Quantitative Trading Course

Lesson 1 • Data Types and Sources

Surveys OHLCV, tick, fundamental, and alternative data. Connects data availability to strategy feasibility throughout the course.

Lesson 2 • Market Microstructure Essentials

Covers order books, bid-ask spreads, and price discovery mechanisms. Establishes the market mechanics that every subsequent strategy must account for.

Lesson 3 • Statistical Foundations for Trading

Reviews probability, distributions, and hypothesis testing as applied to financial returns. Ensures students can evaluate signal significance rigorously.

Lesson 1 • Feature Engineering for Strategies

Transforms raw prices into returns, volatility estimates, and technical features. These engineered inputs feed directly into signal construction.

Lesson 2 • Data Acquisition and Storage

Covers API-based data retrieval, flat-file ingestion, and database storage patterns. Establishes reproducible data pipelines for research.

Lesson 3 • Alternative Data Processing

Handles unstructured text, satellite, and sentiment data for quantitative use. Expands the signal universe beyond traditional price and volume.

Lesson 4 • Research Environment Setup

Configures reproducible Python environments with version control and notebooks. Ensures consistent results across team members and time.

Lesson 1 • Avoiding Overfitting in Signal Research

Applies walk-forward validation, deflated Sharpe, and combinatorial purging to prevent false discoveries. Protects research integrity before backtesting.

Lesson 2 • Signal Construction Techniques

Converts raw data into tradable signals using ranking, z-scoring, and composite methods. Directly produces the inputs used in backtesting.

Lesson 3 • Sources of Alpha

Categorizes alpha into momentum, mean reversion, value, and carry. Frames where to look for edge before building any signal.

Lesson 4 • Factor Model Fundamentals

Introduces single-factor and multi-factor return models with cross-sectional regression. Provides the analytical backbone for systematic signal evaluation.

Lesson 1 • Backtesting Architecture

Compares event-driven and vectorized engines, covering data flow and execution logic. Choosing the right architecture determines simulation fidelity.

Lesson 2 • Bias Identification and Elimination

Identifies look-ahead, survivorship, and selection biases that inflate backtest results. Eliminating these biases is mandatory before trusting any result.

Lesson 3 • Performance Metrics and Reporting

Calculates Sharpe, Sortino, max drawdown, Calmar, and turnover metrics. Standardized reporting enables objective strategy comparison.

Lesson 4 • Realistic Cost Modeling

Incorporates commissions, slippage, market impact, and financing costs into simulations. Accurate cost modeling separates paper profits from real edge.

Lesson 1 • Risk Parity and Factor Allocation

Allocates capital by equalizing risk contributions across assets and factors. Diversifies risk rather than capital, improving drawdown characteristics.

Lesson 2 • Mean-Variance Optimization

Derives efficient frontiers and optimal weights using expected returns and covariance. Establishes the classical framework before addressing its limitations.

Lesson 3 • Constraints and Practical Limits

Incorporates position limits, turnover constraints, and liquidity filters into optimization. Bridges theoretical optima and real-world implementability.

Lesson 4 • Multi-Strategy Portfolio Assembly

Combines uncorrelated strategies into a master portfolio using correlation and capacity analysis. Maximizes diversification benefit across the full book.

Lesson 1 • Stress Testing and Scenario Analysis

Simulates historical crises and hypothetical shocks to reveal hidden portfolio vulnerabilities. Prepares strategies for tail events before they occur.

Lesson 2 • Real-Time Risk Monitoring

Builds dashboards and automated alerts for live exposure, P&L, and limit breaches. Enables rapid response to deteriorating risk conditions.

Lesson 3 • Tail Risk and Hedging Strategies

Uses options, volatility instruments, and correlation hedges to reduce tail exposure. Complements position sizing with structural portfolio protection.

Lesson 4 • Drawdown Control and Position Sizing

Applies Kelly criterion, fixed fractional, and volatility-scaled sizing to limit drawdowns. Directly links position sizing to capital preservation goals.

Lesson 1 • Low-Latency Infrastructure Basics

Introduces co-location, network optimization, and hardware considerations for speed-sensitive strategies. Establishes when latency investment is justified.

Lesson 2 • Algorithmic Execution Strategies

Implements TWAP, VWAP, implementation shortfall, and adaptive algorithms. Reduces market impact and timing risk for large orders.

Lesson 3 • Transaction Cost Analysis

Measures pre-trade, in-flight, and post-trade costs to evaluate execution quality. Feeds cost data back into strategy and portfolio optimization.

Lesson 4 • Order Management and Reconciliation

Covers OMS design, fill confirmation, position reconciliation, and error handling. Ensures operational integrity between strategy signals and broker records.

Lesson 1 • Strategy Adaptation and Retraining

Applies rolling retraining, regime detection, and parameter updating to maintain edge. Balances responsiveness to change against overfitting to recent data.

Lesson 2 • Production Deployment Pipeline

Covers code promotion, paper trading, and staged live rollout with kill switches. Reduces deployment risk through systematic validation gates.

Lesson 3 • Shutdown and Post-Mortem Analysis

Defines criteria for strategy retirement and conducts structured post-mortems. Converts failures into institutional knowledge for future research.

Lesson 4 • Live Performance Monitoring

Tracks live P&L, signal behavior, and execution quality against backtest expectations. Early detection of drift prevents large unexpected losses.