How to Build an Audit‑Ready Backtesting Report for Fundraising or Broker Audits

Introduction — Why an audit‑ready backtesting report matters

When you seek capital or undergo a broker or clearing‑level review, your backtests are evaluated not only for returns but for process, transparency and model governance. Investors and compliance teams expect clear documentation, reproducible results, and evidence you understood model risk and overfitting. Industry platforms and research communities emphasise hypothesis‑driven development, clear out‑of‑sample procedures and limits on post‑hoc tuning to reduce overfitting risk.

This guide condenses the practical content fund managers, retail quants and founders need to include in a single, audit‑ready deliverable: what to document, what tests to run, how to package code/data/execution logs, and governance items auditors typically request.

Core contents: the checklist every audit‑ready backtest report should include

Below are the mandatory sections and the minimum level of detail to include for each. Present the report so an independent reviewer (engineer, quant or compliance officer) can reproduce P&L and validate claims without additional assumptions.

1. Executive summary — concise strategy description, instruments/timeframes, live capacity estimate, claimed performance metrics (CAGR, Sharpe, max drawdown), and a short list of the datasets and versions used.
2. Scope and purpose — clarify that tests are research/backtest/proof‑of‑concept vs. live track records; list dates of creation and last update; disclose any known limitations.
3. Data provenance & cleaning — vendor names, tick vs. aggregated bars, timezone, adjustments (corporate actions for equities), survivorship bias, and precisely how missing data were handled. Provide cryptographic or file‑hashes for delivered datasets if possible.
4. Code & environment — a single reproducible artifact: code repository link (or attached repo), commit hash, dependency manifest (requirements.txt / environment.yml / Dockerfile), and exact engine/version used for backtests. Prefer container images or pinned package versions for auditability.
5. Backtest configuration — start/end dates, warm‑up/washout periods, out‑of‑sample holdouts, random seeds, parameter values, and number of runs. If walk‑forward, specify window sizes and re‑training cadence.
6. Execution assumptions & transaction modelling — fills (mid/ask/bid), slippage model, latency assumptions, per‑trade commissions, lot sizing rules, rounding logic, and any liquidity filters. Provide sensitivity tables showing how returns change under conservative vs. optimistic slippage/costs.
7. Robustness tests — walk‑forward validation, Monte Carlo resampling of trade sequences, parameter sensitivity analyses, regime‑based out‑of‑sample tests, and stress scenarios. Show how performance and drawdown behave across these tests.
8. Multiple testing disclosure — document how many candidate strategies/parameter sets were tested and your procedure for selecting the presented variant (to avoid the multiple‑testing / data‑snooping problem).
9. Reproducible replication instructions — step‑by‑step commands to recreate results, expected compute/time, and a small smoke test with known outputs (hashable CSV or sample plot) so the auditor can confirm environment correctness quickly.
10. Appendices & raw deliverables — raw trade logs, order fills, P&L time series, hourly/daily snapshots, code snippets for key functions (entry, exit, sizing), and hashes for all files. Prefer machine‑readable CSV/JSON for trade histories and a PDF summary for human review.

Regulators and supervisory bodies are increasingly specific about model validation and backtesting evidence (exception counts, PLA/PLA‑type tests and governance), particularly for models used in capital or margin frameworks—so report quantitative backtesting outcomes and the test logic used to derive them.

Practical reproducibility & governance — what auditors and investors actually verify

Auditors will try to reproduce key numbers and probe the assumptions that materially change conclusions. To pass these checks you must:

• Make results reproducible in one command — provide a Dockerfile or pinned environment and a script (e.g., ./run_replicate.sh) that produces the primary P&L CSV and summary PDF.
• Include deterministic seeds and explain randomness — for any stochastic procedure (Monte Carlo, bootstrap, randomized parameter search) include the seed(s) used. Also provide aggregated results across multiple seeds if results vary materially.
• Provide execution & broker evidence — if you have live or paper records, include broker statements, trade confirmations or execution logs that link simulated trades to real fills; for fundraising, brokers and institutional investors will often ask for this or at least for execution modelling details.
• Independent validation and periodic review — document your model validation policy (frequency, independent reviewer, KPIs for re‑validation). Supervisory guidance and widely used model‑risk frameworks require ongoing validation and independent review commensurate with model complexity.

Don’t understate trial counts or optimization steps. Auditors treat an undisclosed large number of tested variants as a red flag; explicitly report the number of backtests and the selection procedure. Transparency here builds trust.

Delivery checklist, sample report outline and closing recommendations

Use the checklist below as the final packaging step before you hand material to a prospective investor, auditor or broker compliance team. Keep a short, reproducible delivery for the first review (one ZIP or container) and a full folder with raw data/code for deeper validation.

Minimum deliverables

• README (purpose + how to run replication in 3 steps)
• Container or pinned dependency file + scripted run
• Trade log CSV, P&L time series CSV, summary PDF with charts
• Data manifest with vendor names, versions, and file hashes
• Key tests & results: walk‑forward table, Monte Carlo distribution, sensitivity tables
• Independent validation note: who reviewed it and when (or plan to commission an independent review)

Sample short report outline (1–3 pages)

1. One‑page strategy summary and capacity estimate
2. Top‑line performance table and risk metrics
3. High‑level robustness tests + concluding statement on readiness for live deployment

Finally, keep governance simple but demonstrable: maintain version control, a validation log, and a change log for data or code updates. Regulators and counterparties in different jurisdictions are tightening model validation requirements and introducing phased standards for independent validation — plan to provide stronger evidence as you scale or enter institutional markets.

Need a downloadable template or checklist in a specific format (PDF, Markdown, or a Dockerized example)? Tell me which format you prefer and I’ll generate a starter repo structure and a one‑page reproducible example you can run locally.