MGPraxis6,400 papers identified by name
MarketGraphs Working Paper II · The Praxis Engine

How a claim becomes a verdict.

“The first principle is that you must not fool yourself — and you are the easiest person to fool.”— R. P. Feynman, Caltech commencement, 1974

Abstract

The pipeline behind every Praxis verdict: a crawler reduces the research firehose to finance-relevant papers (§1), extraction distils each into a structured spec (§2), the consequential ones are rebuilt end-to-end under documented deviations, known-value tests and negative controls (§3), then pushed through the C0–C7 gate ladder (§4) to a fixed-function verdict (§5). Nothing here forecasts. The engine describes what a published rule did: out of sample, net of costs.

1 · The corpus§12 · Extraction§23 · Reconstruction§34 · The gate suite§45 · The verdict§56 · Access§6References·

1The corpus

Everything downstream depends on knowing what exists. The crawler runs daily across the open research firehose. To date: 2 million records screened, 6,400 strategy papers identified by name, over 100 new a month in 2026.

■ equities■ volatility and options■ multi-asset■ crypto■ other, or not named1995200020052010201528420204539832026■ equities■ volatility and options■ multi-asset■ crypto■ other, or not named1995200020052010201528420204539832026
Figure 1. Strategy papers identified by name, by year and asset class, 1994–2026. The last year runs to 25 September. n = 6,400.
EquitiesVol &optionsMulti-assetCryptoFXCommoditiesRates &creditOtherNot named191411129125243Momentum19645221624Factor investing1063111144122Machine learning9723744130News sentiment8921113Value investing4621012143Portfolio optimization464022237Volatility risk premium5134733220Pairs trading33441222Options strategies3512713337Market timing40318Event-driven strategies254513245Macro signals2113Low-volatility anomaly187Quality factors1813Size premium103223Hedging15112Liquidity premium11132Carry trade12113Short-term reversal3436Market making141Seasonality115Prediction markets
EquitiesVol & optionsMulti-assetCryptoFXCommoditiesRates & creditOtherNot namedMomentum191411129125243Factor investing19645221624Machine learning1063111144122News sentiment9723744130Value investing8921113Portfolio optimization4621012143Volatility risk premium464022237Pairs trading5134733220Options strategies33441222Market timing3512713337Event-driven strategies40318Macro signals254513245Low-volatility anomaly2113Quality factors187Size premium1813Hedging103223Liquidity premium15112Carry trade11132Short-term reversal12113Market making3436Seasonality141Prediction markets115
Figure 2. Family × asset class. Fill ∝ papers, a dashed cell has none. n = 1,684. The asset × method grid, the recombination map, and the white space they expose ride with Praxis Pro. Become a member ↗

2Extraction

A paper is only comparable to another paper once both are the same shape.

Definition 1 (Structured spec).

A paper reduced to fixed pillars: asset · data · predictor · method · universe · validation. Each field is either quoted from the text or marked undisclosed.

The pillars are the engine’s coordinate system: they make every spec searchable over MCP, rank which claims deserve a week of reproduction, and expose the white space no single paper can see.

Momentum289Factors240ML forecast153Sentiment148Value106Portfolio105Vol premium102Pairs93Options84Timing80Events52Macro49
Figure 3a. Families, top 12 of 22. Area ∝ papers. n = 1,684.
Linear factor1,226Neural nets379Time series260Derivation222LLM195RL165Bayesian135State-space134Trees108Symbolic97Quantum26
Figure 3b. Method, coloured by family: classical · neural · RL · LLM. n = 2,369.

3Reconstruction

The rule, rebuilt from the paper’s own description and nothing else.

Each reproduction is built from the published text alone. Where the paper is silent, the gap is filled with a documented estimate and flagged as such; where its data cannot be sourced, the closest survivorship-clean proxy is substituted and recorded. The deviation ledger ships with every report: the reader never has to trust us on what changed.

