What you would learn in Python and Machine Learning in Financial Analysis course?
This course will teach you how to perform highly specialized financial analysis. Technical and fundamental analysis will be covered, and different tools will be used for your analysis. You will learn about technical and fundamental analysis and will be able to use different tools to perform your analysis. You will be able to use Python in a completely new way. Deep learning algorithms and artificial neural networks will be taught to improve your financial analysis skills greatly.
This tutorial will show you how to download financial data and prepare it for modeling. We examine the statistical properties of the asset returns and prices, as well as the existence of "stylized facts." Then, we calculate the most popular indicators for technical analysis (such Bollinger Bands and Moving Average Convergence Divergence(MACD) and Relative Strength Index) and backtest automated trading strategies based on them.
Next, we will introduce time series analysis. We also explore popular models like exponential smoothing (ARIMA), AutoRegressive Integrated Movement Average (ARIMA), Generalized Autoregressive Conditional Heteroskedasticity(GARCH) (including multivariate specification). We also present factor models such as the Fama-French three-factor model and the Capital Asset Pricing Model (CAPM). This section ends with a demonstration of different strategies to optimize asset allocation. We use Monte Carlo simulations to perform tasks such as calculating American options' prices or estimating Value at Risk (VaR).
The final part of the course involves a complete data science project in the financial domain. Advanced classifiers like Random Forest, XGBoost, and LightGBM are used to tackle credit card fraud/default issues. We tune the hyperparameters and deal with class imbalance. The book concludes with a demonstration of how deep learning (using PyTorch) can solve many financial problems.
The functions will allow you to download financial data from many sources and preprocess it for further analysis.
You'll be able to see patterns in various metrics that are most frequently used (e.g., MACD and RSI).
This article explains the basics of time-series modeling. Next, we will look at exponential smoothing and ARIMA class model.
This tutorial will show you how to calculate various factor models in Python. One, three, four, and five-factor models.
This article explains the basics of volatility forecasting using (G.ARCH) class models. It also demonstrates how to select the most-suited model and how to interpret your results.
Introduces Monte Carlo simulations. They can simulate stock prices, valuations of European/American options, and calculate the VaR.
This article introduces the Modern Portfolio Theory. It also demonstrates how to get the Efficient Frontier in Python. How to assess the performance of such portfolios.
This case demonstrates how machine learning can be used to predict credit default. Learn how to tune hyperparameters and manage imbalances in the models.
This article will introduce you to advanced classifiers, including stacking multiple models. It also explains how Bayesian optimization can be used to address the class imbalance.
Demonstrates how deep learning can be used to work with tabular and time-series data. PyTorch will be used to train the networks.
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