Who this is for. Anyone who has read a promising paper and wants to know whether the result is real before building on it. Assumes Python and a basic backtesting setup.

How to use it. Read the steps in order; each one assumes the previous. The “why here” line says what the step adds. Where a step is a tool, run it on your own numbers before moving on. The exemplar papers at the end are the archive’s highest-rigor papers for the path’s methods; read two of them with the checklists in hand.

Steps

  1. How to Read a Quant Finance Paper (Without Wasting Your Afternoon)

    A triage framework for reading quantitative finance papers: the 10-minute pass, the two axes that matter, section-by-section priorities, and the red flags that end a read early.

    Why here: Decide in minutes whether the paper deserves hours.

  2. How to Choose Which Papers to Replicate First

    A prioritization framework for replication: the expected-information calculation, the five selection criteria, portfolio-of-replications thinking, and which paper types repay the effort.

    Why here: Not every paper is worth replicating; this ranks candidates by expected information.

  3. How to Find the Datasets Used in Quant-Finance Papers

    Locating the data behind quant finance papers: the standard dataset zoo (CRSP, TAQ, FI-2010, Kaggle mirrors), decoding data sections, access tiers, and legitimate substitutes when the original is unreachable.

    Why here: Most replications fail at the data step, before any code.

  4. A Checklist for Reproducing Quant Research

    A six-stage checklist for reproducing quantitative finance papers: acquisition, alignment, independent reimplementation, reconciliation, stress testing, and documentation — with the failure modes at each stage.

    Why here: The checklist to follow while reproducing the headline table.

  5. Look-Ahead Bias and Point-in-Time Data: The Complete Taxonomy

    Every way future information leaks into backtests: restated fundamentals, index membership, same-bar execution, timestamp semantics, and LLM training-data leakage — with detection tests for each.

    Why here: The leak that most often explains a result you cannot reproduce.

  6. Walk-Forward Analysis and Out-of-Sample Testing, Done Honestly

    How to run out-of-sample tests that mean something: why k-fold fails on market data, purging and embargoes, anchored vs rolling walk-forward, and the evaluate-once discipline.

    Why here: Re-run the strategy on data the authors never saw.

  7. Transaction Costs, Slippage, and Market Impact: From Paper Alpha to Tradeable Alpha

    How to model transaction costs in backtests: the four cost components, the square-root impact law, honest cost ranges by asset class, and the turnover arithmetic that kills most published strategies.

    Why here: Paper alpha to tradeable alpha.

  8. What Is Backtest Overfitting? Definition, Detection, and Defenses

    Backtest overfitting explained: how selection among many trials fits noise, a concrete numerical demonstration, the PBO/deflated-Sharpe detection tools, and the defenses that actually work.

    Why here: Why the best-looking variant is usually the luckiest.

  9. The Deflated Sharpe Ratio, Explained with Real Numbers

    How the deflated Sharpe ratio corrects for multiple testing, fat tails, and short samples: the intuition, the formulas, and the worked example — 100 random backtests produce a Sharpe ≈ 2.5 by luck alone.

    Why here: The haircut for the number of things you tried.

  10. Deflated Sharpe Ratio & Minimum Track Record Calculator tool

    Interactive deflated Sharpe ratio calculator: probabilistic Sharpe ratio, expected maximum Sharpe under N zero-skill trials, DSR, and minimum track record length — with skew, kurtosis, and sample length. Runs in your browser.

    Why here: Compute the deflated Sharpe and minimum track record for your own replication.

  11. Research-to-Production Checklist for an Automated Trading Strategy (2026)

    The complete checklist for taking a backtested strategy live: validation gates, execution safety, kill switches, monitoring, reconciliation, and the go-live protocol.

    Why here: What has to be true before the strategy runs with money.

Exemplar papers

The highest-rigor papers in the archive that publish their code, so the replication path above can be walked end to end:

63 papers link a repository · ranked by rigor-weighted score

All paths: learning paths. Methods, with their own guides and exemplars: methods pages. Programmatic access to the papers: agents page.