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How to Build Targets That Make Sense in Financial ML
Why Your Model Is Only as Good as the Target You Train It On
Nov 28
•
Lucas
4
1
How to Survive the Multiple Hypothesis Bias
Why Most Strategies Fail Before They Even Reach Live Trading
Nov 21
•
Lucas
3
1
Integrating Exogenous Drivers into Market Models
How global indices like S&P 500, Gold, or Oil can reveal hidden dependencies across assets.
Nov 14
•
Lucas
4
Adapting Strategies to Market Structure
Understanding when (and where) your strategy truly performs.
Nov 7
•
Lucas
1
October 2025
Cross-Asset Learning: Finding True Structure Beyond Noise
When the same features drive predictability across multiple markets, robustness stops being an accident.
Oct 31
•
Lucas
9
2
Filtering Noise, Finding Structure
A robust statistical approach to understanding how assets synchronize during stress.
Oct 24
•
Lucas
Statistics vs. Economics in Trading Strategies
Balancing hard data with market intuition to build robust alphas.
Oct 17
•
Lucas
Alpha vs. Strategy: What’s the Difference?
Why formulas create signals, but only strategies create profits.
Oct 10
•
Lucas
Feature Engineering: The First Step Toward Predictability
Isolating volatility and skewness to make markets easier to model.
Oct 3
•
Lucas
4
2
September 2025
Predictability in Financial Time Series
How measuring uncertainty helps you separate signal from noise.
Sep 26
•
Lucas
9
7
How to Land a Quant Trading Job
A diploma gets you noticed, but a project gets you hired.
Sep 19
•
Lucas
5
1
The 5 Best Books to Learn Quant Trading
Each of these books shaped my journey, here’s how they can shape yours.
Sep 12
•
Lucas
8
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