Python framework optimized for running backtests on multiple assetss
Project description
btester - Trading Strategy Backtesting Framework
btester
is a Python framework optimized for running backtests on multiple assets and supports full portfolio backtesting. It provides tools for backtesting trading strategies based on historical market data. The framework includes classes for managing financial positions, completed trades, and a flexible abstract base class for implementing custom trading strategies.
Installation
You can install btester
using pip. Simply run the following command:
pip install btester
Usage
-
Define your custom trading strategy by creating a class that inherits from the
Strategy
abstract class. -
Implement the required methods in your custom strategy:
init
for initialization andnext
for the core strategy logic. -
Instantiate the
Backtest
class with your custom strategy, historical market data, and other parameters. -
Run the backtest using the
run
method, which returns aResult
object containing backtest results.
Classes
Position
Class
Represents an open financial position with attributes such as symbol, open date, open price, and current value. It allows for updating the position based on the latest market data.
Trade
Class
Represents a completed financial transaction, including details like symbol, open and close dates, open and close prices, position size, profit/loss, and trade commission.
Result
Class
A container class holding the results of a backtest, including returns, a list of trades, and a list of open positions.
Strategy
Class (Abstract Base Class)
An abstract base class that defines the structure for implementing trading strategies. It includes methods for initialization, the core strategy logic, opening positions, and closing positions.
Backtest
Class
Responsible for running a backtest on a given strategy using historical market data.
Example Usage
# Example usage of the btester
from btester import Strategy, Backtest
import pandas as pd
# Define a custom strategy by inheriting from the abstract Strategy class
class MyStrategy(Strategy):
def init(self):
# Custom initialization logic for the strategy
pass
def next(self, i: int, record: Dict[Hashable, Any]):
# Custom strategy logic for each time step
pass
# Load historical market data
data = pd.read_csv('historical_data.csv', parse_dates=['Date'])
data.set_index('Date', inplace=True)
# Initialize and run the backtest
backtest = Backtest(strategy=MyStrategy, data=data, cash=10000, commission=0.01)
result = backtest.run()
# Access backtest results
returns_series = result.returns
completed_trades = result.trades
remaining_positions = result.open_positions
Examples
Check out the examples in the examples
directory for additional use cases and demonstrations. The examples cover various scenarios and strategies to help you understand the versatility of the btester
.
- Example 1: Multiple Assets Breakout Strategy
- Example 2: Multiple Assets Moving Average Crossover Strategy
- Example 3: Single Asset Breakout Strategy
- Example 4: Single Asset Moving Average Crossover Strategy
Feel free to explore and adapt these examples to suit your specific needs and trading strategies.
Project details
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