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options_backtester/backtester/examples/backtester_example.ipynb
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Javier Rodríguez Chatruc 1bda04d30b Updated example notebooks
2020-03-25 10:54:25 -03:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Backtester Examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data files"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import sys\n",
"\n",
"BACKTESTER_DIR = os.path.realpath(os.path.join(os.getcwd(), '..', '..'))\n",
"TEST_DATA_DIR = os.path.join(BACKTESTER_DIR, 'backtester', 'test', 'test_data')\n",
"SAMPLE_STOCK_DATA = os.path.join(TEST_DATA_DIR, 'test_data_stocks.csv')\n",
"SAMPLE_OPTIONS_DATA = os.path.join(TEST_DATA_DIR, 'test_data_options.csv')\n",
"\n",
"sys.path.append(BACKTESTER_DIR) # Add backtester base dir to $PYTHONPATH"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sample backtest"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from backtester import Backtest, Stock, Type, Direction\n",
"from backtester.datahandler import HistoricalOptionsData, TiingoData\n",
"from backtester.strategy import Strategy, StrategyLeg"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we construct an options datahandler."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"options_data = HistoricalOptionsData(SAMPLE_OPTIONS_DATA)\n",
"options_schema = options_data.schema"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we'll create a toy options strategy."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"sample_strategy = Strategy(options_schema)\n",
"\n",
"leg1 = StrategyLeg('leg_1', options_schema, option_type=Type.CALL, direction=Direction.BUY)\n",
"leg1.entry_filter = ((options_schema.contract == 'SPX170317C00300000') &\n",
" (options_schema.dte == 73)) | ((options_schema.contract == 'SPX170421C00500000') &\n",
" (options_schema.dte == 51))\n",
"\n",
"leg1.exit_filter = (options_schema.dte == 44) | (options_schema.dte == 18)\n",
"\n",
"leg2 = StrategyLeg('leg_2', options_schema, option_type=Type.PUT, direction=Direction.BUY)\n",
"leg2.entry_filter = ((options_schema.contract == 'SPX170317P00300000') &\n",
" (options_schema.dte == 73)) | ((options_schema.contract == 'SPX170421P01375000') &\n",
" (options_schema.dte == 51))\n",
"\n",
"leg2.exit_filter = (options_schema.dte == 44) | (options_schema.dte == 18)\n",
"\n",
"sample_strategy.add_legs([leg1, leg2])\n",
"sample_strategy.add_exit_thresholds(profit_pct=0.2, loss_pct=0.2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We do the same for stocks: create a datahandler together with a list of the stocks we want in our inventory and their corresponding weights."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"stocks_data = TiingoData(SAMPLE_STOCK_DATA)\n",
"stocks = [Stock('VOO', 0.4), Stock('TUR', 0.1), Stock('RSX', 0.5)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We set our portfolio allocation, i.e. how much of our capital will be invested in stocks, options and cash."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"allocation = {'stocks': 0.5, 'options': 0.5, 'cash': 0.0}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we create the `Backtest` object."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"bt = Backtest(allocation, initial_capital=1_000_000)\n",
"\n",
"bt.stocks = stocks\n",
"bt.stocks_data = stocks_data\n",
"\n",
"bt.options_strategy = sample_strategy\n",
"bt.options_data = options_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And run the backtest with a rebalancing period of one month."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"0% [██████████████████████████████] 100% | ETA: 00:00:00\n",
"Total time elapsed: 00:00:00\n"
]
},
{
"data": {
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"<div>\n",
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"\n",
