Book Image

Hands-On Financial Trading with Python

By : Jiri Pik, Sourav Ghosh
Book Image

Hands-On Financial Trading with Python

By: Jiri Pik, Sourav Ghosh

Overview of this book

Creating an effective system to automate your trading can help you achieve two of every trader’s key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage. This practical Python book will introduce you to Python and tell you exactly why it’s the best platform for developing trading strategies. You’ll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources. Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics. As you progress, you’ll pick up lots of skills like time series forecasting, covering pmdarima and Facebook Prophet. By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization to help you get —and stay—ahead of the markets.
Table of Contents (15 chapters)
1
Section 1: Introduction to Algorithmic Trading
3
Section 2: In-Depth Look at Python Libraries for the Analysis of Financial Datasets
9
Section 3: Algorithmic Trading in Python

Data types used with NumPy ndarrays

NumPy ndarrays are homogenous—that is, each element in an ndarray has the same data type. This is different from Python lists, which can have elements with different data types (heterogenous).

The np.array(...) method accepts an explicit dtype= parameter that lets us specify the data type that the ndarray should use. Common data types used are np.int32, np.float64, np.float128, and np.bool. Note that np.float128 is not supported on Windows.

The primary reason why you should be conscious about the various numeric types for ndarrays is the memory usage—the more precision the data type provides, the larger memory requirements it has. For certain operations, a smaller data type may be just enough.

Creating a numpy.float64 array

To create a 128-bit floating-values array, use the following code:

np.array([-1, 0, 1], dtype=np.float64)

The output is shown here:

array([-1.,  0.,  1.], dtype=float64)
...