Big discount on Martyn's tool for subscribers: https://www.algoadvantage.io/toolbox/Watch Part 1 first! https://youtu.be/Kxvp00VbLx0My detailed write up on Walk Forward Correlation Analysis: https://www.algoadvantage.io/podcast/053-martyn-tinsley-2/Martyn introduces Walk Forward Correlation (WFC) as a diagnostic for two problems that sit at the heart of systematic trading: over-fitting and structural edge. Traditional walk-forward analysis typically optimizes a strategy on an in-sample window, picks the “best” parameter set, then tests that one choice out-of-sample. Used the wrong way, there’s a potential flaw here: one parameter set can look good out-of-sample purely by accident. That tells you very little about whether the underlying model is genuinely robust.Tinsley’s move is simple, but useful. Instead of judging one selected point, he looks at all parameter combinations in the optimisation grid and asks a harder question: does strong in-sample performance tend to map to strong out-of-sample performance across the whole space? If yes, you may have something real. If no, you’re probably flattering noise.Contents:0:00 Walk Forward Correlation Explained 4:22 Best Metrics for Strategy Selection9:27 Building a Combined Performance Metric13:05 Objective Functions and Walk Forward Tests17:30 In-Sample vs Out-of-Sample Validation22:28 Pre-Live Optimization for Live Trading25:14 Why Traditional Walk Forward Falls Short28:59 Walk Forward Correlation Method32:28 Measuring Predictive Power in Trading39:25 Reading Correlation Chart Scenarios41:48 Trade Counts and Statistical Significance45:52 Go/No-Go Gates for Robust Strategies51:03 Optimize Strategy Software Overview56:43 Final Thoughts for Systematic Traders

Martyn's process. Dealing with common trader pitfalls. Defining steps and methods for avoiding over-fitting."Opt My Strategy" the Robustness Testing Application built by Martyn Tinsley. Up to 25% off for Algo Advantage Subscribers!! https://www.algoadvantage.io/toolboxMartyn's paper on his new technique, "Walk Forward Correlation A Diagnostic for Over-Fitting and Structural Edge in Trading Strategy Optimisation": Our courses, community & toolbox: https://algoadvantage.ioContents:00:00 Introduction and Setup02:02 Martyn's Trading Journey12:07 Transition to Algorithmic Trading20:02 Common Pitfalls in Trading30:11 Developing Robust Trading Strategies31:55 Understanding Parameter Optimization and Performance Metrics39:43 The Impact of Economic News on Trading Strategies44:38 Identifying the True Edge of Trading Strategies52:05 Noise Reduction Techniques in Algorithmic Trading01:01:49 Research Phase vs. Optimization in Trading Strategies01:07:33 Reassessing Trading Strategies01:08:00 The Importance of Statistical Significance01:09:00 Understanding Sample Size in Trading01:10:00 Methodology for Backtesting Strategies01:11:59 The Role of Edge in Trading Strategies01:15:03 Randomness vs. Genuine Edge01:17:59 Long-Term Performance and Sample Size01:19:52 Confidence in Trading Results01:22:00 Increasing Sample Size for Better Results01:24:01 Testing Across Multiple Assets01:26:04 Optimizing Across Timeframes01:30:01 Generalizing Strategies Across Markets01:31:57 Diversification in Trading Strategies01:35:05 Final Thoughts on Strategy Optimization

What does a quantum physicist & inventor bring to quant trading? He thinks differently and is purposefully anti-alpha - instead focusing on risk management. After years of trying conventional risk models, Samir’s conclusion was not that risk is impossible to model. It was that most people are solving the wrong problem. They try to predict exact future risk levels. His approach shifted to classifying market states instead: when risk is low, be exposed; when risk is high, reduce or eliminate exposure.That is a profound change in mindset.Prediction asks for precision.Classification asks for usefulness.And in markets, usefulness usually wins.My in-depth analysis and write-up: https://algoadvantage.substack.comCourses & Community: https://algoadvantage.io

Where Real Edge in Quant Trading Actually Comes FromDo not watch this podcast. This is Part 1 with Samir Varma, and in Part 2 we go into great detail about his quantitative trading. In the Collective, he gives our members some specific instructions on how to measure risk differently – this stuff isn’t fluff. But in Part 1, I got derailed into quantum physics, determinism, AI, Asimov’s three laws of robotics and more. One of my favourite shows – but the first show I’ve done that isn’t about trading! It’s the warm-up you need to make the most of Part 2 though, and if I didn’t publish it, I’d be depriving a great many of you who will no doubt find this stuff as fascinating as myself! Still, if you only have time for strict ‘trading content’, fair warning, skip this. Let me know your thoughts…

Crypto Trader's Edge Course: https://www.algoadvantage.io/academy/crypto-traders-edge/Most crypto traders are still thinking like coin pickers when they should be thinking like portfolio architects. High-performance systematic crypto trading is not about chasing narratives — it is about robust portfolio construction, trend following, mean reversion, risk management, alpha stacking, diversification, and building strategies that can survive extreme volatility.This pod with David Bush breaks down how to build a smarter algorithmic crypto trading portfolio using proven trading logic, better R&D, and an all-weather mindset. If you want to trade crypto like a serious systematic trader — not a gambler — this is worth your time.#CryptoTrading #AlgorithmicTrading #SystematicTrading #QuantTrading #CryptoPortfolio #PortfolioConstruction #RiskManagement #TrendFollowing #MeanReversion #TradingStrategy #Backtesting #RobustTrading #QuantResearch #Alpha #CryptoMarkets

