manifoldbt 0.20.0

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 329 ms. 79x faster than vectorbt, and 311x once you also want drawdown and Sharpe. Measured in public CI, every run linked.
• Expressive: fluent DSL with 63 indicators and 38 candlestick patterns, 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.

Staying up to date

manifoldbt asks PyPI once a day, in the background, whether a newer release exists, and prints a one-line notice under the banner when one does. It never delays an import (the notice is the previous run's answer, read from a local cache) and it sends nothing: the request is a plain GET of a public JSON document. Set MANIFOLDBT_NO_UPDATE_CHECK=1 to turn it off, and mbt.check_for_update() to ask on demand -- it returns the newer version, or None when you are current.

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")

Market data connectors: Binance, Bybit, Hyperliquid, dYdX, Bitstamp, Yahoo Finance (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")

Yahoo Finance covers stocks, ETFs, indices (^GSPC), FX (EURUSD=X), futures (ES=F) and crypto (BTC-USD) without an API key. Prices are dividend-adjusted, like yfinance's auto_adjust=True; pass dataset="raw" for unadjusted quotes. Yahoo's own history limits apply: 1m over the last 30 days, 1h over ~2 years, daily back to the listing date.

store = mbt.ingest(provider="yahoo", symbol="AAPL", symbol_id=1, interval="1d",
                   asset_class="equity",
                   start="2015-01-01T00:00:00Z", end="2026-01-01T00:00:00Z")

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 ...

Higher timeframes

Declare the timeframes you want alongside the simulation one, then read them with mbt.tf(...). Columns are forward-filled onto the simulation grid, and a bar's value only becomes readable once that bar has closed, so there is no look-ahead.

config = mbt.BacktestConfig(
    ...,
    bar_interval=Interval.minutes(1),                 # simulate on 1m
    extra_timeframes={"1h": Interval.hours(1)},       # also resample to 1h
)

h1 = mbt.tf("1h")
h1.close          # the last closed hourly close, held across the minute bars

For an indicator on a higher timeframe, use .apply(...). It evaluates the expression on that timeframe's own grid, so the period counts in its bars:

from manifoldbt.indicators import close, sma

band = mbt.tf("1h").apply(sma(close, 20))     # mean of 20 HOURLY closes

Careful: sma(mbt.tf("1h").close, 20) is not the same thing. That reads the step-held hourly series on the simulation grid, so the period counts in simulation bars: on a 1m simulation it is a 20-minute smoothing of an hourly staircase. Use .apply(...) whenever you want an indicator of the higher timeframe.

Sweeping a choice, not just a number

mbt.param(...) sweeps numbers. mbt.choice(...) sweeps expressions: the selector becomes a grid axis, and each combination resolves to its branch before the simulation runs, so the branches it did not pick cost nothing.

band = mbt.choice("band", {
    "30m": mbt.tf("30m").apply(sma(close, mbt.param("len"))),
    "1h":  mbt.tf("1h").apply(sma(close, mbt.param("len"))),
    "2h":  mbt.tf("2h").apply(sma(close, mbt.param("len"))),
})

sweep = mbt.run_sweep(strategy, {"band": ["30m", "1h", "2h"],
                                 "len": range(10, 210, 10)}, config, store)

The branches can hold any expression, so the same mechanism sweeps which exogenous column to use, which asset to reference, or which indicator to apply.

Examples

Look-ahead

mbt.detect_lookahead re-runs a strategy over different windows and compares the trades they have in common. That is what isolates bias coming from the engine or from a strategy's own use of time.

A parameter derived from the data before the backtest is a different question: a threshold computed over the whole history in a notebook and then passed in as a number is the same number in every run, so no re-run-based method can weigh it. Treat any parameter that came from data as part of the pipeline, re-derive it on the window under test, and compare the results.

examples/25_lookahead_trap.py runs both on the same strategy — including perturbing every future bar — and prints what each method concludes, so the difference is visible rather than asserted.

Correctness

Speed is worth nothing if the fills are wrong. Everything in this section is a test in this repository, and it runs in CI against the wheel published on PyPI, not against the source, on five Python versions. No licence is configured in that job on purpose, so what it exercises is the experience of someone who has just run pip install manifoldbt, and the run prints which tests skipped rather than showing a green tick that hides them.

Fill-level parity with vectorbt. test_parity_vectorbt.py checks where a trade actually filled and why it exited, not a summary statistic. Each scenario is a short series built to produce one clean round trip, and the entry price, the exit price, the exit reason and the final return are all asserted against vectorbt: market take-profit, market stop-loss, stop-loss/take-profit brackets, shorts and trailing stops. A further scenario pins the fee arithmetic across two round trips. The equity curve is deliberately left out, because a Community build caps it to daily resolution and it would not be an apples-to-apples comparison.

Resting limit entries are checked without vectorbt, which has no resting order to compare against. They are pinned instead by a NumPy model that computes the fill from raw OHLC with no call into the engine. Read it for what it is: it catches an implementation drifting away from the documented rule, and it does not independently prove the rule is the right one, because the rule was measured from the engine before being re-implemented.

Look-ahead. Two regression tests in python/tests/, the stricter of which corrupts every bar after a cut point, re-runs, and requires the equity before that point to come back bit-identical: any decision that read a future bar moves the prefix. They sit alongside the worked example above, which exists to demonstrate a leak the detector cannot see and to say so plainly.

Execution semantics

The rules below are pinned by tests. They are written out here so you can check them against the wheel you installed rather than take them on faith. Where a choice was available, the pessimistic one was taken.

What is not covered

The edges, stated rather than left to be discovered:

• No general liquidation model. Margin force-close exists for short options only. A leveraged spot or perpetual position is not liquidated by the engine.
• No corporate actions. Yahoo prices arrive dividend-adjusted from the source (dataset="raw" opts out) and splits are whatever the provider returns. Nothing in the engine reconstructs either.
• The cross-engine scenarios run on short synthetic series, each built to isolate one behaviour. They are not a long backtest over market data compared trade for trade.
• Perpetual funding is exercised by the engine's own tests, but has no cross-engine parity test.
• The largest universe under test is a handful of instruments. Cross-sectional research across thousands of assets is exercised by the sweep benchmarks, not by the correctness suite.
• Look-ahead coverage is two regression tests and one worked example, not a systematic battery of leak archetypes.

The engine's own Rust suite is not published, which is why the rules above are written out rather than linked. Independent verification is welcome, and a reproduction showing a fill this engine gets wrong is the most useful report this project can receive.

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: #13 ran on Linux x86_64, 4 vCPU (AMD EPYC 7763), Python 3.12, manifoldbt 0.18.0 / vectorbt 0.28.4 / raptorbt 0.9.0, 3 interleaved repetitions, medians reported.

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 102 seconds, because it defers the equity curve until a risk metric needs it and then has to build one.

The last two rows are the ones where ManifoldBT does worst, and they are published for that reason. Broadcasting a column per asset is close to free for vectorbt, while walking five books is not free for anything. And on a stop-loss/take-profit bracket, raptorbt is level with us: the intra-bar check that decides which of the two triggers first is a sequential walk in both engines, so there is no vectorization left to win with.

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, on a developer machine and not the CI runner, so it is not comparable line-for-line with the table above.

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. Treat it as an order of magnitude, not a benchmark: the two engines are not doing the same thing.

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.