manifoldbt 0.14.1

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ManifoldBT
Rust-powered backtesting engine for quantitative research

Website · Documentation · Examples

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ManifoldBT is a Python backtesting library with a Rust core. Strategies are written in a fluent Python DSL, compiled to a vectorized Rust expression graph, then run through a sequential fill simulation with realistic fees, slippage, funding and look-ahead protection. Vectorized speed with event-driven execution realism.

Why ManifoldBT

• Fast — 500K bars in ~13 ms. 353x faster than vectorbt, ~3,500x 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              # engine only: backtests, sweeps, metrics
pip install manifoldbt[plot]        # + interactive charts and native windows (show=True)
pip install manifoldbt[all]         # everything: plots, windows, PNG export, pandas/polars

The base install stays light (no browser, no GUI) for scripts, servers and CI. [plot] adds plotly and a native window backend; [all] also pulls kaleido for static PNG/SVG export (which bundles a headless Chromium).

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.ingest(provider="binance", symbol="BTCUSDT", symbol_id=1,
                   start="2022-01-01T00:00:00Z", end="2025-01-01T00:00:00Z", interval="1h")
result = mbt.run(strategy, config, store)
print(result.summary())

Loading data

Bring your own data, or pull it from a built-in connector — both return a DataStore ready for mbt.run(...).

CSV — free on all tiers, auto-detects standard / MetaTrader 4 / MetaTrader 5:

store = mbt.import_csv("EURUSD_1m.csv", symbol="EURUSD", symbol_id=1,
                       interval="1m", asset_class="forex")

Exchange connectors — Binance, Bybit, Hyperliquid, dYdX, Bitstamp (free); Databento, Massive (Pro):

store = mbt.ingest(provider="binance", symbol="BTCUSDT", symbol_id=1,
                   start="2024-01-01T00:00:00Z", end="2025-01-01T00:00:00Z")

Or from the CLI:

manifoldbt import-csv data.csv --symbol EURUSD --symbol-id 1 --interval 1m
manifoldbt ingest --provider binance --symbol BTCUSDT --symbol-id 1 --start ... --end ...

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
12	Diagnostics	Lookahead & exposure safety checks
13	Stochastic Simulation	SDE path simulation (GBM, Heston, …)
14	Multi-Timeframe	Combining signals across timeframes
15	Cross-Exchange	Signal on one venue, execute on another
16	Exogenous Data	External series (e.g. hashrate) as a signal
17	Per-Venue Fees	Per-venue funding & borrow costs
18	CSV Import	Load OHLCV from CSV (standard / MT4 / MT5)

Performance

EMA(12/26) + RSI(14) on 500K synthetic 1-min bars (manifoldbt/vectorbt: median of 5 runs; backtrader: median of 3):

Engine	Time	vs ManifoldBT
ManifoldBT (Rust)	13 ms	1x
vectorbt (NumPy)	4,662 ms	353x slower
backtrader (Python)	46,944 ms	~3,556x slower

ManifoldBT and vectorbt produce identical results (−30.23% vs −30.24% return, same trade count); backtrader's event-driven fills give a different PnL.

Reproduce: python benchmarks/bench_vs_competitors.py --rows 500000 --runs 5

How it compares

	ManifoldBT	vectorbt	backtrader	Nautilus
Engine	Rust (vectorized + sequential fills)	Numba/NumPy (vectorized)	Python (event-driven)	Rust/Python (event-driven)
Execution realism¹	High	Basic	High	High
Focus	Backtesting + research	Backtesting at scale	Backtest + live	Backtest + live (production)

¹ fees, slippage, funding, partial fills, look-ahead detection.

On GPU (Pro), the Monte Carlo engine runs ~36x faster than the all-core CPU path (SDE path simulation, RTX 3090, f32).

Documentation

Full API reference, indicator list, configuration guide, and best practices:

www.manifoldbt.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, Bybit, Hyperliquid)	Yes	Yes
Databento & Massive connectors	-	Yes
Safety checks (lookahead, exposure)	-	Yes
Tearsheets & export	-	Yes

License

Apache 2.0 with Commons Clause. The source is available, free to use, modify and self-host. Reselling the software or offering it as a paid hosted service is not permitted. See LICENSE for the full text.