by The Algorithmic Advantage
The Algorithmic Advantage is a podcast about quantitative trading and investing. We're here to expand the toolkit of the quant-trading community and introduce investors to the many advantages of systematic trading. Our goal is to educate and inspire as we embark on a captivating journey into the vast knowledge and experience of leading portfolio managers and other experts in the field!
Language
🇺🇲
Publishing Since
8/17/2023
Email Addresses
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April 24, 2025
<p>Kevin’s systematic approach melds rigorous quantitative testing with pragmatic risk management and monthly maintenance protocols. By enforcing single-pass optimizations, extensive real-time validation, and lean portfolio sizes, he constructs a robust trading framework designed for consistency and longevity. Advanced traders can draw from his workshop principles to refine strategy design, navigate common back-testing pitfalls, and build diversified, adaptive portfolios capable of weathering market uncertainties.</p><p><br></p><p>Topics: </p><p>Strategy Design Principles</p><p>Walk Forward Analysis: Best Practices and Common Mistakes</p><p>Robustness Testing Beyond Walk Forward</p><p>Tech Stack and Automation Tools</p><p>Portfolio Construction Process</p><p>Monthly Maintenance and Rebalancing</p><p>Risk Management and Psychological Preparedness</p><p>Performance Benchmarks and Goals</p>
April 18, 2025
<p>In the cutthroat world of algorithmic futures trading, a structured process is non-negotiable. Kevin Davey’s approach—defining objectives, rigorous validation via walk-forward and Monte Carlo methods, live incubation, and proactive portfolio management—offers advanced quantitative traders a framework to thrive in. By blending engineering precision with market adaptability, his methodology underscores that success lies not just in the strategies themselves, but in the disciplined process behind them.</p><p>www.thealgorithmicadvantage.com</p>
March 29, 2025
<p>Many trading strategies are developed using extensive historical data to calibrate model parameters. However, this process often leads to over-optimization, where the strategy is too finely tuned to past market conditions. Two things stand out:</p><p><br /></p><p>Noise vs. Signal: Financial markets inherently contain a high degree of randomness. A model that fits historical data exceptionally well may simply be capturing random fluctuations rather than a persistent trading edge. Regime Shifts: Markets change over time. A strategy that works during a bull market might not perform in a bear market or during periods of high volatility.</p><p><br /></p><p>Enter Walk-Forward Analysis. It's also not easy, but if done right can create an incredible method to solve for over-fitting in a systematic manner, leading to:</p><p><br /></p><p>Realistic Performance Metrics: By testing on entirely out-of-sample data (not just one out of sample period), traders can obtain performance metrics that are closer to what would be experienced in real-world trading. Adaptive Strategies: Walk forward analysis inherently forces a re-optimization process. This means the model is continually updated to reflect more recent market conditions, thereby reducing the risk that it’s built solely on outdated historical data. Robust Parameter Selection: Instead of selecting a single “optimal” parameter set that may be an outlier, traders can identify a plateau of robust parameters that perform consistently across multiple windows. This approach minimizes the risk of curve fitting, ensuring the strategy’s parameters are not overly sensitive to one specific dataset.</p>
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