" .dataframe thead tr th {\n",
" text-align: left;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th></th>\n",
" <th colspan=\"7\" halign=\"left\">leg_1</th>\n",
" <th colspan=\"7\" halign=\"left\">leg_2</th>\n",
" <th colspan=\"3\" halign=\"left\">totals</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>contract</th>\n",
" <th>underlying</th>\n",
" <th>expiration</th>\n",
" <th>type</th>\n",
" <th>strike</th>\n",
" <th>cost</th>\n",
" <th>order</th>\n",
" <th>contract</th>\n",
" <th>underlying</th>\n",
" <th>expiration</th>\n",
" <th>type</th>\n",
" <th>strike</th>\n",
" <th>cost</th>\n",
" <th>order</th>\n",
" <th>cost</th>\n",
" <th>qty</th>\n",
" <th>date</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>SPX170317C00300000</td>\n",
" <td>SPX</td>\n",
" <td>2017-03-17</td>\n",
" <td>call</td>\n",
" <td>300</td>\n",
" <td>195010.0</td>\n",
" <td>Order.BTO</td>\n",
" <td>SPX170317P00300000</td>\n",
" <td>SPX</td>\n",
" <td>2017-03-17</td>\n",
" <td>put</td>\n",
" <td>300</td>\n",
" <td>5.0</td>\n",
" <td>Order.BTO</td>\n",
" <td>195015.0</td>\n",
" <td>2.0</td>\n",
" <td>2017-01-03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>SPX170317C00300000</td>\n",
" <td>SPX</td>\n",
" <td>2017-03-17</td>\n",
" <td>call</td>\n",
" <td>300</td>\n",
" <td>-197060.0</td>\n",
" <td>Order.STC</td>\n",
" <td>SPX170317P00300000</td>\n",
" <td>SPX</td>\n",
" <td>2017-03-17</td>\n",
" <td>put</td>\n",
" <td>300</td>\n",
" <td>-0.0</td>\n",
" <td>Order.STC</td>\n",
" <td>-197060.0</td>\n",
" <td>2.0</td>\n",
" <td>2017-02-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>SPX170421C00500000</td>\n",
" <td>SPX</td>\n",
" <td>2017-04-21</td>\n",
" <td>call</td>\n",
" <td>500</td>\n",
" <td>189250.0</td>\n",
" <td>Order.BTO</td>\n",
" <td>SPX170421P01375000</td>\n",
" <td>SPX</td>\n",
" <td>2017-04-21</td>\n",
" <td>put</td>\n",
" <td>1375</td>\n",
" <td>40.0</td>\n",
" <td>Order.BTO</td>\n",
" <td>189290.0</td>\n",
" <td>2.0</td>\n",
" <td>2017-03-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>SPX170421C00500000</td>\n",
" <td>SPX</td>\n",
" <td>2017-04-21</td>\n",
" <td>call</td>\n",
" <td>500</td>\n",
" <td>-185650.0</td>\n",
" <td>Order.STC</td>\n",
" <td>SPX170421P01375000</td>\n",
" <td>SPX</td>\n",
" <td>2017-04-21</td>\n",
" <td>put</td>\n",
" <td>1375</td>\n",
" <td>-0.0</td>\n",
" <td>Order.STC</td>\n",
" <td>-185650.0</td>\n",
" <td>2.0</td>\n",
" <td>2017-04-03</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" leg_1 \\\n",
" contract underlying expiration type strike cost order \n",
"0 SPX170317C00300000 SPX 2017-03-17 call 300 195010.0 Order.BTO \n",
"1 SPX170317C00300000 SPX 2017-03-17 call 300 -197060.0 Order.STC \n",
"2 SPX170421C00500000 SPX 2017-04-21 call 500 189250.0 Order.BTO \n",
"3 SPX170421C00500000 SPX 2017-04-21 call 500 -185650.0 Order.STC \n",
"\n",
" leg_2 \\\n",
" contract underlying expiration type strike cost order \n",
"0 SPX170317P00300000 SPX 2017-03-17 put 300 5.0 Order.BTO \n",
"1 SPX170317P00300000 SPX 2017-03-17 put 300 -0.0 Order.STC \n",
"2 SPX170421P01375000 SPX 2017-04-21 put 1375 40.0 Order.BTO \n",
"3 SPX170421P01375000 SPX 2017-04-21 put 1375 -0.0 Order.STC \n",
"\n",
" totals \n",
" cost qty date \n",
"0 195015.0 2.0 2017-01-03 \n",
"1 -197060.0 2.0 2017-02-01 \n",
"2 189290.0 2.0 2017-03-01 \n",
"3 -185650.0 2.0 2017-04-03 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bt.run(rebalance_freq=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The trade log (`bt.trade_log`) shows we executed 4 trades: we bought 2 calls and 2 puts on _2017-01-03_ and _2017-03-01_, and exited those positions on _2017-02-01_ and _2017-04-03_ respectively."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The balance data structure shows how our positions evolved in time:\n",
"- We started with $1000000 on _2017-01-02_\n",
"- `total capital` is the sum of `cash`, `stocks capital` and `options capital`\n",
