rustybt 0.3.2

Modern Python backtesting engine built on Zipline-Reloaded, enhanced with Decimal precision, Polars data engine, and live trading capabilities

RustyBT

Modern Python backtesting engine built on Zipline-Reloaded, enhanced with Decimal precision, Polars data engine, and live trading capabilities

Overview

RustyBT is a next-generation Python-based algorithmic trading framework that extends Zipline-Reloaded with modern enhancements:

• Decimal Precision: Financial-grade arithmetic using Python's Decimal type for audit-compliant calculations
• Polars Data Engine: 5-10x faster data processing with lazy evaluation and efficient memory usage
• Parquet Storage: Industry-standard columnar format for OHLCV data (50-80% smaller than HDF5)
• Live Trading: Production-ready engine for executing strategies in real-time markets
• Modern Python: Requires Python 3.12+ for structural pattern matching and enhanced type hints

Key Differences from Zipline-Reloaded

Feature	Zipline-Reloaded	RustyBT
Numeric Type	float64	Decimal (configurable precision)
Data Engine	pandas	polars (pandas compatible)
Storage Format	bcolz/HDF5	Parquet (Arrow-based)
Python Version	3.10+	3.12+
Live Trading	No	Yes (multiple brokers)
Performance	Baseline	Optimized (Rust modules planned)

Installation

Prerequisites

• Python 3.12 or higher
• pip (included with Python)

From PyPI (Recommended)

Install the latest stable release from PyPI:

# Install RustyBT
pip install rustybt

# Install with all optional features (recommended for most users)
pip install rustybt[full]          # All user-facing features (optimization + benchmarks)

# Or install specific features
pip install rustybt[optimization]  # Strategy optimization tools
pip install rustybt[benchmarks]    # Performance profiling tools
pip install rustybt[dev]           # Development tools (for contributors)
pip install rustybt[test]          # Testing tools (for contributors)

Available extras:

• full / all - All optional features (optimization + benchmarks) - ~500MB
• optimization - Strategy optimization (scikit-learn, genetic algorithms, ray)
• benchmarks - Performance profiling (pytest-benchmark, memory_profiler, snakeviz)
• dev - Development tools (jupyter, jupyterlab, ruff, mypy, black, type stubs)
• test - Testing tools (pytest, hypothesis, coverage)
• docs - Documentation generation (MkDocs with Material theme)

From Source (Development)

For contributors or those who want the latest development version:

# Clone the repository
git clone https://github.com/jerryinyang/rustybt.git
cd rustybt

# Using uv (recommended for development)
uv sync --extra dev --extra test

# Or using pip
python3.12 -m venv .venv
source .venv/bin/activate  # On Unix/macOS
# or .venv\Scripts\activate on Windows
pip install -e ".[dev,test]"

Quick Start

RustyBT maintains backward compatibility with Zipline API:

from rustybt.api import order_target, record, symbol

def initialize(context):
    context.i = 0
    context.asset = symbol('AAPL')

def handle_data(context, data):
    # Skip first 300 days to get full windows
    context.i += 1
    if context.i < 300:
        return

    # Compute averages
    short_mavg = data.history(context.asset, 'price', bar_count=100, frequency="1d").mean()
    long_mavg = data.history(context.asset, 'price', bar_count=300, frequency="1d").mean()

    # Trading logic
    if short_mavg > long_mavg:
        order_target(context.asset, 100)
    elif short_mavg < long_mavg:
        order_target(context.asset, 0)

    # Save values for later inspection
    record(AAPL=data.current(context.asset, 'price'),
           short_mavg=short_mavg,
           long_mavg=long_mavg)

Run the backtest:

# First, ingest sample data (only needed once)
rustybt ingest -b yfinance-profiling

# Run the backtest
rustybt run -f strategy.py -b yfinance-profiling --start 2024-01-01 --end 2025-09-30

Key Features

Decimal Precision (Epic 2)

from decimal import Decimal
from rustybt.finance.decimal import DecimalLedger

# Financial calculations with audit-compliant precision
ledger = DecimalLedger(starting_cash=Decimal("100000.00"))

Modern Data Architecture (Epic 3)

import polars as pl
from rustybt.data.adapters import YFinanceAdapter, CCXTAdapter

# Multiple data sources with intelligent caching
yf_adapter = YFinanceAdapter()
crypto_adapter = CCXTAdapter(exchange_id='binance')

# Fast data processing with Parquet storage
data = pl.read_parquet("ohlcv_data.parquet")

Enhanced Transaction Costs (Epic 4)

from rustybt.finance.slippage import VolumeShareSlippage
from rustybt.finance.commission import TieredCommission

# Realistic transaction cost modeling
slippage = VolumeShareSlippage(volume_limit=0.025)
commission = TieredCommission(tiers=[(0, 0.001), (100000, 0.0005)])

