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In [1]:
import os
import sys
from pathlib import Path
BACKTESTER_DIR = Path(".").resolve().parent.parent

DATA_DIR = BACKTESTER_DIR / '../mori_opt_earnings/data/interim/optm_lz/opprcd'
print(DATA_DIR)
OPTIONS_DATA = os.path.join(DATA_DIR, 'SPX_2017.csv')
STOCKS_DATA = os.path.join(DATA_DIR, 'portfolio_data_2017.csv')
sys.path.append(BACKTESTER_DIR) # Add backtester base dir to $PYTHONPATH
/media/wassname/SGIronWolf/projects5/investing/options/options_backtester/../mori_opt_earnings/data/interim/optm_lz/opprcd
In [2]:
%load_ext autoreload
%autoreload 2
In [3]:
import pyfolio as pf
import matplotlib.pyplot as plt

from backtester import Backtest
from backtester.strategy import Strategy, StrategyLeg
from backtester.enums import Type, Direction, Stock
from backtester.datahandler import HistoricalOptionsData, TiingoData, Schema, OptionMetricsData
from backtester.statistics import monthly_returns_heatmap, returns_histogram, returns_chart
WARNING (theano.link.c.cmodule): install mkl with `conda install mkl-service`: No module named 'mkl'
In [4]:
plt.style.use("seaborn")
plt.rcParams["figure.figsize"] = (14, 8)

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.

Stock data is taken from Tiingo.

In [5]:
# %env TIINGO_API_KEY=your_tiingo_api_key
from dotenv import load_dotenv
import os
load_dotenv()
Out [5]:
True

Data

get_data_tiingo

In [6]:
import datetime
import pandas_datareader as pdr
# restricting to 1996-01-04 00:00:00 2019-06-28 00:00:00
api_key = os.environ["TIINGO_API_KEY"]
start = datetime.datetime(1996, 1, 4)
end = datetime.datetime(2019, 6, 28)
tickers = ["MSFT", "AAPL", "XOM"] #, "TUR", "RSX", "EWY", "EWS", "VTIP", "TLT", "BWX", "PDBC", "IAU", "VNQI"]

symbols = pdr.get_data_tiingo(tickers, api_key=api_key, start=start, end=end)
symbols
Out [6]:
/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
  return pd.concat(dfs, self._concat_axis)
close high low open volume adjClose adjHigh adjLow adjOpen adjVolume divCash splitFactor
symbol date
MSFT 1996-01-04 00:00:00+00:00 87.37 87.50 84.87 87.25 6397100 3.439386 3.444504 3.340972 3.434662 102353600 0.0 1.0
1996-01-05 00:00:00+00:00 86.37 87.62 86.12 86.25 3880200 3.400020 3.449227 3.390179 3.395296 62083200 0.0 1.0
1996-01-08 00:00:00+00:00 86.25 87.62 86.12 86.50 711100 3.395296 3.449227 3.390179 3.405138 11377600 0.0 1.0
1996-01-09 00:00:00+00:00 80.19 86.00 79.87 86.00 11089000 3.156740 3.385455 3.144143 3.385455 177424000 0.0 1.0
1996-01-10 00:00:00+00:00 82.37 83.25 80.48 80.50 11348200 3.242557 3.277199 3.168156 3.168943 181571200 0.0 1.0
... ... ... ... ... ... ... ... ... ... ... ... ... ...
XOM 2019-06-24 00:00:00+00:00 76.95 77.76 76.81 77.53 10012236 64.097147 64.771854 63.980531 64.580270 10012236 0.0 1.0
2019-06-25 00:00:00+00:00 76.27 77.22 76.19 77.08 9739970 63.530727 64.322049 63.464089 64.205433 9739970 0.0 1.0
2019-06-26 00:00:00+00:00 76.60 77.13 76.47 76.68 11365476 63.805607 64.247082 63.697321 63.872245 11365476 0.0 1.0
2019-06-27 00:00:00+00:00 75.82 76.75 75.76 76.59 8147803 63.155889 63.930553 63.105911 63.797277 8147803 0.0 1.0
2019-06-28 00:00:00+00:00 76.63 76.73 75.94 76.19 14781298 63.830596 63.913893 63.255846 63.464089 14781298 0.0 1.0

17736 rows × 12 columns

In [7]:
save_path = os.path.join(DATA_DIR, 'portfolio_data_2017.csv')
symbols.to_csv(save_path)
In [ ]:

Use TiingoData to load your stocks csv. Data must include date, adjClose and symbol columns to work.