"- `% change` shows the inter day change in `total capital`\n",
"- `accumulated return` gives the compounded return in `total capital` since the start of the backtest"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>total capital</th>\n",
" <th>cash</th>\n",
" <th>VOO</th>\n",
" <th>TUR</th>\n",
" <th>RSX</th>\n",
" <th>options qty</th>\n",
" <th>calls capital</th>\n",
" <th>puts capital</th>\n",
" <th>stocks qty</th>\n",
" <th>options capital</th>\n",
" <th>stocks capital</th>\n",
" <th>% change</th>\n",
" <th>accumulated return</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2017-01-02</th>\n",
" <td>1.000000e+06</td>\n",
" <td>1000000.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2017-01-03</th>\n",
" <td>9.990300e+05</td>\n",
" <td>110117.405920</td>\n",
" <td>199872.763320</td>\n",
" <td>49993.281167</td>\n",
" <td>249986.549593</td>\n",
" <td>2.0</td>\n",
" <td>389060.0</td>\n",
" <td>0.0</td>\n",
" <td>16186.0</td>\n",
" <td>389060.0</td>\n",
" <td>499852.594080</td>\n",
" <td>-0.000970</td>\n",
" <td>0.999030</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-04</th>\n",
" <td>1.004228e+06</td>\n",
" <td>110117.405920</td>\n",
" <td>201052.238851</td>\n",
" <td>50072.862958</td>\n",
" <td>251605.333911</td>\n",
" <td>2.0</td>\n",
" <td>391380.0</td>\n",
" <td>0.0</td>\n",
" <td>16186.0</td>\n",
" <td>391380.0</td>\n",
" <td>502730.435720</td>\n",
" <td>0.005203</td>\n",
" <td>1.004228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-05</th>\n",
" <td>1.002706e+06</td>\n",
" <td>110117.405920</td>\n",
" <td>200897.553535</td>\n",
" <td>49865.950301</td>\n",
" <td>250564.686850</td>\n",
" <td>2.0</td>\n",
" <td>391260.0</td>\n",
" <td>0.0</td>\n",
" <td>16186.0</td>\n",
" <td>391260.0</td>\n",
" <td>501328.190686</td>\n",
" <td>-0.001516</td>\n",
" <td>1.002706</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-01-06</th>\n",
" <td>1.003201e+06</td>\n",
" <td>110117.405920</td>\n",
" <td>201680.647945</td>\n",
" <td>49372.543196</td>\n",
" <td>248830.275081</td>\n",
" <td>2.0</td>\n",
" <td>393200.0</td>\n",
" <td>0.0</td>\n",
" <td>16186.0</td>\n",
" <td>393200.0</td>\n",
" <td>499883.466222</td>\n",
" <td>0.000494</td>\n",
" <td>1.003201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-12</th>\n",
" <td>1.008599e+06</td>\n",
" <td>507298.036182</td>\n",
" <td>203126.836072</td>\n",
" <td>50621.271130</td>\n",
" <td>247552.567157</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>16415.0</td>\n",
" <td>0.0</td>\n",
" <td>501300.674359</td>\n",
" <td>-0.002805</td>\n",
" <td>1.008599</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-15</th>\n",
" <td>1.015904e+06</td>\n",
" <td>507298.036182</td>\n",
" <td>204154.459189</td>\n",
" <td>51695.869759</td>\n",
" <td>252755.284844</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>16415.0</td>\n",
" <td>0.0</td>\n",
" <td>508605.613792</td>\n",
" <td>0.007243</td>\n",
" <td>1.015904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-16</th>\n",
" <td>1.014651e+06</td>\n",
" <td>507298.036182</td>\n",
" <td>204117.427725</td>\n",
" <td>51811.005327</td>\n",
" <td>251424.357064</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>16415.0</td>\n",
" <td>0.0</td>\n",
" <td>507352.790116</td>\n",
" <td>-0.001233</td>\n",
" <td>1.014651</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-17</th>\n",
" <td>1.005524e+06</td>\n",
" <td>507298.036182</td>\n",
" <td>200497.602152</td>\n",
" <td>50659.649653</td>\n",
" <td>247068.593419</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>16415.0</td>\n",
" <td>0.0</td>\n",
" <td>498225.845224</td>\n",
" <td>-0.008995</td>\n",
" <td>1.005524</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2017-05-18</th>\n",
" <td>1.001336e+06</td>\n",
" <td>507298.036182</td>\n",
" <td>201228.973560</td>\n",
" <td>49853.700681</td>\n",
