manifoldbt/benchmarks/vs_vectorbt/README.md

manifoldbt vs vectorbt

An engine-to-engine benchmark you can re-run yourself. It installs both engines from PyPI, generates its own data, checks that the two engines produced the same result, and only then reports how long each took.

pip install manifoldbt vectorbt
pip install -r requirements-lock.txt
python bench.py --bars 10000 100000 1000000 --reps 7 --cold-start-reps 3 --memory-bars 2000000 --out results.json
python report.py results.json

No dataset to download, no API key, no configuration. The same command runs in GitHub Actions on a public runner, so every published number has a run URL behind it.

The rule this harness is built around

A speed comparison between two backtesters is worthless unless both engines did the same work. So parity comes first:

1. each workload runs once per engine;
2. total return, round-trip count and fees are compared;
3. a workload the engines disagree on gets no published timing.

Three verdicts come out of that gate:

Verdict	Meaning	What gets published
exact	agreement down to float-reordering noise (relative tolerance 1e-9)	the timing, in the headline table
documented	the engines disagree, the workload declared it in advance, and the cause is written down	the timing, in an annex, with the cause and its measured size
failed	the engines disagree and nobody predicted it	nothing. The run exits non-zero

The failed path is not decoration. It is the reason the other numbers can be trusted, and it makes the benchmark fail loudly if a future release of either engine changes a fill rule.

Method

Interleaved repetitions. The engines alternate inside each repetition (A, B, A, B, ...) rather than running in two blocks. On a shared cloud runner that slows down halfway through, two blocks would hand the penalty to whichever engine ran second; alternating splits it evenly.

The ratio is the headline, milliseconds are context. Each ratio comes from two measurements taken seconds apart on the same machine. Absolute timings from a shared runner are worth much less than the ratio between them.

Dispersion is published, and noise is flagged. Every point carries min, median, max and interquartile range. If the IQR exceeds 15% of the median the point is marked noisy and is not headline material, however good it looks.

Warmup is discarded, and that favours vectorbt on purpose. The first call of each engine is thrown away, which is where vectorbt pays its numba compilation. Charging a one-off JIT cost to every repetition would inflate the result.

Data loading is excluded on both sides. manifoldbt is handed a prepared store, vectorbt is handed prepared Series. What is timed is indicators plus simulation plus reading the headline metrics, nothing else.

Only public APIs. manifoldbt is driven through bt.run(strategy, config, store), the documented entry point, not through an internal fast path.

What is compared

Workload	What it exercises	Parity
sma_cross	SMA 10/50 crossover, long-only, no cost	exact
ema_rsi_fees	EMA 12/26 crossover with an RSI(14) filter and a 5 bps taker fee	exact
sma_cross_metrics	the same simulation, plus max drawdown, Sharpe, Sortino and volatility	exact
bracket_sl_tp	the same entry with a 15 bps stop and a 30 bps target	documented divergence

Each of those runs across a range of series lengths. Two further axes, cold start and memory, are measured in their own processes because they cannot be measured honestly inside the main one.

Scope, stated twice on purpose. sma_cross and sma_cross_metrics run the identical simulation; only the second one also produces a performance summary. manifoldbt computes that summary inside run() whether or not you read it, while vectorbt defers the equity curve until a risk metric asks for it and then pays to materialise it. Reporting both scopes is the only honest way to present the result: a reader who only wants a total return should look at the first number, and a reader who wants a Sharpe should look at the second. Parity on the summary workload is gated on total return, round-trip count and max drawdown, which match exactly; the ratios agree to about 3e-4, because manifoldbt buckets its daily returns slightly differently, and that residual is reported rather than smoothed over.

The vectorbt side of the summary is written out in pandas rather than through pf.sharpe_ratio() for two reasons, both in vectorbt's favour or neutral. manifoldbt computes its ratios on daily returns annualised by sqrt(365) and its drawdown at full bar resolution, so the native accessors would return different numbers and the comparison would be timing two different computations; and the hand-written version is measurably faster than a single native accessor on the same data, so vectorbt is credited with the quicker of its two paths.

Cold start. The steady-state table discards a warmup call, which is where vectorbt compiles its numba kernels. A user pays that cost in every new notebook, script or CI job, so it is measured rather than waved away: a fresh process, an engine it has never imported, one backtest. The Python, numpy and pandas baseline is measured the same way and reported alongside, so the engine's own share can be read off instead of argued about.

Memory added by the run. Resident memory sampled while the backtest executes, after a warmup. vectorbt materialises the simulation as arrays and its footprint grows with the series; manifoldbt streams bars out of its store. What is compared is what running a backtest costs on top of already holding the data: building each engine's data representation is a different question and is excluded on both sides, in manifoldbt's case a deliberately unflattering choice since its one-off ingest is the memory-hungry part.

Why the fee workload sizes in units

With FractionOfEquity sizing and a non-zero fee the engines size differently: manifoldbt charges the fee on top of a full-equity notional, vectorbt reserves it out of cash first. Both are legitimate product decisions, and comparing them would compare policy rather than speed or correctness. Sizing in fixed units isolates the fee arithmetic, which is the thing both engines must agree on.

The documented divergence, in full

When a bracket fires intrabar and the entry condition still holds at that bar's close, manifoldbt books two orders on that bar: the stop or target exit, then a fresh entry at the close. vectorbt processes one order per bar and re-enters on the next bar instead. Neither is wrong. On controlled bars the bracket fills themselves match exactly, which the cross-engine parity suite shipped with the library pins test by test; the divergence is purely about when a re-entry is allowed.

The harness counts the affected round-trips rather than hand-waving at them, so the report states what share of the trades the difference touches.

Alignment choices, and why each one exists

Two engines only produce identical numbers if they are told to do the same thing. These are the conventions the harness sets, all of them visible in engine_mbt.py and engine_vbt.py:

• signal_delay=0 with execution_price="AtClose", matching what Portfolio.from_signals does by default: a signal fills at the close of the bar that produced it.
• warmup_bars=0, so the indicator's own NaN warmup is what suppresses early signals, identically on both sides.
• Signals are fed to vectorbt as a level (entries = the condition holds, exits = it no longer holds) rather than as transitions, which reproduces manifoldbt's target-position semantics.
• Indicators are mirrored on manifoldbt's exact definitions. The EMA seeds on the first observation with alpha = 2/(span+1), which ewm(span=n, adjust=False) reproduces exactly. The RSI is Wilder's, seeded with the simple average of the first period deltas and emitted from bar period: a plain ewm(alpha=1/period) over the whole delta series is a different indicator, and using it would have made the engines disagree for a reason that has nothing to do with either engine.
• The generated bars have no opening gap (open == previous close). A bar that gaps through a stop is the one case where two engines can legitimately book different fill prices while both being correct, so that class of false failures is removed from the data rather than argued about in the report.

Reading the numbers honestly

• On a shared runner with 4 vCPUs, an engine that parallelises is understated. These are floors, not peaks.
• The published wheels are CPU-only, and GitHub-hosted runners have no GPU, so nothing here says anything about GPU performance.
• vectorbt is the open-source package (pip install vectorbt), not vectorbtpro.

Files

• data.py - deterministic OHLCV generator and its content digest
• probe_child.py - the cold-start and memory probes, each in a fresh process
• workloads.py - the parameters both engines read, and the declared parity status
• engine_mbt.py / engine_vbt.py - one adapter per engine
• parity.py - the gate
• bench.py - the runner
• report.py - JSON to Markdown, and to the GitHub job summary
• .github/workflows/bench-vs-vectorbt.yml - the workflow that runs all of the above on a GitHub-hosted runner