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:

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.

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:

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is continually updated to reflect more recent market conditions, thereby reducing the risk that it’s built solely on outdated historical data.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.

Of the two biggest problems quantitative traders probably face, the first is over-optimization and the second is likely finding inspiration for new ideas. In-depth interviews with market wizards surely has to be one of the best ways to learn quickly, avoid common pitfalls and find untold amounts of inspiration hidden between the lines. Listening to experts that have been at it for decades, for me anyway, is an incredible education. In this show I invite you to again spend over an hour with Bob Pardo on the ins and outs of his trading, his philosophy and his edge. And the best bit is, this is just part 1 of 2. In the second part I'm going to deep-dive walk forward analysis with him and I'm sure I'll be walking away with some highly practical tips and tricks.

Bob’s career spans several decades of evolving market dynamics, groundbreaking system development, and a philosophy rooted in adaptability and robustness. His journey—from early days on the trading floor to pioneering walk forward analysis and working with the likes of Solomon Brothers, Dunn Capital, Daiwa Securities & Goldman Sachs—offers a compelling narrative for quantitative traders seeking both inspiration and technical insights.

Intra-day Futures Traders and others - grab a chamomile tea and enjoy!

www.thealgorithmicadvantage.com for contacts and more.

The A-Z of building a systematic futures portfolio

In this episode, seasoned trader Rob Carver shared his nuanced approach to building and managing a diversified futures portfolio—a methodology that appeals to advanced, technical traders, while we also covered off some of the 'basics' of futures trading, such as rolling, back-adjusting, and so on. I did my best to break down the key elements of his strategy, from market selection to dynamic optimization and continuous trading. A couple of interesting things came up, there's a lot of detail in here, and luckily you can go to his blog and books for all the technical detail.

For the long-term futures trader with a smaller account, this is essential listening. How much diversification across markets and models is enough? How can we capture the benefits of this diversification with a limited account size? Rob has innovative approaches to both market diversification and model diversification to generate a highly capital efficient approach.

For futures data, check out Norgate on our site: https://thealgorithmicadvantage.com/tools/

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Building Better Strategies with Good Science

It was strangely comforting talking to Ernie Chan. Whilst I was completely out of my depth talking about AI and Machine Learning, I came away broadly reinforced in my own belief that great trading still requires a human touch, and that the best niche's in the market are best discovered by applying a certain kind of wisdom, experience and competitive approach. The machine learning techniques and computer power needed to make them work are, however, quickly catching up, so how long we have is anyone's guess.

For now, however, even Ernie is on the same page: that causal strategies (ones you can say 'why' they work) are still superior, more robust, easier to tweak if they should begin to decay. Furthermore, diversification across strategy types is key, merging long and short vol strategies, diversifying between trend and mean reversion. Avoiding over-fitting these strategies is best done by applying the scientific method: create a hypothesis of what should work in the market, then try to invalidate it with a logical analysis of the data. Well, that's nicely validating for my approach, so I'm happy.

More detail / notes over at www.thealgorithmicadvantage.com

In the domain of quantitative finance, the juxtaposition of mean reversion and trend-following strategies constitutes a pivotal dialogue in the formulation of robust trading paradigms. Each methodology is underpinned by unique theoretical and empirical foundations, presenting distinct opportunities and inherent vulnerabilities. However, when synthesized within a cohesive portfolio framework, these strategies reveal a profound synergy that not only enhances diversification but also attenuates systemic risks. This discourse delves into the nuances of each strategy and elucidates their integrative potential.

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Wayne Himelsein, President and CIO of Logica Capital Advisors, has developed a robust approach to options trading centred on long volatility strategies that balance systematic rigor with human oversight. His methodology involves "gross long volatility," rejecting short volatility trades to ensure full protection during market downturns, and dynamically adjusting positions through a technique he calls "scalping to fund long vol." This process leverages mean-reverting market behaviours to offset the inherent costs of options while maintaining asymmetric risk-reward structures like straddles and strangles.

Supported by extensive quantitative analysis and adaptability to varying volatility regimes, Wayne’s strategies exemplify a nuanced blend of art and science in trading.

More over at www.thealgorithmicadvantage.com

The concept of the “Cockroach Portfolio” is a novel take on building a robust investment strategy that thrives across diverse market conditions. Drawing inspiration from one of nature's most resilient creatures, this approach emphasizes adaptability, diversification, and risk mitigation.

Jason Buck runs Mutiny Funds with a core belief that: “Offense wins games. Defense wins championships.” Mutiny’s version of a diversified, all-weather portfolio therefore combines defensive-minded strategies, such as long volatility and trend, with offensive-minded strategies, such as stocks and bonds. Ensuring survival, and reducing draw-downs through time, provides the best opportunity for long-term capital growth.

This show is all about risk management. If you don’t know what ergodicity is, or how you can drown in a river that is 2 feet deep on average, listen in.

Loads more, including contact links and a detailed write-up over at www.thealgorithmicadvantage.com

One of the standout points from Eric’s approach is the focus on constructing a durable, all-weather portfolio. Eric designed his fund with the idea that it should be able to endure the most uncertain future scenarios, a goal he finds essential for long-term wealth preservation. His strategy blends managed futures, global equities, and laddered treasury bills, with a focus on equal risk contribution across futures & equities.

Eric emphasizes simplicity and robustness over complexity, believing that this provides the best opportunity for consistent performance in both good times and bad.

For Eric, effective trading is a blend of art and science—knowing when to trust the data and when to recognize that the future will always hold surprises. It was extremely interesting to hear Eric’s wisdom that “direct attempts to create a smoother equity curve usually cause more harm than good. They create more problems than they solve”, adding that it is akin to asking for “more infrequent iceberg risk” in order to avoid “frequent, survivable, nuisance risk”. The solution being to build something that is robust and durable on a stand-alone basis, then something else, and then something else. Pick the ones that play well together and combine into a portfolio!

More detail, tools and so on over at www.thealgorithmicadvantage.com

Reach out with questions / suggestions!

Today we spoke with Corey Hoffstein, a well-known market practitioner with a deep and broad knowledge across quantitative trading & trend following, but also across developing investment products for wider advisor distribution. I’m super interested in almost every aspect of the financial markets, because I feel like a broad and generalist knowledge helps me make better trading and business decisions. Corey hits the nail on the head when he says that a given industry participant may be making optimal business decisions which are sub-optimal trading decisions.

If we are aware of this kind of behaviour, it can help shape the way we trade. More than that, understanding market participants, infrastructure, technology, business motivations, and so on, is critical to effective trading. At a basic level, this would include knowing the exact differences between the execution of a market versus a limit order for example. It builds from there, but the point is, it doesn’t hurt you to gain more and more knowledge of how the markets ‘actually work’.

So much more over on the website:

www.thealgorithmicadvantage.com

Gary Antonacci is back on the show after having released a new research paper with Carlo Zarattini from Concretum Research which constitutes a 100-year study on trend following US sectors. The strategy deployed in the paper has an impressive long-term track record, averaging an annual return of 18.2% with 12.6% volatility and a Sharpe Ratio of 1.39. Using Keltner and Donchian channels for entries & edits, volatility-based position sizing and a universe of 48 sectors, the simple model is surprisingly robust and a testament to the enduring power of trend following.

Get all the links over at www.thealgorithmicadvantage.com