Newsletter: When Trading Systems Break Down, Causes of Decay and Stop Criteria

Diagnosing Strategy Decay and Establishing Exit Conditions in Systematic Trading

A key challenge in system development is that trading performance often deteriorates after going live. In this issue, we look at why this happens by examining the post-publication decay of stock anomalies, and we address a practical question faced by every trader: when a system is losing money, is it simply in a drawdown or has it stopped working altogether?

Web-only posts Recap

Below is a summary of the web-only posts I published during last week.

The Volatility Risk Premium Around Macroeconomic Announcements

AI Aging: Model Quality Degradation

How Credit Risks Affect Momentum Strategies Returns

Do Commodities Lead the Equity Markets?

Diffusive Volatility and Jump Risks

Hedge Effectiveness Under a Four-State Regime Switching Model

Upcoming Conference

The CBOE Quant Conference is a gathering of thought leaders from academia and industry to examine the future of quantitative finance. This year, they feature fantastic speakers such as Jim Gatheral and Julien Guyon. If you're a member of an academic institution, you can receive an 80% discount. Email CboeRMC@cboe.com for the discount code. For the full agenda, follow the link below:

RMC Quant Conference 2025

Why and How Systematic Trading Strategies Decay After Going Live

Testing and validating a trading strategy is an important step in trading system development. It’s a commonly known fact that a well-optimized trading strategy’s performance often deteriorates after it goes live. Thus, developing a robust strategy that performs well out-of-sample is quite a challenge.

Reference [1] attempts to answer the question: why a strategy’s performance decays after going live.

Findings

-The paper investigates which ex-ante characteristics can predict the out-of-sample decline in risk-adjusted performance of published stock anomalies.

-The analysis covers a broad cross-section of anomalies documented in finance and academic journals, with the post-publication period defined as out-of-sample.

-Predictors of performance decay are based on two hypotheses: (1) arbitrage capital flowing into newly published strategies, and (2) in-sample overfitting due to multiple hypothesis testing.

-Publication year alone accounts for 30% of the variance in Sharpe ratio decay, with Sharpe decay increasing by 5 percentage points annually for newly published factors.

-Three overfitting-related variables—signal complexity (measured by the number of operations required) and two measures of in-sample sensitivity to outliers—add another 15% of explanatory power.

-Arbitrage-related variables are statistically significant but contribute little additional predictive power.

-The study tests both hypotheses using explanatory variables and univariate regressions, finding significant coefficients from both sets.

In short, the results indicate that performance decay is driven jointly by overfitting and arbitrage effects.

Reference

[1] Falck, Antoine Rej, Adam and Thesmar, David, Why and How Systematic Strategies Decay, SSRN 3845928

When to Stop Trading a Strategy?

When a trading system is losing money, an important question one should ask is: Are we in a drawdown, or has the system stopped working? The distinction is crucial because the two situations require different solutions. If we are in a drawdown, it means that our system is still working and we just have to ride out the losing streak. On the other hand, if our system has stopped working, we need to take action and find a new system.

Reference [2] attempted to answer this question.

Findings

-The paper examines how to distinguish between normal unlucky streaks and genuine degradation in trading strategies.

-It argues that excessively long or deep drawdowns should trigger a downward revision of the strategy’s assumed Sharpe ratio.

-A quantitative framework is developed using exact probability distributions for the length and depth of the last drawdown in upward-drifting Brownian motions.

-The analysis shows that both managers and investors systematically underestimate the expected length and depth of drawdowns implied by a given Sharpe ratio.

I found that the authors have some good points. But I don’t think that the assumption that the log P&L of a strategy follows a drifted Brownian process is realistic.

Note that a trading strategy’s P&L can often exhibit serial correlation. This is in contradiction with the assumption above.

Reference

[2] Adam Rej, Philip Seager, Jean-Philippe Bouchaud, You are in a drawdown. When should you start worrying? arxiv.org/abs/1707.01457v2

Closing Thoughts

Both papers address the critical issue of strategy persistence and performance decay, though from different perspectives. The first highlights how published anomalies tend to lose risk-adjusted returns over time, with evidence pointing to both overfitting in backtests and arbitrage capital crowding as drivers of performance decay. The second provides a quantitative framework for assessing when drawdowns signal genuine deterioration rather than normal variance, showing that investors often underestimate the length and depth of drawdowns implied by a given Sharpe ratio. Taken together, these studies underscore the need for investors to treat historical performance with caution, monitor strategies rigorously, and account for both statistical fragility and realistic drawdown expectations in portfolio management.

Educational Video

Automating Robustness Analysis of Trading Strategy Development Processes

In this video Edwin Stang demonstrates a trading-platform built to automate robustness testing across the entire strategy development pipeline. He explains how he extended a long-running system with machine-learning strategy generation (differential evolution / genetic programming), a domain-specific expression language to formalize every decision point (entry/exit, order types, position sizing, risk rules, portfolio selection), and nested robustness loops — walk-forward testing, Monte-Carlo repeats, and White’s Reality Check — so that the platform measures repeatability rather than one-off backtest fits.

Edwin highlights key engineering choices that make the approach practical: a high-performance Java implementation with constant folding, bitset optimizations, and zero-copy execution to run hundreds of thousands to millions of backtests per second; UIs for step-through backtests and visual diagnostics; and an automated filter/selector that turns candidate generators into tradable ensembles. His empirical comparisons (signal vs breakout rules on EUR/USD) show many false positives from automated generation and demonstrate why layered robustness checks and rigorous selection criteria are essential before moving strategies live.

Around the Quantosphere

- The Wharton School launches Master of Science in Quantitative Finance with $60M gift from Bruce I. Jacobs (upenn)

-Up to 50% of quant researchers in hedge fund pods have stressful, miserable jobs (efinancialcareers)

-DE Shaw raising $5 billion in its first hedge fund run by humans (bloomberg)

- How to get a $20k a month trading firm internship (efinancialcareers)

-AI-driven hedge funds: Hidden risks of algorithmic fragility and systemic underestimation (ainvest)

-Interns are offered $14,000 a month pay by quant firms in India (bloomberg)

- Michael Saylor says Bitcoin may Go ‘Boring’ as Institutional Money Kills Volatility (yahoo finance)

Recent Newsletters

Below is a summary of the weekly newsletters I sent out recently

-Volatility Targeting Across Asset Pricing Factors and Industry Portfolios (12 min)

-Tail Risk Hedging Using Option Signals and Bond ETFs (12 min)

-Stochastic Volatility Models for Capturing ETF Dynamics and Option Term Structures (11 min)

-Cross-Sectional Momentum: Results from Commodities and Equities (11 min)

-Predictive Information of Options Volume in Equity Markets (11 min)

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