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David Brazda
2024-08-30 20:49:53 +02:00
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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, DATA_DIR\n",
"import pandas as pd\n",
"import numpy as np\n",
"import vectorbtpro as vbt\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"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"batch_id = \"e44a5075\"\n",
"# res, df = load_batch(batch_id=batch_id,\n",
"# space_resolution_evenly=False,\n",
"# indicators_columns=[\"Rsi14\"],\n",
"# main_session_only=True)\n",
"# if res < 0:\n",
"# print(\"Error\" + str(res) + str(df))\n",
"# df = df[\"bars\"]\n",
"# df.info()\n",
"# df.head()\n",
"# #df.ptable()\n",
"# df.to_pickle(DATA_DIR+\"/\"+f'{batch_id}.pickle')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## FILTERING"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_pickle(DATA_DIR+\"/\"+f'{batch_id}.pickle')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\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",
"#filter date\n",
"#basic_data = basic_data.transform(lambda df: df[df.index.date == start_date.date()])\n",
"#filter range\n",
"basic_data = basic_data.transform(lambda df: df[(df.index >= start_date) & (df.index <= end_date)])\n",
"#filtered_data = basic_data.transform(lambda df: df[(df.index >= start_date) & (df.index <= end_date)])\n",
"# #range filtered_data = data[(data.index >= start_date) & (data.index <= end_date)\n",
"#df.between_time('09:30', '16:00')\n",
"#(df.index.time >= pd.Timestamp('09:30').time()) & (df.index.time <= pd.Timestamp('16:00').time())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# basic_data.data[\"BAC\"]\n",
"rsi14"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#b = filtered_data.get().iloc[100:200] #b[[\"Open\",\"High\"]]\n",
"rsi14 = basic_data.data[\"BAC\"][\"Rsi14\"].rename(\"Rsi14\")\n",
"#create subploit\n",
"fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}], [{\"secondary_y\": False}]])\n",
"rsi14.vbt.plot(add_trace_kwargs=dict(row=1, col=1, secondary_y=True),fig=fig)\n",
"basic_data.data[\"BAC\"].vbt.ohlcv.plot(add_trace_kwargs=dict(row=1, col=1, secondary_y=False), fig=fig)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"run_rsi = vbt.talib_func(\"rsi\")\n",
"rsi_new = run_rsi(basic_data.vwap, timeperiod=15)\n",
"rsi_new = rsi_new.fillna(0)\n",
"# print(rsi_new)\n",
"# print(dir(rsi_new))\n",
"rsi14 = basic_data.data[\"BAC\"][\"Rsi14\"]\n",
"# print(rsi14)\n",
"\n",
"#zkombinujeme do stejneho dataframe skrz sloupce (axis1)\n",
"# combined_df = pd.concat([rsi_new, rsi14], axis=1)\n",
"# combined_df\n",
"\n",
"#create subplot\n",
"fig = vbt.make_subplots(rows=1, cols=1, shared_xaxes=True)\n",
"\n",
"plot_rsi = vbt.talib_plot_func(\"rsi\")\n",
"plot_rsi(rsi_new, fig=fig)\n",
"plot_rsi(rsi14, fig=fig)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"entries = rsi_new.vbt.crossed_below(30)\n",
"entries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"exits = rsi_new.vbt.crossed_above(70)\n",
"exits "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def plot_rsi(close, rsi, entries, exits):\n",
" fig = vbt.make_subplots(rows=2, cols=1, shared_xaxes=True, specs=[[{\"secondary_y\": True}], [{\"secondary_y\": False}]])\n",
" basic_data.data[\"BAC\"].vbt.ohlcv.plot(add_trace_kwargs=dict(row=1, col=1, secondary_y=True),fig=fig)\n",
" rsi.vbt.plot(fig=fig, add_trace_kwargs=dict(row=1, col=1, secondary_y=False), trace_kwargs=dict(line=dict(color='grey', width=1.5)))\n",
" #close.vbt.plot(fig=fig, add_trace_kwargs=dict(row=1, col=1, secondary_y=True))\n",
" entries.vbt.signals.plot_as_entries(y=close, fig=fig, add_trace_kwargs=dict(row=1, col=1, secondary_y=True))\n",
" exits.vbt.signals.plot_as_exits(y=close, fig=fig, add_trace_kwargs=dict(row=1, col=1, secondary_y=True))\n",
" return fig\n",
"\n",
"close = basic_data.get(\"Close\")\n",
"\n",
"print(entries)\n",
"\n",
"plot_rsi(close, rsi_new, entries, exits).show()\n",
"\n",
"clean_entries, clean_exits = entries.vbt.signals.clean(exits) \n",
"\n",
"plot_rsi(close, rsi_new, clean_entries, clean_exits).show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"clean_entries.vbt.signals.total() \n",
