#### Overview of this book

Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach.
Preface
Financial Data and Preprocessing
Free Chapter
Technical Analysis in Python
Identifying Credit Default with Machine Learning
Advanced Machine Learning Models in Finance
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# Finding the Efficient Frontier using convex optimization with cvxpy

In the previous recipe, Finding the Efficient Frontier using optimization with scipy, we found the Efficient Frontier, using numerical optimization with scipy. We used the portfolio volatility as the metric we wanted to minimize. However, it is also possible to state the same problem a bit differently and use convex optimization to find the Efficient Frontier.

We can reframe the mean-variance optimization problem into a risk-aversion framework, in which the investor wants to maximize the risk-adjusted return:

Here, γ ∈ [0, ∞) is the risk-aversion parameter, and the constraints specify that the weights must sum up to 1, and short-selling is not allowed. The higher...