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\n", "Loading ITables v2.0.1 from the init_notebook_mode cell...\n", "(need help?)\n", "
\n", "\n", "\n", "\n", "\n" ], "text/plain": [ " Open High Low Close Volume \\\n", "Open time \n", "2024-04-29 09:30:00-04:00 37.8400 37.940 37.7600 37.870 476819.0 \n", "2024-04-29 09:31:00-04:00 37.8750 37.985 37.7800 37.815 147198.0 \n", "2024-04-29 09:32:00-04:00 37.8050 37.850 37.7750 37.800 107729.0 \n", "2024-04-29 09:33:00-04:00 37.7935 37.880 37.7935 37.860 52305.0 \n", "2024-04-29 09:34:00-04:00 37.8600 37.890 37.8350 37.860 41294.0 \n", "... ... ... ... ... ... \n", "2024-05-06 11:33:00-04:00 37.4800 37.485 37.4700 37.475 25795.0 \n", "2024-05-06 11:34:00-04:00 37.4700 37.490 37.4700 37.485 25650.0 \n", "2024-05-06 11:35:00-04:00 37.4900 37.525 37.4900 37.500 76940.0 \n", "2024-05-06 11:36:00-04:00 37.5100 37.510 37.4950 37.505 29354.0 \n", "2024-05-06 11:37:00-04:00 37.5000 37.511 37.4900 37.505 49202.0 \n", "\n", " Trade count VWAP \n", "Open time \n", "2024-04-29 09:30:00-04:00 2738 37.855352 \n", "2024-04-29 09:31:00-04:00 975 37.904413 \n", "2024-04-29 09:32:00-04:00 875 37.812062 \n", "2024-04-29 09:33:00-04:00 517 37.842597 \n", "2024-04-29 09:34:00-04:00 403 37.855641 \n", "... ... ... \n", "2024-05-06 11:33:00-04:00 192 37.475600 \n", "2024-05-06 11:34:00-04:00 210 37.481738 \n", "2024-05-06 11:35:00-04:00 433 37.506907 \n", "2024-05-06 11:36:00-04:00 188 37.501721 \n", "2024-05-06 11:37:00-04:00 293 37.505465 \n", "\n", "[2083 rows x 7 columns]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "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()\n", "\n", "basic_data.data[\"BAC\"]" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
OpenHighLowCloseVolume
Open time
\n", "\n", "
\n", "Loading ITables v2.0.1 from the init_notebook_mode cell...\n", "(need help?)
\n", "\n" ], "text/plain": [ " Open High Low Close Volume\n", "Open time \n", "2024-04-29 09:30:00-04:00 37.8400 37.9850 37.7349 37.8800 2098173.0\n", "2024-04-29 09:45:00-04:00 37.8750 38.0300 37.8607 37.9850 1030038.0\n", "2024-04-29 10:00:00-04:00 37.9900 38.0600 37.9600 38.0145 933777.0\n", "2024-04-29 10:15:00-04:00 38.0100 38.0300 37.9500 37.9650 657084.0\n", "2024-04-29 10:30:00-04:00 37.9650 37.9950 37.9250 37.9600 617961.0\n", "... ... ... ... ... ...\n", "2024-05-06 10:30:00-04:00 37.6253 37.6465 37.5650 37.5993 925904.0\n", "2024-05-06 10:45:00-04:00 37.5994 37.6200 37.5400 37.5600 626772.0\n", "2024-05-06 11:00:00-04:00 37.5600 37.5700 37.5100 37.5343 659209.0\n", "2024-05-06 11:15:00-04:00 37.5300 37.5600 37.4650 37.4900 870303.0\n", "2024-05-06 11:30:00-04:00 37.4950 37.5250 37.4700 37.5050 312117.0\n", "\n", "[191 rows x 5 columns]" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "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_data = m15_data.transform(lambda x: x.between_time(\"9:30\",\"15:59\"))\n", "\n", "m15 = m15_data.data[\"BAC\"]\n", "\n", "m15.vbt.ohlcv.plot()\n", "\n", "m15\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": [ "# Calculate VWAP" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "VWAP.run(\n", " high,\n", " low,\n", " close,\n", " volume,\n", " anchor=Default(value='D'),\n", " short_name='vwap',\n", " hide_params=None,\n", " hide_default=True,\n", " **kwargs\n", "):\n", " Run `VWAP` indicator.\n", " \n", " * Inputs: `high`, `low`, `close`, `volume`\n", " * Parameters: `anchor`\n", " * Outputs: `vwap`\n", " \n", " Pass a list of parameter names as `hide_params` to hide their column levels, or True to hide all.\n", " Set `hide_default` to False to show the column levels of the parameters with a default value.\n", " \n", " Other keyword arguments are passed to `VWAP.run_pipeline`.\n" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "close": [ 37.87, 37.815, 37.8, 37.86, 37.86, 37.78, 37.815, 37.8737, 37.885, 37.885, 37.88, 37.92, 37.845, 37.86, 37.88, 37.87, 37.93, 37.96, 37.9642, 37.985, 37.975, 38.02, 38, 37.975, 38.01, 38.02, 37.985, 38.0086, 37.985, 37.985, 38, 38.025, 38.05, 38.035, 38.03, 38.035, 38.035, 38.05, 38.015, 38.005, 38.005, 38.005, 38.005, 38.0166, 38.0145, 38.015, 38.025, 38.025, 38.01, 38.005, 37.9799, 37.985, 38, 38, 37.9724, 37.98, 37.9775, 37.975, 37.95, 37.965, 37.95, 37.96, 37.965, 37.985, 37.9877, 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vwap_anchorarray
symbolBAC
Open time
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\n", "\n" ], "text/plain": [ "vwap_anchor array\n", "symbol BAC\n", "Open time \n", "2024-04-29 09:30:00-04:00 37.856667\n", "2024-04-29 09:31:00-04:00 37.857453\n", "2024-04-29 09:32:00-04:00 37.850221\n", "2024-04-29 09:33:00-04:00 37.849840\n", "2024-04-29 09:34:00-04:00 37.850432\n", "... ...\n", "2024-05-06 11:33:00-04:00 37.572246\n", "2024-05-06 11:34:00-04:00 37.571982\n", "2024-05-06 11:35:00-04:00 37.571400\n", "2024-05-06 11:36:00-04:00 37.571175\n", "2024-05-06 11:37:00-04:00 37.570794\n", "\n", "[2083 rows x 1 columns]" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#WWAP\n", "vbt.phelp(vbt.VWAP.run)\n", "close = m1_data.close\n", "high = m1_data.high\n", "low = m1_data.low\n", "volume = m1_data.volume\n", "vwapD = vbt.VWAP.run(high, low, close, volume, anchor=\"D\")\n", "# vwapT = vbt.VWAP.run(high, low, close, volume, anchor=\"T\")\n", "\n", "#vwap.vwap\n", "\n", "fig = m1_data.data[\"BAC\"].vbt.ohlcv.plot()\n", "vwapD.vwap.vbt.plot(fig=fig)\n", "#vwapT.vwap.vbt.plot(fig=fig)\n", "fig.show()\n", "\n", "vwapD.vwap\n", "\n", "#vwap = vbt.VWAP.run()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
OpenHighLowCloseVolume
Open time
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\n", "\n" ], "text/plain": [ " Open High Low Close Volume\n", "Open time \n", "2024-04-29 09:30:00-04:00 37.8400 37.9850 37.7349 37.8800 2098173.0\n", "2024-04-29 09:45:00-04:00 37.8750 38.0300 37.8607 37.9850 1030038.0\n", "2024-04-29 10:00:00-04:00 37.9900 38.0600 37.9600 38.0145 933777.0\n", "2024-04-29 10:15:00-04:00 38.0100 38.0300 37.9500 37.9650 657084.0\n", "2024-04-29 10:30:00-04:00 37.9650 37.9950 37.9250 37.9600 617961.0\n", "... ... ... ... ... ...\n", "2024-05-06 09:45:00-04:00 37.5250 37.5700 37.4250 37.4900 1015426.0\n", "2024-05-06 10:00:00-04:00 37.4950 37.6400 37.4700 37.6350 1420282.0\n", "2024-05-06 10:15:00-04:00 37.6350 37.6900 37.5910 37.6250 1592625.0\n", "2024-05-06 10:30:00-04:00 37.6253 37.6465 37.5650 37.5993 925904.0\n", "2024-05-06 10:45:00-04:00 37.5994 37.6200 37.5400 37.5600 626772.0\n", "\n", "[141 rows x 5 columns]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m1_data.data[\"BAC\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# defining ENTRY WINDOW and forced EXIT window" ] }, { "cell_type": "code", "execution_count": 63, "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 = 360\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": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
count
(14, BAC)
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\n", "Loading ITables v2.0.1 from the init_notebook_mode cell...\n", "(need help?)