"clean_exits.vbt.signals.total() "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"index = basic_data.wrapper.index\n",
"\n",
"#minutes from market open\n",
"market_open_time = pd.to_timedelta('09:30:00')\n",
"\n",
"# Calculate the market open datetime for each day\n",
"market_opens = index.normalize() + market_open_time\n",
"\n",
"minutes_from_open = (index - market_opens).total_seconds() / 60\n",
"# Ensuring the result is a Series\n",
"minutes_from_open = pd.Series(minutes_from_open, index=index)\n",
"\n",
"#minutes_from_open.values\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"symbol_wrapper = basic_data.get_symbol_wrapper()\n",
"\n",
"@vbt.njit\n",
"def elapsed_minutes_from_open_nb(time_in_ns):\n",
" market_opens_in_minute = 570 # 9 hours * 60 minutes + 30 minutes\n",
" current_minute = vbt.dt_nb.hour_nb(time_in_ns) * 60 + vbt.dt_nb.minute_nb(time_in_ns)\n",
" #print(\"current_minutes\", current_minutes)\n",
" # Calculate elapsed minutes since market open at 9:30 AM\n",
" minutes_from_open = current_minute - market_opens_in_minute\n",
" print( \"elapsed_from_open\", minutes_from_open)\n",
" return minutes_from_open if minutes_from_open >= 0 else 0\n",
"\n",
"@vbt.njit\n",
"def entry_place_func_nb(c, low, close, time_in_ns, rsi14, window_open, window_close):\n",
" # if c.from_i == 0: # (1)!\n",
" # c.out[0] = True\n",
" # return -1\n",
" # print(\"ted\")\n",
" # print(c.from_i)\n",
" #exit_i = c.from_i - c.wait # (2)!\n",
" #exit_price = close[exit_i, c.col] # (3)!\n",
" #hit_price = exit_price * (1 - th)\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",
"#whether the date changed\n",
"# day_changed_nb(\n",
"# ts1,\n",
"# ts2\n",
"# )\n",
"\n",
"# h_ns(ts1) int\n",
"\n",
"\n",
"@vbt.njit\n",
"def 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",
"\n",
" \n",
"\n",
"\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 high[i, c.col] >= hit_price or close[i, c.col] <= stop_price :\n",
" return out_i\n",
" return -1\n",
"\n",
"index = basic_data.wrapper.index\n",
"\n",
"#minutes from market open\n",
"market_open_time = pd.to_timedelta('09:30:00')\n",
"\n",
"# Calculate the market open datetime for each day\n",
"market_opens = index.normalize() + market_open_time\n",
"\n",
"minutes_from_open = (index - market_opens).total_seconds() / 60\n",
"\n",
"print(minutes_from_open)\n",
"\n",
"\n",
"#index 9:30 az 10:00\n",
"time_entry_window = ((index.time >= pd.Timestamp(\"09:30:00\").time())&\n",
" (index.time <= pd.Timestamp(\"14:00:00\").time()))\n",
"\n",
"\n",
"print(time_entry_window)\n",
"print(rsi_new)\n",
"rsi_entries = rsi_new.vbt.crossed_below(64)\n",
"rsi_entries = rsi_entries < 40\n",
"rsi_entries_array = rsi_entries.vbt.to_1d_array()\n",
"print(rsi_entries_array)\n",
"\n",
"\n",
"entries, exits = vbt.pd_acc.signals.generate_both( # (6)!\n",
" symbol_wrapper.shape,\n",
" entry_place_func_nb=entry_place_func_nb,\n",
" #timeindex to ns to numba\n",
" entry_place_args=(vbt.Rep(\"low\"), vbt.Rep(\"close\"), vbt.dt.to_ns(basic_data.wrapper.index), vbt.Rep(\"rsi14\"), 0, 380), # (7)!\n",
" exit_place_func_nb=exit_place_func_nb,\n",
" exit_place_args=(vbt.Rep(\"high\"), vbt.Rep(\"close\"), vbt.dt.to_ns(basic_data.wrapper.index), 0.001, 0.001),\n",
" wrapper=symbol_wrapper,\n",
" broadcast_named_args=dict( # (8)!\n",
" high=basic_data.get(\"High\"),\n",
" low=basic_data.get(\"Low\"),\n",
" close=basic_data.get(\"Close\"),\n",
" rsi14=basic_data.get(\"Rsi14\"),\n",
" window_open=10,\n",
" window_close=60\n",
" ),\n",
" broadcast_kwargs=dict(post_func=np.asarray) # (9)!\n",
")\n",
"\n",
"\n",
"plot_rsi(close, rsi_new, entries, exits).show()\n",
"\n",
"# fig = basic_data.plot(\n",
"# symbol=\"BAC\", \n",
"# ohlc_trace_kwargs=dict(opacity=0.5), \n",
"# plot_volume=False\n",
"# )\n",
"\n",
"# #rsi_entries.vbt.plot(fig=fig)\n",
"# entries.vbt.signals.plot_as_entries(\n",
"# y=close, fig=fig)\n",
"# exits.vbt.signals.plot_as_exits(\n",
"# y=close, fig=fig)\n",
"# fig.show() # (10)!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
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