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! www.algoadvantage.io

Critical formulas for Bollinger Band trading - John Bollinger - 056

What does 46 years in markets teach you about building trading systems that actually last?John Bollinger joins me to discuss simplicity, robustness, volatility, market breadth, position sizing and why traders are paid to accept risk.We also cover Bollinger Bands inside Keltner Channels, price-pattern confirmation, geometric growth, risk of ruin, the danger of optimisation and why old systems can still be incredibly valuable.John even reveals a “secret sauce” trading system along the way.Write Up & Research: https://algoadvantage.substack.comCourses & Community: https://algoadvantage.io/collectiveMusic:Intro & Outro created by me on Suno - Algo Analogue I call it.Pre-Intro - Your Destiny from HookSounds - No subscription licensing.#AlgorithmicTrading #QuantTrading #SystematicTrading Contents:

09-03
01:03:57

055 - Toby Crabel - Short-Term Futures Trading with Size!

Toby Crabel — founder of Crabel Capital Management (~$5B AUM) and author of the legendary *Day Trading with Short Term Price Patterns and Intraday Breakouts* (1990), the book that gave the world the opening range breakout and NR4/NR7 patterns — joins the show for a rare, wide-ranging conversation. Toby traces his path from a pro tennis career to the Chicago trading floors, his formative stints with Victor Niederhoffer and his early connections to Monroe Trout and Paul Tudor Jones, and how zero-commission floor trading shaped his short-term edge from day one. He unpacks why the "clean open" that powered ORB for decades has eroded under 24-hour markets and institutional flow, why studying historical price shocks (1987, COVID) is non-negotiable for systematic survival, and why PhDs and machine learning are no substitute for a causal, market-structure-driven research process. For the solo systematic trader, Toby's advice is refreshingly practical: start with one market, build strict rules around a single idea, and know exactly when your edge has died. A must-watch for anyone serious about the history, robustness, and future of short-term systematic trading.Research: https://algoadvantage.substack.comCourses & Community: https://algoadvantage.ioMusic:Intro & Outro created by me on Suno - Algo Analogue I call it.Pre-Intro - Your Destiny from HookSounds - No subscription licensing.Contents:0:00 AI, Quant Research and Market Regimes5:50 Toby Crabel’s Systematic Trading Origins13:00 How the Opening Range Breakout Was Built18:36 Lessons from Legendary Traders25:04 Why Traders Must Study Market History30:22 How 24-Hour Markets Changed Trading38:02 How Systematic Trading Has Evolved45:17 Crabel’s Multi-Market Strategy Portfolio53:32 Trading as a Business59:58 Price, Volume and Wyckoff Principles1:06:31 Trading Short-Term Strategies at Scale1:14:00 Capacity, Execution and Market Impact1:22:00 Systematic Risk and Portfolio Management1:30:00 Advice for the newer trader1:38:00 The Future of Systematic Trading

08-14
01:43:44

054 - Kieran Duff - Trading for a Living

Trading your own account was never going to replace a salary — the compounding you need gets wiped out by the withdrawals you need to live on. The more commercial option is to trade investor capital, but the options are limited.In this video we get a look inside a trader's journey with Darwinex, quickly establishing a track record and attracting external capital.In the Substack article I break down why prop firm evaluations are built for the firm to win, not you: daily loss limits, trailing drawdown, and consistency rules that quietly punish traders with genuine edge. I talk about why fixed stop-losses backfire to explain exactly why trailing drawdown is the worst offender, and why the industry's real ~10% pass rate says far more about the rules than about trader skill. Then I cover the alternative most traders never consider: platforms like Darwinex, where there's no evaluation to survive, just a certified track record and capital that's actually incentivised to see you succeed.Check it out: https://algoadvantage.substack.com/publish/post/207723117I've just released an incredible 'Trading Breakthroughs with AI course' for members of the Collective. You'll also get the bonus chat with Kieran (and all my other guests).https://algoadvantage.io/collectiveContents:0:00 From Crypto to Systematic Trading7:31 Switching From Discretionary to Systematic12:44 Building a Live Track Record on Darwinex18:05 Trading Styles That Attract AUM25:47 FX, Breakout and Trend Following Systems32:20 Choosing Timeframes and Trade Frequency37:18 Mentor Lessons for Trading Psychology42:48 Scaling Into Futures and Better Execution49:12 Metrics Darwinex Uses to Fund Traders57:03 How Darwinex Allocates Trader Capital1:00:33 Track Record Length and Strategy Fit1:06:59 Using AI and Claude Code for Trading

07-20
01:13:16

053 - Martyn Tinsley - 2 of 2 - Walk Forward Correlation: A New Tool for Robust Strategy Design!

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

05-26
01:00:08

052 - Martyn Tinsley - 1 of 2 - Building Robust Trading Strategies - The Masterclass

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

05-11
01:24:56

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