\n", "\n" ], "text/plain": [ "(14, BAC)\n", "False 127\n", "True 14\n", "Name: count, dtype: int64" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "close = m1_data.close\n", "\n", "rsi = vbt.RSI.run(close, window=14)\n", "\n", "long_entries = (rsi.rsi.vbt.crossed_below(28) & 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": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
symbolBAC
Open time
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\n", "Loading ITables v2.0.1 from the init_notebook_mode cell...\n", "(need help?)
\n", "\n" ], "text/plain": [ "symbol BAC\n", "Open time \n", "2024-04-29 09:30:00-04:00 37.8800\n", "2024-04-29 09:45:00-04:00 37.9850\n", "2024-04-29 10:00:00-04:00 38.0145\n", "2024-04-29 10:15:00-04:00 37.9650\n", "2024-04-29 10:30:00-04:00 37.9600\n", "... ...\n", "2024-05-06 09:45:00-04:00 37.4900\n", "2024-05-06 10:00:00-04:00 37.6350\n", "2024-05-06 10:15:00-04:00 37.6250\n", "2024-05-06 10:30:00-04:00 37.5993\n", "2024-05-06 10:45:00-04:00 37.5600\n", "\n", "[141 rows x 1 columns]" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "close" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4c11ec2e1b754bf597d0856020d41755", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureWidget({\n", " 'data': [{'name': 'BAC',\n", " 'showlegend': True,\n", " 'type': 'scatter',\n", " 'uid': '8eef4694-305a-4f38-9916-7a6176babe2e',\n", " 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63.81222428, 62.53094439, 51.18605355, 42.31368311,\n", " 46.19427053, 49.22362537, 51.12854244, 43.91693211, 45.40855923,\n", " 39.57845299, 40.75017744, 42.0014731 , 39.82751096, 37.89142552,\n", " 38.47702499, 36.09475359, 36.42084984, 39.09825262, 39.44154839,\n", " 47.39886335, 49.58049766, 47.21690866, 47.21690866, 45.45139516,\n", " 47.22928014, 49.00493469, 50.44721236, 48.24310899, 47.70440622,\n", " 42.53284237, 49.42662469, 49.42662469, 69.4366945 , 73.02816179,\n", " 61.24707816, 58.06165861, 60.28603947, 57.80951201, 59.59692731,\n", " 58.41247201, 58.41247201, 59.80228634, 59.09336858, 60.11181234,\n", " 60.39322483, 56.71049769, 57.32908325, 54.39912248, 58.62798422,\n", " 58.1810198 , 58.52156521, 57.51282127, 52.6281247 , 53.05748703,\n", " 48.33916888, 53.2627497 , 54.11894932, 56.68271896, 57.94799997,\n", " 73.32914926, 69.82845181, 75.12633475, 74.15920112, 71.60783584,\n", " 67.76830357]),\n", " 'yaxis': 'y'},\n", " {'marker': {'color': '#37B13F',\n", " 'line': {'color': 'rgb(38,123,44)', 'width': 1},\n", " 'size': 8,\n", " 'symbol': 'triangle-up'},\n", " 'mode': 'markers',\n", " 'name': 'Entries',\n", " 'showlegend': True,\n", " 'type': 'scatter',\n", " 'uid': '20a40d53-2d3b-4567-adc5-7b239dac13ad',\n", " 'x': array([datetime.datetime(2024, 4, 30, 15, 15, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x',\n", " 'y': array([37.085]),\n", " 'yaxis': 'y2'},\n", " {'marker': {'color': '#EA4335',\n", " 'line': {'color': 'rgb(181,31,18)', 'width': 1},\n", " 'size': 8,\n", " 'symbol': 'triangle-down'},\n", " 'mode': 'markers',\n", " 'name': 'Exits',\n", " 'showlegend': True,\n", " 'type': 'scatter',\n", " 'uid': '1b39b0d3-7bb7-46dc-abdb-e5ee1c782bc6',\n", " 'x': array([datetime.datetime(2024, 4, 29, 14, 0, tzinfo=),\n", " datetime.datetime(2024, 4, 29, 15, 30, tzinfo=),\n", " datetime.datetime(2024, 4, 29, 15, 45, tzinfo=),\n", " datetime.datetime(2024, 4, 30, 15, 30, tzinfo=),\n", " datetime.datetime(2024, 4, 30, 15, 45, tzinfo=),\n", " datetime.datetime(2024, 5, 1, 15, 30, tzinfo=),\n", " datetime.datetime(2024, 5, 1, 15, 45, tzinfo=),\n", " datetime.datetime(2024, 5, 2, 15, 30, tzinfo=),\n", " datetime.datetime(2024, 5, 2, 15, 45, tzinfo=),\n", " datetime.datetime(2024, 5, 3, 9, 45, tzinfo=),\n", " datetime.datetime(2024, 5, 3, 15, 30, tzinfo=),\n", " datetime.datetime(2024, 5, 3, 15, 45, tzinfo=),\n", " datetime.datetime(2024, 5, 6, 9, 30, tzinfo=),\n", " datetime.datetime(2024, 5, 6, 10, 0, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x',\n", " 'y': array([37.75 , 37.53 , 37.56 , 37.07 , 37.01 , 37.165, 36.945, 36.805, 36.89 ,\n", " 37.415, 37.205, 37.235, 37.525, 37.635]),\n", " 'yaxis': 'y2'}],\n", " 'layout': {'annotations': [{'font': {'size': 16},\n", " 'showarrow': False,\n", " 'text': 'RSI',\n", " 'x': 0.47,\n", " 'xanchor': 'center',\n", " 'xref': 'paper',\n", " 'y': 1.0,\n", " 'yanchor': 'bottom',\n", " 'yref': 'paper'}],\n", " 'height': 350,\n", " 'legend': {'orientation': 'h',\n", " 'traceorder': 'normal',\n", " 'x': 1,\n", " 'xanchor': 'right',\n", " 'y': 1.02,\n", " 'yanchor': 'bottom'},\n", " 'margin': {'b': 30, 'l': 30, 'r': 30, 't': 30},\n", " 'shapes': [{'fillcolor': 'mediumslateblue',\n", " 'layer': 'below',\n", " 'line': {'width': 0},\n", " 'opacity': 0.2,\n", " 'type': 'rect',\n", " 'x0': Timestamp('2024-04-29 09:30:00-0400', tz='America/New_York'),\n", " 'x1': Timestamp('2024-05-06 10:45:00-0400', tz='America/New_York'),\n", " 'xref': 'x',\n", " 'y0': 30,\n", " 'y1': 70,\n", " 'yref': 'y'}],\n", " 'template': '...',\n", " 'width': 1280,\n", " 'xaxis': {'anchor': 'y', 'domain': [0.0, 0.94]},\n", " 'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'range': [-5, 105]},\n", " 'yaxis2': {'anchor': 'x', 'overlaying': 'y', 'side': 'right'}}\n", "})" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "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(close, fig=fig, add_trace_kwargs=dict(secondary_y=True)) \n", " exits.vbt.signals.plot_as_exits(close, fig=fig, add_trace_kwargs=dict(secondary_y=True)) \n", " return fig\n", "\n", "plot_rsi(rsi, close, long_entries, long_exits)\n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
rsi_window14
symbolBAC
Open time
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\n", "\n" ], "text/plain": [ "rsi_window 14\n", "symbol BAC\n", "Open time \n", "2024-04-29 09:30:00-04:00 NaN\n", "2024-04-29 09:45:00-04:00 NaN\n", "2024-04-29 10:00:00-04:00 NaN\n", "2024-04-29 10:15:00-04:00 NaN\n", "2024-04-29 10:30:00-04:00 NaN\n", "... ..\n", "2024-05-05 15:45:00-04:00 NaN\n", "2024-05-06 09:30:00-04:00 NaN\n", "2024-05-06 09:45:00-04:00 NaN\n", "2024-05-06 10:00:00-04:00 NaN\n", "2024-05-06 10:15:00-04:00 NaN\n", "\n", "[186 rows x 1 