The Algorithmic Advantage

Ex-prop trader on why solo trading is a different game, and how to win with real edges & portfolios of noisy strategies

Kris Longmore of Robot Wealth went from aimless data mining to an equity partnership at a Sydney prop firm, all because a professional trader asked him one question: "What's your edge?" In this episode, he explains why solo trading is a completely different game from professional trading, and why success comes down to picking games you can actually win. We cover why keeping your day job isn't a failure, the two fundamental ways traders get paid, and how to build a portfolio in layers: robust risk premia at the base, with flow and seasonality trades stacked on top.

Kris also shares specific strategy ideas, including the SPY/TLT month-end rebalance, the bond end-of-month effect, equity stat arb and long volatility into earnings. He explains why breadth beats polish when your portfolio is small, why you should automate only what you need to, and why machine learning is a tool, not an edge. Members can catch the bonus segment, where Kris breaks down his research process: the three questions he asks of every idea, why he doesn't hold out data, and why a slam-dunk result should make you nervous.

My Write Up: https://algoadvantage.substack.com/p/the-part-time-trader-part-1

Research: https://algoadvantage.substack.com

Courses & Community: https://algoadvantage.io/collective

Music:

Intro & Outro created by me on Suno - Algo Analogue I call it.

Pre-Intro - Your Destiny from HookSounds - No subscription licensing.

Tags: #SystematicTrading #QuantTrading #SoloTrader

Contents:

0:00 What’s Your Trading Edge?

8:30 The Question That Changed Everything

17:58 How Solo Traders Should Get Started

27:00 Building a Diversified Trading Portfolio

36:03 Exploiting Predictable Rebalancing Flows

45:17 How to Find Real Trading Edges

52:52 The Systematic Trader’s Tech Stack

59:12 R vs Python for Quant Trading

1:04:16 Why Machine Learning Isn’t an Edge

1:07:10 The Four Hats of a Solo Trader

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.com

Courses & Community: https://algoadvantage.io/collective

Music:

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:

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.com

Courses & Community: https://algoadvantage.io

Music:

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 Regimes

5:50 Toby Crabel’s Systematic Trading Origins

13:00 How the Opening Range Breakout Was Built

18:36 Lessons from Legendary Traders

25:04 Why Traders Must Study Market History

30:22 How 24-Hour Markets Changed Trading

38:02 How Systematic Trading Has Evolved

45:17 Crabel’s Multi-Market Strategy Portfolio

53:32 Trading as a Business

59:58 Price, Volume and Wyckoff Principles

1:06:31 Trading Short-Term Strategies at Scale

1:14:00 Capacity, Execution and Market Impact

1:22:00 Systematic Risk and Portfolio Management

1:30:00 Advice for the newer trader

1:38:00 The Future of Systematic Trading

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/207723117

I'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/collective

Contents:

0:00 From Crypto to Systematic Trading

7:31 Switching From Discretionary to Systematic

12:44 Building a Live Track Record on Darwinex

18:05 Trading Styles That Attract AUM

25:47 FX, Breakout and Trend Following Systems

32:20 Choosing Timeframes and Trade Frequency

37:18 Mentor Lessons for Trading Psychology

42:48 Scaling Into Futures and Better Execution

49:12 Metrics Darwinex Uses to Fund Traders

57:03 How Darwinex Allocates Trader Capital

1:00:33 Track Record Length and Strategy Fit

1:06:59 Using AI and Claude Code for Trading

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

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

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

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…

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-wallace

Courses, community & more: https://www.algoadvantage.io