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 Evaluate a Machine-Learning Trading Paper

   A checklist for evaluating ML trading papers: baseline honesty, leakage-prone pipelines, accuracy-vs-P&L confusion, seed variance, and the questions that separate signal from citation bait.

   Why here: The master checklist; everything below expands one of its items.

2. 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: Feature construction is where ML papers leak the future.

3. Combinatorially Symmetric Cross-Validation (CSCV) Explained

   CSCV and the Probability of Backtest Overfitting (PBO) explained: the block-combination construction, pseudocode, how to read PBO, and the method's honest limitations.

   Why here: Cross-validation that respects time and counts trials.

4. P-Hacking in Financial Research: The Practices, the Tells, the Fixes

   How p-hacking works in finance: the seven questionable research practices, the tells visible in published papers, and the reader-side corrections that keep you from funding other people's noise.

   Why here: The tells of a result selected from many.

5. Statistical Significance vs Economic Significance in Trading Research

   Why t-statistics mislead in finance: multiple testing and the factor zoo, the t > 3 hurdle, economic magnitude after costs, and the two-by-two matrix for judging any empirical result.

   Why here: Accuracy above 50% is not a strategy.

6. 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: High-turnover ML signals live or die here.

7. A Minimal ML Experiment-Tracking Stack for Quant Research

   Experiment tracking for quant ML: what to record, the finance-specific requirements (trial counts, temporal splits, leakage audits), tool tiers from SQLite to MLflow/W&B, and the minimal stack that suffices.

   Why here: Record the trial count so the haircut is honest.

8. 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: Apply the haircut.

9. 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: Run the numbers for the paper you are reading.

10. A Visual Map of Machine Learning in Asset Pricing

    A Visual Map of Machine Learning in Asset Pricing: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored.

    Why here: Where the ML-in-asset-pricing literature stands.

Related

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

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 Evaluate a Factor-Investing Paper

   A checklist for evaluating factor-investing and cross-sectional asset-pricing papers: multiple-testing hurdles, portfolio construction choices, cost realism, and the questions that expose a factor-zoo entry.

   Why here: The checklist.

2. How to Interpret a Factor-Zoo Paper

   Reading factor-zoo and replication meta-studies: what the multiple-testing corrections mean, why replication rates differ so wildly between studies, and what survives for practitioners.

   Why here: Hundreds of factors, few survivors: how to read the meta-literature.

3. Alpha, Beta, and Alternative Risk Premia: The Difference That Prices Everything

   The alpha/beta/ARP taxonomy: what each actually is, the regression that sorts any return stream, why the boundaries move over time, and what each category should cost.

   Why here: Is it alpha, beta, or a risk premium in disguise?

4. Survivorship Bias in Quantitative Finance: How Dead Companies Fake Alpha

   How survivorship bias inflates backtests, how large the effect is by asset class, the five-dead-tickers test for any dataset, and how to build survivorship-clean universes.

   Why here: Dead companies fake alpha.

5. Cross-Sectional vs Time-Series Predictability: Two Different Claims

   The difference between cross-sectional and time-series predictability in finance: what each claim means, how evidence standards differ, and why conflating them produces phantom strategies.

   Why here: Two different claims that papers blur.

6. Cross-Sectional Momentum vs Time-Series Momentum

   The two momentum families compared: construction, crash profiles, evidence bases, and why they are different strategies that happen to share a name.

   Why here: The worked example of that distinction.

7. What Makes a Factor Tradable?

   The gap between a published factor premium and a tradable one: implementation costs, capacity, crowding, borrow reality, and the checklist that converts paper premia into honest expectations.

   Why here: From a sorted portfolio to a position you can hold.

8. Why Turnover Can Destroy Factor Returns

   The turnover arithmetic that decides factor profitability: signal decay vs trading cost, the rebalance-frequency trade-off, turnover-reduction techniques, and the reporting gap in academic papers.

   Why here: Turnover is the hidden fee.

9. Capacity Constraints in Quantitative Strategies

   How to estimate a strategy's capacity: the impact arithmetic, the capacity hierarchy by strategy type, self-competition effects, and why capacity is the number papers never report.

   Why here: How much money the premium can absorb.

10. Portfolio Optimization: Estimation Error and Regularization

    Why mean-variance optimization amplifies estimation error, the error-maximization mechanism, the regularization toolkit (shrinkage, constraints, resampling), and the 1/N benchmark that keeps everyone honest.

    Why here: Combining factors without amplifying noise.

11. Risk Parity, Minimum Variance, and Maximum Diversification

    The μ-free allocation family compared: what risk parity, minimum variance, and maximum diversification each optimize, the leverage that makes risk parity work, and the shared failure modes.

    Why here: The construction choices that need no return forecast.

Related

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

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:

Related

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

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. OHLCV, Trade, Quote, and Order-Book Data: What Each Can Answer

   The market-data hierarchy from daily bars to full order books: what each granularity can and cannot answer, the storage and cost jumps between levels, and matching data type to research question.