columns]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rsi.rsi" ] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Portfolio.from_signals(\n", " close,\n", " entries=None,\n", " exits=None,\n", " *,\n", " direction=None,\n", " long_entries=None,\n", " long_exits=None,\n", " short_entries=None,\n", " short_exits=None,\n", " adjust_func_nb=None,\n", " adjust_args=(),\n", " signal_func_nb=None,\n", " signal_args=(),\n", " post_segment_func_nb=None,\n", " post_segment_args=(),\n", " order_mode=False,\n", " size=None,\n", " size_type=None,\n", " price=None,\n", " fees=None,\n", " fixed_fees=None,\n", " slippage=None,\n", " min_size=None,\n", " max_size=None,\n", " size_granularity=None,\n", " leverage=None,\n", " leverage_mode=None,\n", " reject_prob=None,\n", " price_area_vio_mode=None,\n", " allow_partial=None,\n", " raise_reject=None,\n", " log=None,\n", " val_price=None,\n", " accumulate=None,\n", " upon_long_conflict=None,\n", " upon_short_conflict=None,\n", " upon_dir_conflict=None,\n", " upon_opposite_entry=None,\n", " order_type=None,\n", " limit_delta=None,\n", " limit_tif=None,\n", " limit_expiry=None,\n", " limit_reverse=None,\n", " upon_adj_limit_conflict=None,\n", " upon_opp_limit_conflict=None,\n", " use_stops=None,\n", " stop_ladder=None,\n", " sl_stop=None,\n", " tsl_stop=None,\n", " tsl_th=None,\n", " tp_stop=None,\n", " td_stop=None,\n", " dt_stop=None,\n", " stop_entry_price=None,\n", " stop_exit_price=None,\n", " stop_exit_type=None,\n", " stop_order_type=None,\n", " stop_limit_delta=None,\n", " upon_stop_update=None,\n", " upon_adj_stop_conflict=None,\n", " upon_opp_stop_conflict=None,\n", " delta_format=None,\n", " time_delta_format=None,\n", " open=None,\n", " high=None,\n", " low=None,\n", " init_cash=None,\n", " init_position=None,\n", " init_price=None,\n", " cash_deposits=None,\n", " cash_earnings=None,\n", " cash_dividends=None,\n", " cash_sharing=None,\n", " from_ago=None,\n", " call_seq=None,\n", " attach_call_seq=None,\n", " ffill_val_price=None,\n", " update_value=None,\n", " fill_pos_info=None,\n", " save_state=None,\n", " save_value=None,\n", " save_returns=None,\n", " max_order_records=None,\n", " max_log_records=None,\n", " in_outputs=None,\n", " seed=None,\n", " group_by=None,\n", " broadcast_named_args=None,\n", " broadcast_kwargs=None,\n", " template_context=None,\n", " jitted=None,\n", " chunked=None,\n", " staticized=None,\n", " bm_close=None,\n", " records=None,\n", " return_preparer=False,\n", " return_prep_result=False,\n", " return_sim_out=False,\n", " **kwargs\n", "):\n", " Simulate portfolio from entry and exit signals.\n", " \n", " Supports the following modes:\n", " \n", " 1. `entries` and `exits`:\n", " Uses `vectorbtpro.portfolio.nb.from_signals.dir_signal_func_nb` as `signal_func_nb`\n", " if an adjustment function is provided (not cacheable), otherwise translates signals using\n", " `vectorbtpro.portfolio.nb.from_signals.dir_to_ls_signals_nb` then simulates statically (cacheable)\n", " 2. `entries` (acting as long), `exits` (acting as long), `short_entries`, and `short_exits`:\n", " Uses `vectorbtpro.portfolio.nb.from_signals.ls_signal_func_nb` as `signal_func_nb`\n", " if an adjustment function is provided (not cacheable), otherwise simulates statically (cacheable)\n", " 3. `order_mode=True` without signals:\n", " Uses `vectorbtpro.portfolio.nb.from_signals.order_signal_func_nb` as `signal_func_nb` (not cacheable)\n", " 4. `signal_func_nb` and `signal_args`: Custom signal function (not cacheable)\n", " \n", " Prepared by `vectorbtpro.portfolio.preparing.FSPreparer`.\n", " \n", " Args:\n", " close (array_like, OHLCDataMixin, FSPreparer, or PFPrepResult): See `Portfolio.from_orders`.\n", " entries (array_like of bool): Boolean array of entry signals.\n", " Defaults to True if all other signal arrays are not set, otherwise False. Will broadcast.\n", " \n", " * If `short_entries` and `short_exits` are not set: Acts as a long signal if `direction`\n", " is 'all' or 'longonly', otherwise short.\n", " * If `short_entries` or `short_exits` are set: Acts as `long_entries`.\n", " exits (array_like of bool): Boolean array of exit signals.\n", " Defaults to False. Will broadcast.\n", " \n", " * If `short_entries` and `short_exits` are not set: Acts as a short signal if `direction`\n", " is 'all' or 'longonly', otherwise long.\n", " * If `short_entries` or `short_exits` are set: Acts as `long_exits`.\n", " direction (Direction or array_like): See `Portfolio.from_orders`.\n", " \n", " Takes only effect if `short_entries` and `short_exits` are not set.\n", " long_entries (array_like of bool): Boolean array of long entry signals.\n", " Defaults to False. Will broadcast.\n", " long_exits (array_like of bool): Boolean array of long exit signals.\n", " Defaults to False. Will broadcast.\n", " short_entries (array_like of bool): Boolean array of short entry signals.\n", " Defaults to False. Will broadcast.\n", " short_exits (array_like of bool): Boolean array of short exit signals.\n", " Defaults to False. Will broadcast.\n", " adjust_func_nb (path_like or callable): User-defined function to adjust the current simulation state.\n", " Defaults to `vectorbtpro.portfolio.nb.from_signals.no_adjust_func_nb`.\n", " \n", " Passed as argument to `vectorbtpro.portfolio.nb.from_signals.dir_signal_func_nb`,\n", " `vectorbtpro.portfolio.nb.from_signals.ls_signal_func_nb`, and\n", " `vectorbtpro.portfolio.nb.from_signals.order_signal_func_nb`. Has no effect\n", " when using other signal functions.\n", " \n", " Can be a path to a module when using staticizing.\n", " adjust_args (tuple): Packed arguments passed to `adjust_func_nb`.\n", " signal_func_nb (path_like or callable): Function called to generate signals.\n", " \n", " See `vectorbtpro.portfolio.nb.from_signals.from_signal_func_nb`.\n", " \n", " Can be a path to a module when using staticizing.\n", " signal_args (tuple): Packed arguments passed to `signal_func_nb`.\n", " post_segment_func_nb (path_like or callable): Post-segment function.\n", " \n", " See `vectorbtpro.portfolio.nb.from_signals.from_signal_func_nb`.\n", " \n", " Can be a path to a module when using staticizing.\n", " post_segment_args (tuple): Packed arguments passed to `post_segment_func_nb`.\n", " order_mode (bool): Whether to simulate as orders without signals.\n", " size (float or array_like): See `Portfolio.from_orders`.\n", " \n", " !!! note\n", " Negative size is not allowed. You must express direction using signals.\n", " size_type (SizeType or array_like): See `Portfolio.from_orders`.\n", " \n", " Only `SizeType.Amount`, `SizeType.Value`, `SizeType.Percent(100)`, and\n", " `SizeType.ValuePercent(100)` are supported. Other modes such as target percentage\n", " are not compatible with signals since their logic may contradict the direction of the signal.\n", " \n", " !!! note\n", " `SizeType.Percent(100)` does not support position reversal. Switch to a single\n", " direction or use `OppositeEntryMode.Close` to close the position first.\n", " \n", " See warning in `Portfolio.from_orders`.\n", " price (array_like of float): See `Portfolio.from_orders`.\n", " fees (float or array_like): See `Portfolio.from_orders`.\n", " fixed_fees (float or array_like): See `Portfolio.from_orders`.\n", " slippage (float or array_like): See `Portfolio.from_orders`.\n", " min_size (float or array_like): See `Portfolio.from_orders`.\n", " max_size (float or array_like): See `Portfolio.from_orders`.\n", " \n", " Will be partially filled if exceeded. You might not be able to properly close\n", " the position if accumulation is enabled and `max_size` is too low.\n", " size_granularity (float or array_like): See `Portfolio.from_orders`.\n", " leverage (float or array_like): See `Portfolio.from_orders`.\n", " leverage_mode (LeverageMode or array_like): See `Portfolio.from_orders`.\n", " reject_prob (float or array_like): See `Portfolio.from_orders`.