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options_backtester/backtester/examples/main_example_mjc.ipynb
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wassname 6764b03b04 make it a package, get it working with my data
2022-07-04 13:19:01 +08:00

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{
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/media/wassname/SGIronWolf/projects5/investing/options/options_backtester/../mori_opt_earnings/data/interim/optm_lz/opprcd\n"
]
}
],
"source": [
"import os\n",
"import sys\n",
"from pathlib import Path\n",
"BACKTESTER_DIR = Path(\".\").resolve().parent.parent\n",
"\n",
"DATA_DIR = BACKTESTER_DIR / '../mori_opt_earnings/data/interim/optm_lz/opprcd'\n",
"print(DATA_DIR)\n",
"OPTIONS_DATA = os.path.join(DATA_DIR, 'SPX_2017.csv')\n",
"STOCKS_DATA = os.path.join(DATA_DIR, 'portfolio_data_2017.csv')\n",
"sys.path.append(BACKTESTER_DIR) # Add backtester base dir to $PYTHONPATH"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
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"start_time": "2022-07-04T05:14:26.135988Z"
}
},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:14:27.614543Z",
"start_time": "2022-07-04T05:14:26.198651Z"
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},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING (theano.link.c.cmodule): install mkl with `conda install mkl-service`: No module named 'mkl'\n"
]
}
],
"source": [
"import pyfolio as pf\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from backtester import Backtest\n",
"from backtester.strategy import Strategy, StrategyLeg\n",
"from backtester.enums import Type, Direction, Stock\n",
"from backtester.datahandler import HistoricalOptionsData, TiingoData, Schema, OptionMetricsData\n",
"from backtester.statistics import monthly_returns_heatmap, returns_histogram, returns_chart"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"ExecuteTime": {
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"start_time": "2022-07-04T05:14:27.616095Z"
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},
"outputs": [],
"source": [
"plt.style.use(\"seaborn\")\n",
"plt.rcParams[\"figure.figsize\"] = (14, 8)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As an example, we run a 2017 backtest of a portfolio consisting of a 97% allocation in stocks and 3% in options. The backtest will run for the time period spanned by the data, performing periodic rebalances with a user given frequency. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Stock data is taken from [Tiingo](https://api.tiingo.com)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:14:27.653688Z",
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"data": {
"text/plain": [
"True"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# %env TIINGO_API_KEY=your_tiingo_api_key\n",
"from dotenv import load_dotenv\n",
"import os\n",
"load_dotenv()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## get_data_tiingo"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:14:35.155346Z",
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},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/wassname/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas_datareader/tiingo.py:234: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
" return pd.concat(dfs, self._concat_axis)\n"
]
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" <th></th>\n",
" <th></th>\n",
" <th>close</th>\n",
" <th>high</th>\n",
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" <th rowspan=\"5\" valign=\"top\">MSFT</th>\n",
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" <th>1996-01-05 00:00:00+00:00</th>\n",
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" <th>1996-01-08 00:00:00+00:00</th>\n",
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" <td>3.242557</td>\n",
" <td>3.277199</td>\n",
" <td>3.168156</td>\n",
" <td>3.168943</td>\n",
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" <th>2019-06-26 00:00:00+00:00</th>\n",
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"text/plain": [
" close high low open volume \\\n",
"symbol date \n",
"MSFT 1996-01-04 00:00:00+00:00 87.37 87.50 84.87 87.25 6397100 \n",
" 1996-01-05 00:00:00+00:00 86.37 87.62 86.12 86.25 3880200 \n",
" 1996-01-08 00:00:00+00:00 86.25 87.62 86.12 86.50 711100 \n",
" 1996-01-09 00:00:00+00:00 80.19 86.00 79.87 86.00 11089000 \n",
" 1996-01-10 00:00:00+00:00 82.37 83.25 80.48 80.50 11348200 \n",
"... ... ... ... ... ... \n",
"XOM 2019-06-24 00:00:00+00:00 76.95 77.76 76.81 77.53 10012236 \n",
" 2019-06-25 00:00:00+00:00 76.27 77.22 76.19 77.08 9739970 \n",
" 2019-06-26 00:00:00+00:00 76.60 77.13 76.47 76.68 11365476 \n",
" 2019-06-27 00:00:00+00:00 75.82 76.75 75.76 76.59 8147803 \n",
" 2019-06-28 00:00:00+00:00 76.63 76.73 75.94 76.19 14781298 \n",
"\n",
" adjClose adjHigh adjLow adjOpen \\\n",
"symbol date \n",
"MSFT 1996-01-04 00:00:00+00:00 3.439386 3.444504 3.340972 3.434662 \n",
" 1996-01-05 00:00:00+00:00 3.400020 3.449227 3.390179 3.395296 \n",
" 1996-01-08 00:00:00+00:00 3.395296 3.449227 3.390179 3.405138 \n",
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" 1996-01-10 00:00:00+00:00 3.242557 3.277199 3.168156 3.168943 \n",
"... ... ... ... ... \n",