In [8]:
stock_data = TiingoData(STOCKS_DATA)
# remove tz
stock_data['date']=stock_data['date'].dt.tz_localize(None)
stock_data.start_date=stock_data.start_date.tz_localize(None)
stock_data.end_date=stock_data.end_date.tz_localize(None)
stock_data.head()
Out [8]:
symbol date close high low open volume adjClose adjHigh adjLow adjOpen adjVolume divCash splitFactor
0 MSFT 1996-01-04 87.37 87.50 84.87 87.25 6397100 3.439386 3.444504 3.340972 3.434662 102353600 0.0 1.0
1 MSFT 1996-01-05 86.37 87.62 86.12 86.25 3880200 3.400020 3.449227 3.390179 3.395296 62083200 0.0 1.0
2 MSFT 1996-01-08 86.25 87.62 86.12 86.50 711100 3.395296 3.449227 3.390179 3.405138 11377600 0.0 1.0
3 MSFT 1996-01-09 80.19 86.00 79.87 86.00 11089000 3.156740 3.385455 3.144143 3.385455 177424000 0.0 1.0
4 MSFT 1996-01-10 82.37 83.25 80.48 80.50 11348200 3.242557 3.277199 3.168156 3.168943 181571200 0.0 1.0

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.

In [9]:
MSFT = Stock('MSFT', 0.3)
AAPL = Stock('AAPL', 0.3)
XOM = Stock('XOM', 0.4)
# EWY = Stock('EWY', 0.05)
# EWS = Stock('EWS', 0.05)
# VTIP = Stock('VTIP', 0.10)
# TLT = Stock('TLT', 0.20)
# BWX = Stock('BWX', 0.10)
# PDBC = Stock('PDBC', 0.05)
# IAU = Stock('IAU', 0.15)
# VNQI = Stock('VNQI', 0.10)

Make sure the sum of the percentages is 1.

In [10]:
0.10 + 0.05 + 0.05 + 0.05 + 0.05 + 0.10 + 0.20 + 0.10 + 0.05 + 0.15 + 0.10
Out [10]:
1.0000000000000002
In [11]:
stocks = [
    MSFT,
    AAPL,
    XOM,
    # EWY,
    # EWS,
    # VTIP,
    # TLT,
    # BWX,
    # PDBC,
    # IAU,
    # VNQI
]

Options

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:

In [12]:
options_schema = Schema.options()
options_schema.update({
    'underlying': 'stock',
            'contract': 'optionroot',
            'date': 'quotedate'})
options_schema
Out [12]:
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')])

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).

In [13]:
options_data = OptionMetricsData(["MSFT", "AAPL", "XOM"], schema=None)
schema = options_data.schema
options_data.head()
Out [13]:
date symbol symbol_flag exdate last_date cp_flag strike_price best_bid best_offer volume ... am_settlement contract_size ss_flag forward_price expiry_indicator root suffix secid ticker dte
0 1996-01-04 09D05.1F 0 1996-01-20 NaT P 55000.0 0.000 0.0625 0.0 ... 0.0 100.0 0 87.580727 09D05 1F 107525.0 MSFT 16
1 1996-01-04 09907.E9 0 1996-02-17 1996-01-04 C 80000.0 9.375 9.7500 2.0 ... 0.0 100.0 0 87.960975 09907 E9 107525.0 MSFT 44
2 1996-01-04 09E5B.96 0 1996-07-20 1996-01-04 C 80000.0 14.250 14.7500 1.0 ... 0.0 100.0 0 89.972692 09E5B 96 107525.0 MSFT 198
3 1996-01-04 09D7E.EF 0 1997-01-18 NaT P 70000.0 3.125 3.5000 0.0 ... 0.0 100.0 0 92.275048 09D7E EF 107525.0 MSFT 380
4 1996-01-04 09EF9.89 0 1996-04-20 1996-01-04 C 90000.0 6.500 6.8750 135.0 ... 0.0 100.0 0 88.796785 09EF9 89 107525.0 MSFT 107