\n", " price_area_vio_mode (PriceAreaVioMode or array_like): See `Portfolio.from_orders`.\n", " allow_partial (bool or array_like): See `Portfolio.from_orders`.\n", " raise_reject (bool or array_like): See `Portfolio.from_orders`.\n", " log (bool or array_like): See `Portfolio.from_orders`.\n", " val_price (array_like of float): See `Portfolio.from_orders`.\n", " accumulate (bool, AccumulationMode or array_like): See `vectorbtpro.portfolio.enums.AccumulationMode`.\n", " If True, becomes 'both'. If False, becomes 'disabled'. Will broadcast.\n", " \n", " When enabled, `Portfolio.from_signals` behaves similarly to `Portfolio.from_orders`.\n", " upon_long_conflict (ConflictMode or array_like): Conflict mode for long signals.\n", " See `vectorbtpro.portfolio.enums.ConflictMode`. Will broadcast.\n", " upon_short_conflict (ConflictMode or array_like): Conflict mode for short signals.\n", " See `vectorbtpro.portfolio.enums.ConflictMode`. Will broadcast.\n", " upon_dir_conflict (DirectionConflictMode or array_like): See `vectorbtpro.portfolio.enums.DirectionConflictMode`.\n", " Will broadcast.\n", " upon_opposite_entry (OppositeEntryMode or array_like): See `vectorbtpro.portfolio.enums.OppositeEntryMode`.\n", " Will broadcast.\n", " order_type (OrderType or array_like): See `vectorbtpro.portfolio.enums.OrderType`.\n", " \n", " Only one active limit order is allowed at a time.\n", " limit_delta (float or array_like): Delta from `price` to build the limit price.\n", " Will broadcast.\n", " \n", " If NaN, `price` becomes the limit price. Otherwise, applied on top of `price` depending\n", " on the current direction: if the direction-aware size is positive (= buying), a positive delta\n", " will decrease the limit price; if the direction-aware size is negative (= selling), a positive delta\n", " will increase the limit price. Delta can be negative.\n", " \n", " Set an element to `np.nan` to disable. Use `delta_format` to specify the format.\n", " limit_tif (frequency_like or array_like): Time in force for limit signals.\n", " Will broadcast.\n", " \n", " Any frequency-like object is converted using `vectorbtpro.utils.datetime_.to_timedelta64`.\n", " Any array must either contain timedeltas or integers, and will be cast into integer format\n", " after broadcasting. If the object provided is of data type `object`, will be converted\n", " to timedelta automatically.\n", " \n", " Measured in the distance after the open time of the signal bar. If the expiration time happens\n", " in the middle of the current bar, we pessimistically assume that the order has been expired.\n", " The check is performed at the beginning of the bar, and the first check is performed at the\n", " next bar after the signal. For example, if the format is `TimeDeltaFormat.Rows`, 0 or 1 means\n", " the order must execute at the same bar or not at all; 2 means the order must execute at the\n", " same or next bar or not at all.\n", " \n", " Set an element to `-1` to disable. Use `time_delta_format` to specify the format.\n", " limit_expiry (frequency_like, datetime_like, or array_like): Expiration time.\n", " Will broadcast.\n", " \n", " Any frequency-like object is used to build a period index, such that each timestamp in the original\n", " index is pointing to the timestamp where the period ends. For example, providing \"d\" will\n", " make any limit order expire on the next day. Any array must either contain timestamps or integers\n", " (not timedeltas!), and will be cast into integer format after broadcasting. If the object\n", " provided is of data type `object`, will be converted to datetime and its timezone will\n", " be removed automatically (as done on the index).\n", " \n", " Behaves in a similar way as `limit_tif`.\n", " \n", " Set an element to `-1` or `pd.Timestamp.max` to disable. Use `time_delta_format` to specify the format.\n", " limit_reverse (bool or array_like): Whether to reverse the price hit detection.\n", " Will broadcast.\n", " \n", " If True, a buy/sell limit price will be checked against high/low (not low/high).\n", " Also, the limit delta will be applied above/below (not below/above) the initial price.\n", " upon_adj_limit_conflict (PendingConflictMode or array_like): Conflict mode for limit and user-defined\n", " signals of adjacent sign. See `vectorbtpro.portfolio.enums.PendingConflictMode`. Will broadcast.\n", " upon_opp_limit_conflict (PendingConflictMode or array_like): Conflict mode for limit and user-defined\n", " signals of opposite sign. See `vectorbtpro.portfolio.enums.PendingConflictMode`. Will broadcast.\n", " use_stops (bool): Whether to use stops.\n", " Defaults to None, which becomes True if any of the stops are not NaN or\n", " the adjustment function is not the default one.\n", " \n", " Disable this to make simulation a bit faster for simple use cases.\n", " stop_ladder (bool or StopLadderMode): Whether and which kind of stop laddering to use.\n", " See `vectorbtpro.portfolio.enums.StopLadderMode`.\n", " \n", " If so, rows in the supplied arrays will become ladder steps. Make sure that\n", " they are increasing. If one column should have less steps, pad it with NaN\n", " for price-based stops and -1 for time-based stops.\n", " \n", " Rows in each array can be of an arbitrary length but columns must broadcast against\n", " the number of columns in the data. Applied on all stop types.\n", " sl_stop (array_like of float): Stop loss.\n", " Will broadcast.\n", " \n", " Set an element to `np.nan` to disable. Use `delta_format` to specify the format.\n", " tsl_stop (array_like of float): Trailing stop loss for the trailing stop loss.\n", " Will broadcast.\n", " \n", " Set an element to `np.nan` to disable. Use `delta_format` to specify the format.\n", " tsl_th (array_like of float): Take profit threshold for the trailing stop loss.\n", " Will broadcast.\n", " \n", " Set an element to `np.nan` to disable. Use `delta_format` to specify the format.\n", " tp_stop (array_like of float): Take profit.\n", " Will broadcast.\n", " \n", " Set an element to `np.nan` to disable. Use `delta_format` to specify the format.\n", " td_stop (frequency_like or array_like): Timedelta-stop.\n", " Will broadcast.\n", " \n", " Set an element to `-1` to disable. Use `time_delta_format` to specify the format.\n", " dt_stop (frequency_like, datetime_like, or array_like): Datetime-stop.\n", " Will broadcast.\n", " \n", " Set an element to `-1` to disable. Use `time_delta_format` to specify the format.\n", " stop_entry_price (StopEntryPrice or array_like): See `vectorbtpro.portfolio.enums.StopEntryPrice`.\n", " Will broadcast.\n", " \n", " If provided on per-element basis, gets applied upon entry. If a positive value is provided,\n", " used directly as a price, otherwise used as an enumerated value.\n", " stop_exit_price (StopExitPrice or array_like): See `vectorbtpro.portfolio.enums.StopExitPrice`.\n", " Will broadcast.\n", " \n", " If provided on per-element basis, gets applied upon entry. If a positive value is provided,\n", " used directly as a price, otherwise used as an enumerated value.