Yet another backtesting engine
Project description
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 and scipy.
Installation
pip install yatbe
Usage
Below is an example usage (the performance of the example strategy won't be good).
import inspect
from pathlib import Path
import pandas as pd
from yabte.backtest import Strategy, StrategyRunner, Order, Book
from yabte.utilities.strategy_helpers import crossover
data_dir = Path(inspect.getfile(Strategy)).parents[2] / "tests/data/nasdaq"
class SMAXO(Strategy):
def init(self):
# enhance data with simple moving averages
p = self.params
days_short = p.get("days_short", 10)
days_long = p.get("days_long", 20)
close_sma_short = (
self.data.loc[:, (slice(None), "Close")]
.rolling(days_short)
.mean()
.rename({"Close": "CloseSMAShort"}, axis=1, level=1)
)
close_sma_long = (
self.data.loc[:, (slice(None), "Close")]
.rolling(days_long)
.mean()
.rename({"Close": "CloseSMALong"}, axis=1, level=1)
)
self.data = pd.concat(
[self.data, close_sma_short, close_sma_long], axis=1
).sort_index(axis=1)
def on_close(self):
# create some orders
for symbol in ["GOOG", "MSFT"]:
df = self.data[symbol]
ix_2d = df.index[-2:]
data = df.loc[ix_2d, ("CloseSMAShort", "CloseSMALong")].dropna()
if len(data) == 2:
if crossover(data.CloseSMAShort, data.CloseSMALong):
self.orders.append(Order(asset_name=symbol, size=-100))
elif crossover(data.CloseSMALong, data.CloseSMAShort):
self.orders.append(Order(asset_name=symbol, 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])
# create a book with 100000 cash
book = Book(name="PrimaryBook", cash="100000")
# run our strategy
sr = StrategyRunner(
data=pd.concat([df_goog, df_msft], axis=1),
asset_meta=asset_meta,
strats=[SMAXO],
books=[book],
)
sr.run()
# see the trades or book history
th = sr.trade_history
bch = sr.book_history.loc[:, (slice(None), "cash")]
# plot the trades against book value
bvh = sr.book_history.loc[:, (slice(None), "value")].droplevel(axis=1, level=1)
ax = bvh.plot(title="Book Value History")
for symbol, scol, lcol in [("GOOG", "red", "green"), ("MSFT", "blue", "yellow")]:
long_ix = th.query(f"asset_name == '{symbol}' and quantity > 0").ts
short_ix = th.query(f"asset_name == '{symbol}' and quantity < 0").ts
bvh.loc[long_ix].rename(columns={"PrimaryBook": f"{symbol} Short"}).plot(color=scol, marker="v", markersize=5, linestyle="None", ax=ax)
bvh.loc[short_ix].rename(columns={"PrimaryBook": f"{symbol} Long"}).plot(color=lcol, marker="^", markersize=5, linestyle="None", ax=ax)
Examples
Jupyter notebook examples can be found under the notebooks folder.
Documentation
Documentation can be found on Read the Docs.
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