yabte 0.1.2

Yet another backtesting engine

yabte - Yet Another BackTesting Engine

Python module for backtesting trading strategies.

Support event driven backtesting, ie on_open, on_close, etc. Also supports multiple assets.

Very basic statistics like book cash, mtm and total value. Currently, everything else needs to be deferred to a 3rd party module like empyrical.

There are some basic tests but use at your own peril. It's not production level code.

Core dependencies

The core module uses pandas.

Installation

pip install yatbe

Usage

Below is an example usage (the performance of the example strategy won't be good).

from pathlib import Path
import pandas as pd

import yabte
from yabte import Strategy, StrategyRunner, Order
from yabte.utils import crossover

data_dir = Path(yabte.__file__).parents[1] / "tests/data"


class SmokeStrat1(Strategy):
    def init(self):
        # enhance data with simple moving averages
        csma10 = (
            self.data.loc[:, (slice(None), "Close")]
            .rolling(10)
            .mean()
            .rename({"Close": "CloseSMA10"}, axis=1, level=1)
        )
        csma20 = (
            self.data.loc[:, (slice(None), "Close")]
            .rolling(20)
            .mean()
            .rename({"Close": "CloseSMA20"}, axis=1, level=1)
        )
        self.data = pd.concat([self.data, csma10, csma20], axis=1).sort_index(axis=1)

    def on_close(self):
        # create some orders
        data_2d = self.data.iloc[-2:]
        for sym in ["GOOG", "MSFT"]:
            data = data_2d[sym].loc[:, ("CloseSMA10", "CloseSMA20")].dropna()
            if len(data) == 2:
                if crossover(data.CloseSMA10, data.CloseSMA20):
                    self.orders.append(Order(asset_name=sym, size=100))
                elif crossover(data.CloseSMA20, data.CloseSMA10):
                    self.orders.append(Order(asset_name=sym, size=-100))


# load some data
asset_meta = {"GOOG": {"denom": "USD"}, "MSFT": {"denom": "USD"}}

df_goog = pd.read_csv(data_dir / "GOOG.csv", index_col=0, parse_dates=[0])
df_goog.columns = pd.MultiIndex.from_tuples([("GOOG", f) for f in df_goog.columns])

df_msft = pd.read_csv(data_dir / "MSFT.csv", index_col=0, parse_dates=[0])
df_msft.columns = pd.MultiIndex.from_tuples([("MSFT", f) for f in df_msft.columns])

# run our strategy
sr = StrategyRunner(
    data=pd.concat([df_goog, df_msft], axis=1),
    asset_meta=asset_meta,
    strats=[SmokeStrat1],
)
sr.run()

# see the trades or book history
th = sr.trade_history
bch = sr.book_history.loc[:, (slice(None), "cash")]