+ ManifoldBT
+ Rust-powered backtesting engine for quantitative research
+
+ Website · + Documentation · + Examples +
-ManifoldBT is a high-performance backtesting framework with a Python DSL that compiles strategies into an optimized Rust expression graph. It is designed for speed, correctness, and ergonomics. +--- -## Highlights +ManifoldBT compiles Python strategy definitions into an optimized Rust expression graph. +Write strategies in a fluent Python DSL — execute them on a vectorized Rust engine. -- **Rust core** — vectorized engine handles 1-minute resolution across years of data -- **Python DSL** — fluent strategy builder with indicators, signals, and sizing -- **Monte Carlo** — permutation-based simulation for robustness testing -- **Walk-Forward** — out-of-sample validation with rolling windows -- **Parameter Sweeps** — 2D heatmaps and 3D surface plots -- **Portfolio** — multi-strategy portfolio with risk rules and rebalancing +## Why ManifoldBT + +- **Fast** — 500K bars in ~26ms. 161x faster than vectorbt, 1000x+ faster than backtrader. +- **Expressive** — fluent DSL with 30+ indicators, conditional logic, cross-asset references +- **Rigorous** — Monte Carlo, walk-forward, parameter sweeps, lookahead detection, exposure diagnostics +- **Portable** — `pip install`, no Rust toolchain needed. Works on Python 3.9+. ## Installation @@ -19,7 +27,7 @@ ManifoldBT is a high-performance backtesting framework with a Python DSL that co pip install manifoldbt ``` -With plotting support: +With all extras (plotting, pandas, polars): ```bash pip install manifoldbt[all] @@ -32,11 +40,9 @@ import manifoldbt as mbt from manifoldbt.indicators import close, ema from manifoldbt.helpers import time_range, Interval, Slippage -# Define indicators fast = ema(close, 12) slow = ema(close, 26) -# Build strategy strategy = ( mbt.Strategy.create("ema_crossover") .signal("fast", fast) @@ -45,8 +51,8 @@ strategy = ( .size(mbt.col("signal") * mbt.lit(0.25)) ) -# Configure backtest start, end = time_range("2022-01-01", "2025-01-01") + config = mbt.BacktestConfig( universe=[1], time_range_start=start, @@ -59,7 +65,6 @@ config = mbt.BacktestConfig( warmup_bars=30, ) -# Run store = mbt.DataStore(data_root="data", metadata_db="metadata/metadata.sqlite") result = mbt.run(strategy, config, store) print(result.summary()) @@ -67,34 +72,48 @@ print(result.summary()) ## Examples -See the [examples/](examples/) directory for complete runnable strategies: - -| # | Example | Description | -|---|---------|-------------| +| # | Example | What it shows | +|---|---------|---------------| | 00 | [Template](examples/00_template.py) | Minimal starting point | -| 01 | [Trend Following](examples/01_trend_following.py) | EMA crossover with stop-loss and volume filter | +| 01 | [Trend Following](examples/01_trend_following.py) | EMA crossover, volume filter, stop-loss | | 02 | [Mean Reversion](examples/02_mean_reversion.py) | EMA crossover with parameter sweep | -| 03 | [Multi-Asset Momentum](examples/03_multi_asset_momentum.py) | Cross-asset momentum signals | +| 03 | [Multi-Asset Momentum](examples/03_multi_asset_momentum.py) | Cross-asset signals | | 04 | [Linear Regression](examples/04_linear_regression.py) | Regression-based signal | -| 05 | [Statistical Arbitrage](examples/05_stat_arb.py) | Pairs trading with spread z-score | -| 06 | [Full Visualization](examples/06_full_visualization.py) | Complete tearsheet and charts | +| 05 | [Statistical Arbitrage](examples/05_stat_arb.py) | Pairs trading, spread z-score | +| 06 | [Full Visualization](examples/06_full_visualization.py) | Tearsheet and charts | | 07 | [Walk-Forward](examples/07_walk_forward.py) | Out-of-sample validation | -| 08 | [2D Sweep Heatmap](examples/08_sweep_2d_heatmap.py) | Parameter grid search | -| 09 | [3D Surface](examples/09_surface_3d.py) | 3D parameter surface plot | +| 08 | [2D Sweep](examples/08_sweep_2d_heatmap.py) | Parameter grid heatmap | +| 09 | [3D Surface](examples/09_surface_3d.py) | Parameter surface plot | | 10 | [Monte Carlo](examples/10_monte_carlo.py) | Permutation-based robustness | | 11 | [Portfolio](examples/11_portfolio.py) | Multi-strategy portfolio | -## Documentation - -- [Strategy Authoring Guide](docs/strategy-authoring.md) — full DSL reference - ## Performance -Run the benchmark yourself: +EMA(12/26) + RSI(14) on 500K synthetic 1-min bars (median of 5 runs): -```bash -python benchmarks/bench_vs_competitors.py --rows 500000 --runs 5 -``` +| Engine | Time | vs ManifoldBT | +|--------|------|---------------| +| **ManifoldBT** (Rust) | **26 ms** | 1x | +| vectorbt (NumPy) | 4,094 ms | 161x slower | +| backtrader (Python) | — | ~1000x slower | + +Reproduce: `python benchmarks/bench_vs_competitors.py --rows 500000 --runs 5` + +## Documentation + +Full API reference, indicator list, configuration guide, and best practices: + +**[rustbt.vercel.app/docs/documentation.html](https://rustbt.vercel.app/docs/documentation.html)** + +## Community vs Pro + +| | Community | Pro | +|---|---|---| +| Output resolution | Daily | 1m, 5m, 15m, 1h | +| Monte Carlo | 1K sims | Unlimited | +| Walk-Forward | - | Anchored + Rolling | +| Parameter Stability | - | Yes | +| Data Connectors | - | Binance, Polygon, DataBento | ## License