" <td>242954.816643</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>16415.0</td>\n",
" <td>0.0</td>\n",
" <td>494037.490883</td>\n",
" <td>-0.004165</td>\n",
" <td>1.001336</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>96 rows × 13 columns</p>\n",
"</div>"
],
"text/plain": [
" total capital cash VOO TUR \\\n",
"2017-01-02 1.000000e+06 1000000.000000 NaN NaN \n",
"2017-01-03 9.990300e+05 110117.405920 199872.763320 49993.281167 \n",
"2017-01-04 1.004228e+06 110117.405920 201052.238851 50072.862958 \n",
"2017-01-05 1.002706e+06 110117.405920 200897.553535 49865.950301 \n",
"2017-01-06 1.003201e+06 110117.405920 201680.647945 49372.543196 \n",
"... ... ... ... ... \n",
"2017-05-12 1.008599e+06 507298.036182 203126.836072 50621.271130 \n",
"2017-05-15 1.015904e+06 507298.036182 204154.459189 51695.869759 \n",
"2017-05-16 1.014651e+06 507298.036182 204117.427725 51811.005327 \n",
"2017-05-17 1.005524e+06 507298.036182 200497.602152 50659.649653 \n",
"2017-05-18 1.001336e+06 507298.036182 201228.973560 49853.700681 \n",
"\n",
" RSX options qty calls capital puts capital \\\n",
"2017-01-02 NaN NaN NaN NaN \n",
"2017-01-03 249986.549593 2.0 389060.0 0.0 \n",
"2017-01-04 251605.333911 2.0 391380.0 0.0 \n",
"2017-01-05 250564.686850 2.0 391260.0 0.0 \n",
"2017-01-06 248830.275081 2.0 393200.0 0.0 \n",
"... ... ... ... ... \n",
"2017-05-12 247552.567157 0.0 0.0 0.0 \n",
"2017-05-15 252755.284844 0.0 0.0 0.0 \n",
"2017-05-16 251424.357064 0.0 0.0 0.0 \n",
"2017-05-17 247068.593419 0.0 0.0 0.0 \n",
"2017-05-18 242954.816643 0.0 0.0 0.0 \n",
"\n",
" stocks qty options capital stocks capital % change \\\n",
"2017-01-02 NaN 0.0 0.000000 NaN \n",
"2017-01-03 16186.0 389060.0 499852.594080 -0.000970 \n",
"2017-01-04 16186.0 391380.0 502730.435720 0.005203 \n",
"2017-01-05 16186.0 391260.0 501328.190686 -0.001516 \n",
"2017-01-06 16186.0 393200.0 499883.466222 0.000494 \n",
"... ... ... ... ... \n",
"2017-05-12 16415.0 0.0 501300.674359 -0.002805 \n",
"2017-05-15 16415.0 0.0 508605.613792 0.007243 \n",
"2017-05-16 16415.0 0.0 507352.790116 -0.001233 \n",
"2017-05-17 16415.0 0.0 498225.845224 -0.008995 \n",
"2017-05-18 16415.0 0.0 494037.490883 -0.004165 \n",
"\n",
" accumulated return \n",
"2017-01-02 NaN \n",
"2017-01-03 0.999030 \n",
"2017-01-04 1.004228 \n",
"2017-01-05 1.002706 \n",
"2017-01-06 1.003201 \n",
"... ... \n",
"2017-05-12 1.008599 \n",
"2017-05-15 1.015904 \n",
"2017-05-16 1.014651 \n",
"2017-05-17 1.005524 \n",
"2017-05-18 1.001336 \n",
"\n",
"[96 rows x 13 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bt.balance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Statistics and Plots"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from backtester.statistics import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Summary table of our options strategy."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
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"<style type=\"text/css\" >\n",
"</style><table id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fdd\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >Strategy</th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddlevel0_row0\" class=\"row_heading level0 row0\" >Total trades</th>\n",
" <td id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddrow0_col0\" class=\"data row0 col0\" >2</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddlevel0_row1\" class=\"row_heading level0 row1\" >Number of wins</th>\n",
" <td id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddrow1_col0\" class=\"data row1 col0\" >1</td>\n",
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" <tr>\n",
" <th id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddlevel0_row2\" class=\"row_heading level0 row2\" >Number of losses</th>\n",
" <td id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddrow2_col0\" class=\"data row2 col0\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddlevel0_row3\" class=\"row_heading level0 row3\" >Win %</th>\n",
