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Financial Market Data Access in Python 7 Financial Market Data Access in Python is chapter outlines several key market data sources, ranging from free to paid data sources. A more complete list of available resources can be obtained from https://github.com/wilsonfreitas/awesome-quant#data-sources. e quality of algorithmic trading models’ signals fundamentally depends on the quality of market data being analyzed. Has the market data b.. 2023. 3. 20.
Statistical Estimation, Inference, and Prediction 6 Statistical Estimation, Inference, and Prediction In this chapter, we introduce four key statistical libraries in Python statsmodels, pmdarima, fbprophet, and scikitlearn by outlining key examples. ese libraries are used to model time series and provide their forecast values, along with confidence intervals. In addition, we demonstrate how to use a classification model to predict percentage ch.. 2023. 3. 20.
Data Visualization Using Matplotlib 5 Data Visualization Using Matplotlib Data visualization allows comprehending numerical data significantly more easily than reading pure tables of numbers. Getting instant insight into data and the identification of patterns, trends, and outliers are the primary uses of charting libraries. When deciding which stock may be suitable for which algorithmic trading strategy, creating a chart of the s.. 2023. 3. 20.
Data Manipulation and Analysis with pandas 4 Data Manipulation and Analysis with pandas In this chapter, you will learn about the Python pandas library built upon NumPy, which provides data manipulation and analysis methods for structured data frames. e name pandas is derived from panel data, an econometrics term for multidimensional structured datasets, according to the Wikipedia page on pandas. e pandas library contains two fundamental.. 2023. 3. 20.
High-Speed Scientific Computing Using 3 High-Speed Scientific Computing Using NumPy is chapter introduces us to NumPy, a high-speed Python library for matrix calculations. Most data science/algorithmic trading libraries are built upon NumPy’s functionality and conventions. In this chapter, we are going to cover the following key topics: Introduction to NumPy Creating NumPy n-dimensional arrays (ndarrays) Data types used with NumPy a.. 2023. 3. 20.
Exploratory Data Analysis in Python 2 Exploratory Data Analysis in Python is chapter focuses on exploratory data analysis (EDA), which is the first step in processing any dataset. e objective of EDA is to load data into data structures most suitable for further analysis to identify and rectify any wrong/bad data and get basic insight into the data the types of fields there are; whether they are categorical or not; how many missing.. 2023. 3. 20.
PyQt6 and PySide6 —What’s the difference? Appendix C: PyQt6 and PySide6 — What’s the difference? If you start building Python application with Qt6 you’ll soon discover that there are in fact two packages which you can use to do this — PyQt6 and PySide6. In this short chapter I’ll run through why exactly this is, whether you need to care (spoiler: you really don’t), what the (few) differences are and how to work around them. By the end y.. 2023. 3. 16.
Translating C++ Appendix B: Translating C++ Examples to Python When writing applications with PyQt6 we are really writing applications with Qt. PyQt6 acts as a wrapper around the Qt libraries, translating Python method calls to C++, handling type conversions and transparently creating Python objects to represent Qt objects in your applications. The result of all this cleverness is that you can use Qt from Pytho.. 2023. 3. 16.
Installing PyQt6 Appendix A: Installing PyQt6 Before you start coding you will first need to have a working installation of PyQt6 on your system. If you don’t have PyQt6 set up yet, the following sections will guide you through how to do this on Windows, macOS and Linux. q Note that the following instructions are only for installation of the GPL licensed version of PyQt. If you need to use PyQt in a non-GPL proj.. 2023. 3. 16.

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