{ "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": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Activating profile profile1\n",
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\n", "This is the init_notebook_mode cell from ITables v2.0.1
\n", "(you should not see this message - is your notebook trusted?)\n", "
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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": 2, "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": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "frozen_cfg({'silence_warnings': False})" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#asic_data.stats()\n", "basic_data.wrapper.index.normalize().nunique()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "t1data = basic_data[['open', 'high', 'low', 'close', 'volume','vwap','buyvolume','sellvolume']].resample(\"1T\")" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
buyvolume
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sellvolume
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'2739f9c4-6a09-4b94-8c8d-d4f4acfdc248',\n", " 'x': array([datetime.datetime(2024, 2, 12, 10, 30, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 31, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 32, tzinfo=),\n", " ...,\n", " datetime.datetime(2024, 2, 14, 11, 58, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 11, 59, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 12, 0, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x',\n", " 'y': array([33.385, 33.39 , 33.39 , ..., 33.025, 33.016, 32.98 ]),\n", " 'yaxis': 'y'},\n", " {'name': 'None',\n", " 'showlegend': True,\n", " 'type': 'scatter',\n", " 'uid': '631932f0-7128-4768-9c43-db8ef6a72ea7',\n", " 'x': array([datetime.datetime(2024, 2, 12, 10, 30, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 31, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 32, tzinfo=),\n", " ...,\n", " datetime.datetime(2024, 2, 14, 11, 58, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 11, 59, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 12, 0, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x',\n", " 'y': array([-5368., -1457., 762., ..., -4431., 773., 411.]),\n", " 'yaxis': 'y2'},\n", " {'name': 'None',\n", " 'showlegend': True,\n", " 'type': 'scatter',\n", " 'uid': '380226e9-d10f-4c7e-ba3f-fc93a97f44ee',\n", " 'x': array([datetime.datetime(2024, 2, 12, 10, 30, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 31, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 32, tzinfo=),\n", " ...,\n", " datetime.datetime(2024, 2, 14, 11, 58, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 11, 59, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 12, 0, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x',\n", " 'y': array([0.42117749, 0.7756045 , 1.13110805, ..., 0.30678974, 1.36342266,\n", " 1.03594228]),\n", " 'yaxis': 'y2'},\n", " {'name': 'buyvolume',\n", " 'showlegend': True,\n", " 'type': 'bar',\n", " 'uid': '2dcd3ba2-1485-4ec9-8df2-98a878de2abc',\n", " 'x': array([datetime.datetime(2024, 2, 12, 10, 30, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 31, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 32, tzinfo=),\n", " ...,\n", " datetime.datetime(2024, 2, 14, 11, 58, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 11, 59, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 12, 0, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x2',\n", " 'y': array([ 3906., 5036., 6574., ..., 1961., 2900., 11846.]),\n", " 'yaxis': 'y3'},\n", " {'name': 'sellvolume',\n", " 'showlegend': True,\n", " 'type': 'bar',\n", " 'uid': '0c088e56-556e-4803-aa78-1194cbf2e31d',\n", " 'x': array([datetime.datetime(2024, 2, 12, 10, 30, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 31, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 32, tzinfo=),\n", " ...,\n", " datetime.datetime(2024, 2, 14, 11, 58, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 11, 59, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 12, 0, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x2',\n", " 