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release: v0.19.0
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@@ -8,14 +8,7 @@
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</p>
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<p align="center">
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<a href="https://discord.gg/bvU6Wjc72d"><img src="https://img.shields.io/badge/Discord-Join%20the%20community-5865F2?style=for-the-badge&logo=discord&logoColor=white" alt="Join the ManifoldBT Discord" height="34"></a>
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</p>
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<p align="center">
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<a href="https://pypi.org/project/manifoldbt/"><img src="https://img.shields.io/pypi/v/manifoldbt?logo=pypi&logoColor=white&color=2f6fed" alt="PyPI"></a>
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<img src="https://img.shields.io/badge/python-3.9%2B-3776AB?logo=python&logoColor=white" alt="Python 3.9+">
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<img src="https://img.shields.io/badge/core-Rust-dea584?logo=rust&logoColor=white" alt="Rust core">
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<a href="https://github.com/manifoldbt/manifoldbt/actions/workflows/bench-vs-vectorbt.yml"><img src="https://img.shields.io/badge/benchmarks-public%20CI-2ea44f?logo=githubactions&logoColor=white" alt="Benchmarks in public CI"></a>
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<a href="https://discord.gg/bvU6Wjc72d"><img src="https://img.shields.io/badge/Discord-join%20the%20community-5865F2?logo=discord&logoColor=white" alt="Discord"></a>
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</p>
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<p align="center">
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@@ -33,7 +26,7 @@ sequential fill simulation with realistic fees, slippage, funding and look-ahead
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## Why ManifoldBT
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- **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.
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- **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.
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- **Expressive**: fluent DSL with 30+ indicators, conditional logic, cross-asset references
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- **Rigorous**: Monte Carlo, walk-forward, parameter sweeps, lookahead detection, exposure diagnostics
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- **Portable**: `pip install`, no Rust toolchain needed. Works on Python 3.9+.
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@@ -107,13 +100,26 @@ store = mbt.import_csv("EURUSD_1m.csv", symbol="EURUSD", symbol_id=1,
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interval="1m", asset_class="forex")
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```
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**Exchange connectors**: Binance, Bybit, Hyperliquid, dYdX, Bitstamp (free); Databento, Massive (Pro):
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**Market data connectors**: Binance, Bybit, Hyperliquid, dYdX, Bitstamp, Yahoo Finance (free);
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Databento, Massive (Pro):
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```python
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store = mbt.ingest(provider="binance", symbol="BTCUSDT", symbol_id=1,
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start="2024-01-01T00:00:00Z", end="2025-01-01T00:00:00Z")
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```
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Yahoo Finance covers stocks, ETFs, indices (`^GSPC`), FX (`EURUSD=X`), futures
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(`ES=F`) and crypto (`BTC-USD`) without an API key. Prices are dividend-adjusted,
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like `yfinance`'s `auto_adjust=True`; pass `dataset="raw"` for unadjusted quotes.
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Yahoo's own history limits apply: 1m over the last 30 days, 1h over ~2 years,
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daily back to the listing date.
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```python
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store = mbt.ingest(provider="yahoo", symbol="AAPL", symbol_id=1, interval="1d",
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asset_class="equity",
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start="2015-01-01T00:00:00Z", end="2026-01-01T00:00:00Z")
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```
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Or from the CLI:
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```bash
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@@ -121,59 +127,6 @@ manifoldbt import-csv data.csv --symbol EURUSD --symbol-id 1 --interval 1m
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manifoldbt ingest --provider binance --symbol BTCUSDT --symbol-id 1 --start ... --end ...
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```
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## Higher timeframes
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Declare the timeframes you want alongside the simulation one, then read them
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with `mbt.tf(...)`. Columns are forward-filled onto the simulation grid, and a
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bar's value only becomes readable once that bar has closed, so there is no
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look-ahead.
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```python
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config = mbt.BacktestConfig(
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...,
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bar_interval=Interval.minutes(1), # simulate on 1m
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extra_timeframes={"1h": Interval.hours(1)}, # also resample to 1h
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)
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h1 = mbt.tf("1h")
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h1.close # the last closed hourly close, held across the minute bars
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```
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For an **indicator** on a higher timeframe, use `.apply(...)`. It evaluates the
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expression on that timeframe's own grid, so the period counts in *its* bars:
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```python
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from manifoldbt.indicators import close, sma
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band = mbt.tf("1h").apply(sma(close, 20)) # mean of 20 HOURLY closes
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```
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> Careful: `sma(mbt.tf("1h").close, 20)` is **not** the same thing. That reads
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> the step-held hourly series on the simulation grid, so the period counts in
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> simulation bars: on a 1m simulation it is a 20-*minute* smoothing of an hourly
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> staircase. Use `.apply(...)` whenever you want an indicator *of* the higher
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> timeframe.
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## Sweeping a choice, not just a number
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`mbt.param(...)` sweeps numbers. `mbt.choice(...)` sweeps *expressions*: the
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selector becomes a grid axis, and each combination resolves to its branch before
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the simulation runs, so the branches it did not pick cost nothing.
