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docs: improve README with real benchmarks, docs link, community vs pro table

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# ManifoldBT
<p align="center">
<strong>ManifoldBT</strong><br>
Rust-powered backtesting engine for quantitative research
</p>
**Rust-powered backtesting engine for quantitative research.**
<p align="center">
<a href="https://rustbt.vercel.app">Website</a> &middot;
<a href="https://rustbt.vercel.app/docs/documentation.html">Documentation</a> &middot;
<a href="examples/">Examples</a>
</p>
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