manifoldbt/README.md

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

Telemetry

Every install sends one anonymous ping a day: an anonymous install id, the ManifoldBT version, your OS, CPU architecture, whether it is a CUDA build, your Python major version, and Community or Pro. No email, no license key, no strategy, no data, no results. It tells us which versions and platforms are still in use, so we know what we can stop supporting.

Turn it off with either of:

export MANIFOLDBT_NO_TELEMETRY=1
export DO_NOT_TRACK=1

CI runners are skipped automatically. Pro license validation is a separate mechanism and is not affected by these variables.

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.