"XOM 2019-06-24 00:00:00+00:00 64.097147 64.771854 63.980531 64.580270 \n",
" 2019-06-25 00:00:00+00:00 63.530727 64.322049 63.464089 64.205433 \n",
" 2019-06-26 00:00:00+00:00 63.805607 64.247082 63.697321 63.872245 \n",
" 2019-06-27 00:00:00+00:00 63.155889 63.930553 63.105911 63.797277 \n",
" 2019-06-28 00:00:00+00:00 63.830596 63.913893 63.255846 63.464089 \n",
"\n",
" adjVolume divCash splitFactor \n",
"symbol date \n",
"MSFT 1996-01-04 00:00:00+00:00 102353600 0.0 1.0 \n",
" 1996-01-05 00:00:00+00:00 62083200 0.0 1.0 \n",
" 1996-01-08 00:00:00+00:00 11377600 0.0 1.0 \n",
" 1996-01-09 00:00:00+00:00 177424000 0.0 1.0 \n",
" 1996-01-10 00:00:00+00:00 181571200 0.0 1.0 \n",
"... ... ... ... \n",
"XOM 2019-06-24 00:00:00+00:00 10012236 0.0 1.0 \n",
" 2019-06-25 00:00:00+00:00 9739970 0.0 1.0 \n",
" 2019-06-26 00:00:00+00:00 11365476 0.0 1.0 \n",
" 2019-06-27 00:00:00+00:00 8147803 0.0 1.0 \n",
" 2019-06-28 00:00:00+00:00 14781298 0.0 1.0 \n",
"\n",
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},
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"metadata": {},
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],
"source": [
"import datetime\n",
"import pandas_datareader as pdr\n",
"# restricting to 1996-01-04 00:00:00 2019-06-28 00:00:00\n",
"api_key = os.environ[\"TIINGO_API_KEY\"]\n",
"start = datetime.datetime(1996, 1, 4)\n",
"end = datetime.datetime(2019, 6, 28)\n",
"tickers = [\"MSFT\", \"AAPL\", \"XOM\"] #, \"TUR\", \"RSX\", \"EWY\", \"EWS\", \"VTIP\", \"TLT\", \"BWX\", \"PDBC\", \"IAU\", \"VNQI\"]\n",
"\n",
"symbols = pdr.get_data_tiingo(tickers, api_key=api_key, start=start, end=end)\n",
"symbols"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:14:35.290745Z",
"start_time": "2022-07-04T05:14:35.156786Z"
}
},
"outputs": [],
"source": [
"save_path = os.path.join(DATA_DIR, 'portfolio_data_2017.csv')\n",
"symbols.to_csv(save_path)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use *TiingoData* to load your stocks csv. Data must include `date`, `adjClose` and `symbol` columns to work."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:14:35.456256Z",
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"outputs": [
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" <td>3.168943</td>\n",
" <td>181571200</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" symbol date close high low open volume adjClose adjHigh \\\n",
"0 MSFT 1996-01-04 87.37 87.50 84.87 87.25 6397100 3.439386 3.444504 \n",
"1 MSFT 1996-01-05 86.37 87.62 86.12 86.25 3880200 3.400020 3.449227 \n",
"2 MSFT 1996-01-08 86.25 87.62 86.12 86.50 711100 3.395296 3.449227 \n",
"3 MSFT 1996-01-09 80.19 86.00 79.87 86.00 11089000 3.156740 3.385455 \n",
"4 MSFT 1996-01-10 82.37 83.25 80.48 80.50 11348200 3.242557 3.277199 \n",
"\n",
" adjLow adjOpen adjVolume divCash splitFactor \n",
"0 3.340972 3.434662 102353600 0.0 1.0 \n",
"1 3.390179 3.395296 62083200 0.0 1.0 \n",
"2 3.390179 3.405138 11377600 0.0 1.0 \n",
"3 3.144143 3.385455 177424000 0.0 1.0 \n",
"4 3.168156 3.168943 181571200 0.0 1.0 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stock_data = TiingoData(STOCKS_DATA)\n",
"# remove tz\n",
"stock_data['date']=stock_data['date'].dt.tz_localize(None)\n",
"stock_data.start_date=stock_data.start_date.tz_localize(None)\n",
"stock_data.end_date=stock_data.end_date.tz_localize(None)\n",
"stock_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To create a stock portfolio, use `Stock` as Stock(*name*, *percentage*), where *name* should match the name given in the `symbol` column and *percentage* is the percentage (from 0 to 1) allocated to that specific asset. Afterwards, store them in a list."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:14:35.474794Z",
"start_time": "2022-07-04T05:14:35.457851Z"
}
},
"outputs": [],
"source": [
"MSFT = Stock('MSFT', 0.3)\n",
"AAPL = Stock('AAPL', 0.3)\n",
"XOM = Stock('XOM', 0.4)\n",
"# EWY = Stock('EWY', 0.05)\n",
"# EWS = Stock('EWS', 0.05)\n",
"# VTIP = Stock('VTIP', 0.10)\n",
"# TLT = Stock('TLT', 0.20)\n",
"# BWX = Stock('BWX', 0.10)\n",
"# PDBC = Stock('PDBC', 0.05)\n",
"# IAU = Stock('IAU', 0.15)\n",
"# VNQI = Stock('VNQI', 0.10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Make sure the sum of the percentages is 1."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:14:35.498740Z",
"start_time": "2022-07-04T05:14:35.476159Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1.0000000000000002"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0.10 + 0.05 + 0.05 + 0.05 + 0.05 + 0.10 + 0.20 + 0.10 + 0.05 + 0.15 + 0.10"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:14:35.514662Z",
"start_time": "2022-07-04T05:14:35.500251Z"
}
},
"outputs": [],
"source": [
"stocks = [\n",
" MSFT,\n",
" AAPL,\n",
" XOM,\n",
" # EWY,\n",
" # EWS,\n",
" # VTIP,\n",
" # TLT,\n",
" # BWX,\n",
" # PDBC,\n",
" # IAU,\n",
" # VNQI\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Options"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To load your options data, use `HistoricalOptionsData`. Both this and `Stock` make use of an underlying `schema` to map the expected column names to the ones in your data. *Schema.stocks()* and *Schema.options()* will return the default mappings (i.e, the ones that don't change anything). If you need to change some values in them use *update* like so:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:14:35.532640Z",
