Afleveringen
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Big discount on Martyn's tool for subscribers: https://www.algoadvantage.io/toolbox/
Watch Part 1 first! https://youtu.be/Kxvp00VbLx0
My 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 Selection
9:27 Building a Combined Performance Metric
13:05 Objective Functions and Walk Forward Tests
17:30 In-Sample vs Out-of-Sample Validation
22:28 Pre-Live Optimization for Live Trading
25:14 Why Traditional Walk Forward Falls Short
28:59 Walk Forward Correlation Method
32:28 Measuring Predictive Power in Trading
39:25 Reading Correlation Chart Scenarios
41:48 Trade Counts and Statistical Significance
45:52 Go/No-Go Gates for Robust Strategies
51:03 Optimize Strategy Software Overview
56:43 Final Thoughts for Systematic Traders
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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/toolbox
Martyn'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.io
Contents:
00:00 Introduction and Setup
02:02 Martyn's Trading Journey
12:07 Transition to Algorithmic Trading
20:02 Common Pitfalls in Trading
30:11 Developing Robust Trading Strategies
31:55 Understanding Parameter Optimization and Performance Metrics
39:43 The Impact of Economic News on Trading Strategies
44:38 Identifying the True Edge of Trading Strategies
52:05 Noise Reduction Techniques in Algorithmic Trading
01:01:49 Research Phase vs. Optimization in Trading Strategies
01:07:33 Reassessing Trading Strategies
01:08:00 The Importance of Statistical Significance
01:09:00 Understanding Sample Size in Trading
01:10:00 Methodology for Backtesting Strategies
01:11:59 The Role of Edge in Trading Strategies
01:15:03 Randomness vs. Genuine Edge
01:17:59 Long-Term Performance and Sample Size
01:19:52 Confidence in Trading Results
01:22:00 Increasing Sample Size for Better Results
01:24:01 Testing Across Multiple Assets
01:26:04 Optimizing Across Timeframes
01:30:01 Generalizing Strategies Across Markets
01:31:57 Diversification in Trading Strategies
01:35:05 Final Thoughts on Strategy Optimization
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Zijn er afleveringen die ontbreken?
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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.com
Courses & Community: https://algoadvantage.io
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Where Real Edge in Quant Trading Actually Comes From
Do 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…
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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
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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-wallace
Courses, community & more: https://www.algoadvantage.io
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Courses, community & more: https://www.algoadvantage.io
This 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.
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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
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Detailed write-up on all of the concepts discussed here: https://www.algoadvantage.io/podcast/045-rob-hanna
Rob 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
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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
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Can caveman-simple trading rules still work in today’s markets? Brent Penfold says yes. In this interview, he reveals why old strategy rules remain powerful, why portfolio-level thinking is the real edge, and how diversification and discipline create timeless success.Brent talks about trading patterns in a range of 30 futures markets, deploying 20 strategies diversified across mean reversion and trend following principles. I write an article inspired by each podcast which are full of my insights and practical tips. Check them out on the website: https://www.thealgorithmicadvantage.comLinks for Quant Strats conference in London on the 14th-15th October 2025:
https://www.alphaevents.com/events-quantstratsuk
--- 10% off using ALGOADVANTAGE10
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Round II of a systematic trading masterclass with Laurens Bensdorp: architect non-correlated, purpose-built portfolios—mix trend following, mean reversion, and long-volatility hedges to drive smoother, higher risk-adjusted returns.
We unpack the “paradox of diversification” (Parrondo’s paradox) to turn “ugly” equity curves into compounding machines, and when (not) to switch systems off to avoid recency bias and overfitting.
Plus: robust portfolio construction, capital allocation, and highlights from Laurens’ latest book, Trading Retirement Accounts.