'y': array([ 9274., 6493., 5812., ..., 6392., 2127., 11435.]),\n", " 'yaxis': 'y3'},\n", " {'name': 'volume',\n", " 'showlegend': True,\n", " 'type': 'bar',\n", " 'uid': '8eb849fe-6629-4a37-a46c-95311cb245b6',\n", " 'x': array([datetime.datetime(2024, 2, 12, 10, 30, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 31, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 32, tzinfo=),\n", " ...,\n", " datetime.datetime(2024, 2, 14, 11, 58, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 11, 59, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 12, 0, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x2',\n", " 'y': array([26275., 29950., 65481., ..., 14018., 7571., 67175.]),\n", " 'yaxis': 'y3'}],\n", " 'layout': {'annotations': [{'font': {'size': 16},\n", " 'showarrow': False,\n", " 'text': 'Price and Indicators',\n", " 'x': 0.47,\n", " 'xanchor': 'center',\n", " 'xref': 'paper',\n", " 'y': 1.0,\n", " 'yanchor': 'bottom',\n", " 'yref': 'paper'},\n", " {'font': {'size': 16},\n", " 'showarrow': False,\n", " 'text': 'Volume',\n", " 'x': 0.47,\n", " 'xanchor': 'center',\n", " 'xref': 'paper',\n", " 'y': 0.49,\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", " 'template': '...',\n", " 'width': 1280,\n", " 'xaxis': {'anchor': 'y', 'domain': [0.0, 0.94], 'matches': 'x2', 'showticklabels': False},\n", " 'xaxis2': {'anchor': 'y3', 'domain': [0.0, 0.94]},\n", " 'yaxis': {'anchor': 'x', 'domain': [0.51, 1.0]},\n", " 'yaxis2': {'anchor': 'x', 'overlaying': 'y', 'side': 'right'},\n", " 'yaxis3': {'anchor': 'x2', 'domain': [0.0, 0.49]}}\n", "})" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "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": 38, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "close": [ 33.425, 33.485, 33.58, 33.655, 33.695, 33.75, 33.695, 33.71, 33.72, 33.7475, 33.78, 33.775, 33.835, 33.8901, 33.965, 33.825, 33.78, 33.775, 33.755, 33.675, 33.715, 33.62, 32.89, 32.725, 32.81, 32.845, 32.885, 32.805, 32.85, 32.88, 32.875, 32.86, 32.855, 32.8199, 32.8926, 32.815, 32.745, 32.6999, 32.685, 32.664, 32.5504, 32.505, 32.495, 32.485, 32.415, 32.43, 32.639, 32.76, 33.084, 33.095, 33.015, 33.125, 33.06, 33.01, 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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": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
close
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\n", "\n" ], "text/plain": [ "time\n", "2024-02-12 10:30:00-05:00 33.425\n", "2024-02-12 10:45:00-05:00 33.485\n", "2024-02-12 11:00:00-05:00 33.580\n", "2024-02-12 11:15:00-05:00 33.655\n", "2024-02-12 11:30:00-05:00 33.695\n", " ... \n", "2024-02-13 11:00:00-05:00 32.850\n", "2024-02-13 11:15:00-05:00 32.880\n", "2024-02-13 11:30:00-05:00 32.875\n", "2024-02-13 11:45:00-05:00 32.860\n", "2024-02-13 12:00:00-05:00 32.855\n", "Name: close, Length: 33, dtype: float64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t2data.close\n", "\n", "#in df remove rows with nan\n" ] }, { "cell_type": "code", "execution_count": 25, "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": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
vwap
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close
time
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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": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "DatetimeIndex: 8469 entries, 2024-02-12 14:14:00-05:00 to 2024-03-13 12:00:00-04:00\n", "Series name: 15\n", "Non-Null Count Dtype \n", "-------------- ----- \n", "8469 non-null float64\n", "dtypes: float64(1)\n", "memory usage: 132.3 KB\n" ] } ], "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": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
