High-Performance Backtest Engines
How to build a backtest engine that runs hundreds of times faster without changing a single PnL number — data layout, caching, adaptive resolution, and architecture, from first speedups to production internals.
- 01
Jun 26, 2026 #algotradingThe Backtest Speed Ladder: 298x on a Laptop CPU, Identical PnL to the Last Trade
Five implementations of the same 80-combo parameter sweep, all verified to produce identical PnL: pandas rolling.apply takes 69.9 seconds, numpy 3.1, numba 2.0, parallel numba 0.23 — a measured 298x speedup on an Apple M2 Max with zero hardware changes, and still ~13x over a competent vectorized baseline. What each rung buys, why a GPU is not the missing piece, and where the real bottleneck in mass parameter search lives.
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Jul 2, 2026 #algotradingThe Framework Tax: When Your Backtest Library Is Slower Than a Naive Pandas Loop
We benchmarked eight backtest engines on one identical parameter sweep — 150k bars, 80 HMA-cross combos, trade-count parity locked at 2707. Two of the most popular event-driven frameworks came in slower than a hand-written pandas loop, while a vectorized/compiled engine ran the same work ~13,000× faster. A study of the per-bar overhead that popular libraries were never built to amortize.
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Mar 16, 2026 #algotradingAggregated Parquet Cache: How to Speed Up Multi-Timeframe Backtests by Hundreds of Times
How to precompute timeframes and indicators from minute candles, save them to parquet, and use them for mass strategy testing without redundant recalculations.
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Jul 3, 2026 #algotradingThe Two-Axis Parameter Space: Why Most of Your Sweep Should Be Nearly Free
Not all parameters cost the same to search. A strategy's parameters split into an expensive axis (indicators — recomputed over the whole series) and a cheap axis (decision thresholds — an O(n) pass over precomputed signals). Because indicators are invariant to thresholds, you compute them once and sweep thousands of threshold configs at ~5,600 cfg/s — roughly 1,600x cheaper than recomputing per config. A re-pricing of the curse of dimensionality.
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Mar 17, 2026 #algotradingAdaptive Drill-Down: Backtest with Variable Granularity from Minutes to Raw Trades
How adaptive data granularity speeds up backtests and saves storage: drill-down from 1m to 1s, 100ms, and raw trades only where price moved significantly or volume spiked, not across the entire historical series.
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Jul 5, 2026 #algotradingThe Fidelity Gate: Coarse-to-Fine Backtesting Fools You Faster Unless the Cheap Proxy Ranks Like the Expensive One
Drill-down / multi-fidelity search (ASHA, successive halving, Hyperband) screens thousands of configs cheaply and promotes only survivors to the expensive full evaluation. It is a genuine speedup — but it collapses silently if the low-fidelity ranking disagrees with the high-fidelity one. We measured the fold-rank correlation: at one fold Spearman ρ can be 0.03 (ranks almost randomly), climbing to 0.43, 0.67, 0.78, 0.91 as folds accumulate. The fix is one mandatory gate — measure ρ(cheap, full) first, and auto-raise the minimum fidelity to the first rung where ρ ≥ 0.5.
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Jul 4, 2026 #algotradingRandom vs Smart Search: The Crossover Is Eval Cost, Not the Algorithm
When one backtest is cheap, dumb scrambled Sobol wins on raw throughput — smart samplers (TPE, CMA-ES, ASHA) pay a Python ask/tell tax that drops them 20x, so they evaluate far fewer points at equal wall-clock and lose. Make each eval expensive (multi-TF + walk-forward folds) and the crossover flips. We measured both regimes, and why fold-rank fidelity (ρ@1 rising 0.03→0.43) is the precondition for pruning to pay off.
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Jul 6, 2026 #algotradingThe GPU Precision Trap: How an fp32 Backtest on Apple Metal Silently Returns Garbage
Apple's Metal GPU has no float64. Port a vectorized backtest to it naively and the tempting prefix-sum WMA overflows fp32 — max relative error 211× — yet it still runs and returns plausible-looking numbers. The fix is not more precision; it is a different formulation: a direct windowed convolution, fp32-safe to 8×10⁻⁷ and 55.9× faster than single-thread numba. The trap, the arithmetic, and how to prove you didn't fall in.
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Jul 7, 2026 #algotradingWhen the GPU Pays Off: The Parameter-Sweep Roofline, Where a Headline 167x Is Really 27x Algorithm Times 6.2x Hardware
The GPU's lead over CPU grows with batch size — 54.5x at one combo per call up to 359.6x at 61 on our multi-timeframe indicator precompute — because a small sweep cannot amortize kernel-launch and transfer overhead. We decompose a headline 167x into a 27x algorithmic win that also helps the CPU and a 6.2x hardware win, show the true GPU-vs-best-CPU lead is only 3.2x single-timeframe and 6.2x multi, and give a decision guide for how wide a sweep must be before a GPU is worth buying into.
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Jun 30, 2026 #algotradingThe IPC Tax: Put the Backtest Engine Behind a Socket and Lose 13% — Almost None of It to the Socket
We ported a numba backtest kernel line-for-line to Rust and called it across a process boundary four ways, with an equivalence gate confirming identical PnL to the last trade. Shipping the entire 1.2 MB price series through a Unix socket costs ~2 ms — about 0.1% of the job. JSON-encoding the same payload costs 1348x more than raw bytes, chatty per-combo calls re-ship the data 80 times, and a per-bar call pattern would pay 2.1 s of pure IPC on a 2.0 s job. The boundary is cheap; the tax is in how you cross it.