"start_time": "2022-07-04T05:14:35.515795Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Schema([Field(name='underlying', mapping='stock'), Field(name='underlying_last', mapping='underlying_last'), Field(name='date', mapping='quotedate'), Field(name='contract', mapping='optionroot'), Field(name='type', mapping='type'), Field(name='expiration', mapping='expiration'), Field(name='strike', mapping='strike'), Field(name='bid', mapping='bid'), Field(name='ask', mapping='ask'), Field(name='volume', mapping='volume'), Field(name='open_interest', mapping='open_interest')])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"options_schema = Schema.options()\n",
"options_schema.update({\n",
" 'underlying': 'stock',\n",
" 'contract': 'optionroot',\n",
" 'date': 'quotedate'})\n",
"options_schema"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the last cell we updated the schema so that the *contract* and *date* columns are mapped to *optionroot* and *quotedate*, as they are in our data. This isn't needed in our case, since the default schema used by the backtester already uses this mapping; in general though, to use a custom schema pass it to `Stock` or `HistoricalOptionsData` when loading the csv (*schema=None* uses the default schema)."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:14.396502Z",
"start_time": "2022-07-04T05:14:35.533916Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>date</th>\n",
" <th>symbol</th>\n",
" <th>symbol_flag</th>\n",
" <th>exdate</th>\n",
" <th>last_date</th>\n",
" <th>cp_flag</th>\n",
" <th>strike_price</th>\n",
" <th>best_bid</th>\n",
" <th>best_offer</th>\n",
" <th>volume</th>\n",
" <th>...</th>\n",
" <th>am_settlement</th>\n",
" <th>contract_size</th>\n",
" <th>ss_flag</th>\n",
" <th>forward_price</th>\n",
" <th>expiry_indicator</th>\n",
" <th>root</th>\n",
" <th>suffix</th>\n",
" <th>secid</th>\n",
" <th>ticker</th>\n",
" <th>dte</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1996-01-04</td>\n",
" <td>09D05.1F</td>\n",
" <td>0</td>\n",
" <td>1996-01-20</td>\n",
" <td>NaT</td>\n",
" <td>P</td>\n",
" <td>55000.0</td>\n",
" <td>0.000</td>\n",
" <td>0.0625</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>87.580727</td>\n",
" <td></td>\n",
" <td>09D05</td>\n",
" <td>1F</td>\n",
" <td>107525.0</td>\n",
" <td>MSFT</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1996-01-04</td>\n",
" <td>09907.E9</td>\n",
" <td>0</td>\n",
" <td>1996-02-17</td>\n",
" <td>1996-01-04</td>\n",
" <td>C</td>\n",
" <td>80000.0</td>\n",
" <td>9.375</td>\n",
" <td>9.7500</td>\n",
" <td>2.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>87.960975</td>\n",
" <td></td>\n",
" <td>09907</td>\n",
" <td>E9</td>\n",
" <td>107525.0</td>\n",
" <td>MSFT</td>\n",
" <td>44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1996-01-04</td>\n",
" <td>09E5B.96</td>\n",
" <td>0</td>\n",
" <td>1996-07-20</td>\n",
" <td>1996-01-04</td>\n",
" <td>C</td>\n",
" <td>80000.0</td>\n",
" <td>14.250</td>\n",
" <td>14.7500</td>\n",
" <td>1.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>89.972692</td>\n",
" <td></td>\n",
" <td>09E5B</td>\n",
" <td>96</td>\n",
" <td>107525.0</td>\n",
" <td>MSFT</td>\n",
" <td>198</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1996-01-04</td>\n",
" <td>09D7E.EF</td>\n",
" <td>0</td>\n",
" <td>1997-01-18</td>\n",
" <td>NaT</td>\n",
" <td>P</td>\n",
" <td>70000.0</td>\n",
" <td>3.125</td>\n",
" <td>3.5000</td>\n",
" <td>0.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>92.275048</td>\n",
" <td></td>\n",
" <td>09D7E</td>\n",
" <td>EF</td>\n",
" <td>107525.0</td>\n",
" <td>MSFT</td>\n",
" <td>380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1996-01-04</td>\n",
" <td>09EF9.89</td>\n",
" <td>0</td>\n",
" <td>1996-04-20</td>\n",
" <td>1996-01-04</td>\n",
" <td>C</td>\n",
" <td>90000.0</td>\n",
" <td>6.500</td>\n",
" <td>6.8750</td>\n",
" <td>135.0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>100.0</td>\n",
" <td>0</td>\n",
" <td>88.796785</td>\n",
" <td></td>\n",
" <td>09EF9</td>\n",
" <td>89</td>\n",
" <td>107525.0</td>\n",
" <td>MSFT</td>\n",
" <td>107</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 28 columns</p>\n",
"</div>"
],
"text/plain": [
" date symbol symbol_flag exdate last_date cp_flag \\\n",
"0 1996-01-04 09D05.1F 0 1996-01-20 NaT P \n",
"1 1996-01-04 09907.E9 0 1996-02-17 1996-01-04 C \n",
"2 1996-01-04 09E5B.96 0 1996-07-20 1996-01-04 C \n",
"3 1996-01-04 09D7E.EF 0 1997-01-18 NaT P \n",
"4 1996-01-04 09EF9.89 0 1996-04-20 1996-01-04 C \n",
"\n",
" strike_price best_bid best_offer volume ... am_settlement \\\n",
"0 55000.0 0.000 0.0625 0.0 ... 0.0 \n",
"1 80000.0 9.375 9.7500 2.0 ... 0.0 \n",
"2 80000.0 14.250 14.7500 1.0 ... 0.0 \n",
"3 70000.0 3.125 3.5000 0.0 ... 0.0 \n",
"4 90000.0 6.500 6.8750 135.0 ... 0.0 \n",
"\n",
" contract_size ss_flag forward_price expiry_indicator root suffix \\\n",
"0 100.0 0 87.580727 09D05 1F \n",
"1 100.0 0 87.960975 09907 E9 \n",
"2 100.0 0 89.972692 09E5B 96 \n",
"3 100.0 0 92.275048 09D7E EF \n",
"4 100.0 0 88.796785 09EF9 89 \n",
"\n",
" secid ticker dte \n",
"0 107525.0 MSFT 16 \n",
"1 107525.0 MSFT 44 \n",
"2 107525.0 MSFT 198 \n",
"3 107525.0 MSFT 380 \n",