5 rows × 28 columns

In [14]:
options_data.ticker.unique()
Out [14]:
array(['MSFT', 'AAPL', 'XOM'], dtype=object)
In [15]:
# d0 = max(options_data['date'].min(), stock_data['date'].min())
# d1 = min(options_data['date'].max(), stock_data['date'].max())
# print("restricting to", d0, d1)
# stock_data = stock_data[(stock_data.date <= d1) & (stock_data.date >= d0)]
date_col = stock_data.schema['date']
stock_data.start_date = stock_data._data[date_col].min()
stock_data.end_date = stock_data._data[date_col].max()
# # remove tz
# stock_data['date']=stock_data.date.dt.tz_localize(None)
# stock_data.head()
# # options_data._data = options_data_data[(options_data.date <= d1) & (options_data.date >= d0)]

Strat

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.

In [16]:
long_straddle = Strategy(schema)

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.

In [17]:
leg1 = StrategyLeg("leg_1", schema, option_type=Type.CALL, direction=Direction.BUY)

leg2 = StrategyLeg("leg_2", schema, option_type=Type.PUT, direction=Direction.BUY)

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).

In [18]:
# options_data.head()
In [19]:
leg1.entry_filter = (schema.dte >= 31) & (schema.dte <= 60) & (schema.strike >= schema.underlying_last * 0.95) & (schema.strike <= schema.underlying_last * 1.05)
leg2.entry_filter = (schema.dte >= 31) & (schema.dte <= 60) & (schema.strike >= schema.underlying_last * 0.95) & (schema.strike <= schema.underlying_last * 1.05)

leg1.exit_filter = (schema.dte <= 60)
leg2.exit_filter = (schema.dte <= 60)

long_straddle.add_legs([leg1, leg2]);
In [20]:
# 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)
# 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)

# leg1.exit_filter = (schema.dte <= 60)
# leg2.exit_filter = (schema.dte <= 60)

# long_straddle.add_legs([leg1, leg2]);

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.

In [21]:
stocks
Out [21]:
[Stock(symbol='MSFT', percentage=0.3),
 Stock(symbol='AAPL', percentage=0.3),
 Stock(symbol='XOM', percentage=0.4)]

Run

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.

In [41]:
bt = Backtest({'stocks': 0.5, 'options': 0.5, 'cash': 0})
bt.stocks = stocks
bt.options_strategy = long_straddle
bt.options_data = options_data
bt.stocks_data = stock_data
In [42]:
options_data['date'].unique()
Out [42]:
array(['1996-01-04T00:00:00.000000000', '1996-01-05T00:00:00.000000000',
       '1996-01-08T00:00:00.000000000', ...,
       '2019-06-26T00:00:00.000000000', '2019-06-27T00:00:00.000000000',
       '2019-06-28T00:00:00.000000000'], dtype='datetime64[ns]')
In [43]:
stock_data['date'].unique()
stock_data.start_date
Out [43]:
Timestamp('1996-01-04 00:00:00')

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.

In [44]:
bt.run(rebalance_freq=1)
Out [44]:
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:31
leg_1 leg_2 totals
contract underlying expiration type strike cost order contract underlying expiration type strike cost order cost qty date
In [ ]:

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.

