# 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 publishes a hosted MCP documentation endpoint so AI agents can look up the authoring reference before writing a strategy. Install with `pip install manifoldbt`. Research software, not financial advice. The complete link index. For a shorter, curated version see https://www.manifoldbt.com/llms.txt. ## Documentation - [Documentation: Python Backtesting API](https://www.manifoldbt.com/documentation): strategy authoring reference: indicators, execution config, parameter sweeps, walk-forward optimization, Monte Carlo analysis. - [Interactive Python Backtesting Notebook](https://www.manifoldbt.com/notebook): step-by-step walkthrough: strategy definition, parameter sweep, walk-forward validation, Monte Carlo analysis. - [Backtesting MCP Server for AI Agents](https://www.manifoldbt.com/mcp): official Model Context Protocol server to drive the Rust engine from Claude Code, Codex, Cursor, and other MCP clients. - [The Rust Backtesting Engine](https://www.manifoldbt.com/features/rust-backtesting-engine): A million bars in 22 ms on one core, a grid swept 25x faster than the same backtests one at a time, and where that advantage stops. - [The Expression DSL](https://www.manifoldbt.com/features/expression-dsl): Signals declared as composable expressions and evaluated vectorized in Rust: 104 indicators, 38 candlestick patterns, 74 methods, cross-asset references, parameters that sweep. - [The Research Workflow](https://www.manifoldbt.com/features/research-workflow): Parameter maps, stability scores, walk-forward folds, Monte Carlo resampling and look-ahead detection, each answering a question one backtest cannot. - [Execution Realism](https://www.manifoldbt.com/features/execution-realism): Slippage, fees, funding, borrow, partial fills and decision-to-fill delay, each an independent model rather than one flat cost assumption. - [Multi-Asset Backtesting](https://www.manifoldbt.com/features/multi-asset-backtesting): Rank a universe on every bar and trade the cross-section: nine perpetuals in one call, one shared account, portfolio-level exposure and fees. - [GPU Acceleration](https://www.manifoldbt.com/features/gpu-acceleration): Parameter sweeps and Monte Carlo on your own NVIDIA GPU, bit-identical to the CPU path, and the measured grid size below which the CPU still wins. - [Options Backtesting](https://www.manifoldbt.com/features/options-backtesting): Contracts that expire, cash-settle at intrinsic value, and post margin under a named model: a real multi-leg spread, held to expiry, on live Deribit data. ## Benchmarks - [Benchmark Results: Manifold-BT vs vectorbt and RaptorBT](https://www.manifoldbt.com/benchmarks): the full result of a public GitHub Actions run, every engine installed from PyPI: single-backtest latency, parameter sweep wall time and memory, cold start, and the parity check each timing is gated on. Figures live on the page, which tracks the latest run; quoting one here would go stale between runs. - [Raw benchmark data](https://www.manifoldbt.com/benchmarks.json): the exact JSON artifact that run produced, with the run URL, commit, engine versions and runner hardware it came from. ## Guides - [What Is Backtesting?](https://www.manifoldbt.com/guide/what-is-backtesting): What backtesting is, why it works, where it lies to you, and how to do it honestly in Python. - [How to Backtest a Trading Strategy](https://www.manifoldbt.com/guide/how-to-backtest-a-trading-strategy): A step-by-step Python walkthrough: from raw bars to a realistic backtest and a tearsheet. - [Algorithmic Trading in Python](https://www.manifoldbt.com/guide/algorithmic-trading-python): The libraries, the workflow, and a runnable parameter sweep to research systematic strategies. - [Walk-Forward Optimization in Python](https://www.manifoldbt.com/guide/walk-forward-optimization-python): Optimize in-sample, validate out-of-sample, fold by fold, the honest way to tune parameters. ## Strategies - [Python Trading Strategies, Backtested](https://www.manifoldbt.com/strategies): index of every strategy below, each with runnable Python code and a realistic backtest. - [Mean Reversion in Python: Strategy & Backtest](https://www.manifoldbt.com/strategies/mean-reversion-python): Build and backtest a mean reversion strategy in Python: z-score entries, realistic costs, and a tearsheet, powered by the Manifold-BT Rust engine. - [Momentum Strategy in Python (Backtested)](https://www.manifoldbt.com/strategies/momentum-strategy-python): Code and backtest a momentum strategy in Python: ride trends with a rolling-return signal, realistic execution, and a tearsheet, on the Manifold-BT engine. - [Breakout Strategy in Python (Backtested)](https://www.manifoldbt.com/strategies/breakout-strategy-python): Build a breakout strategy in Python with Donchian-style range logic, then backtest it with realistic costs and a tearsheet on the Manifold-BT