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Courses

Follow the structured path from coding fundamentals to finding statistically valid market edges.

Learning Path

1
Python Fundamentals
Write clean Python scripts and solve core programming tasks confidently.
Focus: Syntax and variables · Loops and functions · Data structures
2
Pandas for Data Analysis
Prepare raw market datasets into model-ready tables.
Focus: DataFrames · Cleaning and joins · Feature tables
3
Statistics Fundamentals
Evaluate signals with statistical discipline instead of intuition alone.
Focus: Distributions · Hypothesis tests · Confidence intervals
4
Statistical Edges
Identify and validate repeatable alpha candidates.
Focus: Signal validation · Bias checks · Robust backtesting
5
Probability for Trading
Quantify uncertainty and make probability-driven decisions.
Focus: Conditional probability · Bayes rule · Expected value
6
Calculus for Quants
Understand optimization mechanics behind modern quant models.
Focus: Derivatives · Gradients · Optimization basics
7
Basic Quant Strategies
Build and compare baseline strategies with realistic assumptions.
Focus: Momentum · Mean reversion · Risk controls
8
Machine Learning
Train and validate ML models correctly on financial data.
Focus: Feature engineering · Cross-validation · Diagnostics
9
Machine Learning Strategies
Convert predictions into executable and risk-aware strategy logic.
Focus: Signal-to-trade mapping · Position sizing · Cost-aware evaluation
10
Frequency Scaling
Adapt strategy design across multiple trading frequencies.
Focus: Horizon effects · Noise handling · Execution constraints
11
Market Microstructure
Understand how market mechanics affect fills, costs, and edge decay.
Focus: Order books · Spread and impact · Execution tactics
Step 1 Beginner Enrolled

Python Fundamentals

Learn the basics of Python programming — variables, types, control flow, and functions. The foundation for everything else.

4 chapters 10 exercises
65%
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Step 2 Beginner

Pandas for Data Analysis

Work with DataFrames, cleaning, filtering, and transformations so you can structure market datasets effectively.

5 chapters 12 exercises
Requires:
Python Fundamentals
Step 3 Intermediate

Statistics Fundamentals

Build core statistical intuition: distributions, hypothesis testing, confidence intervals, and correlation vs causation.

5 chapters 12 exercises
Requires:
Pandas for Data Analysis
Step 4 Advanced

Statistical Edges

Turn statistical concepts into practical strategy ideas, edge validation, and robust backtesting workflows.

5 chapters 12 exercises
Requires:
Statistics Fundamentals
Step 5 Intermediate

Probability for Trading

Master conditional probability, Bayes intuition, and expected value for decision-making under uncertainty.

4 chapters 10 exercises
Requires:
Statistical Edges
Step 6 Intermediate

Calculus for Quants

Cover derivatives, gradients, optimization basics, and continuous-time intuition used in quant modeling.

4 chapters 9 exercises
Requires:
Probability for Trading
Step 7 Intermediate

Basic Quant Strategies

Implement momentum, mean reversion, and simple factor ideas with clean backtests and risk controls.

5 chapters 12 exercises
Requires:
Calculus for Quants
Step 8 Advanced

Machine Learning

Learn core supervised learning workflows, feature engineering, validation, and model diagnostics.

5 chapters 12 exercises
Requires:
Basic Quant Strategies
Step 9 Advanced

Machine Learning Strategies

Translate ML predictions into tradable portfolios with position sizing, costs, and robustness checks.

5 chapters 12 exercises
Requires:
Machine Learning
Step 10 Advanced

Frequency Scaling

Adapt ideas from daily to intraday horizons while handling noise, latency assumptions, and execution impact.

4 chapters 10 exercises
Requires:
Machine Learning Strategies
Step 11 Advanced

Market Microstructure

Study order books, spread dynamics, adverse selection, and execution tactics in real trading environments.

5 chapters 12 exercises
Requires:
Frequency Scaling