manifoldbt 0.18.0

ManifoldBT
Rust-powered backtesting engine for quantitative research

Website · Documentation · Examples

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: 10M bars in 317 ms. 78x faster than vectorbt, 308x once you also want drawdown and Sharpe, ~3,500x faster than backtrader. Measured in public CI, every run linked.
• 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
pip install manifoldbt[gpu]         # + NVIDIA runtime compiler, for device="cuda" (Pro)

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).

The Linux and Windows x86_64 wheels already carry the CUDA kernels, so [gpu] only adds the NVIDIA runtime compiler (~180 MB) that compiles them on your machine. Skip it if you already have a CUDA toolkit installed. An NVIDIA driver is required, and GPU acceleration is a Pro feature; everything else runs at full speed on the CPU.

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

Performance

Every number below comes from a benchmark that runs in public CI on a standard GitHub runner, and links back to the run that produced it. It installs each engine from PyPI the way a user would, generates its own data, checks that the engines produced the same result, and only then reports how long each took: a workload they disagree on gets no published timing at all.

Latest run: #11 ran on Linux x86_64, 4 vCPU, Python 3.12, manifoldbt 0.17.3 / vectorbt 0.28.4 / raptorbt 0.9.0, 3 interleaved repetitions.

The second row is the one worth reading twice. Asking for a performance summary costs ManifoldBT nothing measurable, because it computes one during the run whether you read it or not, and costs vectorbt 73 seconds, because it defers the equity curve until a risk metric needs it and then has to build one.

The fifth row is the one where ManifoldBT does worst, and it is published for that reason: broadcasting a column per asset is close to free for vectorbt, while walking five books is not free for anything.

Parameter sweeps

Past a certain grid the question stops being speed. vectorbt materialises the simulation per combination, 1.57 MB of it at 20,000 bars, so the second row would ask a machine for tens of gigabytes. ManifoldBT runs it in ten seconds inside 79 MB.

Reproduce any of it yourself: fork the repository and press Run workflow on the benchmark, or run it locally from benchmarks/vs_vectorbt/. The method, the parity gate and the known divergences are written up in its README.

Against an event-driven engine

backtrader runs the same EMA(12/26) + RSI(14) strategy on 500K 1-minute bars in 46,944 ms, against 13 ms for ManifoldBT: a factor of 3,556. Measured with benchmarks/bench_vs_competitors.py, median of 3 runs. It sits outside the CI suite because its event-driven fills produce a different PnL, and the parity gate publishes no timing for engines that did not do the same work.

How it compares

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

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.