This interview with Michael Wallace (who was inspired by Larry Williams & Ralph Vince) brings a few things to mind. First is the absolute centrality of the role of position sizing in trading, second is the nature of ‘probabilities’ in trading. They are highly related obviously. Sizing is not an afterthought; it can change everything. Presuming an ‘average win rate’ is going to apply to your next 10 trades is not a wise way to proceed either. You want to be more ‘statistically minded’ than that – just toss a coin 10 times, and do that 10 times, the number of heads you get in each group of 10 is going to vary wildly no doubt. Toss it 10,000 times and ‘averages will tend to show up, this is the law of large numbers, but accounts can blow up a long time before averages play out. Because... sequencing risk.SEE MY FULL WRITE UP ON POSITION SIZING: https://www.algoadvantage.io/podcast/048-michael-wallaceCourses, community & more: https://www.algoadvantage.io

Courses, community & more: https://www.algoadvantage.ioThis is part II, part I is Episode 46.I know we all want “quick, actionable take-aways”, but the reality is that foundational principles of strategy development process is at the core of successful trading, and you more than likely do not have half of this in place like you should. So, while this is ‘foundational’, and can only be covered briefly, don’t skimp on reviewing this stuff. It’s only in the Algo Collective that we’ll be able to take the time to deep-dive how to set this all up in a highly practical way. Believe me, once you have a pipeline for strategy development, you’re done! You churn out strategies that are more robust, quickly drop bad ideas and refine your portfolio quickly. You can focus on risk management, other research and constant review, while your trading takes place automatically in the background. At least, that’s my approach.

Detailed write up on how institutions trade differently: https://www.algoadvantage.io/podcast/046-tom-starke/Part 2: coming soon!Dr Tom Starke trades significant institutional capital as a quant trader for a private fund. In Part 1, we cover the common pitfalls of 'retail' or newer traders. Tom makes the case that institutions 'think differently', applying an extra dimension to their thinking, as compared to retail traders. A significant result of this is the critical role a systematic R&D process plays in strategy development. The development pipeline is a 'research first', 'hypothesis testing' laboratory, designed to invalidate bad ideas quickly, and push viable ideas through a strict robustness testing framework to ensure out-of-sample results. Applying a scientific approach (which is just good data science), means letting the data speak, rather than squeezing it for the answers we want! The result is a process designed to minimize overfitting and produce the highest risk-adjusted returns for the pre-defined objectives.Courses, Community & More: https://algoadvantage.ioContents:0:00 Introduction to Systematic Trading and Research6:47 Tom Stark’s Journey: From Physics to Trading13:16 The Scientific Approach: Pros and Cons in Trading19:30 Avoiding Analysis Paralysis in Quant Trading26:02 The Transition: Retail vs Institutional Trading32:28 The Motivation Behind Teaching and Mentoring Traders38:04 Mindset Shifts: From Retail to Institutional Thinking44:34 Risk Management: How Institutions Approach Risk51:08 Defining Trading Objectives: A Key Starting Point57:06 Portfolio Construction: Balancing Risk and Return1:03:10 Diversification: The Key to Long-Term Success1:09:30 Position Sizing: Crucial for Strategy Success1:15:00 Machine Learning’s Role in Systematic Trading1:21:10 Python: The Essential Tool for Quantitative Research1:27:00 Back-testing and Strategy Evaluation: Avoiding Overfitting

Detailed write-up on all of the concepts discussed here: https://www.algoadvantage.io/podcast/045-rob-hannaRob Hanna has been trading since the mid 90's and has slowly progressed from discretionary swing trading to a systematic, research driven approach, while still carrying some of those qualitative features into his quant trading. He trades a diversified set of strategies in equities and ETFs, with a focus on the shorter term (and particularly mean-reversion) models. Of particular interest to me was his VIX trading strategies due to their usefulness as a hedge in times of crises, and because they employ more than just price data (they look to the VIX futures curve - whether in backwardation or contango as a critical filter to his models). Trading volatility (through the futures, options or ETFs) can be extremely risky, but given the strong edges that are present in trading a consistent down-trending market, it's always of interest to me how traders find a way to profit while minimizing the risks inherent in these models. Rob has been trading the VIX long enough to share some invaluable insights. Enjoy!The only reliable source for trading COURSES, COMMUNITY & more: https://algoadvantage.io

I think Nick Radge’s edge is actually an architecture: robust, simple, momentum-driven systems stitched together into a portfolio that survives, adapts, and compounds. Across nearly four decades, he’s traded through crashes, chop, and melt-ups; shifted from futures to equities for business reasons; and kept his build-process stubbornly logic-first and comfortingly boring—by design. The pro vs amateur divide, per Nick: pros ride the drawdowns and are present for the next outlier. They profit from human bias—fear, greed, crowding—by refusing to trust their own emotions and by outsourcing discretion to rules they can defend under pressure. Write the plan. Build the engines. Diversify the return streams. Rebuke complexity. Then let compounding do its weird, beautiful work.COURSES, COMMUNITY & MORE OVER ON THE WEBSITE:https://www.algoadvantage.io