" <td id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddrow3_col0\" class=\"data row3 col0\" >50.00%</td>\n",
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" <tr>\n",
" <th id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddlevel0_row4\" class=\"row_heading level0 row4\" >Largest loss</th>\n",
" <td id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddrow4_col0\" class=\"data row4 col0\" >$7280.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddlevel0_row5\" class=\"row_heading level0 row5\" >Profit factor</th>\n",
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" <tr>\n",
" <th id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddlevel0_row6\" class=\"row_heading level0 row6\" >Average profit</th>\n",
" <td id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddrow6_col0\" class=\"data row6 col0\" >$-1595.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddlevel0_row7\" class=\"row_heading level0 row7\" >Average P&L %</th>\n",
" <td id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddrow7_col0\" class=\"data row7 col0\" >0.00%</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddlevel0_row8\" class=\"row_heading level0 row8\" >Total P&L %</th>\n",
" <td id=\"T_ae9adb42_6a2e_11ea_9301_38f9d3530fddrow8_col0\" class=\"data row8 col0\" >99.68%</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x11424c8d0>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(bt.trade_log, bt.balance)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot of the accumulated returns over time."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
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"text/plain": [
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},
"execution_count": 12,
"metadata": {},
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],
"source": [
"returns_chart(bt.balance)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Daily returns histogram."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
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],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"returns_histogram(bt.balance)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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" } else if (typeof vegaEmbed === \"function\") {\n",
" displayChart(vegaEmbed);\n",
" } else {\n",
" loadScript(\"vega\")\n",
" .then(() => loadScript(\"vega-lite\"))\n",
" .then(() => loadScript(\"vega-embed\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-346a4f2528f883c1edf82f98636126f5\"}, \"mark\": \"rect\", \"encoding\": {\"color\": {\"type\": \"quantitative\", \"aggregate\": \"mean\", \"field\": \"total capital\", \"scale\": {\"scheme\": \"redyellowgreen\"}, \"title\": \"Return\"}, \"tooltip\": {\"type\": \"quantitative\", \"aggregate\": \"mean\", \"field\": \"total capital\", \"format\": \".2f\"}, \"x\": {\"type\": \"ordinal\", \"field\": \"date\", \"timeUnit\": \"year\", \"title\": \"Year\"}, \"y\": {\"type\": \"ordinal\", \"field\": \"date\", \"timeUnit\": \"month\", \"title\": \"Month\"}}, \"title\": \"Monthly Returns\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.0.2.json\", \"datasets\": {\"data-346a4f2528f883c1edf82f98636126f5\": [{\"date\": \"2017-01-31T00:00:00\", \"total capital\": 0.007052466563358228}, {\"date\": \"2017-02-28T00:00:00\", \"total capital\": -0.004094139749308168}, {\"date\": \"2017-03-31T00:00:00\", \"total capital\": 0.000736452326976833}, {\"date\": \"2017-04-30T00:00:00\", \"total capital\": 0.00950367199049329}, {\"date\": \"2017-05-31T00:00:00\", \"total capital\": -0.011716334863691724}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"monthly_returns_heatmap(bt.balance)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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