Complete Python and Machine Learning in Financial Analysis

• 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. 

Who this course is for:

• Developers
• Financial Analysts
• Data Analysts
• Data Scientists
• Stock and cryptocurrency traders
• Students
• Teachers
• Researchers