Algorithmic Trading Course

Lesson 1 • Algorithmic Trading System Overview

Maps the end-to-end architecture of an algo trading system. Connects data ingestion, signal generation, execution, and risk into one coherent pipeline.

Lesson 2 • Regulatory and Compliance Fundamentals

Introduces market conduct rules, reporting obligations, and algorithmic trading oversight frameworks. Ensures strategies are designed within compliant boundaries from the start.

Lesson 3 • Markets and Microstructure Basics

Covers order types, bid-ask spreads, and price formation mechanics. Establishes market vocabulary needed for every subsequent trading concept.

Lesson 1 • Automating Data Pipelines

Schedules data downloads, transformations, and storage updates with Python scripts. Automation ensures strategies always operate on fresh, consistent data.

Lesson 2 • Statistical Analysis of Returns

Computes return distributions, rolling statistics, and correlation matrices. Provides the statistical intuition underlying signal construction and risk measurement.

Lesson 3 • Data Structures for Market Data

Applies pandas DataFrames and time-series indexing to OHLCV data. Directly supports the data manipulation required in every strategy chapter.

Lesson 4 • Python Environment Setup

Configures a reproducible Python workspace with essential libraries. Removes setup friction so students focus on financial logic immediately.

Lesson 1 • Oscillators and Mean-Reversion Signals

Builds RSI, Stochastic, and CCI to detect overbought and oversold conditions. Contrasts with trend indicators to support mean-reversion strategy design.

Lesson 2 • Combining Indicators into Signals

Designs multi-indicator confirmation logic and signal scoring frameworks. Reduces false positives by requiring agreement across independent indicator families.

Lesson 3 • Custom Indicator Development

Codes proprietary indicators from mathematical formulas and domain hypotheses. Enables differentiated strategies not replicable from standard library functions.

Lesson 4 • Volatility and Volume Indicators

Computes Bollinger Bands, ATR, and volume-weighted metrics to measure market activity. Volatility signals inform position sizing and breakout entry rules.

Lesson 1 • Overfitting and Bias Detection

Identifies look-ahead bias, data snooping, and overfitting through statistical tests. Prevents strategies from failing in live markets due to in-sample illusions.

Lesson 2 • Backtesting Architecture and Data

Structures an event-driven backtesting engine and selects appropriate historical data. Correct architecture prevents look-ahead bias from corrupting results.

Lesson 3 • Using Backtesting Libraries

Implements strategies in established Python backtesting frameworks for speed and reproducibility. Library proficiency accelerates iteration from idea to validated result.

Lesson 4 • Simulating Realistic Execution

Models slippage, commissions, and partial fills to approximate live trading costs. Realistic cost modeling prevents strategies from appearing profitable only in simulation.

Lesson 1 • Statistical Arbitrage and Pairs Trading

Tests cointegration between asset pairs and builds spread-trading strategies. Extends mean reversion to multi-asset relationships for market-neutral exposure.

Lesson 2 • Parameter Optimization Techniques

Applies grid search, random search, and Bayesian optimization to tune strategy parameters. Balances in-sample fit against out-of-sample robustness to avoid overfitting.

Lesson 3 • Mean-Reversion Strategy Construction

Designs Bollinger Band and RSI-based reversion strategies with statistical entry thresholds. Contrasts risk profile and holding period with trend-following approaches.

Lesson 4 • Strategy Stress Testing

Subjects strategies to historical crisis periods, regime shifts, and synthetic shock scenarios. Stress testing reveals fragility before capital is committed to live markets.

Lesson 1 • Position Sizing Models

Implements fixed fractional, Kelly criterion, and volatility-scaled sizing methods. Correct sizing determines whether a profitable strategy survives adverse runs.

Lesson 2 • Risk Metrics and Measurement

Quantifies Value at Risk, Expected Shortfall, and beta to characterize portfolio risk. Accurate measurement is the prerequisite for all subsequent risk control decisions.

Lesson 3 • Portfolio-Level Risk Management

Manages correlation, sector concentration, and gross exposure across multiple strategies. Portfolio-level controls prevent individual strategy losses from cascading.

Lesson 4 • Stop-Loss and Drawdown Controls

Codes hard stops, trailing stops, and portfolio-level drawdown circuit breakers. Automated controls enforce discipline when discretionary judgment may fail.

Lesson 1 • Broker API Integration

Authenticates and communicates with broker REST and WebSocket APIs to place orders. API integration is the critical bridge between strategy logic and live markets.

Lesson 2 • Execution Quality Analysis

Measures slippage, fill rates, and implementation shortfall against benchmarks post-trade. Execution analysis closes the feedback loop between strategy design and live performance.

Lesson 3 • Order Types and Routing Logic

Implements market, limit, stop, and conditional orders with smart routing rules. Proper order selection minimizes execution cost and slippage in live conditions.

Lesson 4 • Real-Time Order Monitoring

Builds a live dashboard tracking open orders, fills, and position reconciliation. Real-time monitoring enables rapid response to execution anomalies and system failures.

Lesson 1 • Strategy Lifecycle Management

Defines criteria for strategy retirement, reoptimization, and capital reallocation over time. Lifecycle management prevents capital from remaining in strategies that have lost their edge.

Lesson 2 • Production Infrastructure Setup

Configures cloud servers, containerized deployments, and secure credential management. Reliable infrastructure prevents downtime from causing missed trades or runaway positions.

Lesson 3 • Live Performance Attribution

Decomposes live P&L into signal, execution, and cost components for attribution analysis. Attribution identifies whether underperformance stems from strategy logic or execution quality.

Lesson 4 • Paper Trading and Staged Rollout

Validates strategies in paper trading before committing real capital through staged rollout. Staged deployment catches live-environment bugs without material financial exposure.