   Why here: What OHLCV, trades, quotes and order books can and cannot tell you.

2. How to Evaluate a Market-Microstructure Paper

   A checklist for evaluating market-microstructure papers: dataset provenance, venue and period specificity, theoretical assumption audits, and the generalization trap.

   Why here: The checklist for the literature.

3. Limit-Order-Book Imbalance: What It Measures and What It Misses

   Order-book imbalance as a predictor: why it works at tick horizons, the spoofing and iceberg problems, the monetization gap, and how to evaluate imbalance-based research.

   Why here: The canonical short-horizon signal and its limits.

4. A Visual Map of Limit-Order-Book Prediction Research

   A Visual Map of Limit-Order-Book Prediction Research: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored.

   Why here: LOB-prediction research mapped by score.

5. Market Making: Inventory Risk, Adverse Selection, and Spread Capture

   The three forces of market making — spread capture, inventory risk, and adverse selection — with the Avellaneda-Stoikov intuition, the profitability identity, and the production failure catalog.

   Why here: Inventory, adverse selection, spread capture.

6. 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: Impact models and the square-root law.

7. Microstructure Noise and Realized Volatility

   Why high-frequency volatility estimates explode: bid-ask bounce, discreteness, the signature plot, noise-robust estimators, and the practical sampling rules for realized volatility.

   Why here: Why high-frequency volatility estimates need care.

8. How to Store Tick Data Efficiently

   Practical tick-data storage: partitioning schemes, columnar formats, compression choices, type discipline, and the layout decisions that make years of ticks queryable on one machine.

   Why here: Storage layout for the data you now need.

9. TimescaleDB vs ClickHouse vs DuckDB vs kdb+ for Tick Data Research (2026)

   An honest comparison of TimescaleDB, ClickHouse, DuckDB, QuestDB, and kdb+ for storing and querying tick data in quant research — by workload, not by benchmark marketing.

   Why here: Choosing the database.

10. Tick Data Storage Sizer & Cost Estimator tool

    Interactive tick data storage calculator: raw and compressed GB per day, year, and total for trades, quotes, L2 depth, or full order book across your universe — with October 2026 cost estimates for NVMe, S3, ClickHouse Cloud, Timescale Cloud, QuestDB Cloud, and a Hetzner box, plus the data-feed bill.

    Why here: Size the storage before buying it.

11. Free vs Paid Market Data: What Changes in Real Research

    What separates free market data from paid: the six quality dimensions that matter for research, where free data is genuinely sufficient, and the failure modes that only surface after you've built on it.

    Why here: What changes when you pay for data.

Related

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

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. A Visual Map of Reinforcement Learning for Trading

   A Visual Map of Reinforcement Learning for Trading: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored.

   Why here: The lay of the land: which problems RL papers attack and how the archive scores them.

2. How to Evaluate a Reinforcement-Learning Trading Paper

   A checklist for evaluating RL trading papers: environment leakage, reward hacking, the sample-efficiency problem in non-stationary markets, and where RL claims are actually credible.

   Why here: The checklist: seeds, environment realism, baselines, costs.

3. Market Making: Inventory Risk, Adverse Selection, and Spread Capture

   The three forces of market making — spread capture, inventory risk, and adverse selection — with the Avellaneda-Stoikov intuition, the profitability identity, and the production failure catalog.

   Why here: The problem RL most plausibly helps with, and the static baseline it must beat.

4. A Visual Map of Market-Making Research

   A Visual Map of Market-Making Research: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored.

   Why here: Market-making research mapped by score.

5. 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: Reward functions that ignore impact train agents that trade too much.

6. Regime Dependence: When a Historical Edge Stops Working

   Regime dependence in trading strategies: why edges are conditional on market states, how to detect regime-carried backtests, decay vs regime-shift diagnosis, and what to do when an edge goes quiet.

   Why here: A policy trained on one regime is a bet on that regime.

7. A Minimal ML Experiment-Tracking Stack for Quant Research

   Experiment tracking for quant ML: what to record, the finance-specific requirements (trial counts, temporal splits, leakage audits), tool tiers from SQLite to MLflow/W&B, and the minimal stack that suffices.

   Why here: Many seeds and many configs need bookkeeping.

8. Quant Researcher Compute-Cost Calculator tool

   Interactive calculator: local GPU vs cloud GPU vs API costs for quant research. Editable assumptions, break-even hours, and a monthly budget for your whole stack.

   Why here: What the honest version of the experiment costs to run.

Related

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

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 Build a Quant Research Pipeline: From Idea to Evidence, Repeatably

   The seven-stage quant research pipeline — idea intake, data, features, backtest, validation, paper trading, production — with the artifact each stage must produce and the gate it must pass.

   Why here: The end-to-end design: idea to evidence, repeatably.

