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Finance11

Fundamental Algorithmic Trading Strategies 9 Fundamental Algorithmic Trading Strategies is chapter outlines several algorithms profitable on the given stock, given a time window and certain parameters, with the aim of helping you to formulate an idea of how to develop your own trading strategies. In this chapter, we will discuss the following topics: What is an algorithmic trading strategy? Learning momentum-based/trend-following strateg.. 2023. 3. 20.
Introduction to Zipline and PyFolio 8 Introduction to Zipline and PyFolio In this chapter, you will learn about the Python libraries known as Zipline and PyFolio, which abstract away the complexities of the backtesting and performance/risk analysis aspects of algorithmic trading strategies. ey allow you to completely focus on the trading logic. For this, we are going to cover the following main topics: Introduction to Zipline and .. 2023. 3. 20.
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.
3. High-Speed Scientific Computing UsingNumPy • Introduction to NumPy • Creating NumPy n-dimensional arrays (ndarrays) • Data types used with NumPy arrays • Indexing of ndarrays • Basic ndarray operations • File operations on ndarrays 2023. 2. 14.

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