\"pivotinfo\", up_th=0.003, down_th=0.002)\n",
"# #pivot_info.plot()\n",
"# pivot_info.plot(fig=fig, conf_value_trace_kwargs=dict(visible=True))\n",
"# fig.show()\n",
"\n",
"\n",
"# rsi14 = basic_data.data[\"BAC\"][\"Rsi14\"].rename(\"Rsi14\")\n",
"\n",
"# rsi14.vbt.plot().show()\n",
"#basic_data.xloc[\"09:30\":\"10:00\"].data[\"BAC\"].vbt.ohlcv.plot().show()\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": [
"# 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 = 1\n",
"entry_window_closes = 350\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",
"long_entries = (rsi.rsi.vbt.crossed_below(20) & entry_window_open)\n",
"long_exits = (rsi.rsi.vbt.crossed_above(70) | forced_exit)\n",
"#long_entries.info()\n",
"#number of trues and falses in long_entries\n",
"long_entries.value_counts()\n",
"#long_exits.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_rsi(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(rsi.rsi, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
" exits.vbt.signals.plot_as_exits(rsi.rsi, fig=fig, add_trace_kwargs=dict(secondary_y=False)) \n",
" return fig\n",
"\n",
"plot_rsi(rsi, close, long_entries, long_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, entries=long_entries, sl_stop=sl_stop, tp_stop = sl_stop, exits=long_exits,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[(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(50, 'Total Return [%]')\n",
"#stats_df.info()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pf[(0.0011,0.0013)].plot()\n",
"\n",
"#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",
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