Python Finance Course

Lesson 1 • Control Flow and Functions

Teaches if-else logic, loops, and reusable functions for automating repetitive finance tasks. Students write modular code that scales across datasets.

Lesson 2 • Core Python Syntax and Data Types

Covers variables, arithmetic operators, strings, booleans, and type conversion. These primitives underpin every financial calculation built later.

Lesson 3 • Setting Up the Python Environment

Install Python, Anaconda, and Jupyter Notebook for a finance-ready workspace. Establishes the technical baseline every subsequent chapter depends on.

Lesson 4 • Python Data Structures

Introduces lists, tuples, dictionaries, and sets as containers for financial records. Choosing the right structure improves code efficiency and clarity.

Lesson 1 • Vectorised Mathematical Operations

Applies element-wise arithmetic, universal functions, and broadcasting to price series. Eliminates Python loops for dramatic speed gains on large datasets.

Lesson 2 • Linear Algebra with NumPy

Uses matrix multiplication, inverses, and eigenvalues for portfolio and regression maths. Provides the algebraic tools required in later risk and optimisation chapters.

Lesson 3 • NumPy Arrays and Creation Methods

Covers ndarray creation, shapes, dtypes, and indexing for financial series. Arrays form the numerical backbone of all quantitative work ahead.

Lesson 1 • Loading and Cleaning Financial Data

Reads CSV, Excel, and JSON market files, then handles missing values and duplicates. Clean data is the prerequisite for accurate financial analysis.

Lesson 2 • Merging and Reshaping DataFrames

Combines multiple financial datasets using merge, join, and concat, then reshapes with melt and stack. Prepares multi-source data for unified analysis.

Lesson 3 • Time Series Handling in Pandas

Manages DatetimeIndex, resampling, and rolling windows for price and return series. Time-aware operations are central to every financial analysis workflow.

Lesson 4 • Filtering, Sorting, and Grouping

Applies boolean filters, sort operations, and groupby aggregations to segment financial data. Enables sector-level and time-period comparisons across large datasets.

Lesson 1 • Candlestick and OHLC Charts

Renders open-high-low-close charts with volume bars using mplfinance. Provides the standard visual format used in equity and commodity trading analysis.

Lesson 2 • Matplotlib Fundamentals for Finance

Builds line, bar, and scatter plots of financial data with proper labels and formatting. Establishes the plotting foundation extended by all subsequent visualisation work.

Lesson 3 • Advanced Matplotlib Techniques

Uses subplots, twin axes, and annotations to display multi-panel financial dashboards. Enables side-by-side comparison of related financial metrics.

Lesson 1 • Performance Ratios and Benchmarking

Computes Sharpe, Sortino, Calmar, and information ratios to evaluate risk-adjusted returns. Benchmarking against a market index contextualises portfolio performance.

Lesson 2 • Value at Risk and Expected Shortfall

Estimates VaR and CVaR using historical, parametric, and Monte Carlo methods. These tail-risk measures are standard requirements in risk management reporting.

Lesson 3 • Computing Asset Returns

Calculates simple, log, and cumulative returns from price series using Pandas and NumPy. Return computation is the entry point for all performance and risk analysis.

Lesson 4 • Correlation and Covariance Analysis

Builds correlation and covariance matrices across asset returns to measure co-movement. These matrices are the direct inputs to portfolio optimisation in the next chapter.

Lesson 1 • Implementing Optimisation with SciPy

Uses scipy.optimise to minimise portfolio variance subject to weight constraints. Students translate mathematical optimisation problems directly into executable Python code.

Lesson 2 • Portfolio Rebalancing and Constraints

Adds sector caps, turnover limits, and transaction costs to realistic optimisation problems. Bridges theoretical optimisation and practical portfolio management requirements.

Lesson 3 • Efficient Frontier Visualisation

Simulates thousands of random portfolios and plots the efficient frontier curve. Visual output confirms optimiser results and communicates trade-offs to stakeholders.

Lesson 4 • Advanced Portfolio Construction Methods

Applies risk parity, Black-Litterman, and hierarchical risk parity as alternatives to mean-variance. Addresses estimation error and concentration weaknesses of classical optimisation.

Lesson 1 • Evaluating Backtest Results

Analyses equity curves, drawdowns, win rates, and risk-adjusted returns from backtest output. Rigorous evaluation separates genuinely profitable strategies from overfitted ones.

Lesson 2 • Technical Indicators and Signal Generation

Computes moving averages, RSI, MACD, and Bollinger Bands from OHLC data. Indicators translate raw price data into actionable buy and sell signals.

Lesson 3 • Avoiding Overfitting and Bias

Applies walk-forward testing, out-of-sample validation, and parameter sensitivity checks. These techniques prevent strategies from being tuned to historical noise rather than signal.

Lesson 4 • Strategy Logic and Position Sizing

Encodes entry and exit rules, then sizes positions using fixed fractional and volatility-based methods. Position sizing directly controls the risk taken per trade.

Lesson 1 • Greeks and Hedging Strategies

Computes and interprets option Greeks, then constructs delta-neutral and gamma-neutral hedges. Hedging with Greeks is the core risk management practice for derivatives books.

Lesson 2 • Options Fundamentals and Payoff Diagrams

Defines calls, puts, moneyness, and expiry, then plots payoff and profit diagrams. Conceptual clarity here is essential before implementing any pricing model.

Lesson 3 • Black-Scholes-Merton Model

Implements the closed-form BSM formula for European option pricing and the Greeks. BSM is the industry benchmark against which all other models are compared.

Lesson 4 • Monte Carlo Option Pricing

Simulates geometric Brownian motion paths to price European and exotic options. Monte Carlo extends pricing to path-dependent payoffs beyond analytical reach.