Combining losing investments into a winner:
https://blog.ephorie.de/parrondos-paradox-in-finance-combine-two-losing-investments-into-a-winner
"The Paradox of Diversification" paper:
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1567126
More on our site: https://www.thealgorithmicadvantage.com
#Quant #SystematicTrading #AlgorithmicTrading #PortfolioConstruction #Diversification #RiskManagement #TrendFollowing #MeanReversion #Volatility #Hedging #Backtesting #Robustness
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Dive into the deep experience of quantitative trading with Cesar Alvarez (trader first, martial artist second), a veteran trader known for his mastery in mean reversion, breakouts, momentum, ETF and volatility strategies. Discover his innovative methods building a dynamic portfolio, retiring strategies, parameter sensitivity tests, strategy robustness checks, and the art of balancing risk and return to ensure long-term trading success. Cesar’s insights highlight essential strategies for thriving in volatile markets, fine-tuning strategy components, and avoiding the trap of overfitting. Perfect for systematic traders looking for practical edges!
#QuantTrading #MeanReversion #AlgorithmicTrading #ETFStrategies #QuantTrading
Contents:
0:00 Cesar's Journey: Discretionary to Quant Trading
3:59 Inside Connors Research: Mean Reversion Insights
5:48 Cesar's Current Quant Trading Portfolio
8:35 Tactical ETF Strategies & Retirement Focus
14:34 Designing Quant Strategies: Goals & Principles
17:07 Robustness Testing & Avoiding Overfitting
22:49 Knowing When to Retire a Trading Strategy
29:53 Amibroker vs RealTest: Tools for Systematic Traders
34:03 Cesar’s Featured Quant Trading Strategies
37:03 Short Selling & Mean Reversion in Bear Markets
41:12 Breakout & Momentum Strategies for Stocks
43:53 Navigating Volatility: Trading VIX & SVIX ETFs
50:50 Secrets to Effective Mean Reversion TradingWhat could it be?
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A Smart Portfolio of Trend Following, Mean Reversion & Hedging Strategies
Unlock insane returns with quant crypto trading! Discover how Pavel from Robuxio builds robust portfolios combining mean reversion, momentum, and hedging strategies—even with limited historical data. Learn essential techniques for managing crypto volatility, optimizing execution, and leveraging diversified strategies. Curious? Dive into the show!
#QuantTrading #CryptoTrading #Momentum #MeanReversion #Hedging #AlgorithmicTrading #CryptoStrategies
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Psychology for Quant Traders? Really?
Quantitative futures traders like to think in code, not clichés—but Dr Brett Steenbarger makes a compelling case that mindset is part of the edge. In this interview, Brett argues that the same statistical rigor quants apply to markets should be applied to the grey matter behind the keyboard. Here's a guide for the advanced systematic trader who suspects “psy-stuff” might be more than motivational posters.
The punch-line from Brett’s research is simple: systematic trading is less “set-and-forget” and more Formula 1 pit-crew—engineering precision plus real-time human performance. Code finds edges; psychology keeps you creative enough to refresh them. Or, as one of Brett’s blog posts puts it, “We can’t run robust systems from brittle minds.” Not a bad mantra to stick on your trading monitor!
#traderpsychology #tradermindset #tradinginthezone
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Finishing our little mini-series on shorter-term futures trading we talk to Andrea Unger and happily inject some click-bait in the form of gloating about his 672% return in a single year when he won the World Trading Competition. Naturally, we know that this kind of return is generated by specifically trying to win the comp, and taking on the associated risks! If you've been asleep the first two guests in this series were Bob Pardo and Kevin Davey. Between the three we've got a complete masterclass in shorter-term, diversified and responsive futures trading!
Andrea Unger is actually a four-time World Trading Champion, and here he offers a comprehensive and structured approach to quantitative trading in futures markets, emphasizing practical methods for strategy design, robustness testing, portfolio construction, and system deployment.
www.thealgorithmicadvantage.com
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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.
Topics:
Strategy Design Principles
Walk Forward Analysis: Best Practices and Common Mistakes
Robustness Testing Beyond Walk Forward
Tech Stack and Automation Tools
Portfolio Construction Process
Monthly Maintenance and Rebalancing
Risk Management and Psychological Preparedness
Performance Benchmarks and Goals
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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.
www.thealgorithmicadvantage.com
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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.
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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.
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