Rust engine. - [VWAP Strategy in Python (Backtested)](https://www.manifoldbt.com/strategies/vwap-strategy-python): Code a VWAP strategy in Python: trade around the volume-weighted average price, backtested with realistic intraday costs on the Manifold-BT Rust engine. - [MACD Trading Strategy in Python (Backtested)](https://www.manifoldbt.com/strategies/macd-strategy-python): Build a MACD trading strategy in Python: signal-line crossovers, realistic costs, and a tearsheet, backtested on the Manifold-BT Rust engine. - [Moving Average Crossover in Python](https://www.manifoldbt.com/strategies/moving-average-crossover-python): Code the classic moving average crossover in Python (golden cross), then backtest it with realistic costs and a tearsheet on the Manifold-BT Rust engine. - [Grid Trading in Python (Backtested)](https://www.manifoldbt.com/strategies/grid-trading-python): Build a grid trading strategy in Python that ladders into positions as price moves, backtested with realistic costs on the Manifold-BT Rust engine. - [Market Making in Python (Backtested)](https://www.manifoldbt.com/strategies/market-making-python): A market making strategy in Python that fades deviations from micro fair value, backtested with realistic costs on the Manifold-BT Rust engine. - [Pairs Trading in Python (Backtested)](https://www.manifoldbt.com/strategies/pairs-trading-python): Build a pairs trading strategy in Python: trade the ETH/BTC spread with z-score entries, backtested with realistic costs on the Manifold-BT Rust engine. - [Crypto Arbitrage in Python (Backtested)](https://www.manifoldbt.com/strategies/crypto-arbitrage-python): Build a crypto arbitrage strategy in Python: trade the cross-exchange basis between two venues, backtested with realistic costs on the Manifold-BT engine. - [Scalping Strategy in Python (Backtested)](https://www.manifoldbt.com/strategies/scalping-strategy-python): Build a scalping strategy in Python: fast EMA signals with tight stops on one-minute bars, backtested with realistic costs on the Manifold-BT Rust engine. - [RSI Strategy in Python (Backtested)](https://www.manifoldbt.com/strategies/rsi-strategy-python): Build and backtest an RSI strategy in Python: oversold entries with realistic costs and a tearsheet, on the Manifold-BT Rust engine. - [Swing Trading in Python (Backtested)](https://www.manifoldbt.com/strategies/swing-trading-python): Build a swing trading strategy in Python: trend-filtered entries on daily bars, backtested with realistic costs on the Manifold-BT Rust engine. - [Intraday Trading in Python (Backtested)](https://www.manifoldbt.com/strategies/intraday-trading-python): Build an intraday trading strategy in Python: a VWAP and EMA filter on 5-minute bars, backtested with realistic costs on the Manifold-BT engine. - [Trend Following in Python (Backtested)](https://www.manifoldbt.com/strategies/trend-following-python): Build a trend following strategy in Python: a long/short Donchian breakout, backtested with realistic costs on the Manifold-BT Rust engine. ## Comparisons - [Manifold-BT vs vectorbt](https://www.manifoldbt.com/vs/vectorbt): Rust engine with vectorized signals and a sequential fills pass, versus vectorized NumPy: speed, realism, path-dependent logic. - [Manifold-BT vs Backtrader](https://www.manifoldbt.com/vs/backtrader): A fast, realistic research engine versus a mature pure-Python framework, and when each fits. - [Manifold-BT vs Freqtrade](https://www.manifoldbt.com/vs/freqtrade): An honest comparison: fast, realistic backtesting versus live crypto trading, and when to use each. - [Manifold-BT vs RaptorBT](https://www.manifoldbt.com/vs/raptorbt): Two Rust engines, benchmarked head to head: both fast on one call, but Manifold-BT is 25x faster on the parameter sweeps that validate a strategy. - [Manifold-BT vs NautilusTrader](https://www.manifoldbt.com/vs/nautilustrader): A research backtesting library with fast parameter sweeps and GPU, versus a full event-driven platform built for live trading and backtest-to-live parity. ## Deep dives - [Realistic Backtest Execution](https://www.manifoldbt.com/blog/realistic-backtest-execution): Slippage, market impact, funding, partial fills, and signal delay: why most backtests lie, and how to fix it. ## Company - [About Manifold-BT](https://www.manifoldbt.com/about): why we built a Rust-powered Python backtesting engine for systematic trading research. - [Team: Managed Compute for Research Desks](https://www.manifoldbt.com/team): everything in Pro for 5 seats, managed cloud compute, hosted data credits (Databento, Massive), usage dashboard and spend caps. - [Firm: Managed Research Infrastructure](https://www.manifoldbt.com/firm): hosted market data, team licenses, custom integrations, and priority support for trading firms. ## Optional - [Terms](https://www.manifoldbt.com/terms): terms of service. - [Privacy](https://www.manifoldbt.com/privacy): privacy policy.