"4 107525.0 MSFT 107 \n",
"\n",
"[5 rows x 28 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"options_data = OptionMetricsData([\"MSFT\", \"AAPL\", \"XOM\"], schema=None)\n",
"schema = options_data.schema\n",
"options_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:14.658790Z",
"start_time": "2022-07-04T05:15:14.397527Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['MSFT', 'AAPL', 'XOM'], dtype=object)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"options_data.ticker.unique()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:14.674665Z",
"start_time": "2022-07-04T05:15:14.659925Z"
}
},
"outputs": [],
"source": [
"# d0 = max(options_data['date'].min(), stock_data['date'].min())\n",
"# d1 = min(options_data['date'].max(), stock_data['date'].max())\n",
"# print(\"restricting to\", d0, d1)\n",
"# stock_data = stock_data[(stock_data.date <= d1) & (stock_data.date >= d0)]\n",
"date_col = stock_data.schema['date']\n",
"stock_data.start_date = stock_data._data[date_col].min()\n",
"stock_data.end_date = stock_data._data[date_col].max()\n",
"# # remove tz\n",
"# stock_data['date']=stock_data.date.dt.tz_localize(None)\n",
"# stock_data.head()\n",
"# # options_data._data = options_data_data[(options_data.date <= d1) & (options_data.date >= d0)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Strat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the options part of the backtest, we create a `Strategy` to determine when to enter on certain contracts. Remember to pass the options schema when creating it."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:14.690535Z",
"start_time": "2022-07-04T05:15:14.675626Z"
}
},
"outputs": [],
"source": [
"long_straddle = Strategy(schema)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each strategy consists of one or more legs created with `StrategyLeg`. A leg consists of a type of contract (*put* or *call*) and a direction to enter (*buy* or *sell*). Our long straddle will have two legs, which will buy a call and a put respectively."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:14.706476Z",
"start_time": "2022-07-04T05:15:14.691543Z"
}
},
"outputs": [],
"source": [
"leg1 = StrategyLeg(\"leg_1\", schema, option_type=Type.CALL, direction=Direction.BUY)\n",
"\n",
"leg2 = StrategyLeg(\"leg_2\", schema, option_type=Type.PUT, direction=Direction.BUY)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Legs have entry and exit filters, to determine the conditions a contract from that leg has to satisfy to select it for entry/exit. Filters are created with the following syntax, using the options schema to refer to column names (such as `underlying`, `dte` or `strike`)."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:14.722390Z",
"start_time": "2022-07-04T05:15:14.707404Z"
}
},
"outputs": [],
"source": [
"# options_data.head()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:14.739166Z",
"start_time": "2022-07-04T05:15:14.723369Z"
}
},
"outputs": [],
"source": [
"leg1.entry_filter = (schema.dte >= 31) & (schema.dte <= 60) & (schema.strike >= schema.underlying_last * 0.95) & (schema.strike <= schema.underlying_last * 1.05)\n",
"leg2.entry_filter = (schema.dte >= 31) & (schema.dte <= 60) & (schema.strike >= schema.underlying_last * 0.95) & (schema.strike <= schema.underlying_last * 1.05)\n",
"\n",
"leg1.exit_filter = (schema.dte <= 60)\n",
"leg2.exit_filter = (schema.dte <= 60)\n",
"\n",
"long_straddle.add_legs([leg1, leg2]);"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:14.754983Z",
"start_time": "2022-07-04T05:15:14.740098Z"
}
},
"outputs": [],
"source": [
"# leg1.entry_filter = (schema.underlying == \"SPX\") & (schema.dte >= 31) & (schema.dte <= 60) & (schema.strike >= schema.underlying_last * 0.95) & (schema.strike <= schema.underlying_last * 1.05)\n",
"# leg2.entry_filter = (schema.underlying == \"SPX\") & (schema.dte >= 31) & (schema.dte <= 60) & (schema.strike >= schema.underlying_last * 0.95) & (schema.strike <= schema.underlying_last * 1.05)\n",
"\n",
"# leg1.exit_filter = (schema.dte <= 60)\n",
"# leg2.exit_filter = (schema.dte <= 60)\n",
"\n",
"# long_straddle.add_legs([leg1, leg2]);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, the first leg will look for contracts with *SPX* as the underlying which have between 31 and 60 days to expiration (*dte*) and are at the money (*ATM*), i.e, their strike price is close to their spot. After entering, the exit filter will exit contracts having 60 or less *dte*, which for this given strategy means we will always exit contracts. This effectively means we are buying ATM put and call contracts, keeping them for a month and then selling them."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:14.777101Z",
"start_time": "2022-07-04T05:15:14.755936Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[Stock(symbol='MSFT', percentage=0.3),\n",
" Stock(symbol='AAPL', percentage=0.3),\n",
" Stock(symbol='XOM', percentage=0.4)]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stocks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To put it all together, create a `Backtest` object and pass it a map specifying the percentage allocated to stocks, options and cash. Then set the stock portfolio, the options strategy and the data and run it."
