polarbt 0.1.0

Lightweight backtesting library built on polars

PolarBT

A lightweight, high-performance backtesting library for trading strategy development and optimization. Built on Polars for fast vectorized data processing with an event-driven execution loop for flexible strategy logic.

Features

• Hybrid architecture — vectorized preprocessing (Polars) + event-driven execution loop
• 30+ built-in indicators — SMA, EMA, RSI, MACD, Bollinger Bands, ATR, SuperTrend, ADX, and more
• Complete order system — market, limit, stop, stop-limit, bracket orders, day/GTC orders
• Risk management — stop-loss, take-profit, trailing stops, position size limits, drawdown stops
• Short selling — negative positions, borrow costs, position reversals
• Margin & leverage — configurable leverage, margin tracking, margin calls
• Commission models — percentage, fixed, maker/taker, volume-tiered, custom
• Position sizing — fixed, percent, fixed-risk, Kelly, volatility-based
• Multi-asset — pass a dict of DataFrames for portfolio strategies
• Parallel optimization — grid search, multi-objective Pareto, Bayesian optimization
• Walk-forward analysis — rolling and anchored train/test splits
• Advanced analysis — Monte Carlo simulation, look-ahead bias detection, permutation testing
• Visualization — interactive Plotly charts (price, equity, drawdown, trade markers, heatmaps)
• Data utilities — validation, cleaning, OHLCV resampling
• Optional TA-Lib integration — wrap any TA-Lib function into Polars expressions

Installation

pip install polarbt

Or with optional extras:

pip install polarbt[plotting]   # Plotly charts
pip install polarbt[talib]      # TA-Lib integration

Install from source:

git clone [e-mail]:nikkisora/PolarBT.git
cd PolarBT
pip install -e .

Quick Start

import polars as pl
from polarbt import Strategy, backtest
from polarbt import indicators as ind
from polarbt.core import BacktestContext


class SMACross(Strategy):
    def preprocess(self, df: pl.DataFrame) -> pl.DataFrame:
        return df.with_columns([
            ind.sma("close", 10).alias("sma_fast"),
            ind.sma("close", 30).alias("sma_slow"),
            ind.crossover("sma_fast", "sma_slow").alias("buy"),
            ind.crossunder("sma_fast", "sma_slow").alias("sell"),
        ])

    def next(self, ctx: BacktestContext) -> None:
        if ctx.row.get("buy"):
            ctx.portfolio.order_target_percent("asset", 1.0)
        elif ctx.row.get("sell"):
            ctx.portfolio.close_position("asset")


results = backtest(
    SMACross,
    pl.DataFrame({"close": [100 + i * 0.5 for i in range(200)]}),
    params={},
    initial_cash=100_000,
)

print(f"Return: {results['total_return']:.2%}")
print(f"Sharpe: {results['sharpe_ratio']:.2f}")

Examples

Example	Description
example.py	Basic SMA crossover
example_sma_crossover_stoploss.py	SMA crossover with ATR stop-loss and trailing stop
example_rsi_bracket_orders.py	RSI mean reversion with bracket orders
example_momentum_rotation.py	Multi-asset momentum rotation
example_ml_strategy.py	ML model integration
example_walk_forward.py	Walk-forward analysis workflow
example_advanced_analysis.py	Full workflow: optimization, heatmaps, Monte Carlo, permutation test
example_limit_orders.py	Limit orders and stop-loss
example_trade_analysis.py	Trade-level analysis
example_plotting.py	Interactive chart generation
example_commission.py	Commission model comparison
example_multi_asset.py	Multi-asset dict input

Documentation

• Getting Started Guide
• API Reference
• Full Feature Documentation

License

MIT