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array([-0.40728376, -0.12637696, 0.06152107, ..., -0.5304681 , 0.15376964,\n", " 0.01765388]),\n", " 'yaxis': 'y2'},\n", " {'connectgaps': True,\n", " 'line': {'color': '#ffe476'},\n", " 'marker': {'color': 'limegreen',\n", " 'line': {'color': 'rgb(34,143,34)', 'width': 1},\n", " 'size': 8,\n", " 'symbol': 'triangle-up'},\n", " 'mode': 'markers',\n", " 'name': 'LONGS',\n", " 'showlegend': True,\n", " 'type': 'scatter',\n", " 'uid': 'f0a2bc78-967f-41f0-8a62-8d194b207dba',\n", " 'x': array([datetime.datetime(2024, 2, 12, 10, 42, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 53, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 13, 5, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 13, 12, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 13, 36, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 14, 29, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 14, 35, tzinfo=),\n", " datetime.datetime(2024, 2, 13, 9, 30, tzinfo=),\n", " datetime.datetime(2024, 2, 13, 12, 56, tzinfo=),\n", " 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12, 10, 31, tzinfo=),\n", " datetime.datetime(2024, 2, 12, 10, 33, tzinfo=),\n", " ...,\n", " datetime.datetime(2024, 2, 14, 11, 56, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 11, 57, tzinfo=),\n", " datetime.datetime(2024, 2, 14, 11, 58, tzinfo=)],\n", " dtype=object),\n", " 'xaxis': 'x',\n", " 'y': array([33.385, 33.39 , 33.36 , ..., 33.046, 33.015, 33.025]),\n", " 'yaxis': 'y'}],\n", " 'layout': {'annotations': [{'font': {'size': 16},\n", " 'showarrow': False,\n", " 'text': 'Price and Indicators',\n", " 'x': 0.47,\n", " 'xanchor': 'center',\n", " 'xref': 'paper',\n", " 'y': 1.0,\n", " 'yanchor': 'bottom',\n", " 'yref': 'paper'},\n", " {'font': {'size': 16},\n", " 'showarrow': False,\n", " 'text': 'Volume',\n", " 'x': 0.47,\n", " 'xanchor': 'center',\n", " 'xref': 'paper',\n", " 'y': 0.66,\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", " 'template': '...',\n", " 'width': 1280,\n", " 'xaxis': {'anchor': 'y',\n", " 'domain': [0.0, 0.94],\n", " 'matches': 'x3',\n", " 'rangeslider': {'visible': False},\n", " 'showticklabels': False},\n", " 'xaxis2': {'anchor': 'y3', 'domain': [0.0, 0.94], 'matches': 'x3', 'showticklabels': False},\n", " 'xaxis3': {'anchor': 'y5', 'domain': [0.0, 0.94]},\n", " 'yaxis': {'anchor': 'x', 'domain': [0.68, 1.0]},\n", " 'yaxis2': {'anchor': 'x', 'overlaying': 'y', 'side': 'right'},\n", " 'yaxis3': {'anchor': 'x2', 'domain': [0.34, 0.66]},\n", " 'yaxis4': {'anchor': 'x2', 'overlaying': 'y3', 'side': 'right'},\n", " 'yaxis5': {'anchor': 'x3', 'domain': [0.0, 0.32]}}\n", "})" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "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": 239, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "Cell \u001b[0;32mIn[53], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m#short_signal = t1slope.real_below(t1_th) & t2slope.real_below(t2_th) & t3slope.real_below(t3_th) & t4slope.real_below(t4_th)\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m#long_signal = t1slope.real_above(t1_th) & t2slope.real_above(t2_th) & t3slope.real_above(t3_th) & t4slope.real_above(t4_th)\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m#test na daily s reversem crossed 0\u001b[39;00m\n\u001b[0;32m----> 5\u001b[0m short_signal \u001b[38;5;241m=\u001b[39m t2slope\u001b[38;5;241m.\u001b[39mvbt \u001b[38;5;241m<\u001b[39m \u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m0.01\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m&\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mt3slope\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvbt\u001b[49m \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m0.01\u001b[39m \u001b[38;5;66;03m#min value of threshold\u001b[39;00m\n\u001b[1;32m 6\u001b[0m long_signal \u001b[38;5;241m=\u001b[39m t2slope\u001b[38;5;241m.\u001b[39mvbt \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.01\u001b[39m \u001b[38;5;241m&\u001b[39m t3slope\u001b[38;5;241m.\u001b[39mvbt \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.01\u001b[39m \u001b[38;5;66;03m#min\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# thirty_up_signal = t3slope.vbt.crossed_above(0.01)\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# thirty_down_signal = t3slope.vbt.crossed_below(-0.01)\u001b[39;00m\n", "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/vectorbtpro/utils/magic_decorators.py:90\u001b[0m, in \u001b[0;36mattach_binary_magic_methods..wrapper..new_method\u001b[0;34m(self, other, _translate_func, _func)\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnew_method\u001b[39m(\n\u001b[1;32m 85\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 86\u001b[0m other: tp\u001b[38;5;241m.