Название: Python for Finance and Algorithmic Trading, 2nd Edition
Автор: Lucas Inglese
Издательство: Quantreo
Год: 2022
Страниц: 325
Язык: английский
Формат: epub, mobi
Размер: 10.8 MB

The book presents the benefits of portfolio management, statistics and machine learning applied to live trading with MetaTrader™ 5. This second version has allowed us to tweak some points of the existing chapters but especially to add 3 new chapters based on your feedbacks of the first version. So I am proud to offer you 3 new chapters: "Advanced backtest methods", ”Features and target engineering" and ”From nothing to a live trading bot".

Why should you read this book?
The financial sector is undergoing significant restructuring. Traders and portfolio managers are increasingly becoming financial data scientists. Banks, hedge funds, and fintech are automating their investments by integrating Machine Learning and Deep Learning algorithms into their decision-making process. This book presents the benefits of portfolio management, statistics, and Machine Learning applied to live trading with MetaTrader 5.

We will talk about the deep neural network DNN, or artificial neural network ANN. Deep learning is a field of machine learning which uses the most powerful algorithms. However, it demands many resources to run. To explain the DNN and create a trading strategy using this algorithm, we will explain the intuition behind the DNN and how to create a DNN classifier and a DNN regressor.

Part 1 is dedicated to portfolio management, risk management, and back testing. These chapters will allow us to understand how to combine strategies and which metrics to look at to understand the strategy robustness.

Part 2 discusses statistical predictive models . We will discuss the statistical arbitrage and autoregressive moving average (ARMA) model and introduce the classification algorithms through logistic regression.

Part 3 gives us an understanding of Machine Learning and Deep Learning predictive models . We will see these algorithms using trading strategies example: Support Vector Machines (SVM), decision tree, random forest, ensemble methods, Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Recurrent Convolutional Neural Network (RCNN)

- Learn portfolio management technics and how to implement your optimization criterion
- How to backtest a strategy using the most valuable metrics in trading
- Import data from your broker to be as close as possible to the market
- Learn statistical arbitrage through pair trading strategies
- Generate market predictions using machine learning, deep learning, and time series analysis
- Learn how to find the best take profit, stop loss, and leverage for your strategies
- Combine trading strategies using portfolio management to increase the robustness of the strategies
- Connect your Python algorithm to your MetaTrader 5 and run it with a demo or live trading account
- Use all codes in the book for live trading or screener if you prefer manual trading