#### 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
Other Books You May Enjoy

# Simulating stock price dynamics using Geometric Brownian Motion

Thanks to the unpredictability of financial markets, simulating stock prices plays an important role in the valuation of many derivatives, such as options. Due to the aforementioned randomness in price movement, these simulations rely on stochastic differential equations (SDE).

A stochastic process is said to follow the Geometric Brownian Motion (GBM) when it satisfies the following SDE:

Here, we have the following:

• S: Stock price
• μ: The drift coefficient, that is, the average return over a given period or the instantaneous expected return
• σ: The diffusion coefficient, that is, how much volatility is in the drift
• Wt: The Brownian Motion

We will not investigate the properties of the Brownian Motion in too much depth, as it is outside the scope of this book. Suffice to say, Brownian increments are calculated...