Master Financial Analysis with Python & Machine Learning

What you will learn:

Acquire and preprocess financial data from various sources.
Interpret technical analysis indicators (MACD, RSI, Bollinger Bands) and build trading strategies.
Master time series modeling with exponential smoothing, ARIMA, and GARCH models.
Implement and interpret multi-factor models (CAPM, Fama-French).
Forecast volatility using ARCH and GARCH models.
Use Monte Carlo simulations for option pricing and VaR calculations.
Optimize asset allocation and find the efficient frontier.
Predict credit default using machine learning algorithms.
Apply advanced machine learning techniques (random forests, XGBoost, LightGBM, stacking).
Leverage deep learning (PyTorch) for time series and tabular data analysis.

Description

Unlock the power of Python, machine learning, and deep learning to revolutionize your financial analysis. This comprehensive course provides a step-by-step journey, equipping you with the skills to tackle real-world financial challenges.

Starting with essential Python programming and data acquisition techniques from sources like Yahoo Finance and Quandl, you'll master data preprocessing, visualization, and identifying key statistical properties. Explore the core concepts of technical analysis, implementing and backtesting strategies using indicators like Bollinger Bands, MACD, and RSI. You'll construct interactive dashboards to visualize your findings.

Delve into the world of time series analysis, using powerful models such as exponential smoothing, ARIMA, and GARCH, including multivariate specifications. Gain a deep understanding of multi-factor models, including CAPM and Fama-French models, and learn to optimize asset allocation with Monte Carlo simulations. Apply Monte Carlo techniques to pricing options and calculating Value at Risk (VaR).

The course culminates in a data science project focused on credit card fraud and default prediction. Master advanced machine learning algorithms like random forests, XGBoost, LightGBM, and stacked models. Fine-tune models with hyperparameter optimization (including Bayesian optimization) and effectively handle class imbalances. Finally, you'll explore the cutting-edge field of deep learning in finance using PyTorch, leveraging neural networks for time series forecasting and other complex financial modeling tasks.

All code is provided, ensuring a hands-on learning experience that empowers you to build sophisticated financial models and solutions.

Curriculum

Financial Data and Preprocessing

This section lays the groundwork for your financial analysis journey. Begin by learning the fundamentals of Python programming in a financial context. You'll discover how to acquire data from reliable sources such as Yahoo Finance and Quandl. Master the essential techniques of converting prices into returns, changing data frequencies, and creating effective visualizations for time series data. Develop your ability to identify outliers and understand the characteristic stylized facts of asset returns. This includes hands-on exercises and detailed code examples for each step.

Technical Analysis in Python

Dive into the world of technical analysis (TA) using Python. Create and interpret candlestick charts, a fundamental tool in financial markets. Learn how to calculate and utilize key technical indicators such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI). You’ll build and backtest automatic trading strategies based on these indicators. This section culminates in the creation of an interactive dashboard for comprehensive TA analysis, enhancing your ability to visualize and interpret market trends.

Time Series Modeling

This section provides a comprehensive introduction to time series analysis techniques. Learn to decompose time series, test for stationarity, and implement corrections to ensure reliable model performance. Master exponential smoothing methods and ARIMA class models for accurate time series forecasting, with practical application to real-world financial data.

Multi-Factor Models

Expand your skillset to incorporate multi-factor models in financial analysis. Gain a strong understanding of the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model, learning how to implement and interpret these models in Python. Explore extensions to four and five-factor models, further refining your ability to analyze asset performance and portfolio risk.

Modeling Volatility with GARCH Class Models

Understand and implement Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to forecast volatility. Start with ARCH models as a foundation, progressing to GARCH models for improved accuracy. Learn to apply multivariate volatility forecasting using the CCC-GARCH and DCC-GARCH models, equipping you with advanced techniques for managing risk in dynamic market conditions.

Monte Carlo Simulations in Finance

Master Monte Carlo simulations, a powerful tool in finance. Simulate stock price dynamics using Geometric Brownian Motion and apply these simulations to price both European and American options. This section also includes learning how to utilize Quantlib for option pricing, and finally estimating Value at Risk (VaR) using Monte Carlo methods for comprehensive risk assessment.

Asset Allocation in Python

Learn the principles of modern portfolio theory and implement asset allocation strategies in Python. Evaluate the performance of basic 1/n portfolios and move on to finding the efficient frontier using Monte Carlo simulations and optimization techniques with SciPy. These techniques will help you optimally construct and manage your investment portfolios.

Identifying Credit Default with Machine Learning

Tackle the challenge of credit default prediction using machine learning. You'll learn how to preprocess and explore financial datasets, address missing values, and encode categorical variables. Build a decision tree classifier, implement scikit-learn's pipelines, and master hyperparameter tuning using grid search and cross-validation for building robust predictive models.

Advanced Machine Learning Models in Finance

Explore advanced classification techniques for enhanced predictive accuracy. You will learn to use a variety of advanced models, including XGBoost and LightGBM, and use stacking to combine predictions. Strategies for dealing with class imbalance and Bayesian optimization for hyperparameter tuning will be covered, creating highly effective credit risk prediction models.

Deep Learning in Finance

This section introduces the cutting edge techniques of deep learning in finance. Learn to use deep learning models for both tabular and time-series data using PyTorch. You will build and train multilayer perceptrons, convolutional neural networks, and recurrent neural networks for advanced time-series forecasting, pushing the boundaries of your financial analytical capabilities.