\u001b[39mAny,\n\u001b[1;32m 87\u001b[0m _translate_func: BinaryTranslateFuncT \u001b[38;5;241m=\u001b[39m translate_func,\n\u001b[1;32m 88\u001b[0m _func: tp\u001b[38;5;241m.\u001b[39mCallable \u001b[38;5;241m=\u001b[39m func,\n\u001b[1;32m 89\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m tp\u001b[38;5;241m.\u001b[39mSeriesFrame:\n\u001b[0;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_translate_func\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_func\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/vectorbtpro/base/accessors.py:417\u001b[0m, in \u001b[0;36mBaseAccessor.\u001b[0;34m(self, other, np_func)\u001b[0m\n\u001b[1;32m 411\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m get_index_ranges(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39many_freq, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 414\u001b[0m BaseAccessorT \u001b[38;5;241m=\u001b[39m tp\u001b[38;5;241m.\u001b[39mTypeVar(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBaseAccessorT\u001b[39m\u001b[38;5;124m\"\u001b[39m, bound\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBaseAccessor\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 417\u001b[0m \u001b[38;5;129m@attach_binary_magic_methods\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;28mself\u001b[39m, other, np_func: \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcombine\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcombine_func\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnp_func\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 418\u001b[0m \u001b[38;5;129m@attach_unary_magic_methods\u001b[39m(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;28mself\u001b[39m, np_func: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply(apply_func\u001b[38;5;241m=\u001b[39mnp_func))\n\u001b[1;32m 419\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBaseAccessor\u001b[39;00m(Wrapping):\n\u001b[1;32m 420\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Accessor on top of Series and DataFrames.\u001b[39;00m\n\u001b[1;32m 421\u001b[0m \n\u001b[1;32m 422\u001b[0m \u001b[38;5;124;03m Accessible via `pd.Series.vbt` and `pd.DataFrame.vbt`, and all child accessors.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 539\u001b[0m \u001b[38;5;124;03m ```\u001b[39;00m\n\u001b[1;32m 540\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[1;32m 543\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mresolve_row_stack_kwargs\u001b[39m(\n\u001b[1;32m 544\u001b[0m \u001b[38;5;28mcls\u001b[39m: tp\u001b[38;5;241m.\u001b[39mType[BaseAccessorT],\n\u001b[1;32m 545\u001b[0m \u001b[38;5;241m*\u001b[39mobjs: tp\u001b[38;5;241m.\u001b[39mMaybeTuple[BaseAccessorT],\n\u001b[1;32m 546\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 547\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m tp\u001b[38;5;241m.\u001b[39mKwargs:\n", "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/vectorbtpro/base/accessors.py:1702\u001b[0m, in \u001b[0;36mBaseAccessor.combine\u001b[0;34m(cls_or_self, obj, combine_func, allow_multiple, keep_pd, to_2d, concat, keys, broadcast_named_args, broadcast_kwargs, template_context, wrap_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1699\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrapper\u001b[38;5;241m.\u001b[39mwrap(out, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmerge_dicts(\u001b[38;5;28mdict\u001b[39m(columns\u001b[38;5;241m=\u001b[39mnew_columns), wrap_kwargs))\n\u001b[1;32m 1700\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1701\u001b[0m \u001b[38;5;66;03m# Combine arguments pairwise into one object\u001b[39;00m\n\u001b[0;32m-> 1702\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mcombining\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcombine_multiple\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcombine_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1703\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrapper\u001b[38;5;241m.