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```python
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band = mbt.choice("band", {
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"30m": mbt.tf("30m").apply(sma(close, mbt.param("len"))),
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"1h": mbt.tf("1h").apply(sma(close, mbt.param("len"))),
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"2h": mbt.tf("2h").apply(sma(close, mbt.param("len"))),
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})
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sweep = mbt.run_sweep(strategy, {"band": ["30m", "1h", "2h"],
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"len": range(10, 210, 10)}, config, store)
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```
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The branches can hold any expression, so the same mechanism sweeps which
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exogenous column to use, which asset to reference, or which indicator to apply.
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## Examples
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| # | Example | What it shows |
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@@ -206,30 +159,26 @@ engine from PyPI the way a user would, generates its own data, checks that the
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engines produced the **same result**, and only then reports how long each took:
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a workload they disagree on gets no published timing at all.
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**Latest run: [#13](https://github.com/manifoldbt/manifoldbt/actions/runs/32469701489)**
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ran on Linux x86_64, 4 vCPU (AMD EPYC 7763), Python 3.12, manifoldbt 0.18.0 /
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vectorbt 0.28.4 / raptorbt 0.9.0, 3 interleaved repetitions, medians reported.
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**Latest run: [#11](https://github.com/manifoldbt/manifoldbt/actions/runs/32396472073)**
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ran on Linux x86_64, 4 vCPU, Python 3.12, manifoldbt 0.17.3 / vectorbt 0.28.4 /
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raptorbt 0.9.0, 3 interleaved repetitions.
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| Workload | Bars | ManifoldBT | vectorbt | raptorbt |
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|---|---:|---:|---:|---:|
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| SMA crossover | 10M | **327 ms** | 26.12 s (x79) | 913 ms (x2.8) |
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| ...with drawdown, Sharpe, Sortino, volatility | 10M | **329 ms** | 102.38 s (**x311**) | 909 ms (x2.8) |
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| ...with a 5 bps fee and 2 bps slippage | 10M | **337 ms** | 26.08 s (x79) | not supported |
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| EMA + RSI filter, 5 bps fee | 1M | **57 ms** | 2.35 s (x40) | not supported |
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| Five assets in one book | 1M | **148 ms** | 2.54 s (x17) | not supported |
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| Stop-loss and take-profit bracket | 10M | **934 ms** | 26.24 s (x28) | 916 ms (**x1.0**) |
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| SMA crossover | 10M | **317 ms** | 24.75 s (x78) | 878 ms (x2.8) |
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| ...with drawdown, Sharpe, Sortino, volatility | 10M | **317 ms** | 97.46 s (**x308**) | 894 ms (x2.8) |
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| ...with a 5 bps fee and 2 bps slippage | 10M | **316 ms** | 24.53 s (x78) | not supported |
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| EMA + RSI filter, 5 bps fee | 1M | **52 ms** | 2.21 s (x41) | not supported |
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| Five assets in one book | 1M | **140 ms** | 2.34 s (x17) | not supported |
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The second row is the one worth reading twice. Asking for a performance summary
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costs ManifoldBT nothing measurable, because it computes one during the run
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whether you read it or not, and costs vectorbt 102 seconds, because it defers
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the equity curve until a risk metric needs it and then has to build one.
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whether you read it or not, and costs vectorbt 73 seconds, because it defers the
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equity curve until a risk metric needs it and then has to build one.
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The last two rows are the ones where ManifoldBT does worst, and they are
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published for that reason. Broadcasting a column per asset is close to free for
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vectorbt, while walking five books is not free for anything. And on a
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stop-loss/take-profit bracket, raptorbt is level with us: the intra-bar check
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that decides which of the two triggers first is a sequential walk in both
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engines, so there is no vectorization left to win with.
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The fifth row is the one where ManifoldBT does worst, and it is published for
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that reason: broadcasting a column per asset is close to free for vectorbt,
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while walking five books is not free for anything.
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### Parameter sweeps
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@@ -254,14 +203,9 @@ The method, the parity gate and the known divergences are written up in
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backtrader runs the same EMA(12/26) + RSI(14) strategy on 500K 1-minute bars in
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**46,944 ms**, against **13 ms** for ManifoldBT: a factor of **3,556**. Measured
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with `benchmarks/bench_vs_competitors.py`, median of 3 runs, on a developer
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machine and not the CI runner, so it is not comparable line-for-line with the
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table above.
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It sits outside the CI suite because its event-driven fills produce a different
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PnL, and the parity gate publishes no timing for engines that did not do the
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same work. Treat it as an order of magnitude, not a benchmark: the two engines
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are not doing the same thing.
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with `benchmarks/bench_vs_competitors.py`, median of 3 runs. It sits outside the
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CI suite because its event-driven fills produce a different PnL, and the parity
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gate publishes no timing for engines that did not do the same work.
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### How it compares
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@@ -289,7 +233,7 @@ Full API reference, indicator list, configuration guide, and best practices:
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| Monte Carlo | 1K sims | Unlimited |
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| Walk-Forward | - | Anchored + Rolling |
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| Parameter Stability | - | Yes |
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| Crypto connectors (Binance, Bybit, Hyperliquid) | Yes | Yes |
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| Free connectors (Binance, Bybit, Hyperliquid, dYdX, Bitstamp, Yahoo) | Yes | Yes |
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| Databento & Massive connectors | - | Yes |
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| Safety checks (lookahead, exposure) | - | Yes |
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| Tearsheets & export | - | Yes |
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