Algorithmic Trading Framework

A Python framework for backtesting and optimizing trading strategies with support for multiple assets, long/short positions, margin trading, and comprehensive performance analytics.

Features

• Multi-Asset Support: Track and trade multiple assets simultaneously.
• Backtesting Engine: Full-featured backtester with support for:
  • Long and short positions
  • Margin trading and margin calls
  • Stop loss and take profit orders
  • Commission and slippage modeling
• Optimization: Parameter optimization using Optuna with persistent storage.
• Performance Metrics: Sharpe ratio, Sortino ratio, Calmar ratio, profit factor, and more.
• Visualization: Equity curve plotting and detailed trade logs.

Installation

pip install -r requirements.txt

Quick Start

from core import Backtester
from strategies import BuyAndHold
from utils import data_downloader

# Download data
data = data_downloader.download("SPY", start_date="2020-01-01", end_date="2023-12-31")

# Initialize strategy and backtester
strategy = BuyAndHold(tickers="SPY", data={"SPY": data})
backtester = Backtester(
    strategy=strategy,
    initial_cash=10000.0,
    commission=0.001,
    slippage=0.0005
)

# Run backtest
backtester.run_backtest()
backtester.print_performance_metrics()
backtester.plot_equity_curve()

Creating Custom Strategies

Extend the Strategy base class and implement the compute_signals() method:

from core import Strategy
from utils import Signal

class MyStrategy(Strategy):
    def compute_signals(self):
        # Your strategy logic here
        for timestamp in self.index:
            # Set signals and allocations
            self.signals["TICKER"].loc[timestamp, "Signal"] = Signal.LONG
            self.signals["TICKER"].loc[timestamp, "Allocation"] = 1.0