MGPraxis6,400 papers identified by name
Strategy Validation Engine

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systematic strategies
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Abstract ↓

Abstract

Praxis is a library of audited systematic trading strategies. Published claims overstate themselves, routinely and measurably [2, 3]. We screened 2 million research records, identified 6,400 strategy papers by name, with over 100 new a month in 2026, and rebuild the most consequential to the paper’s own spec. Each is re-run on bias-free data, out of sample, net of all real costs, across crisis regimes, accounting for overfitting [4] and luck [1]. Every claim leaves validated, overstated, or fails, and its code integrates into IBKR, Bloomberg or LEAN.

Keywords: replication · out-of-sample · deflated Sharpe · alpha decay · MCP
EquitiesVol &optionsMulti-assetCryptoFXCommo-ditiesRates &creditOtherNotnamedMomentum191411129125243Factor investing196452216·24Machine learning1063111144122News sentiment97237441·30Value investing892·11···13Portfolio optimization4621012·1·43Volatility risk premium46402·223·7Pairs trading5134733·220Options strategies334412·22··Market timing35127·13337Event-driven strategies403·····18Macro signals25451324·5Low-volatility anomaly21·····1·3Quality factors18·······7Size premium18··1····3Hedging1032·2···3Liquidity premium15···1·1·2Carry trade1·1·132···Short-term reversal12·1··1··3Market making34·3····6Seasonality141·······Prediction markets1··1···5·
EquitiesVol & optionsMulti-assetCryptoFXCommoditiesRates & creditOtherNot namedMomentum191411129125243Factor investing196452216·24Machine learning1063111144122News sentiment97237441·30Value investing892·11···13Portfolio optimization4621012·1·43Volatility risk premium46402·223·7Pairs trading5134733·220Options strategies334412·22··Market timing35127·13337Event-driven strategies403·····18Macro signals25451324·5Low-volatility anomaly21·····1·3Quality factors18·······7Size premium18··1····3Hedging1032·2···3Liquidity premium15···1·1·2Carry trade1·1·132···Short-term reversal12·1··1··3Market making34·3····6Seasonality141·······Prediction markets1··1···5·
Figure 1. The corpus by family and asset class: all 22 families. Shading ∝ papers. A · marks none. n = 1,684.

1Findings

Table 1. Claimed vs. measured, out-of-sample, net of costs.
#PaperAssetClaimedMeasuredClaimed → MeasuredVerdict
1The Volatility Edge — dual VIX-ETN signalVolSharpe 1.001.03Sharpe 1.00→ 1.03validated
2Network momentum across asset classesX-assetSharpe 1.510.62Sharpe 1.51→ 0.62overstated
3Lazy factor + probability weightingGold·BTC$136,216$4,374$136,216→ $4,374fails
⋮More papers can be viewed in Your Library →
(1)

2Methodology

ReplicationC0100%Trading costsC184%SignificanceC263%Crisis regimesC352%GeneralisationC441%CapacityC534%PersistenceC622%OriginalityC712%

Definition 1 (Measured edge). The measured edge is the paper’s own headline statistic, recomputed from a full re-implementation under three conditions: (i) out of sample, (ii) net of real costs, (iii) on survivorship-clean, point-in-time data. Table 1 reports it beside the claim.

Theorem 1 (Selection). Among N backtests of noise, the expected best Sharpe ratio grows as √(2 ln N) [1]. Publication reports the maximum, so a claimed edge exceeds the true one in expectation. The excess is identified only by re-measurement under Definition 1, and Eq. (1) turns the gap into the verdict.

Remark. The verdict is computed, not argued.

Figure 2. What survives each gate. 100 claims enter, ≈12 leave beta-adjusted. Illustrative until the audited set is large enough to publish measured rates · full gate definitions ↗

3Access

Table 2. Access — reader is free, no card; Praxis Pro is £29 a month.
Reader — freePraxis Pro — £29/mo
A library of clustered systematic trading strategies5 papers6,400
Strategy validation reports under live trading conditions5all
Reproduction code, integrated with Bloomberg, IBKR, etc.5all
Integration with your AI agent — Claude, ChatGPT, Cursor5full corpus
Become a member — £29/mo ↗full corpus · every reproduction · MCP on all 6,400
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works in Claude · ChatGPT · Cursor · any MCP host

4References

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