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:17:12.542568Z",
"start_time": "2022-07-04T05:17:12.528174Z"
}
},
"outputs": [],
"source": [
"bt = Backtest({'stocks': 0.5, 'options': 0.5, 'cash': 0})\n",
"bt.stocks = stocks\n",
"bt.options_strategy = long_straddle\n",
"bt.options_data = options_data\n",
"bt.stocks_data = stock_data"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:17:12.756559Z",
"start_time": "2022-07-04T05:17:12.695626Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['1996-01-04T00:00:00.000000000', '1996-01-05T00:00:00.000000000',\n",
" '1996-01-08T00:00:00.000000000', ...,\n",
" '2019-06-26T00:00:00.000000000', '2019-06-27T00:00:00.000000000',\n",
" '2019-06-28T00:00:00.000000000'], dtype='datetime64[ns]')"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"options_data['date'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:17:12.843180Z",
"start_time": "2022-07-04T05:17:12.828029Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
"Timestamp('1996-01-04 00:00:00')"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stock_data['date'].unique()\n",
"stock_data.start_date"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The *run* method takes the initial capital (default value *1.000.000*) and `rebalance_freq` as arguments. This last one defines how often, in months, a rebalancing of the portfolio is made, so that a value of 1 means a monthly rebalancing and 6 a bi-annual one. Its value defaults to 0, meaning no rebalancing done."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:17:45.459030Z",
"start_time": "2022-07-04T05:17:13.130619Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"0% [██████████████████████████████] 100% | ETA: 00:00:00\n",
"Total time elapsed: 00:00:31\n"
]
},
{
"data": {
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" <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",
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"text/plain": [
"Empty DataFrame\n",
"Columns: [(leg_1, contract), (leg_1, underlying), (leg_1, expiration), (leg_1, type), (leg_1, strike), (leg_1, cost), (leg_1, order), (leg_2, contract), (leg_2, underlying), (leg_2, expiration), (leg_2, type), (leg_2, strike), (leg_2, cost), (leg_2, order), (totals, cost), (totals, qty), (totals, date)]\n",
"Index: []"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bt.run(rebalance_freq=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:43.833604Z",
"start_time": "2022-07-04T05:15:43.817268Z"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When done, the backtester returns a trade log with all the option trades. There is also a balance sheet with daily returns, capital and other information for each stock symbol and options."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:15:43.859515Z",
"start_time": "2022-07-04T05:15:43.834678Z"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:17:45.489105Z",
"start_time": "2022-07-04T05:17:45.460476Z"
}
},
"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>MSFT</th>\n",
" <th>AAPL</th>\n",
" <th>XOM</th>\n",
" <th>options qty</th>\n",
" <th>calls capital</th>\n",
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" <th>AAPL qty</th>\n",
" <th>XOM 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>1996-01-03</th>\n",
" <td>1.000000e+06</td>\n",
" <td>1.000000e+06</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>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>0.000000e+00</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-01-04</th>\n",
" <td>1.000000e+06</td>\n",
" <td>5.000041e+05</td>\n",
" <td>1.499985e+05</td>\n",
" <td>1.499999e+05</td>\n",
" <td>1.999975e+05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>688386.0</td>\n",
" <td>43612.0</td>\n",
" <td>622655.0</td>\n",
" <td>22119.0</td>\n",
" <td>0.0</td>\n",
" <td>4.999959e+05</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-01-05</th>\n",
" <td>1.015984e+06</td>\n",
" <td>5.000041e+05</td>\n",
" <td>1.482817e+05</td>\n",
" <td>1.627850e+05</td>\n",
" <td>2.049133e+05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>688386.0</td>\n",
" <td>43612.0</td>\n",
" <td>622655.0</td>\n",
" <td>22119.0</td>\n",
" <td>0.0</td>\n",
" <td>5.159800e+05</td>\n",
" <td>0.015984</td>\n",
" <td>1.015984</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-01-08</th>\n",
" <td>1.020042e+06</td>\n",
" <td>5.000041e+05</td>\n",
" <td>1.480757e+05</td>\n",
" <td>1.645911e+05</td>\n",
" <td>2.073712e+05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>688386.0</td>\n",
" <td>43612.0</td>\n",
" <td>622655.0</td>\n",
" <td>22119.0</td>\n",
" <td>0.0</td>\n",
" <td>5.200379e+05</td>\n",
" <td>0.003994</td>\n",
" <td>1.020042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1996-01-09</th>\n",
" <td>9.967210e+05</td>\n",
" <td>5.000041e+05</td>\n",
" <td>1.376717e+05</td>\n",
" <td>1.556558e+05</td>\n",
" <td>2.033894e+05</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>688386.0</td>\n",
" <td>43612.0</td>\n",
" <td>622655.0</td>\n",
" <td>22119.0</td>\n",
" <td>0.0</td>\n",
" <td>4.967169e+05</td>\n",
" <td>-0.022863</td>\n",
" <td>0.996721</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
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" <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",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-21</th>\n",
" <td>8.976873e+06</td>\n",
" <td>4.235908e+06</td>\n",
" <td>1.452253e+06</td>\n",
" <td>1.457513e+06</td>\n",
" <td>1.831199e+06</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>69275.0</td>\n",
" <td>10928.0</td>\n",
" <td>30050.0</td>\n",
" <td>28297.0</td>\n",
" <td>0.0</td>\n",
" <td>4.740965e+06</td>\n",
" <td>0.002309</td>\n",
" <td>8.976873</td>\n",
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" <tr>\n",
" <th>2019-06-24</th>\n",
" <td>8.966552e+06</td>\n",
" <td>4.235908e+06</td>\n",
" <td>1.460841e+06</td>\n",
" <td>1.456047e+06</td>\n",
" <td>1.813757e+06</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <td>28297.0</td>\n",
" <td>0.0</td>\n",
" <td>4.730644e+06</td>\n",
" <td>-0.001150</td>\n",
" <td>8.966552</td>\n",
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" <th>2019-06-25</th>\n",
" <td>8.882332e+06</td>\n",
" <td>4.235908e+06</td>\n",
" <td>1.414719e+06</td>\n",
" <td>1.433976e+06</td>\n",
" <td>1.797729e+06</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>69275.0</td>\n",
" <td>10928.0</td>\n",
" <td>30050.0</td>\n",
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" <td>0.0</td>\n",
" <td>4.646424e+06</td>\n",
" <td>-0.009393</td>\n",
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" <tr>\n",
" <th>2019-06-26</th>\n",
" <td>8.926428e+06</td>\n",
" <td>4.235908e+06</td>\n",
" <td>1.420020e+06</td>\n",
" <td>1.464992e+06</td>\n",
