Using Python and machine learning in financial analysis with step-by-step coding (with all codes)
This course includes:
- 20.5 hours on-demand video
- 18 articles
- 18 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion
What you'll learn
- You will be able to use the functions provided to download financial data from a number of sources and preprocess it for further analysis
- You will be able to draw some insights into patterns emerging from a selection of the most commonly used metrics (such as MACD and RSI)
- Introduces the basics of time series modeling. Then, we look at exponential smoothing methods and ARIMA class models.
- shows you how to estimate various factor models in Python. one ,three-, four-, and five-factor models.
- Introduces you to the concept of volatility forecasting using (G)ARCH class models, how to choose the best-fitting model, and how to interpret your results.
- Introduces concept of Monte Carlo simulations and use them for simulating stock prices, the valuation of European/American options and calculating the VaR.
- Introduces the Modern Portfolio Theory and shows you how to obtain the Efficient Frontier in Python. how to evaluate the performance of such portfolios.
- Presents a case of using machine learning for predicting credit default. You will get to know tune the hyperparameters of the models and handle imbalances
- Introduces you to a selection of advanced classifiers (including stacking multiple models)and how to deal with class imbalance, use Bayesian optimization.
- Demonstrates how to use deep learning techniques for working with time series and tabular data. The networks will be trained using PyTorch.
Description
In this comprehensive course, you will become proficient with a variety of current financial analysis methodologies, as well as advanced algorithmic techniques of machine learning in the Python programming environment, where you can execute highly specialized quantitative financial analysis. You will master both technical and fundamental analysis, utilizing state-of-the-art tools and software for your analytical tasks. You will gain complete proficiency in the Python programming environment, learning to leverage its capabilities for robust data analysis and financial modeling. Additionally, you will delve into deep learning algorithms and artificial neural networks that significantly boost your financial analysis prowess and domain expertise.
This tutorial starts with an in-depth exploration of various methods for downloading financial data and preparing it for sophisticated modeling. We examine the essential statistical properties of asset prices and returns, and investigate the presence of stylized facts commonly observed in financial markets. We proceed to compute popular indicators used in technical analysis, such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI), and conduct backtesting on automated trading strategies based on these indicators.
The subsequent section introduces time series analysis and investigates popular models such as Exponential Smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH), including multivariate specifications. You will also learn about factor models, including the widely-regarded Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model. We conclude this section by illustrating various methods to optimize asset allocation, utilizing Monte Carlo simulations for tasks like pricing American options and estimating Value at Risk (VaR).
In the final part of the course, we undertake a comprehensive data science project within the financial sector. We tackle credit card fraud and default prediction problems using advanced classifiers such as Random Forest, XGBoost, LightGBM, stacked models, and many more. We will fine-tune the hyperparameters of these models using sophisticated techniques, including Bayesian optimization, and address class imbalance issues. The course concludes by showcasing how deep learning frameworks (using PyTorch) can solve a wide range of complex financial problems, providing you with cutting-edge skills in financial data science.
Also See : Certified Artificial Intelligence Developer Program
