Epymetheus: Multi-asset Backtesting Framework
Epymetheus is a multi-asset backtesting framework. It features an intuitive user API that lets analysts try out their trade strategies right away.
Installation
$ pip install epymetheus
Features
- Intuitive and Pythonic API
- Epymetheus designs Pythonic API that lets you code your idea intuitively without any fuss.
- Trading strategies can be readily coded as ordinary functions and then you can
run()andscore()it right away.
- Blazingly Fast Computation
- Backtesting is boosted by NumPy and so you can give your own idea a quick try.
- Executions of profit-taking and stop-loss orders are built-in.
- Seamless Connection with Pandas
- You can use
pandas.DataFrameof historical prices as the target of backtesting. - You can view the result of backtesting in Pandas format so that you can analyze and plot it using the familiar Pandas methods.
- You can use
- Full Test Coverage:
- Epymetheus is thoroughly tested by continuous integration with 100% code coverage.
Integrations
Your trading strategy may incorporate various libraries out there, for instance,
- Machine Learning - scikit-learn, PyTorch, TensorFlow, etc.
- Econometrics - statsmodels, Prophet, etc.
- Technical Indicators - TA-Lib, etc.
- Hyperparameter Optimization - Optuna (Example follows), etc.
How to use
Create strategy
Let's construct your trading strategy. The following "dumb strategy" will,
- target a set of stocks whose
DataFrameof historical prices is given byuniverse. - buy the cheapest stock with your monthly allowance $100, and
- place a profit-taking order when your profit exceeds $
profit_takeand place a stop-loss order when your loss exceeds $stop_loss.
Here in the function dumb_strategy, the first argument universe is mandatory while the other arguments are parameters that you can define freely.
import pandas as pd
import epymetheus as ep
def dumb_strategy(universe: pd.DataFrame, profit_take, stop_loss):
# I get $100 allowance on the first business day of each month
allowance = 100
for date in pd.date_range(universe.index[0], universe.index[-1], freq="BMS"):
cheapest_stock = universe.loc[date].idxmin()
# Find the maximum number of shares that I can buy with my allowance
n_shares = allowance // universe.at[date, cheapest_stock]
trade = n_shares * ep.trade(cheapest_stock, date, take=profit_take, stop=stop_loss)
yield trade
You can now create your strategy with specific parameters as:
my_strategy = ep.create_strategy(dumb_strategy, profit_take=20.0, stop_loss=-10.0)
Run strategy
Now you can backtest your strategy with any universe, for instance, US stocks.
from epymetheus.datasets import fetch_usstocks
universe = fetch_usstocks()
universe.head()
# AAPL MSFT AMZN BRK-A JPM JNJ WMT BAC PG XOM
# 2000-01-01 0.785456 37.162327 76.1250 56100 27.773939 27.289129 46.962898 14.527933 31.304089 21.492596
# 2000-01-02 0.785456 37.162327 76.1250 56100 27.773939 27.289129 46.962898 14.527933 31.304089 21.492596
# 2000-01-03 0.855168 37.102634 89.3750 54800 26.053429 26.978193 45.391777 14.021359 30.625511 20.892334
# 2000-01-04 0.783068 35.849308 81.9375 52000 25.481777 25.990519 43.693306 13.189125 30.036228 20.492161
# 2000-01-05 0.794528 36.227283 69.7500 53200 25.324482 26.264877 42.801613 13.333860 29.464787 21.609318
my_strategy.run(universe)
# 240 trades returned: trade(['BAC'], lot=[3.], entry=2019-12-02 00:00:00, take=20.0, stop=-10.0) ... Done. (Runtume: 0.1034 sec)
# 240 trades executed: trade(['BAC'], lot=[3.], entry=2019-12-02 00:00:00, take=20.0, stop=-10.0) ... Done. (Runtime: 0.2304 sec)
# Done. (Runtime: 0.3338 sec)
Trade history and wealth
Trade history can be viewed as:
df_history = my_strategy.history()
df_history.head()
# trade_id asset lot entry close exit take stop pnl
# 0 0 AAPL 115.0 2000-01-03 2005-01-07 None 20.0 -10.0 23.527901
# 1 1 AAPL 129.0 2000-02-01 2000-09-29 None 20.0 -10.0 -48.437450
# 2 2 AAPL 99.0 2000-03-01 2005-07-14 None 20.0 -10.0 24.924913
# 3 3 AAPL 97.0 2000-04-03 2005-07-14 None 20.0 -10.0 22.180065
# 4 4 AAPL 104.0 2000-05-01 2005-05-19 None 20.0 -10.0 20.736752
The time-series of wealth can be viewed as:
series_wealth = my_strategy.wealth()
series_wealth.head()
# 2000-01-01 0.000000
# 2000-01-02 0.000000
# 2000-01-03 0.000000
# 2000-01-04 -8.363557
# 2000-01-05 -7.034265
# Freq: D, dtype: float64
Scores
You can quickly score() the metrics of the strategy.
my_strategy.score("final_wealth")
# 3216.74
my_strategy.score("max_drawdown")
# -989.19
You may compute various time-series.
drawdown = my_strategy.drawdown()
exposure = my_strategy.net_exposure()
More examples
Optimization
You may optimize the parameters of your strategy using Optuna for example.
Remember that optimization for backtesting is dangerous.
import optuna
def objective(trial):
profit_take = trial.suggest_int("profit_take", 10, 100)
stop_loss = trial.suggest_int("stop_loss", -100, -10)
my_strategy = ep.create_strategy(
dumb_strategy,
profit_take=profit_take,
stop_loss=stop_loss,
).run(universe, verbose=False)
return my_strategy.score("final_wealth")
study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=100)
study.best_params
# {'profit_take': 100, 'stop_loss': -42}
Pair trading
Trade can include multiple stocks.
Profit-take and stop-loss will be executed when the total profit/loss exceed thresholds.
def pair_trading_strategy(universe, param_1, ...):
...
# Buy 1 share of "BULLISH_STOCK" and sell 2 shares of "BEARISH_STOCK".
yield [1, -2] * ep.trade(["BULLISH_STOCK", "BEARISH_STOCK"], stop=-100.0)
Contributing
Any contributions are more than welcome.
The maintainer (simaki) is not making further enhancements and appreciates pull requests to make them. See Issue for proposed features. Please take a look at CONTRIBUTING.md before creating a pull request.
7.7k Jan 07, 2023
745 Dec 28, 2022
74 Dec 31, 2022
30 Dec 04, 2022
575 Jan 02, 2023
3 Dec 04, 2021
1 Jun 20, 2022
1 Jan 22, 2022
1.6k Dec 09, 2022
548 Dec 28, 2022
222 Jan 04, 2023
31 Apr 12, 2022
499 Dec 28, 2022
17 Nov 04, 2022
1.6k Jan 06, 2023
253 Dec 21, 2022
2 Dec 25, 2022
70 Aug 07, 2022
20.4k Jan 04, 2023
2 Feb 26, 2022