\n", " stop_exit_type (StopExitType or array_like): See `vectorbtpro.portfolio.enums.StopExitType`.\n", " Will broadcast.\n", " \n", " If provided on per-element basis, gets applied upon entry.\n", " stop_order_type (OrderType or array_like): Similar to `order_type` but for stop orders.\n", " Will broadcast.\n", " \n", " If provided on per-element basis, gets applied upon entry.\n", " stop_limit_delta (float or array_like): Similar to `limit_delta` but for stop orders.\n", " Will broadcast.\n", " upon_stop_update (StopUpdateMode or array_like): See `vectorbtpro.portfolio.enums.StopUpdateMode`.\n", " Will broadcast.\n", " \n", " Only has effect if accumulation is enabled.\n", " \n", " If provided on per-element basis, gets applied upon repeated entry.\n", " upon_adj_stop_conflict (PendingConflictMode or array_like): Conflict mode for stop and user-defined\n", " signals of adjacent sign. See `vectorbtpro.portfolio.enums.PendingConflictMode`. Will broadcast.\n", " upon_opp_stop_conflict (PendingConflictMode or array_like): Conflict mode for stop and user-defined\n", " signals of opposite sign. See `vectorbtpro.portfolio.enums.PendingConflictMode`. Will broadcast.\n", " delta_format (DeltaFormat or array_like): See `vectorbtpro.portfolio.enums.DeltaFormat`.\n", " Will broadcast.\n", " time_delta_format (TimeDeltaFormat or array_like): See `vectorbtpro.portfolio.enums.TimeDeltaFormat`.\n", " Will broadcast.\n", " open (array_like of float): See `Portfolio.from_orders`.\n", " \n", " For stop signals, `np.nan` gets replaced by `close`.\n", " high (array_like of float): See `Portfolio.from_orders`.\n", " \n", " For stop signals, `np.nan` replaced by the maximum out of `open` and `close`.\n", " low (array_like of float): See `Portfolio.from_orders`.\n", " \n", " For stop signals, `np.nan` replaced by the minimum out of `open` and `close`.\n", " init_cash (InitCashMode, float or array_like): See `Portfolio.from_orders`.\n", " init_position (float or array_like): See `Portfolio.from_orders`.\n", " init_price (float or array_like): See `Portfolio.from_orders`.\n", " cash_deposits (float or array_like): See `Portfolio.from_orders`.\n", " cash_earnings (float or array_like): See `Portfolio.from_orders`.\n", " cash_dividends (float or array_like): See `Portfolio.from_orders`.\n", " cash_sharing (bool): See `Portfolio.from_orders`.\n", " from_ago (int or array_like): See `Portfolio.from_orders`.\n", " \n", " Take effect only for user-defined signals, not for stop signals.\n", " call_seq (CallSeqType or array_like): See `Portfolio.from_orders`.\n", " attach_call_seq (bool): See `Portfolio.from_orders`.\n", " ffill_val_price (bool): See `Portfolio.from_orders`.\n", " update_value (bool): See `Portfolio.from_orders`.\n", " fill_pos_info (bool): fill_pos_info (bool): Whether to fill position record.\n", " \n", " Disable this to make simulation faster for simple use cases.\n", " save_state (bool): See `Portfolio.from_orders`.\n", " save_value (bool): See `Portfolio.from_orders`.\n", " save_returns (bool): See `Portfolio.from_orders`.\n", " max_order_records (int): See `Portfolio.from_orders`.\n", " max_log_records (int): See `Portfolio.from_orders`.\n", " in_outputs (mapping_like): Mapping with in-output objects. Only for flexible mode.\n", " \n", " Will be available via `Portfolio.in_outputs` as a named tuple.\n", " \n", " To substitute `Portfolio` attributes, provide already broadcasted and grouped objects,\n", " for example, by using `broadcast_named_args` and templates. Also see\n", " `Portfolio.in_outputs_indexing_func` on how in-output objects are indexed.\n", " \n", " When chunking, make sure to provide the chunk taking specification and the merging function.\n", " See `vectorbtpro.portfolio.chunking.merge_sim_outs`.\n", " \n", " !!! note\n", " When using Numba below 0.54, `in_outputs` cannot be a mapping, but must be a named tuple\n", " defined globally so Numba can introspect its attributes for pickling.\n", " seed (int): See `Portfolio.from_orders`.\n", " group_by (any): See `Portfolio.from_orders`.\n", " broadcast_named_args (dict): Dictionary with named arguments to broadcast.\n", " \n", " You can then pass argument names wrapped with `vectorbtpro.utils.template.Rep`\n", " and this method will substitute them by their corresponding broadcasted objects.\n", " broadcast_kwargs (dict): See `Portfolio.from_orders`.\n", " template_context (mapping): Mapping to replace templates in arguments.\n", " jitted (any): See `Portfolio.from_orders`.\n", " chunked (any): See `Portfolio.from_orders`.\n", " staticized (bool, dict, hashable, or callable): Keyword arguments or task id for staticizing.\n", " \n", " If True or dictionary, will be passed as keyword arguments to\n", " `vectorbtpro.utils.cutting.cut_and_save_func` to save a cacheable version of the\n", " simulator to a file. If a hashable or callable, will be used as a task id of an\n", " already registered jittable and chunkable simulator. Dictionary allows additional options\n", " `override` and `reload` to override and reload an already existing module respectively.\n", " bm_close (array_like): See `Portfolio.from_orders`.\n", " records (array_like): See `Portfolio.from_orders`.\n", " return_preparer (bool): See `Portfolio.from_orders`.\n", " return_prep_result (bool): See `Portfolio.from_orders`.\n", " return_sim_out (bool): See `Portfolio.from_orders`.\n", " **kwargs: Keyword arguments passed to the `Portfolio` constructor.\n", " \n", " All broadcastable arguments will broadcast using `vectorbtpro.base.reshaping.broadcast`\n", " but keep original shape to utilize flexible indexing and to save memory.\n", " \n", " For defaults, see `vectorbtpro._settings.portfolio`. Those defaults are not used to fill\n", " NaN values after reindexing: vectorbt uses its own sensible defaults, which are usually NaN\n", " for floating arrays and default flags for integer arrays. Use `vectorbtpro.base.reshaping.BCO`\n", " with `fill_value` to override.\n", " \n", " Also see notes and hints for `Portfolio.from_orders`.\n", " \n", " Usage:\n", " * By default, if all signal arrays are None, `entries` becomes True,\n", " which opens a position at the very first tick and does nothing else:\n", " \n", " ```pycon\n", " >>> close = pd.Series([1, 2, 3, 4, 5])\n", " >>> pf = vbt.Portfolio.from_signals(close, size=1)\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 0.0\n", " 2 0.0\n", " 3 0.0\n", " 4 0.0\n", " dtype: float64\n", " ```\n", " \n", " * Entry opens long, exit closes long:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]),\n", " ... exits=pd.Series([False, False, True, True, True]),\n", " ... size=1,\n", " ... direction='longonly'\n", " ... )\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 0.0\n", " 2 0.0\n", " 3 -1.0\n", " 4 0.0\n", " dtype: float64\n", " \n", " >>> # Using direction-aware arrays instead of `direction`\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]), # long_entries\n", " ... exits=pd.Series([False, False, True, True, True]), # long_exits\n", " ... short_entries=False,\n", " ... short_exits=False,\n", " ... size=1\n", " ... )\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 0.0\n", " 2 0.0\n", " 3 -1.0\n", " 4 0.0\n", " dtype: float64\n", " ```\n", " \n", " Notice how both `short_entries` and `short_exits` are provided as constants - as any other\n", " broadcastable argument, they are treated as arrays where each element is False.