In [ ]:
In [45]:
bt.balance
Out [45]:
total capital cash MSFT AAPL XOM options qty calls capital puts capital stocks qty MSFT qty AAPL qty XOM qty options capital stocks capital % change accumulated return
1996-01-03 1.000000e+06 1.000000e+06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.000000e+00 NaN NaN
1996-01-04 1.000000e+06 5.000041e+05 1.499985e+05 1.499999e+05 1.999975e+05 0.0 0.0 0.0 688386.0 43612.0 622655.0 22119.0 0.0 4.999959e+05 0.000000 1.000000
1996-01-05 1.015984e+06 5.000041e+05 1.482817e+05 1.627850e+05 2.049133e+05 0.0 0.0 0.0 688386.0 43612.0 622655.0 22119.0 0.0 5.159800e+05 0.015984 1.015984
1996-01-08 1.020042e+06 5.000041e+05 1.480757e+05 1.645911e+05 2.073712e+05 0.0 0.0 0.0 688386.0 43612.0 622655.0 22119.0 0.0 5.200379e+05 0.003994 1.020042
1996-01-09 9.967210e+05 5.000041e+05 1.376717e+05 1.556558e+05 2.033894e+05 0.0 0.0 0.0 688386.0 43612.0 622655.0 22119.0 0.0 4.967169e+05 -0.022863 0.996721
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2019-06-21 8.976873e+06 4.235908e+06 1.452253e+06 1.457513e+06 1.831199e+06 0.0 0.0 0.0 69275.0 10928.0 30050.0 28297.0 0.0 4.740965e+06 0.002309 8.976873
2019-06-24 8.966552e+06 4.235908e+06 1.460841e+06 1.456047e+06 1.813757e+06 0.0 0.0 0.0 69275.0 10928.0 30050.0 28297.0 0.0 4.730644e+06 -0.001150 8.966552
2019-06-25 8.882332e+06 4.235908e+06 1.414719e+06 1.433976e+06 1.797729e+06 0.0 0.0 0.0 69275.0 10928.0 30050.0 28297.0 0.0 4.646424e+06 -0.009393 8.882332
2019-06-26 8.926428e+06 4.235908e+06 1.420020e+06 1.464992e+06 1.805507e+06 0.0 0.0 0.0 69275.0 10928.0 30050.0 28297.0 0.0 4.690520e+06 0.004964 8.926428
2019-06-27 8.909935e+06 4.235908e+06 1.422353e+06 1.464552e+06 1.787122e+06 0.0 0.0 0.0 69275.0 10928.0 30050.0 28297.0 0.0 4.674027e+06 -0.001848 8.909935

5912 rows × 16 columns

In [46]:
bt.balance['total capital'].plot();
In [47]:
bt.balance[[stock.symbol for stock in stocks]].plot();

Pass the balance dataframe to the functions returns_chart, returns_histogram and monthly_returns_heatmap for better visualization.

In [48]:
returns_chart(bt.balance)
Out [48]:
---------------------------------------------------------------------------
MaxRowsError                              Traceback (most recent call last)
~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/v4/api.py in to_dict(self, *args, **kwargs)
    372         copy = self.copy(deep=False)
    373         original_data = getattr(copy, "data", Undefined)
--> 374         copy.data = _prepare_data(original_data, context)
    375 
    376         if original_data is not Undefined:

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/v4/api.py in _prepare_data(data, context)
     87     # convert dataframes  or objects with __geo_interface__ to dict
     88     if isinstance(data, pd.DataFrame) or hasattr(data, "__geo_interface__"):
---> 89         data = _pipe(data, data_transformers.get())
     90 
     91     # convert string input to a URLData

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py in pipe(data, *funcs)
    628     """
    629     for func in funcs:
--> 630         data = func(data)
    631     return data
    632 

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py in __call__(self, *args, **kwargs)
    304     def __call__(self, *args, **kwargs):
    305         try:
--> 306             return self._partial(*args, **kwargs)
    307         except TypeError as exc:
    308             if self._should_curry(args, kwargs, exc):