" <td>1.805507e+06</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>69275.0</td>\n",
" <td>10928.0</td>\n",
" <td>30050.0</td>\n",
" <td>28297.0</td>\n",
" <td>0.0</td>\n",
" <td>4.690520e+06</td>\n",
" <td>0.004964</td>\n",
" <td>8.926428</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2019-06-27</th>\n",
" <td>8.909935e+06</td>\n",
" <td>4.235908e+06</td>\n",
" <td>1.422353e+06</td>\n",
" <td>1.464552e+06</td>\n",
" <td>1.787122e+06</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>69275.0</td>\n",
" <td>10928.0</td>\n",
" <td>30050.0</td>\n",
" <td>28297.0</td>\n",
" <td>0.0</td>\n",
" <td>4.674027e+06</td>\n",
" <td>-0.001848</td>\n",
" <td>8.909935</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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"</div>"
],
"text/plain": [
" total capital cash MSFT AAPL \\\n",
"1996-01-03 1.000000e+06 1.000000e+06 NaN NaN \n",
"1996-01-04 1.000000e+06 5.000041e+05 1.499985e+05 1.499999e+05 \n",
"1996-01-05 1.015984e+06 5.000041e+05 1.482817e+05 1.627850e+05 \n",
"1996-01-08 1.020042e+06 5.000041e+05 1.480757e+05 1.645911e+05 \n",
"1996-01-09 9.967210e+05 5.000041e+05 1.376717e+05 1.556558e+05 \n",
"... ... ... ... ... \n",
"2019-06-21 8.976873e+06 4.235908e+06 1.452253e+06 1.457513e+06 \n",
"2019-06-24 8.966552e+06 4.235908e+06 1.460841e+06 1.456047e+06 \n",
"2019-06-25 8.882332e+06 4.235908e+06 1.414719e+06 1.433976e+06 \n",
"2019-06-26 8.926428e+06 4.235908e+06 1.420020e+06 1.464992e+06 \n",
"2019-06-27 8.909935e+06 4.235908e+06 1.422353e+06 1.464552e+06 \n",
"\n",
" XOM options qty calls capital puts capital \\\n",
"1996-01-03 NaN NaN NaN NaN \n",
"1996-01-04 1.999975e+05 0.0 0.0 0.0 \n",
"1996-01-05 2.049133e+05 0.0 0.0 0.0 \n",
"1996-01-08 2.073712e+05 0.0 0.0 0.0 \n",
"1996-01-09 2.033894e+05 0.0 0.0 0.0 \n",
"... ... ... ... ... \n",
"2019-06-21 1.831199e+06 0.0 0.0 0.0 \n",
"2019-06-24 1.813757e+06 0.0 0.0 0.0 \n",
"2019-06-25 1.797729e+06 0.0 0.0 0.0 \n",
"2019-06-26 1.805507e+06 0.0 0.0 0.0 \n",
"2019-06-27 1.787122e+06 0.0 0.0 0.0 \n",
"\n",
" stocks qty MSFT qty AAPL qty XOM qty options capital \\\n",
"1996-01-03 NaN NaN NaN NaN 0.0 \n",
"1996-01-04 688386.0 43612.0 622655.0 22119.0 0.0 \n",
"1996-01-05 688386.0 43612.0 622655.0 22119.0 0.0 \n",
"1996-01-08 688386.0 43612.0 622655.0 22119.0 0.0 \n",
"1996-01-09 688386.0 43612.0 622655.0 22119.0 0.0 \n",
"... ... ... ... ... ... \n",
"2019-06-21 69275.0 10928.0 30050.0 28297.0 0.0 \n",
"2019-06-24 69275.0 10928.0 30050.0 28297.0 0.0 \n",
"2019-06-25 69275.0 10928.0 30050.0 28297.0 0.0 \n",
"2019-06-26 69275.0 10928.0 30050.0 28297.0 0.0 \n",
"2019-06-27 69275.0 10928.0 30050.0 28297.0 0.0 \n",
"\n",
" stocks capital % change accumulated return \n",
"1996-01-03 0.000000e+00 NaN NaN \n",
"1996-01-04 4.999959e+05 0.000000 1.000000 \n",
"1996-01-05 5.159800e+05 0.015984 1.015984 \n",
"1996-01-08 5.200379e+05 0.003994 1.020042 \n",
"1996-01-09 4.967169e+05 -0.022863 0.996721 \n",
"... ... ... ... \n",
"2019-06-21 4.740965e+06 0.002309 8.976873 \n",
"2019-06-24 4.730644e+06 -0.001150 8.966552 \n",
"2019-06-25 4.646424e+06 -0.009393 8.882332 \n",
"2019-06-26 4.690520e+06 0.004964 8.926428 \n",
"2019-06-27 4.674027e+06 -0.001848 8.909935 \n",
"\n",
"[5912 rows x 16 columns]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bt.balance"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:17:45.619110Z",
"start_time": "2022-07-04T05:17:45.490251Z"
}
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bt.balance['total capital'].plot();"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:17:45.761206Z",
"start_time": "2022-07-04T05:17:45.620973Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bt.balance[[stock.symbol for stock in stocks]].plot();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pass the balance dataframe to \n",
"the functions `returns_chart`, `returns_histogram` and `monthly_returns_heatmap` for better visualization. "
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:17:45.839110Z",
"start_time": "2022-07-04T05:17:45.762662Z"
}
},
"outputs": [
{
"ename": "MaxRowsError",
"evalue": "The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mMaxRowsError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/v4/api.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 373\u001b[0m \u001b[0moriginal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"data\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_prepare_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 376\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moriginal_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/v4/api.py\u001b[0m in \u001b[0;36m_prepare_data\u001b[0;34m(data, context)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;31m# convert dataframes or objects with __geo_interface__ to dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__geo_interface__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_pipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_transformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;31m# convert string input to a URLData\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(data, *funcs)\u001b[0m\n\u001b[1;32m 628\u001b[0m \"\"\"\n\u001b[1;32m 629\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 630\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 631\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 307\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_curry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/data.py\u001b[0m in \u001b[0;36mdefault_data_transformer\u001b[0;34m(data, max_rows)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mcurried\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdefault_data_transformer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcurried\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit_rows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(data, *funcs)\u001b[0m\n\u001b[1;32m 628\u001b[0m \"\"\"\n\u001b[1;32m 629\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 630\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 631\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 307\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_curry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/utils/data.py\u001b[0m in \u001b[0;36mlimit_rows\u001b[0;34m(data, max_rows)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\"than the maximum allowed ({}). \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;34m\"For information on how to plot larger datasets \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m \u001b[0;34m\"in Altair, see the documentation\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 85\u001b[0m )\n\u001b[1;32m 86\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMaxRowsError\u001b[0m: The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation"