\n", " \n", " * Entry opens short, exit closes short:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]),\n", " ... exits=pd.Series([False, False, True, True, True]),\n", " ... size=1,\n", " ... direction='shortonly'\n", " ... )\n", " >>> pf.asset_flow\n", " 0 -1.0\n", " 1 0.0\n", " 2 0.0\n", " 3 1.0\n", " 4 0.0\n", " dtype: float64\n", " \n", " >>> # Using direction-aware arrays instead of `direction`\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=False, # long_entries\n", " ... exits=False, # long_exits\n", " ... short_entries=pd.Series([True, True, True, False, False]),\n", " ... short_exits=pd.Series([False, False, True, True, True]),\n", " ... size=1\n", " ... )\n", " >>> pf.asset_flow\n", " 0 -1.0\n", " 1 0.0\n", " 2 0.0\n", " 3 1.0\n", " 4 0.0\n", " dtype: float64\n", " ```\n", " \n", " * Entry opens long and closes short, exit closes long and opens short:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]),\n", " ... exits=pd.Series([False, False, True, True, True]),\n", " ... size=1,\n", " ... direction='both'\n", " ... )\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 0.0\n", " 2 0.0\n", " 3 -2.0\n", " 4 0.0\n", " dtype: float64\n", " \n", " >>> # Using direction-aware arrays instead of `direction`\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]), # long_entries\n", " ... exits=False, # long_exits\n", " ... short_entries=pd.Series([False, False, True, True, True]),\n", " ... short_exits=False,\n", " ... size=1\n", " ... )\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 0.0\n", " 2 0.0\n", " 3 -2.0\n", " 4 0.0\n", " dtype: float64\n", " ```\n", " \n", " * More complex signal combinations are best expressed using direction-aware arrays.\n", " For example, ignore opposite signals as long as the current position is open:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries =pd.Series([True, False, False, False, False]), # long_entries\n", " ... exits =pd.Series([False, False, True, False, False]), # long_exits\n", " ... short_entries=pd.Series([False, True, False, True, False]),\n", " ... short_exits =pd.Series([False, False, False, False, True]),\n", " ... size=1,\n", " ... upon_opposite_entry='ignore'\n", " ... )\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 0.0\n", " 2 -1.0\n", " 3 -1.0\n", " 4 1.0\n", " dtype: float64\n", " ```\n", " \n", " * First opposite signal closes the position, second one opens a new position:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]),\n", " ... exits=pd.Series([False, False, True, True, True]),\n", " ... size=1,\n", " ... direction='both',\n", " ... upon_opposite_entry='close'\n", " ... )\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 0.0\n", " 2 0.0\n", " 3 -1.0\n", " 4 -1.0\n", " dtype: float64\n", " ```\n", " \n", " * If both long entry and exit signals are True (a signal conflict), choose exit:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]),\n", " ... exits=pd.Series([False, False, True, True, True]),\n", " ... size=1.,\n", " ... direction='longonly',\n", " ... upon_long_conflict='exit')\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 0.0\n", " 2 -1.0\n", " 3 0.0\n", " 4 0.0\n", " dtype: float64\n", " ```\n", " \n", " * If both long entry and short entry signal are True (a direction conflict), choose short:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]),\n", " ... exits=pd.Series([False, False, True, True, True]),\n", " ... size=1.,\n", " ... direction='both',\n", " ... upon_dir_conflict='short')\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 0.0\n", " 2 -2.0\n", " 3 0.0\n", " 4 0.0\n", " dtype: float64\n", " ```\n", " \n", " !!! note\n", " Remember that when direction is set to 'both', entries become `long_entries` and exits become\n", " `short_entries`, so this becomes a conflict of directions rather than signals.\n", " \n", " * If there are both signal and direction conflicts:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=True, # long_entries\n", " ... exits=True, # long_exits\n", " ... short_entries=True,\n", " ... short_exits=True,\n", " ... size=1,\n", " ... upon_long_conflict='entry',\n", " ... upon_short_conflict='entry',\n", " ... upon_dir_conflict='short'\n", " ... )\n", " >>> pf.asset_flow\n", " 0 -1.0\n", " 1 0.0\n", " 2 0.0\n", " 3 0.0\n", " 4 0.0\n", " dtype: float64\n", " ```\n", " \n", " * Turn on accumulation of signals. Entry means long order, exit means short order\n", " (acts similar to `from_orders`):\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]),\n", " ... exits=pd.Series([False, False, True, True, True]),\n", " ... size=1.,\n", " ... direction='both',\n", " ... accumulate=True)\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 1.0\n", " 2 0.0\n", " 3 -1.0\n", " 4 -1.0\n", " dtype: float64\n", " ```\n", " \n", " * Allow increasing a position (of any direction), deny decreasing a position:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]),\n", " ... exits=pd.Series([False, False, True, True, True]),\n", " ... size=1.,\n", " ... direction='both',\n", " ... accumulate='addonly')\n", " >>> pf.asset_flow\n", " 0 1.0 << open a long position\n", " 1 1.0 << add to the position\n", " 2 0.0\n", " 3 -3.0 << close and open a short position\n", " 4 -1.0 << add to the position\n", " dtype: float64\n", " ```\n", " \n", " * Test multiple parameters via regular broadcasting:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]),\n", " ... exits=pd.Series([False, False, True, True, True]),\n", " ... direction=[list(Direction)],\n", " ... broadcast_kwargs=dict(columns_from=pd.Index(vbt.pf_enums.Direction._fields, name='direction')))\n", " >>> pf.asset_flow\n", " direction LongOnly ShortOnly Both\n", " 0 100.0 -100.0 100.0\n", " 1 0.0 0.0 0.0\n", " 2 0.0 0.0 0.0\n", " 3 -100.0 50.0 -200.0\n", " 4 0.0 0.0 0.0\n", " ```\n", " \n", " * Test multiple parameters via `vectorbtpro.base.reshaping.BCO`:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close,\n", " ... entries=pd.Series([True, True, True, False, False]),\n", " ... exits=pd.Series([False, False, True, True, True]),\n", " ... direction=vbt.Param(Direction))\n", " >>> pf.asset_flow\n", " direction LongOnly ShortOnly Both\n", " 0 100.0 -100.0 100.0\n", " 1 0.0 0.0 0.0\n", " 2 0.0 0.0 0.0\n", " 3 -100.0 50.0 -200.0\n", " 4 0.0 0.0 0.0\n", " ```\n", " \n", " * Set risk/reward ratio by passing trailing stop loss and take profit thresholds:\n", " \n", " ```pycon\n", " >>> close = pd.Series([10, 11, 12, 11, 10, 9])\n", " >>> entries = pd.Series([True, False, False, False, False, False])\n", " >>> exits = pd.Series([False, False, False, False, False, True])\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close, entries, exits,\n", " ... tsl_stop=0.1, tp_stop=0.2) # take