manifoldbt/README.md

ManifoldBT
Rust-powered backtesting engine for quantitative research

Website · Documentation · Examples

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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.

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

pip install manifoldbt

With all extras (plotting, pandas, polars):

pip install manifoldbt[all]

Quick Start

import manifoldbt as mbt
from manifoldbt.indicators import close, ema
from manifoldbt.helpers import time_range, Interval, Slippage

fast = ema(close, 12)
slow = ema(close, 26)

strategy = (
    mbt.Strategy.create("ema_crossover")
    .signal("fast", fast)
    .signal("slow", slow)
    .signal("signal", mbt.when(fast > slow, mbt.lit(1.0), mbt.lit(-1.0)))
    .size(mbt.col("signal") * mbt.lit(0.25))
)

start, end = time_range("2022-01-01", "2025-01-01")

config = mbt.BacktestConfig(
    universe=[1],
    time_range_start=start,
    time_range_end=end,
    bar_interval=Interval.hours(12),
    initial_capital=10_000,
    execution=mbt.ExecutionConfig(allow_short=True, max_position_pct=0.5),
    fees=mbt.FeeConfig.binance_perps(),
    slippage=Slippage.fixed_bps(2),
    warmup_bars=30,
)

store = mbt.DataStore(data_root="data", metadata_db="metadata/metadata.sqlite")
result = mbt.run(strategy, config, store)
print(result.summary())

Examples

#	Example	What it shows
00	Template	Minimal starting point
01	Trend Following	EMA crossover, volume filter, stop-loss
02	Mean Reversion	EMA crossover with parameter sweep
03	Multi-Asset Momentum	Cross-asset signals
04	Linear Regression	Regression-based signal
05	Statistical Arbitrage	Pairs trading, spread z-score
06	Full Visualization	Tearsheet and charts
07	Walk-Forward	Out-of-sample validation
08	2D Sweep	Parameter grid heatmap
09	3D Surface	Parameter surface plot
10	Monte Carlo	Permutation-based robustness
11	Portfolio	Multi-strategy portfolio

Performance

EMA(12/26) + RSI(14) on 500K synthetic 1-min bars (median of 5 runs):

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:

manifold-bt.com/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
Crypto connectors (Binance, Hyperliquid)	Yes	Yes
Databento & Massive connectors	-	Yes
Safety checks (lookahead, exposure)	-	Yes
Tearsheets & export	-	Yes

License

MIT