gavindiaz/manifoldbt
1
0
Fork 0
mirror of https://github.com/manifoldbt/manifoldbt.git synced 2026-08-24 22:48:05 +00:00
Code Issues Packages Projects Releases Wiki Activity

release: v0.18.0

Browse Source
This commit is contained in:
github-actions[bot]
2026-08-21 01:48:35 +00:00
parent 6c2f4eda9e
commit b47b07e8e6
9 changed files with 544 additions and 81 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+64 -15
View File
@@ -26,10 +26,10 @@ sequential fill simulation with realistic fees, slippage, funding and look-ahead
## 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+.
- **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](#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
@@ -37,12 +37,19 @@ sequential fill simulation with realistic fees, slippage, funding and look-ahead
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
@@ -83,17 +90,17 @@ print(result.summary())
## Loading data
Bring your own data, or pull it from a built-in connector — both return a
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:
**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):
**Exchange connectors**: Binance, Bybit, Hyperliquid, dYdX, Bitstamp (free); Databento, Massive (Pro):
```python
store = mbt.ingest(provider="binance", symbol="BTCUSDT", symbol_id=1,
@@ -133,17 +140,59 @@ manifoldbt ingest --provider binance --symbol BTCUSDT --symbol-id 1 --start ...
## Performance
EMA(12/26) + RSI(14) on 500K synthetic 1-min bars (manifoldbt/vectorbt: median of 5 runs; backtrader: median of 3):
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.
| Engine | Time | vs ManifoldBT |
|--------|------|---------------|
| **ManifoldBT** (Rust) | **13 ms** | 1x |
| vectorbt (NumPy) | 4,662 ms | 353x slower |
| backtrader (Python) | 46,944 ms | ~3,556x slower |
**Latest run: [#11](https://github.com/manifoldbt/manifoldbt/actions/runs/32396472073)**
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.
ManifoldBT and vectorbt produce identical results (−30.23% vs −30.24% return, same trade count); backtrader's event-driven fills give a different PnL.
| Workload | Bars | ManifoldBT | vectorbt | raptorbt |
|---|---:|---:|---:|---:|
| SMA crossover | 10M | **317 ms** | 24.75 s (x78) | 878 ms (x2.8) |
| ...with drawdown, Sharpe, Sortino, volatility | 10M | **317 ms** | 97.46 s (**x308**) | 894 ms (x2.8) |
| ...with a 5 bps fee and 2 bps slippage | 10M | **316 ms** | 24.53 s (x78) | not supported |
| EMA + RSI filter, 5 bps fee | 1M | **52 ms** | 2.21 s (x41) | not supported |
| Five assets in one book | 1M | **140 ms** | 2.34 s (x17) | not supported |
Reproduce: `python benchmarks/bench_vs_competitors.py --rows 500000 --runs 5`
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
| 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. 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