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Learning

A structured library for learning quant research, from the basics to deep dives.

253 articles · 23 categories · EN / 中文

Learning phases

Nine phases, from market basics to AI-augmented quant.

  1. 01 Financial Markets Market structures, instruments, risk, and returns. Start here. 31 articles
  2. 02 Mathematics & Statistics Probability, statistics, and linear algebra foundations. 16 articles
  3. 03 Quant Trading Onboarding Python basics, Arithmion SDK intro, simple strategies, and backtesting concepts. 8 articles
  4. 04 Advanced Quant Trading Factor models and strategy frameworks: Fama-French, momentum, mean reversion, arbitrage, carry. 8 articles
  5. 05 ML Foundations Supervised learning, unsupervised learning, and neural networks. 8 articles
  6. 06 ML × Quant Trading Feature engineering, alpha factors, and ML strategies with deep validation thinking. 8 articles
  7. 07 Backtesting & Validation Walk-forward analysis, multiple testing, Deflated Sharpe, and avoiding overfitting. 8 articles
  8. 08 AI × Quant Trading LLMs, RAG, and AI-augmented research workflows. Coming soon
  9. 09 Tools & Systems Arithmion SDK deep dive, data pipelines, and deployment. 8 articles

Reference categories

Quant Methodology How to do research. 10 articles
Tools & Code Python, pandas, the Arithmion SDK. 8 articles
Validation & Debunking Deflated Sharpe, PBO, signal red flags. 9 articles

Deep dives

One topic at a time, from derivation to evidence.

Featured learning paths

Quant onboarding From zero to your first validated alpha factor. In progress
Validation deep-dive From a backtest result to honest significance. In progress
Arithmion usage From install to your first custom datasource plugin. In progress

This site is for education and research. It is not financial advice. Arithmion does not sell signals, recommend instruments, or operate as a broker.