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121 lines
3.9 KiB
Markdown
121 lines
3.9 KiB
Markdown
<p align="center">
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<strong>ManifoldBT</strong><br>
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Rust-powered backtesting engine for quantitative research
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</p>
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<p align="center">
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<a href="https://rustbt.vercel.app">Website</a> ·
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<a href="https://rustbt.vercel.app/docs/documentation.html">Documentation</a> ·
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<a href="examples/">Examples</a>
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</p>
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---
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ManifoldBT compiles Python strategy definitions into an optimized Rust expression graph.
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Write strategies in a fluent Python DSL — execute them on a vectorized Rust engine.
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## Why ManifoldBT
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- **Fast** — 500K bars in ~26ms. 161x faster than vectorbt, 1000x+ faster than backtrader.
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- **Expressive** — fluent DSL with 30+ indicators, conditional logic, cross-asset references
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- **Rigorous** — Monte Carlo, walk-forward, parameter sweeps, lookahead detection, exposure diagnostics
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- **Portable** — `pip install`, no Rust toolchain needed. Works on Python 3.9+.
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## Installation
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```bash
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pip install manifoldbt
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```
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With all extras (plotting, pandas, polars):
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```bash
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pip install manifoldbt[all]
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```
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## Quick Start
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```python
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import manifoldbt as mbt
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from manifoldbt.indicators import close, ema
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from manifoldbt.helpers import time_range, Interval, Slippage
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fast = ema(close, 12)
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slow = ema(close, 26)
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strategy = (
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mbt.Strategy.create("ema_crossover")
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.signal("fast", fast)
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.signal("slow", slow)
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.signal("signal", mbt.when(fast > slow, mbt.lit(1.0), mbt.lit(-1.0)))
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.size(mbt.col("signal") * mbt.lit(0.25))
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)
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start, end = time_range("2022-01-01", "2025-01-01")
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config = mbt.BacktestConfig(
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universe=[1],
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time_range_start=start,
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time_range_end=end,
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bar_interval=Interval.hours(12),
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initial_capital=10_000,
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execution=mbt.ExecutionConfig(allow_short=True, max_position_pct=0.5),
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fees=mbt.FeeConfig.binance_perps(),
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slippage=Slippage.fixed_bps(2),
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warmup_bars=30,
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)
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store = mbt.DataStore(data_root="data", metadata_db="metadata/metadata.sqlite")
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result = mbt.run(strategy, config, store)
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print(result.summary())
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```
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## Examples
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| # | Example | What it shows |
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|---|---------|---------------|
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| 00 | [Template](examples/00_template.py) | Minimal starting point |
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| 01 | [Trend Following](examples/01_trend_following.py) | EMA crossover, volume filter, stop-loss |
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| 02 | [Mean Reversion](examples/02_mean_reversion.py) | EMA crossover with parameter sweep |
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| 03 | [Multi-Asset Momentum](examples/03_multi_asset_momentum.py) | Cross-asset signals |
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| 04 | [Linear Regression](examples/04_linear_regression.py) | Regression-based signal |
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| 05 | [Statistical Arbitrage](examples/05_stat_arb.py) | Pairs trading, spread z-score |
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| 06 | [Full Visualization](examples/06_full_visualization.py) | Tearsheet and charts |
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| 07 | [Walk-Forward](examples/07_walk_forward.py) | Out-of-sample validation |
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| 08 | [2D Sweep](examples/08_sweep_2d_heatmap.py) | Parameter grid heatmap |
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| 09 | [3D Surface](examples/09_surface_3d.py) | Parameter surface plot |
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| 10 | [Monte Carlo](examples/10_monte_carlo.py) | Permutation-based robustness |
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| 11 | [Portfolio](examples/11_portfolio.py) | Multi-strategy portfolio |
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## Performance
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EMA(12/26) + RSI(14) on 500K synthetic 1-min bars (median of 5 runs):
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| Engine | Time | vs ManifoldBT |
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|--------|------|---------------|
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| **ManifoldBT** (Rust) | **26 ms** | 1x |
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| vectorbt (NumPy) | 4,094 ms | 161x slower |
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| backtrader (Python) | — | ~1000x slower |
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Reproduce: `python benchmarks/bench_vs_competitors.py --rows 500000 --runs 5`
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## Documentation
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Full API reference, indicator list, configuration guide, and best practices:
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**[rustbt.vercel.app/docs/documentation.html](https://rustbt.vercel.app/docs/documentation.html)**
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## Community vs Pro
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| | Community | Pro |
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|---|---|---|
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| Output resolution | Daily | 1m, 5m, 15m, 1h |
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| Monte Carlo | 1K sims | Unlimited |
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| Walk-Forward | - | Anchored + Rolling |
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| Parameter Stability | - | Yes |
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| Data Connectors | - | Binance, Polygon, DataBento |
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## License
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MIT
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