# Manifold-BT

> Manifold-BT is a Python backtesting library with a Rust core for systematic trading research. It models execution realistically (fees, funding, slippage, partial fills, signal delay), runs backtests and large parameter sweeps in sub-second time, and ships an MCP server so AI agents can drive the engine. Install with `pip install manifoldbt`. Research software, not financial advice.

For Python developers and quants. Every strategy example is in the full index: https://www.manifoldbt.com/llms-full.txt.

## Documentation
- [Documentation: Python Backtesting API](https://www.manifoldbt.com/documentation): indicators, execution config, sweeps, walk-forward, Monte Carlo.
- [Interactive Python Backtesting Notebook](https://www.manifoldbt.com/notebook): runnable walkthrough: strategy, sweep, walk-forward, Monte Carlo.
- [Backtesting MCP Server for AI Agents](https://www.manifoldbt.com/mcp): drive the engine from Claude Code, Codex, and Cursor over MCP.
- [The Rust Backtesting Engine](https://www.manifoldbt.com/features/rust-backtesting-engine): how the engine runs a million bars in 22 ms on one core.
- [The Research Workflow](https://www.manifoldbt.com/features/research-workflow): parameter maps, walk-forward folds, Monte Carlo, look-ahead detection, manifest replay.
- [The Expression DSL](https://www.manifoldbt.com/features/expression-dsl): declare signals as composable expressions, 48 indicators, evaluated vectorized in Rust.
- [Multi-Asset Backtesting](https://www.manifoldbt.com/features/multi-asset-backtesting): cross-sectional ranking across a universe, cross-asset references, one shared account.
- [Execution Realism](https://www.manifoldbt.com/features/execution-realism): independent models for slippage, fees, funding, borrow, partial fills and signal delay.
- [GPU-Accelerated Backtesting in Python](https://www.manifoldbt.com/features/gpu-acceleration): CUDA parameter sweeps and Monte Carlo, bit-identical to the CPU path.

## Guides
- [What Is Backtesting?](https://www.manifoldbt.com/guide/what-is-backtesting): what it is, why it works, where it misleads.
- [How to Backtest a Trading Strategy](https://www.manifoldbt.com/guide/how-to-backtest-a-trading-strategy): from raw bars to a realistic backtest and tearsheet.
- [Algorithmic Trading in Python](https://www.manifoldbt.com/guide/algorithmic-trading-python): libraries, workflow, and a runnable parameter sweep.
- [Walk-Forward Optimization in Python](https://www.manifoldbt.com/guide/walk-forward-optimization-python): optimize in-sample, validate out-of-sample, fold by fold.

## Strategies
- [Python Trading Strategies, Backtested](https://www.manifoldbt.com/strategies): 15 strategies with runnable code and realistic backtests: mean reversion, momentum, breakout, MACD, pairs, and more.

## Comparisons
- [Manifold-BT vs vectorbt](https://www.manifoldbt.com/vs/vectorbt): event-driven Rust execution versus vectorized NumPy.
- [Manifold-BT vs Backtrader](https://www.manifoldbt.com/vs/backtrader): fast research engine versus a mature pure-Python framework.
- [Manifold-BT vs Freqtrade](https://www.manifoldbt.com/vs/freqtrade): realistic backtesting versus live crypto trading.
- [Manifold-BT vs RaptorBT](https://www.manifoldbt.com/vs/raptorbt): two Rust engines benchmarked; faster on parameter sweeps.

## Deep dives
- [Realistic Backtest Execution](https://www.manifoldbt.com/blog/realistic-backtest-execution): slippage, funding, partial fills, signal delay: why backtests lie.

## Company
- [About Manifold-BT](https://www.manifoldbt.com/about): why we built a Rust-powered Python backtesting engine.
- [Team: Managed Compute for Research Desks](https://www.manifoldbt.com/team): Pro for 5 seats, managed cloud compute, hosted data credits, spend caps.
- [Firm: Managed Research Infrastructure](https://www.manifoldbt.com/firm): hosted data, team licenses, support for trading firms.

## Optional
- [Terms](https://www.manifoldbt.com/terms)
- [Privacy](https://www.manifoldbt.com/privacy)