The Algorithmic Advantage

The Algorithmic Advantage
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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

  1. 053 - Martyn Tinsley - 2 of 2 - Walk Forward Correlation: A New Tool for Robust Strategy Design!

    २६ मे

    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/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

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

    ११ मे

    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/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

    १तास २५मि.
  3. 051 - Samir Varma - Classify Risk Don't Chase Alpha

    १४ एप्रि

    051 - Samir Varma - Classify Risk Don't Chase Alpha

    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

    १तास ३मि.
  4. 050 – Samir Varma - When Academic Finance Theory Fails

    ६ एप्रि

    050 – Samir Varma - When Academic Finance Theory Fails

    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…

    १तास ८मि.
  5. 049 - David Bush - Build a High-Performance Quant Crypto Portfolio Without Blowing Yourself Up!

    २६ मार्च

    049 - David Bush - Build a High-Performance Quant Crypto Portfolio Without Blowing Yourself Up!

    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

    ५५ मिनिटे
  6. 048 - Michael Wallace - Dynamic Position Sizing Like You Haven't Seen Before

    ९ मार्च

    048 - Michael Wallace - Dynamic Position Sizing Like You Haven't Seen Before

    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

    १तास ७मि.
  7. 047 - Tom Starke - The Basics of Building a Strategy Development Pipeline

    १८/१२/२०२५

    047 - Tom Starke - The Basics of Building a Strategy Development Pipeline

    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.

    १तास ३५मि.
  8. 046 - Tom Starke - Institutional Quant Trading Fundamentals

    ११/१२/२०२५

    046 - Tom Starke - Institutional Quant Trading Fundamentals

    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

    १तास ४५मि.
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ह्याविषयी

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

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  • निर्माता
    The Algorithmic Advantage
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    क्लीन करा
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    © The Algorithmic Advantage
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    The Algorithmic Advantage

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