profit hit\n", " >>> pf.asset_flow\n", " 0 10.0\n", " 1 0.0\n", " 2 -10.0\n", " 3 0.0\n", " 4 0.0\n", " 5 0.0\n", " dtype: float64\n", " \n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close, entries, exits,\n", " ... tsl_stop=0.1, tp_stop=0.3) # trailing stop loss hit\n", " >>> pf.asset_flow\n", " 0 10.0\n", " 1 0.0\n", " 2 0.0\n", " 3 0.0\n", " 4 -10.0\n", " 5 0.0\n", " dtype: float64\n", " \n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close, entries, exits,\n", " ... tsl_stop=np.inf, tp_stop=np.inf) # nothing hit, exit as usual\n", " >>> pf.asset_flow\n", " 0 10.0\n", " 1 0.0\n", " 2 0.0\n", " 3 0.0\n", " 4 0.0\n", " 5 -10.0\n", " dtype: float64\n", " ```\n", " \n", " * Test different stop combinations:\n", " \n", " ```pycon\n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... close, entries, exits,\n", " ... tsl_stop=vbt.Param([0.1, 0.2]),\n", " ... tp_stop=vbt.Param([0.2, 0.3])\n", " ... )\n", " >>> pf.asset_flow\n", " tsl_stop 0.1 0.2\n", " tp_stop 0.2 0.3 0.2 0.3\n", " 0 10.0 10.0 10.0 10.0\n", " 1 0.0 0.0 0.0 0.0\n", " 2 -10.0 0.0 -10.0 0.0\n", " 3 0.0 0.0 0.0 0.0\n", " 4 0.0 -10.0 0.0 0.0\n", " 5 0.0 0.0 0.0 -10.0\n", " ```\n", " \n", " This works because `pd.Index` automatically translates into `vectorbtpro.base.reshaping.BCO`\n", " with `product` set to True.\n", " \n", " * We can implement our own stop loss or take profit, or adjust the existing one at each time step.\n", " Let's implement [stepped stop-loss](https://www.freqtrade.io/en/stable/strategy-advanced/#stepped-stoploss):\n", " \n", " ```pycon\n", " >>> @njit\n", " ... def adjust_func_nb(c):\n", " ... val_price_now = c.last_val_price[c.col]\n", " ... tsl_init_price = c.last_tsl_info[\"init_price\"][c.col]\n", " ... current_profit = (val_price_now - tsl_init_price) / tsl_init_price\n", " ... if current_profit >= 0.40:\n", " ... c.last_tsl_info[\"stop\"][c.col] = 0.25\n", " ... elif current_profit >= 0.25:\n", " ... c.last_tsl_info[\"stop\"][c.col] = 0.15\n", " ... elif current_profit >= 0.20:\n", " ... c.last_tsl_info[\"stop\"][c.col] = 0.07\n", " \n", " >>> close = pd.Series([10, 11, 12, 11, 10])\n", " >>> pf = vbt.Portfolio.from_signals(close, adjust_func_nb=adjust_func_nb)\n", " >>> pf.asset_flow\n", " 0 10.0\n", " 1 0.0\n", " 2 0.0\n", " 3 -10.0 # 7% from 12 hit\n", " 4 11.16\n", " dtype: float64\n", " ```\n", " \n", " * Sometimes there is a need to provide or transform signals dynamically. For this, we can implement\n", " a custom signal function `signal_func_nb`. For example, let's implement a signal function that\n", " takes two numerical arrays - long and short one - and transforms them into 4 direction-aware boolean\n", " arrays that vectorbt understands:\n", " \n", " ```pycon\n", " >>> @njit\n", " ... def signal_func_nb(c, long_num_arr, short_num_arr):\n", " ... long_num = vbt.pf_nb.select_nb(c, long_num_arr)\n", " ... short_num = vbt.pf_nb.select_nb(c, short_num_arr)\n", " ... is_long_entry = long_num > 0\n", " ... is_long_exit = long_num < 0\n", " ... is_short_entry = short_num > 0\n", " ... is_short_exit = short_num < 0\n", " ... return is_long_entry, is_long_exit, is_short_entry, is_short_exit\n", " \n", " >>> pf = vbt.Portfolio.from_signals(\n", " ... pd.Series([1, 2, 3, 4, 5]),\n", " ... signal_func_nb=signal_func_nb,\n", " ... signal_args=(vbt.Rep('long_num_arr'), vbt.Rep('short_num_arr')),\n", " ... broadcast_named_args=dict(\n", " ... long_num_arr=pd.Series([1, 0, -1, 0, 0]),\n", " ... short_num_arr=pd.Series([0, 1, 0, 1, -1])\n", " ... ),\n", " ... size=1,\n", " ... upon_opposite_entry='ignore'\n", " ... )\n", " >>> pf.asset_flow\n", " 0 1.0\n", " 1 0.0\n", " 2 -1.0\n", " 3 -1.0\n", " 4 1.0\n", " dtype: float64\n", " ```\n", " \n", " Passing both arrays as `broadcast_named_args` broadcasts them internally as any other array,\n", " so we don't have to worry about their dimensions every time we change our data.\n" ] } ], "source": [ "vbt.phelp(vbt.Portfolio.from_signals)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.0003, 0.0008, 0.0013, 0.0018, 0.0023, 0.0028, 0.0033, 0.0038]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/8p/dwqnp65s0s77jdbm4_6z4vp80000gn/T/ipykernel_15645/1972107742.py:9: UserWarning:\n", "\n", "Object has multiple columns. Aggregated some metrics using . Pass column to select a single column/group.\n", "\n" ] }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
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\n", "Loading ITables v2.0.1 from the init_notebook_mode cell...\n", "(need help?)
\n", "\n" ], "text/plain": [ "Start 2024-04-29 09:30:00-04:00\n", "End 2024-05-06 10:45:00-04:00\n", "Period 0 days 00:02:21\n", "Start Value 100.0\n", "Min Value 99.927477\n", " ... \n", "Expectancy -0.072523\n", "Sharpe Ratio -589.996672\n", "Calmar Ratio -50.816428\n", "Omega Ratio 0.0\n", "Sortino Ratio -588.835078\n", "Name: agg_stats, Length: 29, dtype: object" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sl_stop = np.arange(0.03/100, 0.4/100, 0.05/100).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, exits=long_exits, sl_stop=sl_stop, tp_stop = sl_stop, fees=0.0167/100, freq=\"1s\") #sl_stop=sl_stop, tp_stop = sl_stop,\n", "\n", "pf.stats()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "53274e7f95f04df293a52149db1658af", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureWidget({\n", " 'data': [{'legendgroup': '0',\n", " 'line': {'color': '#1f77b4'},\n", " 'mode': 'lines',\n", " 'name': 'Close',\n", " 'showlegend': True,\n", " 'type': 'scatter',\n", " 'uid': 'c9aba84d-aeb8-47df-b5b7-f8041c89e415',\n", " 'x': array([datetime.datetime(2024, 4, 29, 9, 30, tzinfo=),\n", " datetime.datetime(2024, 4, 29, 9, 31, tzinfo=),\n", " datetime.datetime(2024, 4, 29, 9, 32, tzinfo=),\n", " ...,\n", " datetime.datetime(2024, 5, 6, 10, 2, tzinfo=),\n", " datetime.datetime(2024, 5, 6, 10, 3, tzinfo=),\n", " datetime.datetime(2024, 5, 6, 10, 4, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x',\n", " 'y': array([37.87 , 37.815, 37.8 , ..., 37.51 , 37.535, 37.57 ]),\n", " 'yaxis': 'y'},\n", " {'customdata': array([['0', '2024-04-29 14:32:00-04:00', '2024-04-29 14:32:00-04:00',\n", " '2024-04-29 14:32:00-04:00', 2.6545414254197355, 37.665,\n", " 0.0166972115656705, 'Market', 'None'],\n", " ['2', '2024-04-29 15:00:00-04:00', '2024-04-29 15:00:00-04:00',\n", " '2024-04-29 15:00:00-04:00', 2.6592130130953584, 37.575,\n", " 0.016686628137506432, 'Market', 'None'],\n", " ['4', '2024-04-29 15:13:00-04:00', '2024-04-29 15:13:00-04:00',\n", " '2024-04-29 15:13:00-04:00', 2.6656859400641046, 37.46,\n", " 0.016676051417576332, 'Market', 'None'],\n", " ['6', '2024-04-30 12:32:00-04:00', '2024-04-30 12:32:00-04:00',\n", " '2024-04-30 12:32:00-04:00', 2.678171682609611, 37.34,\n", " 0.01670048941498692, 'Market', 'None'],\n", " ['8', '2024-04-30 14:27:00-04:00', '2024-04-30 14:27:00-04:00',\n", " '2024-04-30 14:27:00-04:00', 2.6841657767646168, 37.233,\n", " 0.01668990390916747, 'Market', 'None'],\n", " ['10', '2024-04-30 14:48:00-04:00', '2024-04-30 