Python for Trading Course

Lesson 1 • Core Python Syntax Essentials

Cover variables, data types, operators, and control flow with trading-themed examples. Provides the language fluency required for all data manipulation ahead.

Lesson 2 • Setting Up the Trading Environment

Install Python, Jupyter, and key financial libraries in a reproducible workspace. Establishes the technical baseline every subsequent chapter depends on.

Lesson 3 • Data Structures for Market Data

Explore lists, tuples, dictionaries, and sets as containers for price and trade records. Directly maps to how market data is stored and accessed in practice.

Lesson 1 • Pandas Series and DataFrames

Build and inspect Series and DataFrames representing price histories. Establishes the primary data structure used throughout the entire course.

Lesson 2 • Cleaning and Transforming OHLCV Data

Handle missing values, duplicates, and type mismatches in raw market feeds. Clean data is a prerequisite for reliable signal generation in later chapters.

Lesson 3 • Time-Series Indexing with DatetimeIndex

Parse dates, set DatetimeIndex, and resample data across frequencies. Time-aware indexing is essential for all signal and backtest work ahead.

Lesson 4 • NumPy Arrays and Vectorized Math

Create and operate on arrays to replace slow Python loops in price calculations. Vectorized operations are the performance backbone of all later analytics.

Lesson 1 • Using Financial Data Libraries

Pull historical OHLCV data using purpose-built Python wrappers for market data providers. Reduces boilerplate and accelerates prototyping of trading ideas.

Lesson 2 • Building a Reusable Data Pipeline

Encapsulate fetch, clean, and store steps into a modular, callable pipeline class. Reusable pipelines are the foundation of reproducible research and live trading.

Lesson 3 • Accessing Data via REST APIs

Send authenticated HTTP requests and parse JSON responses into DataFrames. Establishes the pattern for all API-based data ingestion in the course.

Lesson 1 • Volatility and Volume Indicators

Calculate Bollinger Bands, ATR, and volume-based indicators to gauge market conditions. Volatility and volume context improve signal quality and position sizing.

Lesson 2 • Using TA-Lib and pandas-ta

Leverage established indicator libraries to accelerate development and validate custom code. Library-based indicators reduce bugs and enable rapid strategy prototyping.

Lesson 3 • Trend-Following Indicators

Implement moving averages, MACD, and ADX to identify directional price trends. Trend signals form the most common entry logic in systematic strategies.

Lesson 4 • Momentum and Oscillator Indicators

Build RSI, Stochastic, and Rate of Change indicators to measure price momentum. Oscillators complement trend filters by identifying overbought and oversold conditions.

Lesson 1 • Event-Driven Backtesting Concepts

Introduce order-book simulation, fill logic, and slippage modeling for realistic results. Event-driven engines expose execution realities that vectorized tests obscure.

Lesson 2 • Using Backtrader and Zipline Lite

Run strategies through established open-source backtesting frameworks for richer analytics. Frameworks enforce discipline and provide built-in reporting unavailable in custom engines.

Lesson 3 • Avoiding Overfitting and Data Snooping

Apply walk-forward validation and out-of-sample testing to detect spurious results. Overfitting is the primary cause of live trading underperformance after backtesting.

Lesson 4 • Vectorized Backtesting Fundamentals

Translate position signals into daily returns using vectorized pandas operations. Vectorized backtesting is fast and forms the core engine for all strategy evaluation.

Lesson 1 • Risk Metrics and Drawdown Control

Monitor Value at Risk, Expected Shortfall, and rolling drawdown in real time. Continuous risk monitoring prevents catastrophic losses during adverse market regimes.

Lesson 2 • Return and Covariance Estimation

Compute expected returns and covariance matrices from historical price data. Accurate estimates are the inputs that determine the quality of any optimization output.

Lesson 3 • Position Sizing Methods

Implement fixed fractional, Kelly criterion, and volatility-scaled sizing rules. Proper sizing determines how much capital each signal controls, directly affecting drawdown.

Lesson 4 • Mean-Variance Portfolio Optimization

Use scipy and PyPortfolioOpt to solve for efficient frontier and optimal weights. Mean-variance optimization translates return/risk estimates into actionable allocations.

Lesson 1 • Execution Algorithms (TWAP and VWAP)

Build TWAP and VWAP slicing algorithms to reduce market impact on large orders. Execution algorithms are essential when order size is significant relative to volume.

Lesson 2 • Broker API Integration

Authenticate and interact with a broker REST API to place and manage orders. API integration is the bridge between backtested signals and real market execution.

Lesson 3 • Execution Monitoring and Alerting

Log all order events and trigger alerts on anomalies such as missed fills or breached limits. Robust monitoring is the operational safety net for any live trading system.

Lesson 4 • Order Types and Execution Logic

Implement market, limit, stop, and bracket orders with conditional execution logic. Choosing the right order type directly controls fill quality and risk exposure.

Lesson 1 • Model Evaluation for Financial Data

Use purged cross-validation and financial metrics to assess model quality without data leakage. Standard ML metrics are insufficient for serially correlated financial data.

Lesson 2 • Feature Engineering from Price Data

Transform raw OHLCV data into predictive features suitable for ML models. Feature quality determines model predictive power more than algorithm choice.

Lesson 3 • Supervised Classification Models

Train logistic regression, random forest, and gradient boosting classifiers to predict price direction. Classification outputs map directly to long/short signal generation.

Lesson 4 • Unsupervised Learning for Market Regimes

Apply k-means and Gaussian Mixture Models to cluster market states and adapt strategies. Regime detection enables dynamic strategy switching based on market conditions.