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/data.py in default_data_transformer(data, max_rows)
     17 @curried.curry
     18 def default_data_transformer(data, max_rows=5000):
---> 19     return curried.pipe(data, limit_rows(max_rows=max_rows), to_values)
     20 
     21 

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py in pipe(data, *funcs)
    628     """
    629     for func in funcs:
--> 630         data = func(data)
    631     return data
    632 

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py in __call__(self, *args, **kwargs)
    304     def __call__(self, *args, **kwargs):
    305         try:
--> 306             return self._partial(*args, **kwargs)
    307         except TypeError as exc:
    308             if self._should_curry(args, kwargs, exc):

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/utils/data.py in limit_rows(data, max_rows)
     82             "than the maximum allowed ({}). "
     83             "For information on how to plot larger datasets "
---> 84             "in Altair, see the documentation".format(max_rows)
     85         )
     86     return data

MaxRowsError: 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
alt.VConcatChart(...)
In [49]:
monthly_returns_heatmap(bt.balance)
Out [49]:
In [50]:
returns_histogram(bt.balance)
Out [50]:
---------------------------------------------------------------------------
MaxRowsError                              Traceback (most recent call last)
~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/v4/api.py in to_dict(self, *args, **kwargs)
   2018             copy.data = core.InlineData(values=[{}])
   2019             return super(Chart, copy).to_dict(*args, **kwargs)
-> 2020         return super().to_dict(*args, **kwargs)
   2021 
   2022     def add_selection(self, *selections):

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/v4/api.py in to_dict(self, *args, **kwargs)
    372         copy = self.copy(deep=False)
    373         original_data = getattr(copy, "data", Undefined)
--> 374         copy.data = _prepare_data(original_data, context)
    375 
    376         if original_data is not Undefined:

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/v4/api.py in _prepare_data(data, context)
     87     # convert dataframes  or objects with __geo_interface__ to dict
     88     if isinstance(data, pd.DataFrame) or hasattr(data, "__geo_interface__"):
---> 89         data = _pipe(data, data_transformers.get())
     90 
     91     # convert string input to a URLData

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py in pipe(data, *funcs)
    628     """
    629     for func in funcs:
--> 630         data = func(data)
    631     return data
    632 

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py in __call__(self, *args, **kwargs)
    304     def __call__(self, *args, **kwargs):
    305         try:
--> 306             return self._partial(*args, **kwargs)
    307         except TypeError as exc:
    308             if self._should_curry(args, kwargs, exc):

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/vegalite/data.py in default_data_transformer(data, max_rows)
     17 @curried.curry
     18 def default_data_transformer(data, max_rows=5000):
---> 19     return curried.pipe(data, limit_rows(max_rows=max_rows), to_values)
     20 
     21 

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py in pipe(data, *funcs)
    628     """
    629     for func in funcs:
--> 630         data = func(data)
    631     return data
    632 

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/toolz/functoolz.py in __call__(self, *args, **kwargs)
    304     def __call__(self, *args, **kwargs):
    305         try:
--> 306             return self._partial(*args, **kwargs)
    307         except TypeError as exc:
    308             if self._should_curry(args, kwargs, exc):

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/altair/utils/data.py in limit_rows(data, max_rows)
     82             "than the maximum allowed ({}). "
     83             "For information on how to plot larger datasets "
---> 84             "in Altair, see the documentation".format(max_rows)
     85         )
     86     return data

MaxRowsError: 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
alt.Chart(...)

The backtest also supports an sma 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.

In [51]:
bt.run(rebalance_freq=1, sma_days=30);
0% [██████████████████████████████] 100% | ETA: 00:00:00
Total time elapsed: 00:00:29
In [52]:
bt.balance[[stock.symbol for stock in stocks]].plot();

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.

Finally, the daily returns column (% change) can be passed to the Quantopian library pyfolio to obtain summary statistics and a few plots of the returns.