\u001b[39mwrap(out, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mresolve_dict(wrap_kwargs))\n", "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/vectorbtpro/base/combining.py:314\u001b[0m, in \u001b[0;36mcombine_multiple\u001b[0;34m(objs, combine_func, jitted_loop, *args, **kwargs)\u001b[0m\n\u001b[1;32m 312\u001b[0m result \u001b[38;5;241m=\u001b[39m objs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mlen\u001b[39m(objs)):\n\u001b[0;32m--> 314\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mcombine_func\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mobjs\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 315\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", "File \u001b[0;32m~/Documents/Development/python/v2trading/.venv/lib/python3.10/site-packages/vectorbtpro/utils/magic_decorators.py:41\u001b[0m, in \u001b[0;36m\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 10\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m 12\u001b[0m __pdoc__ \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m 14\u001b[0m binary_magic_config \u001b[38;5;241m=\u001b[39m ReadonlyConfig(\n\u001b[1;32m 15\u001b[0m {\n\u001b[1;32m 16\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__eq__\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mdict\u001b[39m(func\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mequal),\n\u001b[1;32m 17\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__ne__\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mdict\u001b[39m(func\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mnot_equal),\n\u001b[1;32m 18\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__lt__\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mdict\u001b[39m(func\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mless),\n\u001b[1;32m 19\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__gt__\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mdict\u001b[39m(func\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mgreater),\n\u001b[1;32m 20\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__le__\u001b[39m\u001b[38;5;124m\"\u001b[39m: 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\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary_magic_config\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 50\u001b[0m ] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\"\"\u001b[39m\u001b[38;5;124mConfig of binary magic methods to be attached to a class.\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;124m```\u001b[39m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;124m\"\"\"\u001b[39m\n", "\u001b[0;31mTypeError\u001b[0m: ufunc 'bitwise_and' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''" ] } ], "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": 29, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9818ffdcb28c43c0b43e070b8b0f244f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureWidget({\n", " 'data': [{'close': array([477.55 , 477.545 , 477.5088, ..., 476.6 , 476.66 , 476.665 ]),\n", " 'decreasing': {'fillcolor': '#ee534f', 'line': {'color': '#ee534f'}},\n", " 'high': array([477.59, 477.66, 477.62, ..., 476.73, 476.66, 476.68]),\n", " 'increasing': {'fillcolor': '#26a69a', 'line': {'color': '#26a69a'}},\n", " 'low': array([477.53, 477.54, 477.49, ..., 476.6 , 476.59, 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"execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# pf.plot()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "
Drawdown IdColumnStart IndexValley IndexEnd IndexStart ValueValley ValueEnd ValueStatus
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\n", "Loading ITables v2.0.1 from the init_notebook_mode cell...\n", "(need help?)
