Advanced Financial Modelling, Quantitative Finance and Econometrics · Physical, Online
Python for Quantitative Finance Training Training Workshop
5 Days4 sessions available Certificate on completion
The Python for Quantitative Finance Training Workshop equips finance professionals, investment analysts, banking specialists, risk managers, economists, and financial data analysts with practical skills to apply Python programming to financial modelling, quantitative analysis, investment research, and data-driven decision-making. The course combines financial theory with hands-on programming exercises to help participants analyse financial markets, evaluate investment opportunities, measure portfolio performance, and develop quantitative models for modern financial management.
Participants learn to use Python libraries such as Pandas, NumPy, Matplotlib, SciPy, Statsmodels, and Scikit-learn to process financial data, calculate returns and risk metrics, perform statistical analysis, model time-series data, optimise investment portfolios, and conduct financial forecasting. The workshop also introduces AI and machine learning applications in quantitative finance, including volatility prediction, credit risk modelling, algorithmic trading concepts, and financial anomaly detection.
Course objectives
Apply Python programming and financial data analysis techniques to real-world quantitative finance problems.
Build reproducible Python workflows and visualisations for financial reporting, investment research, and risk analysis.
Apply portfolio optimisation, statistical modelling, and introductory machine learning techniques to financial decision-making.
Develop quantitative models for financial forecasting, time-series analysis, and investment evaluation.
Quantitative analysts, financial analysts, investment analysts, portfolio managers, investment researchers, risk management professionals, banking and microfinance professionals, treasury officers, financial data analysts, economists, actuaries, fintech professionals, algorithmic trading practitioners, financial modellers, corporate finance professionals, accountants, financial consultants, data scientists working in finance, machine learning practitioners interested in financial applications, financial regulators, financial researchers, university lecturers, postgraduate students in finance, economics, statistics and applied mathematics, and professionals seeking to apply Python programming, statistical modelling, financial forecasting, portfolio optimisation, derivatives pricing, AI and machine learning to investment analysis, financial risk management and quantitative decision-making.
Course content
Module 1: Python Programming Foundations for Quantitative Finance Core programming skills Python environment setup using Anaconda, Jupyter Notebook and Google Colab, Python variables and data types for financial calculations, operators and financial for
Module 2: Numerical Computing and Financial Data Management NumPy, Pandas and data preparation NumPy arrays and vectorised financial calculations, matrix operations and linear algebra, Pandas Series and DataFrames, importing financial statements and mar
Module 3: Financial Data Acquisition and Exploratory Analysis Market data and financial visualisation Historical stock and index data acquisition, financial data APIs and data-source limitations, OHLCV data (open high low close and volume), price adjust
Module 4: Statistical Methods and Econometrics for Finance Statistical inference and financial relationships Probability distributions and random variables, expected value and variance, covariance and correlation, sampling and confidence intervals, hypo
Module 5: Financial Time-Series Analysis and Forecasting Statsmodels, forecasting and volatility Financial time-series characteristics, trend and seasonality, decomposition of time-series data, moving averages and exponential smoothing, autoregressive a
Module 6: Investment Analysis and Portfolio Management Portfolio performance and asset allocation Investment return calculations, portfolio weighting and asset allocation, portfolio expected returns, covariance matrices, diversification and correlation
Module 7: Quantitative Financial Risk Management Market risk, tail risk and stress testing Financial risk concepts, historical and parametric Value at Risk (VaR), Monte Carlo VaR, Expected Shortfall (Conditional VaR), portfolio exposure measurement, vol
Module 8: Financial Modelling and Monte Carlo Simulation Scenario modelling and uncertainty analysis Random number generation and probability distributions, simulation of asset prices and investment returns, geometric Brownian motion, Monte Carlo valuat
Module 9: Derivatives Pricing and Computational Finance Options, bonds and numerical valuation Derivatives markets and financial contracts, forward and futures valuation principles, call and put options, intrinsic and time value, option payoff diagrams,
Module 10: Algorithmic Trading and Quantitative Strategy Development Rules-based investment strategies Principles of systematic trading, moving-average crossover strategies, momentum and mean-reversion concepts, technical indicators, signal generation,
Module 11: AI and Machine Learning for Quantitative Finance Predictive analytics and intelligent financial models Financial feature engineering, training and test datasets for time-dependent data, linear and regularised regression, decision trees and ra
Module 12: Quantitative Banking, Credit Risk and Financial Analytics Banking applications and credit modelling Loan portfolio analysis, borrower financial indicators, credit scoring fundamentals, probability of default estimation, loss given default and
Module 13: Corporate Finance, Treasury and Financial Decision Modelling Business valuation and financial planning Time value of money, discounted cash-flow valuation, net present value and internal rate of return, capital budgeting, cost of capital and
Module 14: Financial Data Automation, Visualisation and Reporting From analytical notebooks to reusable tools Automated Excel and CSV processing, integration with SQL databases, scheduled data-processing workflows, reusable Python functions, Matplotlib
Module 15: Integrated Quantitative Finance Capstone Project End-to-end practical application Definition of a financial research question, identification of appropriate data sources, data cleaning and exploratory analysis, selection of quantitative metho
General notes
Stock market performance analysis: Compare returns, volatility and drawdowns across selected securities.
Algorithmic trading: Develop and backtest a rules-based strategy while accounting for transaction costs and avoiding look-ahead bias.
Stock market performance analysis: Compare returns, volatility and drawdowns across selected securities. Investment portfolio optimisation: Construct a diversified portfolio and compare its risk-adjusted performance with a benchmark.
Derivative valuation: Price a sample European option and assess how the valuation changes with volatility, time and interest rates.
Algorithmic trading: Develop and backtest a rules-based strategy while accounting for transaction costs and avoiding look-ahead bias.
Banking credit risk: Analyse a sample loan dataset to identify risk factors and estimate borrower default probabilities.
Financial forecasting: Model historical revenue, expenditure, exchange rates or asset prices and evaluate forecast accuracy.
Frequently asked questions
Prior Python experience is beneficial but not mandatory if the programme begins with the fundamentals. Participants should ideally have basic financial mathematics, spreadsheet skills, and an interest in quantitative analysis.
Participants will learn Python programming for financial data analysis, statistical modelling, time-series forecasting, portfolio optimisation, quantitative risk measurement, derivatives pricing, algorithmic trading strategy backtesting, and AI-powered financial analytics
The workshop covers Python, Jupyter Notebook, NumPy, Pandas, Matplotlib, SciPy, Statsmodels, and Scikit-learn. Depending on the practical exercises, participants may also use yfinance, Plotly, CVXPY, and Streamlit for financial data acquisition, portfolio optimisation, visualisation, and dashboard development.
Participants will develop practical Python notebooks and financial models for analysing investment returns, measuring portfolio risk, forecasting financial time series, evaluating investment strategies, pricing selected financial derivatives, and assessing credit risk. The course also includes a capstone project that demonstrates the application of quantitative methods to a realistic financial problem.