Polar BackTest

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
• 25+ 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
• Weight-based backtesting — declarative portfolio allocation with backtest_weights(), rebalance scheduling, stop-loss/take-profit, next-actions output
• 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 git@github.com:nikkisora/PolarBT.git
cd PolarBT
pip install -e .

Quick Start

import polars as pl
import yfinance as yf
from polarbt import Engine, Strategy
from polarbt import indicators as ind
from polarbt.core import BacktestContext
from polarbt.plotting import plot_backtest


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"),
        ).with_columns(
            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")


# Download data from Yahoo Finance
ticker = yf.download("AAPL", start="2016-01-01", end="2026-01-01", auto_adjust=True)
ticker = ticker.droplevel("Ticker", axis=1).reset_index()
data = pl.from_pandas(ticker)

# Run backtest
engine = Engine(SMACross(), data, commission=.005, initial_cash=100_000)
results = engine.run()

print(results)

# Interactive chart saved to HTML
fig = plot_backtest(engine, title="SMA Crossover — AAPL", indicators=["sma_fast", "sma_slow"])
fig.write_html("backtest.html")

Equity Final [$]                        366,236.83
Equity Peak [$]                         433,930.72
Return [%]                                  266.24
Buy & Hold Return [%]                      1044.52
Return (Ann.) [%]                            14.08
CAGR [%]                                     14.08
Volatility (Ann.) [%]                        19.78

Sharpe Ratio                                  0.76
Sortino Ratio                                 0.92
Calmar Ratio                                  0.44
Max. Drawdown [%]                           -32.16
Avg. Drawdown Duration [bars]                   38
Max. Drawdown Duration [bars]                  730

# Trades                                        42
Win Rate [%]                                 47.62
Best Trade [%]                               57.11
Worst Trade [%]                             -13.43
Avg. Trade [%]                                3.94
Max. Trade Duration [bars]                     128
Avg. Trade Duration [bars]                      39
Avg. Trade MDD [%]                           -8.78
Profit Factor                                 1.79
Expectancy [$]                             6338.97
SQN                                           1.27
Kelly Criterion                             0.2098

Weight-Based Backtesting

For portfolio allocation strategies, skip the event loop entirely — just supply target weights per (date, symbol):

import polars as pl
from polarbt import backtest_weights

# data: long-format DataFrame with columns date, symbol, close, weight
result = backtest_weights(
    data,
    resample="M",           # rebalance monthly
    resample_offset="2d",   # delay 2 trading days after month boundary
    fee_ratio=0.001,
    stop_loss=0.10,          # 10% per-position stop-loss
    position_limit=0.5,      # max 50% in any single name
    initial_capital=100_000,
)

print(result.metrics)        # standard BacktestMetrics
print(result.trades.head())  # per-trade log
print(result.next_actions)   # forward-looking rebalance actions

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
example_weight_backtest.py	Weight-based portfolio backtest

Documentation

• Getting Started Guide
• API Reference
• Complete Reference

License

MIT