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](#performance), 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
```bash
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
```python
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:
```python
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):
```python
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:
```bash
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.
```python
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:
```python
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.
```python
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
| # | Example | What it shows |
|---|---------|---------------|
| 00 | [Template](https://github.com/manifoldbt/manifoldbt/blob/master/examples/00_template.py) | Minimal starting point |
| 01 | [Trend Following](https://github.com/manifoldbt/manifoldbt/blob/master/examples/01_trend_following.py) | EMA crossover, volume filter, stop-loss |
| 02 | [Mean Reversion](https://github.com/manifoldbt/manifoldbt/blob/master/examples/02_mean_reversion.py) | EMA crossover with parameter sweep |
| 03 | [Multi-Asset Momentum](https://github.com/manifoldbt/manifoldbt/blob/master/examples/03_multi_asset_momentum.py) | Cross-asset signals |
| 04 | [Linear Regression](https://github.com/manifoldbt/manifoldbt/blob/master/examples/04_linear_regression.py) | Regression-based signal |
| 05 | [Statistical Arbitrage](https://github.com/manifoldbt/manifoldbt/blob/master/examples/05_stat_arb.py) | Pairs trading, spread z-score |
| 06 | [Full Visualization](https://github.com/manifoldbt/manifoldbt/blob/master/examples/06_full_visualization.py) | Tearsheet and charts |
| 07 | [Walk-Forward](https://github.com/manifoldbt/manifoldbt/blob/master/examples/07_walk_forward.py) | Out-of-sample validation |
| 08 | [2D Sweep](https://github.com/manifoldbt/manifoldbt/blob/master/examples/08_sweep_2d_heatmap.py) | Parameter grid heatmap |
| 09 | [3D Surface](https://github.com/manifoldbt/manifoldbt/blob/master/examples/09_surface_3d.py) | Parameter surface plot |
| 10 | [Monte Carlo](https://github.com/manifoldbt/manifoldbt/blob/master/examples/10_monte_carlo.py) | Permutation-based robustness |
| 11 | [Portfolio](https://github.com/manifoldbt/manifoldbt/blob/master/examples/11_portfolio.py) | Multi-strategy portfolio |
| 12 | [Diagnostics](https://github.com/manifoldbt/manifoldbt/blob/master/examples/12_diagnostics.py) | Lookahead & exposure safety checks |
| 13 | [Stochastic Simulation](https://github.com/manifoldbt/manifoldbt/blob/master/examples/13_stochastic_simulation.py) | SDE path simulation (GBM, Heston, …) |
| 14 | [Multi-Timeframe](https://github.com/manifoldbt/manifoldbt/blob/master/examples/14_multi_timeframe.py) | Combining signals across timeframes |
| 15 | [Cross-Exchange](https://github.com/manifoldbt/manifoldbt/blob/master/examples/15_cross_exchange.py) | Signal on one venue, execute on another |
| 16 | [Exogenous Data](https://github.com/manifoldbt/manifoldbt/blob/master/examples/16_hashrate_exogene.py) | External series (e.g. hashrate) as a signal |
| 17 | [Per-Venue Fees](https://github.com/manifoldbt/manifoldbt/blob/master/examples/17_per_venue_fees.py) | Per-venue funding & borrow costs |
| 18 | [CSV Import](https://github.com/manifoldbt/manifoldbt/blob/master/examples/18_csv_import.py) | Load OHLCV from CSV (standard / MT4 / MT5) |
## 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](https://github.com/manifoldbt/manifoldbt/actions/runs/32469701489)**
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.
| Workload | Bars | ManifoldBT | vectorbt | raptorbt |
|---|---:|---:|---:|---:|
| SMA crossover | 10M | **327 ms** | 26.12 s (x79) | 913 ms (x2.8) |
| ...with drawdown, Sharpe, Sortino, volatility | 10M | **329 ms** | 102.38 s (**x311**) | 909 ms (x2.8) |
| ...with a 5 bps fee and 2 bps slippage | 10M | **337 ms** | 26.08 s (x79) | not supported |
| EMA + RSI filter, 5 bps fee | 1M | **57 ms** | 2.35 s (x40) | not supported |
| Five assets in one book | 1M | **148 ms** | 2.54 s (x17) | not supported |
| Stop-loss and take-profit bracket | 10M | **934 ms** | 26.24 s (x28) | 916 ms (**x1.0**) |
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
| Bars | Combinations | ManifoldBT | vectorbt | raptorbt |
|---:|---:|---:|---:|---:|
| 20,000 | 5,000 | **446 ms**, 40 MB | 5.84 s, 2.5 GB | 7.08 s |
| 200,000 | 10,000 | **9.96 s**, 79 MB | out of memory | 164.50 s |
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](https://github.com/manifoldbt/manifoldbt/actions/workflows/bench-vs-vectorbt.yml),
or run it locally from
[`benchmarks/vs_vectorbt/`](https://github.com/manifoldbt/manifoldbt/tree/master/benchmarks/vs_vectorbt).
The method, the parity gate and the known divergences are written up in
[its README](https://github.com/manifoldbt/manifoldbt/blob/master/benchmarks/vs_vectorbt/README.md).
### 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
| | 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](https://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](https://github.com/manifoldbt/manifoldbt/blob/master/LICENSE) for the full text.