\n", "\n" ], "text/plain": [ " Drawdown Id Column Start Index \\\n", "0 0 (0.0018, 0.0068) 2024-01-03 09:31:04-05:00 \n", "1 1 (0.0018, 0.0068) 2024-01-03 09:31:07-05:00 \n", "2 2 (0.0018, 0.0068) 2024-01-03 09:31:11-05:00 \n", "3 3 (0.0018, 0.0068) 2024-01-03 09:31:18-05:00 \n", "4 4 (0.0018, 0.0068) 2024-01-03 09:48:07-05:00 \n", ".. ... ... ... \n", "34 34 (0.0018, 0.0068) 2024-01-12 12:23:26-05:00 \n", "35 35 (0.0018, 0.0068) 2024-01-12 12:23:31-05:00 \n", "36 36 (0.0018, 0.0068) 2024-01-12 12:32:23-05:00 \n", "37 37 (0.0018, 0.0068) 2024-01-12 12:32:27-05:00 \n", "38 38 (0.0018, 0.0068) 2024-01-12 12:32:36-05:00 \n", "\n", " Valley Index End Index Start Value \\\n", "0 2024-01-03 09:31:05-05:00 2024-01-03 09:31:06-05:00 100.000000 \n", "1 2024-01-03 09:31:10-05:00 2024-01-03 09:31:11-05:00 100.057784 \n", "2 2024-01-03 09:31:13-05:00 2024-01-03 09:31:16-05:00 100.102472 \n", "3 2024-01-03 09:32:39-05:00 2024-01-03 09:48:05-05:00 100.162057 \n", "4 2024-01-03 09:48:08-05:00 2024-01-03 09:48:11-05:00 100.186396 \n", ".. ... ... ... \n", "34 2024-01-12 12:23:28-05:00 2024-01-12 12:23:29-05:00 100.981534 \n", "35 2024-01-12 12:26:27-05:00 2024-01-12 12:32:23-05:00 101.089545 \n", "36 2024-01-12 12:32:24-05:00 2024-01-12 12:32:25-05:00 101.089545 \n", "37 2024-01-12 12:32:28-05:00 2024-01-12 12:32:36-05:00 101.104975 \n", "38 2024-02-21 09:35:15-05:00 2024-02-28 15:59:59-05:00 101.104975 \n", "\n", " Valley Value End Value Status \n", "0 99.983303 100.057784 Recovered \n", "1 100.042888 100.102472 Recovered \n", "2 100.057784 100.102472 Recovered \n", "3 99.650958 100.171522 Recovered \n", "4 100.171522 100.201269 Recovered \n", ".. ... ... ... \n", "34 100.968881 101.043254 Recovered \n", "35 100.858092 101.089545 Recovered \n", "36 101.074115 101.089545 Recovered \n", "37 101.089545 101.104975 Recovered \n", "38 99.788058 99.788058 Active \n", "\n", "[39 rows x 9 columns]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pf.get_drawdowns().records_readable" ] }, { "cell_type": "code", "execution_count": 12, "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", "
Order IdColumnSignal IndexCreation IndexFill IndexSizePriceFeesSideTypeStop Type
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\n", "\n" ], "text/plain": [ " Order Id Column Signal Index Creation Index \\\n", "0 0 0 2024-01-03 09:31:06-05:00 2024-01-03 09:31:06-05:00 \n", "1 1 0 2024-01-03 09:31:06-05:00 2024-01-03 09:48:40-05:00 \n", "2 2 0 2024-01-04 09:33:14-05:00 2024-01-04 09:33:14-05:00 \n", "3 3 0 2024-01-04 09:33:14-05:00 2024-01-04 09:43:31-05:00 \n", "4 4 0 2024-01-05 09:31:23-05:00 2024-01-05 09:31:23-05:00 \n", ".. ... ... ... ... \n", "51 51 0 2024-02-21 09:34:52-05:00 2024-02-21 09:45:00-05:00 \n", "52 52 0 2024-02-22 09:31:22-05:00 2024-02-22 09:31:22-05:00 \n", "53 53 0 2024-02-22 09:31:22-05:00 2024-02-22 09:38:13-05:00 \n", "54 54 0 2024-02-23 09:31:25-05:00 2024-02-23 09:31:25-05:00 \n", "55 55 0 2024-02-23 09:31:25-05:00 2024-02-23 09:35:02-05:00 \n", "\n", " Fill Index Size Price Fees Side Type \\\n", "0 2024-01-03 09:31:06-05:00 2.981461 33.535000 0.016697 Sell Market \n", "1 2024-01-03 09:48:40-05:00 2.981461 33.357264 0.016609 Buy Market \n", "2 2024-01-04 09:33:14-05:00 2.976739 33.755000 0.016780 Sell Market \n", "3 2024-01-04 09:43:31-05:00 2.976739 33.821568 0.016813 Buy Market \n", "4 2024-01-05 09:31:23-05:00 2.972516 33.725000 0.016741 Sell Market \n", ".. ... ... ... ... ... ... \n", "51 2024-02-21 09:45:00-05:00 3.023216 33.334240 0.016830 Buy Market \n", "52 2024-02-22 09:31:22-05:00 3.001125 33.580000 0.016830 Sell Market \n", "53 2024-02-22 09:38:13-05:00 3.001125 33.620000 0.016850 Buy Market \n", "54 2024-02-23 09:31:25-05:00 2.990315 33.650000 0.016804 Sell Market \n", "55 2024-02-23 09:35:02-05:00 2.990315 33.654100 0.016806 Buy Market \n", "\n", " Stop Type \n", "0 None \n", "1 TP \n", "2 None \n", "3 TSL \n", "4 None \n", ".. ... \n", "51 TSL \n", "52 None \n", "53 TSL \n", "54 None \n", "55 TSL \n", "\n", "[56 rows x 11 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "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": "3.10.10" } }, "nbformat": 4, "nbformat_minor": 2 }