]
},
{
"data": {
"text/plain": [
"alt.VConcatChart(...)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"returns_chart(bt.balance)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:17:45.874199Z",
"start_time": "2022-07-04T05:17:45.840196Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-fcc590ba0b49448ea3535f9feeaf224e\"></div>\n",
"<script type=\"text/javascript\">\n",
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" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-fcc590ba0b49448ea3535f9feeaf224e\") {\n",
" outputDiv = document.getElementById(\"altair-viz-fcc590ba0b49448ea3535f9feeaf224e\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function maybeLoadScript(lib, version) {\n",
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
" return (VEGA_DEBUG[key] == version) ?\n",
" Promise.resolve(paths[lib]) :\n",
" new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" s.async = true;\n",
" s.onload = () => {\n",
" VEGA_DEBUG[key] = version;\n",
" return resolve(paths[lib]);\n",
" };\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" s.src = paths[lib];\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-d77aab98f34e94773a3f2380ca9e1d84\"}, \"mark\": \"rect\", \"encoding\": {\"color\": {\"aggregate\": \"mean\", \"field\": \"total capital\", \"scale\": {\"scheme\": \"redyellowgreen\"}, \"title\": \"Return\", \"type\": \"quantitative\"}, \"tooltip\": {\"aggregate\": \"mean\", \"field\": \"total capital\", \"format\": \".2f\", \"type\": \"quantitative\"}, \"x\": {\"field\": \"date\", \"timeUnit\": \"year\", \"title\": \"Year\", \"type\": \"ordinal\"}, \"y\": {\"field\": \"date\", \"timeUnit\": \"month\", \"title\": \"Month\", \"type\": \"ordinal\"}}, \"title\": \"Monthly Returns\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-d77aab98f34e94773a3f2380ca9e1d84\": [{\"date\": \"1996-01-31T00:00:00\", \"total capital\": -0.012671758862230043}, {\"date\": \"1996-02-29T00:00:00\", \"total capital\": 0.009065706079007585}, {\"date\": 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\"2018-05-31T00:00:00\", \"total capital\": 0.04026262814425863}, {\"date\": \"2018-06-30T00:00:00\", \"total capital\": 0.002246417686430968}, {\"date\": \"2018-07-31T00:00:00\", \"total capital\": 0.012350740853056319}, {\"date\": \"2018-08-31T00:00:00\", \"total capital\": 0.037387540105983774}, {\"date\": \"2018-09-30T00:00:00\", \"total capital\": 0.013601613859161032}, {\"date\": \"2018-10-31T00:00:00\", \"total capital\": -0.02682221582053257}, {\"date\": \"2018-11-30T00:00:00\", \"total capital\": -0.018808732806539608}, {\"date\": \"2018-12-31T00:00:00\", \"total capital\": -0.05838015461304713}, {\"date\": \"2019-01-31T00:00:00\", \"total capital\": 0.026860332984792956}, {\"date\": \"2019-02-28T00:00:00\", \"total capital\": 0.03658679169907075}, {\"date\": \"2019-03-31T00:00:00\", \"total capital\": 0.026971233980473297}, {\"date\": \"2019-04-30T00:00:00\", \"total capital\": 0.023294755668471945}, {\"date\": \"2019-05-31T00:00:00\", \"total capital\": -0.046871906950626596}, {\"date\": \"2019-06-30T00:00:00\", \"total capital\": 0.048519939977912374}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"monthly_returns_heatmap(bt.balance)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:17:45.905568Z",
"start_time": "2022-07-04T05:17:45.875366Z"
}
},
"outputs": [
{
"ename": "MaxRowsError",
"evalue": "The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mMaxRowsError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/v4/api.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2018\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInlineData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2019\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mChart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2020\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2021\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2022\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0madd_selection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mselections\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/v4/api.py\u001b[0m in \u001b[0;36mto_dict\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 373\u001b[0m \u001b[0moriginal_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"data\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 374\u001b[0;31m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_prepare_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moriginal_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 376\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0moriginal_data\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mUndefined\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/v4/api.py\u001b[0m in \u001b[0;36m_prepare_data\u001b[0;34m(data, context)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0;31m# convert dataframes or objects with __geo_interface__ to dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__geo_interface__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 89\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_pipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_transformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;31m# convert string input to a URLData\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(data, *funcs)\u001b[0m\n\u001b[1;32m 628\u001b[0m \"\"\"\n\u001b[1;32m 629\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 630\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 631\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 307\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_curry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/data.py\u001b[0m in \u001b[0;36mdefault_data_transformer\u001b[0;34m(data, max_rows)\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mcurried\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurry\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdefault_data_transformer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mcurried\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit_rows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mto_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(data, *funcs)\u001b[0m\n\u001b[1;32m 628\u001b[0m \"\"\"\n\u001b[1;32m 629\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mfuncs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 630\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 631\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 632\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 305\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 