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https://github.com/bsdz/yabte
Yet another backtesting engine
https://github.com/bsdz/yabte
Last synced: 3 months ago
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Yet another backtesting engine
- Host: GitHub
- URL: https://github.com/bsdz/yabte
- Owner: bsdz
- License: mit
- Created: 2022-12-15T22:08:32.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-11T20:01:15.000Z (6 months ago)
- Last Synced: 2024-06-13T06:29:52.269Z (5 months ago)
- Language: Jupyter Notebook
- Size: 3.09 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-quant - YABTE - Yet Another (Python) BackTesting Engine. (Python / Trading & Backtesting)
README
# yabte - Yet Another BackTesting Engine
Python module for backtesting trading strategies.
Features
* Event driven, ie `on_open`, `on_close`, etc.
* Multiple assets.
* OHLC Asset. Extendable (e.g support additional fields, e.g. Volatility, or entirely different fields, e.g. Barrels per day).
* Multiple books.
* Positional and Basket orders. Extendible (e.g. can support stop loss).
* Batch runs (for optimization).
* Captures book history including transactions & daily cash, MtM and total values.
The module provides basic statistics like book cash, mtm and total value. Currently, everything else needs to be deferred to a 3rd party module like `empyrical`.
## Core dependencies
The core module uses pandas and scipy.
## Installation
```bash
pip install yatbe
```
## Usage
Below is an example usage (the economic performance of the example strategy won't be good).
```python
import pandas as pd
from yabte.backtest import Book, SimpleOrder, Strategy, StrategyRunner
from yabte.tests._helpers import generate_nasdaq_dataset
from yabte.utilities.plot.plotly.strategy_runner import plot_strategy_runner_result
from yabte.utilities.strategy_helpers import crossover
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(SimpleOrder(asset_name=symbol, size=-100))
elif crossover(data.CloseSMALong, data.CloseSMAShort):
self.orders.append(SimpleOrder(asset_name=symbol, size=100))
# load some data
assets, df_combined = generate_nasdaq_dataset()
# create a book with 100000 cash
book = Book(name="Main", cash="100000")
# run our strategy
sr = StrategyRunner(
data=df_combined,
assets=assets,
strategies=[SMAXO()],
books=[book],
)
srr = sr.run()
# see the trades or book history
th = srr.transaction_history
bch = srr.book_history.loc[:, (slice(None), "cash")]
# plot the trades against book value
plot_strategy_runner_result(srr, sr)
```
## Examples
Jupyter notebook examples can be found under the [notebooks folder](https://github.com/bsdz/yabte/tree/main/notebooks).
## Documentation
Documentation can be found on [Read the Docs](https://yabte.readthedocs.io/en/latest/).
## Development
Before commit run following format commands in project folder:
```bash
poetry run black .
poetry run isort . --profile black
poetry run docformatter . --recursive --in-place --black --exclude _unittest_numpy_extensions.py
```