14:48:00-04:00',\n", " '2024-04-30 14:48:00-04:00', 2.688819444902845, 37.145,\n", " 0.01667932511291781, 'Market', 'None'],\n", " ['12', '2024-05-01 12:21:00-04:00', '2024-05-01 12:21:00-04:00',\n", " '2024-05-01 12:21:00-04:00', 2.702027405173107, 36.94,\n", " 0.01666875302197468, 'Market', 'None'],\n", " ['14', '2024-05-01 13:04:00-04:00', '2024-05-01 13:04:00-04:00',\n", " '2024-05-01 13:04:00-04:00', 2.7061754351427645, 36.86,\n", " 0.016658187632074828, 'Market', 'None'],\n", " ['16', '2024-05-01 13:07:00-04:00', '2024-05-01 13:07:00-04:00',\n", " '2024-05-01 13:07:00-04:00', 2.7070305724992725, 36.825,\n", " 0.016647628938997627, 'Market', 'None'],\n", " ['18', '2024-05-02 11:31:00-04:00', '2024-05-02 11:31:00-04:00',\n", " '2024-05-02 11:31:00-04:00', 2.708256495866447, 36.785,\n", " 0.016637076938479822, 'Market', 'None'],\n", " ['20', '2024-05-02 11:35:00-04:00', '2024-05-02 11:35:00-04:00',\n", " '2024-05-02 11:35:00-04:00', 2.7105779336471194, 36.7302,\n", " 0.016626531626286578, 'Market', 'None'],\n", " ['22', '2024-05-03 10:23:00-04:00', '2024-05-03 10:23:00-04:00',\n", " '2024-05-03 10:23:00-04:00', 2.6878547078398904, 37.095,\n", " 0.016650897054688585, 'Market', 'None']], dtype=object),\n", " 'hovertemplate': ('
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}, "execution_count": 115, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pf[0.03].plot_trade_signals()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# pristup k pf jako multi index" ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/var/folders/8p/dwqnp65s0s77jdbm4_6z4vp80000gn/T/ipykernel_13317/946856841.py:3: UserWarning:\n", "\n", "Object has multiple columns. Aggregated some metrics using . Pass column to select a single column/group.\n", "\n" ] }, { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
agg_stats
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\n", "\n" ], "text/plain": [ "Start 2024-04-22 09:30:01-04:00\n", "End 2024-04-29 15:59:59-04:00\n", "Period 51 days 07:53:00\n", "Start Value 100.0\n", "Min Value 99.929678\n", " ... \n", "Expectancy 0.236952\n", "Sharpe Ratio 1.091921\n", "Calmar Ratio 37.489139\n", "Omega Ratio 1.005129\n", "Sortino Ratio 1.549924\n", "Name: agg_stats, Length: 29, dtype: object" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#pf[0.03].plot()\n", "#pf.order_records\n", "pf[(0.03)].stats()" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
Total Return [%]Total TradesWin Rate [%]Expectancy
sl_stoptp_stoprsi_windowsymbol
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\n", "\n" ], "text/plain": [ " Total Return [%] Total Trades \\\n", "sl_stop tp_stop rsi_window symbol \n", "0.0003 0.0003 14 BAC -0.063384 1 \n", " 0.0008 14 BAC -0.063384 1 \n", " 0.0013 14 BAC -0.063384 1 \n", " 0.0018 14 BAC -0.063384 1 \n", " 0.0023 14 BAC -0.063384 1 \n", " 0.0028 14 BAC -0.063384 1 \n", " 0.0033 14 BAC -0.063384 1 \n", " 0.0038 14 BAC -0.063384 1 \n", "0.0008 0.0003 14 BAC -0.073829 1 \n", " 0.0008 14 BAC -0.073829 1 \n", "\n", " Win Rate [%] Expectancy \n", "sl_stop tp_stop rsi_window symbol \n", "0.0003 0.0003 14 BAC 0.0 -0.063384 \n", " 0.0008 14 BAC 0.0 -0.063384 \n", " 0.0013 14 BAC 0.0 -0.063384 \n", " 0.0018 14 BAC 0.0 -0.063384 \n", " 0.0023 14 BAC 0.0 -0.063384 \n", " 0.0028 14 BAC 0.0 -0.063384 \n", " 0.0033 14 BAC 0.0 -0.063384 \n", " 0.0038 14 BAC 0.0 -0.063384 \n", "0.0008 0.0003 14 BAC 0.0 -0.073829 \n", " 0.0008 14 BAC 0.0 -0.073829 " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "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": 175, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d80a6c747f404eef96cc265dc7cf5566", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureWidget({\n", " 'data': [{'legendgroup': '0',\n", " 'line': {'color': '#1f77b4'},\n", " 'mode': 'lines',\n", " 'name': 'Close',\n", " 'showlegend': True,\n", " 'type': 'scatter',\n", " 'uid': '91d1b8b6-43f1-43e4-877f-2776bbf0d549',\n", " 'x': array([datetime.datetime(2024, 4, 22, 9, 30, 1, tzinfo=),\n", " datetime.datetime(2024, 4, 22, 9, 30, 2, tzinfo=),\n", " datetime.datetime(2024, 4, 22, 9, 30, 3, tzinfo=),\n", " ...,\n", " datetime.datetime(2024, 4, 29, 15, 59, 57, tzinfo=),\n", " datetime.datetime(2024, 4, 29, 15, 59, 58, tzinfo=),\n", " datetime.datetime(2024, 4, 29, 15, 59, 59, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x',\n", " 'y': array([37.035, 37. , 37.02 , ..., 37.55 , 37.56 , 37.555]),\n", " 'yaxis': 'y'},\n", " {'customdata': array([['0', '2024-04-22 10:54:51-04:00', '2024-04-22 10:54:51-04:00', ...,\n", " 0.0166972115656705, 'Market', 'None'],\n", " ['2', '2024-04-22 11:16:29-04:00', '2024-04-22 11:16:29-04:00', ...,\n", " 0.0167133347545132, 'Market', 'None'],\n", " ['4', '2024-04-22 12:53:39-04:00', '2024-04-22 12:53:39-04:00', ...,\n", " 0.01672947351224252, 'Market', 'None'],\n", " ...,\n", " ['54', '2024-04-29 09:31:54-04:00', '2024-04-29 09:31:54-04:00', ...,\n", " 0.016771870154130397, 'Market', 'None'],\n", " ['56', '2024-04-29 14:32:18-04:00', '2024-04-29 14:32:18-04:00', ...,\n", " 0.016788065434923283, 'Market', 'None'],\n", " ['58', '2024-04-29 15:03:26-04:00', '2024-04-29 15:03:26-04:00', ...,\n", " 0.01680427635420756, 'Market', 'None']], dtype=object),\n", " 'hovertemplate': ('
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" 'yanchor': 'bottom',\n", " 'yref': 'paper'},\n", " {'font': {'size': 16},\n", " 'showarrow': False,\n", " 'text': 'Cumulative Returns',\n", " 'x': 0.5,\n", " 'xanchor': 'center',\n", " 'xref': 'paper',\n", " 'y': 0.3055555555555555,\n", " 'yanchor': 'bottom',\n", " 'yref': 'paper'}],\n", " 'height': 960,\n", " 'legend': {'orientation': 'h',\n", " 'traceorder': 'normal',\n", " 'x': 1,\n", " 'xanchor': 'right',\n", " 'y': 1.0416666666666667,\n", " 'yanchor': 'bottom'},\n", " 'margin': {'b': 30, 'l': 30, 'r': 30, 't': 30},\n", " 'shapes': [{'line': {'color': 'gray', 'dash': 'dash'},\n", " 'type': 'line',\n", " 'x0': 0.0,\n", " 'x1': 1.0,\n", " 'xref': 'paper',\n", " 'y0': 0,\n", " 'y1': 0,\n", " 'yref': 'y2'},\n", " {'line': {'color': 'gray', 'dash': 'dash'},\n", " 'type': 'line',\n", " 'x0': 0.0,\n", " 'x1': 1.0,\n", " 'xref': 'paper',\n", " 'y0': 1,\n", " 'y1': 1,\n", " 'yref': 'y3'}],\n", " 'showlegend': True,\n", " 'template': '...',\n", " 'width': 1330,\n", " 'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'matches': 'x3', 'showticklabels': False},\n", " 'xaxis2': {'anchor': 'y2', 'domain': [0.0, 1.0], 'matches': 'x3', 'showticklabels': False},\n", " 'xaxis3': {'anchor': 'y3', 'domain': [0.0, 1.0], 'title': {'text': 'Index'}},\n", " 'yaxis': {'anchor': 'x', 'domain': [0.6944444444444444, 1.0], 'title': {'text': 'Price'}},\n", " 'yaxis2': {'anchor': 'x2',\n", " 'domain': [0.3472222222222222, 0.6527777777777777],\n", " 'tickformat': '.2%',\n", " 'title': {'text': 'Trade PnL'}},\n", " 'yaxis3': {'anchor': 'x3', 'domain': [0.0, 0.3055555555555555], 'title': {'text': 'Cumulative returns'}}}\n", "})" ] }, "execution_count": 175, "metadata": {}, "output_type": "execute_result" } ], "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 }