Trading-System Engineering & Strategy Validation

A durable, project-neutral knowledge base for building and validating systematic trading systems. It records what is true about the domain, the methods, and the tools — the architecture of a deterministic backtesting engine, and the statistics of grading a strategy — not the state of any one codebase. Anything that would drift when an implementation changes (field names, file anchors, build status, internal contracts) is deliberately kept out.

The wiki covers two complementary subject areas. The first is about the machine that runs a strategy; the second about judging the numbers that machine produces.

Area I — Backtesting Engine Architecture

How a modern, deterministic, event-driven backtesting and trading engine is built: the architectural commitments that make a backtest fast, reproducible, and free of the future-leaking bugs that silently invalidate results. Start at the architecture overview, then the detail pages:

Event-Driven Backtesting — vectorized vs. event-driven; the data-driven irregular clock; merging heterogeneous timestamped sources into one chronological timeline; backtest/live symmetry.
Look-Ahead Bias and Causality — the cardinal backtesting bug, and how to make it structurally impossible rather than merely discouraged.
Determinism and Reproducibility — same input → identical run; reproduce-from-manifest; embarrassingly-parallel runs; seed-as-input; the record-then-replay boundary for nondeterministic sources.
Reactive Streaming Dataflow — push-based synchronous reactive dataflow on an acyclic graph; freshness-gated recompute and sample-and-hold; feedback via an explicit delay/register; as-of vs. barrier joins.
Columnar Data Layout — structure-of-arrays vs. array-of-structures; why columnar layout is cache- and SIMD-friendly for a streaming numeric hot path.
Signal, Exposure, and Execution — separating the alpha signal (a target-exposure signal) from the execution model; frictionless signal-quality measurement vs. realistic frictions; position management as a derived layer.
Graph Compilation and Optimization — a parameter-generic graph compiled to a flat runnable instance; determinism as the licence for behaviour-preserving rewrites (CSE, dead-code elimination, loop-invariant code motion over a sweep).
Authoring and Deployment Lifecycle — the engine/application split; fast hot-reload iteration vs. a frozen, reproducible deploy artifact.

Area II — Strategy Analysis & Validation

How the equity and return series a backtest produces are analysed, validated, and graded: deciding whether an apparent edge is real and robust or an artefact of luck and overfitting. Start at the validation overview, then:

Metric Catalogue — for each analysis tool and metric family: the exact formula, the input it is computed from, and the conventional benchmark threshold.
Metric Verification Log — how every load-bearing formula and threshold was adversarially source-checked, which were corrected, and the citations.

How to read this wiki

Every load-bearing claim is marked so a definition is never confused with a rule-of-thumb, and sourced with a validated external link (each page carries a grouped ## References section):

[L] law / exact — a definition, identity, or derivation that does not vary.
[C] convention — a practitioner rule-of-thumb that varies by context (asset class, regime, source); never a hard pass/fail gate without checking the primary source.
[CORR] corrected — a value wrong in a common source and fixed here, with the correction cited.

The two areas meet where the engine produces the series the metrics grade: the four classic analysis axes (sweep / optimize / walk-forward / Monte-Carlo) on Strategy Analysis & Validation are validation methods, while Determinism and Reproducibility and Graph Compilation and Optimization describe how an engine constructs and runs those families of backtests efficiently and reproducibly.

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Backtesting Engine Architecture

Strategy Analysis & Validation