In [53]:
pf.create_returns_tear_sheet(returns =  bt.balance['% change'].dropna())
/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
  UserWarning)

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3360             try:
-> 3361                 return self._engine.get_loc(casted_key)
   3362             except KeyError as err:

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'date'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/utils.py in get_symbol_returns_from_yahoo(symbol, start, end)
    435         px = web.get_data_yahoo(symbol, start=start, end=end)
--> 436         px['date'] = pd.to_datetime(px['date'])
    437         px.set_index('date', drop=False, inplace=True)

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas/core/frame.py in __getitem__(self, key)
   3457                 return self._getitem_multilevel(key)
-> 3458             indexer = self.columns.get_loc(key)
   3459             if is_integer(indexer):

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pandas/core/indexes/base.py in get_loc(self, key, method, tolerance)
   3362             except KeyError as err:
-> 3363                 raise KeyError(key) from err
   3364 

KeyError: 'date'

During handling of the above exception, another exception occurred:

AttributeError                            Traceback (most recent call last)
/tmp/ipykernel_1862594/2665695955.py in <module>
----> 1 pf.create_returns_tear_sheet(returns =  bt.balance['% change'].dropna())

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pyfolio/plotting.py in call_w_context(*args, **kwargs)
     50         if set_context:
     51             with plotting_context(), axes_style():
---> 52                 return func(*args, **kwargs)
     53         else:
     54             return func(*args, **kwargs)

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pyfolio/tears.py in create_returns_tear_sheet(returns, positions, transactions, live_start_date, cone_std, benchmark_rets, bootstrap, return_fig)
    442 
    443     if benchmark_rets is None:
--> 444         benchmark_rets = utils.get_symbol_rets('SPY')
    445 
    446     returns = returns[returns.index > benchmark_rets.index[0]]

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pyfolio/utils.py in get_symbol_rets(symbol, start, end)
    598     return SETTINGS['returns_func'](symbol,
    599                                     start=start,
--> 600                                     end=end)

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pyfolio/deprecate.py in wrapper(*args, **kwargs)
     41                 stacklevel=stacklevel
     42             )
---> 43             return fn(*args, **kwargs)
     44         return wrapper
     45     return deprecated_dec

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/pyfolio/utils.py in default_returns_func(symbol, start, end)
    399          - See full explanation in tears.create_full_tear_sheet (returns).
    400     """
--> 401     return empyrical.utils.default_returns_func(symbol, start=None, end=None)
    402 
    403 

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/deprecate.py in wrapper(*args, **kwargs)
     41                 stacklevel=stacklevel
     42             )
---> 43             return fn(*args, **kwargs)
     44         return wrapper
     45     return deprecated_dec

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/utils.py in default_returns_func(symbol, start, end)
    488                                   symbol='SPY',
    489                                   start='1/1/1970',
--> 490                                   end=datetime.now())
    491         rets = rets[start:end]
    492     else:

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/deprecate.py in wrapper(*args, **kwargs)
     41                 stacklevel=stacklevel
     42             )
---> 43             return fn(*args, **kwargs)
     44         return wrapper
     45     return deprecated_dec

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/utils.py in get_returns_cached(filepath, update_func, latest_dt, **kwargs)
    322 
    323     if update_cache:
--> 324         returns = update_func(**kwargs)
    325         try:
    326             ensure_directory(cache_dir())

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/deprecate.py in wrapper(*args, **kwargs)
     41                 stacklevel=stacklevel
     42             )
---> 43             return fn(*args, **kwargs)
     44         return wrapper
     45     return deprecated_dec

~/miniforge3/envs/mori_opt_earnings/lib/python3.7/site-packages/empyrical/utils.py in get_symbol_returns_from_yahoo(symbol, start, end)
    441             'Yahoo Finance read failed: {}, falling back to Google'.format(e),
    442             UserWarning)
--> 443         px = web.get_data_google(symbol, start=start, end=end)
    444         rets = px[['Close']].pct_change().dropna()
    445 

AttributeError: module 'pandas_datareader.data' has no attribute 'get_data_google'
In [ ]:
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