29%21%19%18%6%Excess return1,295Profit928Alpha861Sharpe ratio819t-statistic278Drawdown196Information ratio91
Figure 4. What the papers claim: 4,468 mentions by the measure the abstract names. A paper may name more than one. n = 2,926.
An out-of-sample test954 18.7%
A benchmark598 11.7%
Transaction costs458 9.0%
The size of the universe125 2.5%
Multiple testing51 1.0%
Figure 5. What the abstract states of the test. Each bar is drawn against all 5,096 abstracts. Read by keyword from the abstract, not from the paper.
Algorithm 1. Faithful re-run, expanded
  1. extract the spec · quote every disclosed parameter
  2. source survivorship-clean reference data · record the vintage
  3. rebuild the strategy · fill undisclosed gaps, flag each
  4. test every block against known values · run negative controls
  5. validate through C0–C7 · publish claimed vs. measured

The controls are adversarial by construction. A look-ahead trap feeds tomorrow’s data through the pipeline: if the honest run were peeking it would light up (on the network-momentum paper the trap explodes the Sharpe to 22.3; the honest run reads 0.62). A signal shuffle scrambles the predictor and the edge must collapse to zero (it does: 0.01). Closed-form cases pin the numerics.

# deviation ledger · 361154 · excerpt data: Pinnacle 64 ratio-adjusted futures (1990–2022) -> the project's roll-adjusted reference panel, 40 contracts window: paper OOS 2000–2022 -> 2020–2026 (the paper’s pre-2010 window isn’t publicly reproducible) solver: CVXPY on MOSEK (commercial) -> CLARABEL (open) cross-checked to <1e-2 · exact on the 2-node form controls: peek trap SR 22.3 · shuffle SR 0.01 · honest 0.62

4The gate suite

Eight gates, fixed before any paper is run. A claim that survives all eight has nowhere left to hide.

Table 1. The gate suite: eight fixed tests, set before any paper is run.
GateWhat it tests
C0ReplicationFaithful re-implementation on the paper's own period, universe (or closest free proxy), and cost assumptions. Does the mechanism exist at all?
C1Trading costsRealistic transaction costs, fees and implementation frictions applied: the numbers the paper should have printed.
C2SignificanceStatistical reality: multiple-testing corrections (Bonferroni / Holm / BHY across the paper's own configuration grid) and the Deflated Sharpe Ratio [1].
C3Crisis regimesThe worst regimes: 2008, Volmageddon 2018, COVID 2020, the 2022 rate shock. Advertised protection is tested, not assumed.
C4GeneralisationOther universes, countries and asset classes, where the claim implies them.
C5CapacityCapacity, borrow and tradability stress, with cost sweeps to breakeven.
C6PersistenceTrue post-publication out-of-sample: the window the authors could not have fit.
C7OriginalitySpanning regressions on standard factor models (HAC / Newey–West errors): alpha, or relabelled beta [3]? Probability of Backtest Overfitting via CSCV assessed alongside [2].

4.1The deflation arithmetic

Gate C2 is where most claims die. The engine treats a reported Sharpe as the maximum of a search the authors ran, disclosed or not, and asks what survives once that search is priced in.

deflated Sharpe (Bailey–López de Prado): the claim, haircut for luck · gate C2
probabilistic Sharpe: P(true SR > benchmark) given skew & tails · gate C2
expected max Sharpe under N trials: the overfit baseline any search must beat · gate C2
minimum track-record length: how much history a claim needs before it means anything · gate C2
alpha decay & half-life: how fast the edge dies post-publication · gate C6
probability of backtest overfitting (CSCV): is the in-sample winner an out-of-sample loser? · gate C7

5The verdict

A fixed function, applied after the gates. Never an opinion.

(1)
Table 2. The verdict: a fixed function of measured against claimed, Eq. (1).
VerdictMeaning
validatedThe claim replicates cleanly on honest data.
overstatedThe effect is real but materially smaller than claimed.
failsThe claim does not survive honest reproduction.

Across the audits to date, the median Sharpe haircut on Sharpe-denominated claims is ≈ 60% of the published number, before anyone traded a dollar against it.

6Access

Every verdict is public. Readers open ten exemplar reproductions in full (gates, deviation ledgers, notebooks) and query them over MCP where they already work. The full corpus rides with Praxis Pro. The engine never emits position-level output.

$ subscribe --tier reader$ praxis --status

References

[1]Bailey, D. & López de Prado, M. The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality. J. Portfolio Management, 2014.
[2]Bailey, Borwein, López de Prado & Zhu. The Probability of Backtest Overfitting. J. Computational Finance, 2017.
[3]Harvey, C., Liu, Y. & Zhu, H. …and the Cross-Section of Expected Returns. Review of Financial Studies, 2016.
[4]Ioannidis, J. Why Most Published Research Findings Are False. PLoS Medicine, 2005.
[5]López de Prado, M. Advances in Financial Machine Learning. Wiley, 2018.
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