Multi-Strategy Portfolio Management (Epic 4)

from rustybt.portfolio import PortfolioAllocator
from rustybt.portfolio.allocation import RiskParityAllocator

# Manage multiple strategies with intelligent allocation
allocator = PortfolioAllocator(
    strategies=[strategy1, strategy2, strategy3],
    allocation_algorithm=RiskParityAllocator()
)

Strategy Optimization (Epic 5)

from rustybt.optimization import GridSearchOptimizer, BayesianOptimizer

# Optimize strategy parameters with multiple algorithms
optimizer = BayesianOptimizer(
    param_space={'fast_ma': (10, 50), 'slow_ma': (50, 200)},
    n_iterations=100
)
results = optimizer.optimize(strategy)

Live Trading (Epic 6)

from rustybt.live import LiveTradingEngine
from rustybt.live.brokers import CCXTBrokerAdapter

# Connect to exchange for live trading with multiple brokers
broker = CCXTBrokerAdapter(
    exchange_id='binance',
    api_key='YOUR_API_KEY',
    api_secret='YOUR_API_SECRET',
    testnet=True,
)
engine = LiveTradingEngine(strategy=my_strategy, broker_adapter=broker)
engine.run()

Development

See CONTRIBUTING.md for development setup and contribution guidelines.

Running Tests

# Run full test suite
pytest tests/ -v

# Run with coverage
pytest tests/ --cov=rustybt --cov-report=html

# Run specific test file
pytest tests/finance/test_decimal_ledger.py

Code Quality

# Format code
black rustybt/ tests/

# Lint code
ruff check rustybt/ tests/

# Type check
mypy rustybt/ --strict

Architecture

RustyBT maintains Zipline's proven architecture while adding new capabilities:

rustybt/
├── finance/          # Financial calculations (Decimal-based)
│   └── decimal/      # Decimal arithmetic modules
├── data/             # Data management (Polars-based)
│   ├── polars/       # Polars data layer
│   └── adapters/     # Data source adapters
├── live/             # Live trading engine
│   ├── brokers/      # Broker adapters
│   └── streaming/    # Real-time data feeds
├── assets/           # Asset management (extended for crypto)
├── pipeline/         # Pipeline framework (Polars-compatible)
└── algorithm.py      # TradingAlgorithm (extended)

Acknowledgments

RustyBT is built on the shoulders of giants:

• Zipline: Original backtesting library by Quantopian
• Zipline-Reloaded: Maintained fork by Stefan Jansen
• Machine Learning for Algorithmic Trading: Comprehensive guide by Stefan Jansen

We are grateful to the Quantopian team, Stefan Jansen, and the entire open-source algorithmic trading community.

License

RustyBT is licensed under the Apache License, Version 2.0. See LICENSE for details.

This project incorporates code from Zipline and Zipline-Reloaded, both licensed under Apache 2.0.

Known License Limitations

While RustyBT follows an Apache 2.0/MIT-only dependency policy, the following LGPL dependencies currently exist as transitive dependencies:

Package	License	Type	Source	Status
frozendict	LGPL v3	Production	Via yfinance (Yahoo Finance data)	Tracked for replacement
chardet	LGPL	Development	Via tox (testing tool)	Tracked for replacement

Legal Status: LGPL allows use as a dynamically-linked library without GPL license contamination. RustyBT's Apache 2.0 license remains unaffected.

Mitigation Plans:

• frozendict: Evaluating alternative data providers or forking yfinance with MIT-licensed replacement
• chardet: Considering migration from tox to modern alternatives (nox, hatch)

For details on dependency security and license compliance, see docs/security-audit.md.

Documentation

• Full Documentation: https://jerryinyang.github.io/rustybt/ (GitHub Pages)
• ReadTheDocs: https://rustybt.readthedocs.io/ (Alternative)
• Architecture Docs: docs/architecture/index.md
• User Guides: docs/guides/

Community

• Issues: GitHub Issues
• Discussions: GitHub Discussions

Roadmap

Completed ✅

• Epic 1: Project setup and architecture foundations
• Epic 2: Decimal precision financial calculations
• Epic 3: Modern data architecture (Polars/Parquet with multiple adapters)
• Epic 4: Enhanced transaction costs and multi-strategy portfolio management
• Epic 5: Strategy optimization framework (Grid, Bayesian, Genetic, Walk-Forward)
• Epic 6: Live trading engine with broker integrations (CCXT, IB, Binance, Bybit, Hyperliquid)
• Epic 8: Analytics and production readiness (reporting, attribution, risk analytics)

In Progress 🚧

• Epic 7: Rust performance optimizations (baseline benchmarks complete)
• Epic X2: Production readiness validation and remediation

Planned 📋

• Epic 9: REST API and WebSocket interface
• v1.0.0: Production-ready stable release

See docs/prd/epic-list.md for detailed epic descriptions and stories.

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Note: RustyBT is under active development. APIs may change until version 1.0.0.