ml4t-backtest 0.1.0a6

State-of-the-art event-driven backtesting engine for quantitative trading

ml4t-backtest

Event-driven backtesting engine for quantitative trading strategies with realistic execution modeling.

Part of the ML4T Library Ecosystem

This library is one of five interconnected libraries supporting the machine learning for trading workflow described in Machine Learning for Trading:

Each library addresses a distinct stage: data infrastructure, feature engineering, signal evaluation, strategy backtesting, and live deployment.

What This Library Does

Backtesting requires accurate simulation of order execution, position tracking, and risk management. ml4t-backtest provides:

• Event-driven architecture with point-in-time correctness (no look-ahead bias)
• Exit-first order processing matching real broker behavior
• Configurable execution modes (same-bar or next-bar fills)
• Position-level risk rules (stop-loss, take-profit, trailing stops)
• Portfolio-level constraints (max positions, drawdown limits)
• Cash and margin account policies

The same Strategy class used in backtesting works unchanged in ml4t-live for production deployment.

Installation

pip install ml4t-backtest

Quick Start

from ml4t.backtest import Engine, Strategy, BacktestConfig, DataFeed
from ml4t.backtest.risk import StopLoss, TakeProfit, RuleChain

class TrendFollowing(Strategy):
    def __init__(self, fast=10, slow=30):
        self.fast = fast
        self.slow = slow

    def on_data(self, timestamp, data, context, broker):
        close = data["close"]
        fast_ma = close.rolling(self.fast).mean().iloc[-1]
        slow_ma = close.rolling(self.slow).mean().iloc[-1]

        position = broker.get_position("SPY")

        if fast_ma > slow_ma and position is None:
            broker.submit_order("SPY", quantity=100, side="BUY")
        elif fast_ma < slow_ma and position is not None:
            broker.close_position("SPY")

config = BacktestConfig(
    initial_cash=100_000,
    commission_rate=0.001,
)

feed = DataFeed(price_data)
engine = Engine(feed, TrendFollowing(), config)
result = engine.run()

print(f"Total Return: {result.total_return:.2%}")
print(f"Sharpe Ratio: {result.metrics['sharpe_ratio']:.2f}")

Risk Management

Position-level exit rules:

from ml4t.backtest.risk import StopLoss, TakeProfit, TrailingStop, RuleChain

class MyStrategy(Strategy):
    def on_start(self, broker):
        broker.set_position_rules(RuleChain([
            StopLoss(pct=0.05),
            TakeProfit(pct=0.15),
            TrailingStop(pct=0.03),
        ]))

Portfolio-level controls:

from ml4t.backtest.risk import MaxPositions, MaxDrawdown, DailyLossLimit

Execution Modes

from ml4t.backtest import ExecutionMode, StopFillMode

# Same-bar fills (VectorBT style)
config = BacktestConfig(
    execution_mode=ExecutionMode.SAME_BAR,
    stop_fill_mode=StopFillMode.STOP_PRICE,
)

# Next-bar fills (Backtrader style)
config = BacktestConfig(
    execution_mode=ExecutionMode.NEXT_BAR,
    stop_fill_mode=StopFillMode.STOP_PRICE,
)

Commission and Slippage

from ml4t.backtest import PercentCommission, PercentSlippage

config = BacktestConfig(
    commission_model=PercentCommission(rate=0.001),
    slippage_model=PercentSlippage(rate=0.0005),
)

Multi-Asset Support

class RankingStrategy(Strategy):
    def on_data(self, timestamp, data, context, broker):
        returns = data["close"].pct_change(20)
        ranked = returns.iloc[-1].sort_values(ascending=False)

        # Long top 10
        for asset in ranked.head(10).index:
            if broker.get_position(asset) is None:
                broker.submit_order(asset, quantity=100, side="BUY")

Validation

The library is validated against VectorBT Pro, Backtrader, and Zipline:

• 119,000+ trades verified trade-by-trade across frameworks
• 500 assets x 10 years (2,520 bars) stress testing
• 100% PnL match on common execution scenarios

See validation/README.md for test methodology.

Technical Characteristics

• Event-driven: Each bar processes sequentially with exit-first logic
• Point-in-time: No access to future data within strategy callbacks
• Configurable fills: Match behavior of different backtesting frameworks
• Parquet export: Results serializable for analysis with ml4t-diagnostic

Related Libraries

• ml4t-data: Market data acquisition and storage
• ml4t-engineer: Feature engineering and technical indicators
• ml4t-diagnostic: Signal evaluation and statistical validation
• ml4t-live: Live trading with broker integration

Development

git clone https://github.com/applied-ai/ml4t-backtest.git
cd ml4t-backtest
uv sync
uv run pytest tests/ -q
uv run ty check

Known Limitations

See LIMITATIONS.md for documented assumptions:

• Partial fills not supported (all-or-nothing)
• No intrabar stop simulation (uses bar OHLC)
• Calendar overnight sessions require configuration

License

MIT License - see LICENSE for details.