306\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_partial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 307\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 308\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_curry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/utils/data.py\u001b[0m in \u001b[0;36mlimit_rows\u001b[0;34m(data, max_rows)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\"than the maximum allowed ({}). \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \u001b[0;34m\"For information on how to plot larger datasets \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m \u001b[0;34m\"in Altair, see the documentation\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_rows\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 85\u001b[0m )\n\u001b[1;32m 86\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mMaxRowsError\u001b[0m: The number of rows in your dataset is greater than the maximum allowed (5000). For information on how to plot larger datasets in Altair, see the documentation"
]
},
{
"data": {
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"returns_histogram(bt.balance)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The backtest also supports an [sma](https://www.investopedia.com/terms/s/sma.asp) option. If the argument `sma_days` is passed to the `run` method with a value of n, on each rebalancing day the backtest will only buy shares of a stock symbol if its current price is above the sma of the last n days. To illustrate, we run the same backtest as before but with *sma_days = 30*."
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:18:15.676287Z",
"start_time": "2022-07-04T05:17:45.906736Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"0% [██████████████████████████████] 100% | ETA: 00:00:00\n",
"Total time elapsed: 00:00:29\n"
]
}
],
"source": [
"bt.run(rebalance_freq=1, sma_days=30);"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:18:15.829742Z",
"start_time": "2022-07-04T05:18:15.678122Z"
}
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"bt.balance[[stock.symbol for stock in stocks]].plot();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice how the capital held in certain symbols sometimes drops to zero, meaning on those months we did not buy shares of those symbols because their price was below the sma."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, the daily returns column (`% change`) can be passed to the Quantopian library [pyfolio](https://github.com/quantopian/pyfolio) to obtain summary statistics and a few plots of the returns."
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"ExecuteTime": {
"end_time": "2022-07-04T05:18:17.868307Z",
"start_time": "2022-07-04T05:18:15.832353Z"
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/wassname/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/utils.py:442: UserWarning: Yahoo Finance read failed: 'date', falling back to Google\n",
" UserWarning)\n"
]
},
{
"ename": "AttributeError",
"evalue": "module 'pandas_datareader.data' has no attribute 'get_data_google'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3360\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3361\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'date'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/utils.py\u001b[0m in \u001b[0;36mget_symbol_returns_from_yahoo\u001b[0;34m(symbol, start, end)\u001b[0m\n\u001b[1;32m 435\u001b[0m \u001b[0mpx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data_yahoo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 436\u001b[0;31m \u001b[0mpx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 437\u001b[0m \u001b[0mpx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3457\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3459\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyError\u001b[0m: 'date'",
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"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/utils.py\u001b[0m in \u001b[0;36mdefault_returns_func\u001b[0;34m(symbol, start, end)\u001b[0m\n\u001b[1;32m 488\u001b[0m \u001b[0msymbol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'SPY'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 489\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'1/1/1970'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 490\u001b[0;31m end=datetime.now())\n\u001b[0m\u001b[1;32m 491\u001b[0m \u001b[0mrets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 492\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/deprecate.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m )\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdeprecated_dec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/utils.py\u001b[0m in \u001b[0;36mget_returns_cached\u001b[0;34m(filepath, update_func, latest_dt, **kwargs)\u001b[0m\n\u001b[1;32m 322\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 323\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mupdate_cache\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m \u001b[0mreturns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mupdate_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 325\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 326\u001b[0m \u001b[0mensure_directory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_dir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/deprecate.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m )\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 44\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdeprecated_dec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/utils.py\u001b[0m in \u001b[0;36mget_symbol_returns_from_yahoo\u001b[0;34m(symbol, start, end)\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;34m'Yahoo Finance read failed: {}, falling back to Google'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 442\u001b[0m UserWarning)\n\u001b[0;32m--> 443\u001b[0;31m \u001b[0mpx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mweb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_data_google\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 444\u001b[0m \u001b[0mrets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpct_change\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 445\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: module 'pandas_datareader.data' has no attribute 'get_data_google'"
]
}
],
"source": [
"pf.create_returns_tear_sheet(returns = bt.balance['% change'].dropna())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"toc": {
"base_numbering": 1,
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"title_cell": "Table of Contents",
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"vscode": {
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