2. A Practical Quant Research Stack for a One-Person Shop (2026)

   The complete toolchain for solo quant research in 2026: data, storage, backtesting, compute, and deployment — with honest costs and the mistakes to skip.

   Why here: Concrete tool choices for one person.

3. Market Data for Quant Research: How to Choose a Vendor (2026)

   A decision framework for choosing market data vendors for quant research: survivorship bias, point-in-time integrity, licensing, and the real cost tiers.

   Why here: Where the data comes from.

4. When to Use Parquet, Postgres, or a Columnar Database

   The three storage archetypes for quant research — files, relational, columnar-analytical — matched to workload shapes, with the two-tier default and the migration triggers.

   Why here: Files or a database, and which database.

5. Point-in-Time Data Architecture for Backtesting

   Designing a point-in-time data layer: bitemporal modeling, the as-of query pattern, a practical schema for prices, fundamentals, universes and events, and the migration path from a naive store.

   Why here: The storage design that prevents look-ahead bias by construction.

6. Corporate Actions and Adjusted Price Data

   Corporate actions in quant research: how adjustments work, the dividend/total-return distinction, the actions that break backtests (spinoffs, mergers, delistings), and the store-unadjusted principle.

   Why here: The adjustment logic every price dataset needs.

7. How to Version Datasets and Backtests

   Versioning for quant research: content-addressed datasets, config-hashed backtests, the lineage chain that makes any result re-derivable, and the lightweight tooling that suffices.

   Why here: Make every result reproducible from a hash.

8. Reproducible Quant Research with Docker

   Using Docker for reproducible quant research: what containers do and don't solve, the research-image pattern, determinism beyond the environment, and when containers are overkill.

   Why here: Freeze the environment.

9. A Minimal ML Experiment-Tracking Stack for Quant Research

   Experiment tracking for quant ML: what to record, the finance-specific requirements (trial counts, temporal splits, leakage audits), tool tiers from SQLite to MLflow/W&B, and the minimal stack that suffices.

   Why here: Track trials so the overfitting haircut is honest.

10. Local GPU vs Cloud GPU for Financial NLP and LLM Research (2026)

    When to buy a GPU and when to rent one for financial NLP and LLM research: break-even math, VRAM sizing, data-licensing constraints, and the hybrid default.

    Why here: Compute decisions for NLP and deep-learning work.

11. Quant Researcher Compute-Cost Calculator tool

    Interactive calculator: local GPU vs cloud GPU vs API costs for quant research. Editable assumptions, break-even hours, and a monthly budget for your whole stack.

    Why here: Price the compute.

12. From Notebook to Production: A Minimal Automated-Strategy Operating Stack (2026)

    The minimal operating stack for running an automated trading strategy: one VPS, Docker Compose, Postgres, cron done right, secrets, logs, dead-man alerting, backups, and incident response — with working configs.

    Why here: From research code to a running strategy.

13. 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: The final gate.

Related

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

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. Options-Implied Volatility: A Practical Research Primer

   Implied volatility for researchers: what IV actually is, surface anatomy, the variance risk premium, using IV as a signal, and the data pitfalls that corrupt options research.

   Why here: The surface, its conventions, and what it encodes.

2. Microstructure Noise and Realized Volatility

   Why high-frequency volatility estimates explode: bid-ask bounce, discreteness, the signature plot, noise-robust estimators, and the practical sampling rules for realized volatility.

   Why here: Measuring realized volatility without being fooled by noise.

3. A Visual Map of Volatility Forecasting Research

   A Visual Map of Volatility Forecasting Research: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored.

   Why here: The forecasting literature mapped by score: GARCH to HAR-RV to deep learning.

4. Regime Dependence: When a Historical Edge Stops Working

   Regime dependence in trading strategies: why edges are conditional on market states, how to detect regime-carried backtests, decay vs regime-shift diagnosis, and what to do when an edge goes quiet.

   Why here: Volatility regimes and why models fitted across them mislead.

5. Auditing a Monte Carlo: Method Notes from a Leveraged-ETF Simulation

   Every technique used to take four Monte Carlo simulation scripts apart, find what they were actually measuring, and rebuild them — with the real numbers each step produced.

   Why here: Checking a simulation before trusting a price from it.

6. Volatility Targeting: Benefits, Hidden Leverage, and Drawdown Risk

   Volatility targeting mechanics: why scaling by inverse vol has worked, the leverage it quietly embeds, gap risk and vol-spike deleveraging, and the estimator choices that change everything.

   Why here: Using the forecast: benefits, hidden leverage, drawdown risk.

7. Monte Carlo Equity Curve Simulator tool

   Simulate thousands of trading equity curves from win rate, reward-to-risk, and position size. Percentile bands, drawdown statistics, and risk of ruin — all in your browser.

   Why here: Simulate what volatility targeting does to a path.

Related

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