Machine Trading Analysis with Python

Requirements

• Python programming language needed. Instructions for downloading are included.
• Python Distribution (PD) and Integrated Development Environment (IDE) are highly recommended. Downloading instructions are provided.
• Examples of practical information and Python code files included with the course.
• Basic Python programming language experience is beneficial but not essential.

Description

Learn about machine-based trading analysis by taking an in-depth course using the Python programming language and The S&P 500(r) Index ETF historical data to test back-testing. It covers fundamental concepts from the basic level to an expert that can help you get higher grades, build your academic skills, apply your knowledge in your job or conduct your research as an experienced investor. All this while studying insights from Nobel Prize winners and best practitioners in the area.

Learn to become a machine Trading Analysis Expert in this practical course using Python

• Download or read S&P 500(r) Index ETF price data, and then perform automated trading analysis by installing the related software and running the code in the Python IDE.
• Define the target and predictor algorithm features of the supervised regression task.
• Find relevant predictors and features using univariate filtering methods using deterministic wrapper strategies and embedded techniques.
• Introduce false discovery rate as well as a family-wise error rate for univariate methods Recursive feature elimination for wrapper methods that are deterministic and deterministic, and the least absolute shrinkage as well as selection operator to embed methods.
• Extract predictor has transformations that are based on the principal component analysis.
• Train algorithm to map the best relationship between predictor and target attributes using ensemble methods, methods for maximizing margin and multi-layer perceptron algorithms.
• Apply gradient-boosting machine analysis for group methods, radial basis method support vector regression to methods with maximum margins, and artificial neural network regression to multi-layer perceptron techniques.
• The test method for evaluating the reliability of relationship forecasting was previously improved by measuring scale-dependent metrics.
• When using scale-dependent measures, examine the mean absolute error, the mean squared error, and the root means squared error.
• Machine trading strategies can be calculated using machine learning algorithms with the highest forecasting accuracy.
• Generate buy and sell trade signals based on the target feature predictions crossing the centerline thresholds of cross-over.
• Create long-only trading positions that are linked with trading signals.
• Examine the performance of machine trading strategies against the benchmark for buy and hold with annualized returns Standard deviation for the year, annualized, Sharpe ratios, and a chart of cumulative returns.

Become a Machine Trading Analysis Expert and Put Your Knowledge in Practice

Understanding the machine trading process is essential for careers in finance in fields like computation-based finance, development of computational finance and even computational finance trading, which is primarily within hedge funds. It is also necessary for research and academic career opportunities that deal with computational finance. Experienced investors need to be familiar with computational research and development in finance.

The learning curve could be steeper as complexity increases. This course can help by guiding you step-by-step through the process using S&P 500(r) Index ETF price historical data to back-test to increase your effectiveness.

Content and Overview

The course is practical and includes 41 lectures and six hours of material. It is designed for all machine’s trading analysis levels. An understanding of Python programming is helpful but not essential.

The first step is to learn how to download or read S&P 500(r) Index ETF historical prices to perform automated trading analysis through installing related packages and running the code on Python IDE.

Next, you’ll determine targets and predictors for the supervised regression machine-learning task. Then, you’ll choose the most relevant features for prediction using univariate filtering techniques such as deterministic wrapper strategies as well as embedded techniques. Then you’ll apply the false discovery ratio, combined with the family-wise error rates for univariate methods and recursive feature elimination in deterministic wrapping methods, as well as the minimum absolute shrinkage and the selection method to embed methods. Then, you’ll determine the transformations of predictor features through the principal component method.

In the next phase, you’ll develop an algorithm to map the best relation between the target and predictor elements using ensemble methods such as maximum margin methods and multi-layer perceptron strategies. You’ll then apply gradient-boosting machine regression to ensemble methods, radial base functions, support vector regression to support methods with maximum margins and Artificial Neural Network Regression to multi-layer perceptron strategies. Then, you’ll evaluate an algorithm to evaluate previously improved accuracy in forecasting relationships using scale-dependent and scale-independent metrics. In the next step, you’ll examine the mean absolute error, the mean squared error, and roots mean squared errors for metrics that are scale-dependent.

Then, you’ll determine the strategies of machine trading for algorithms that provide the best forecasting accuracy. You’ll then generate trade signals to buy or sell based on the target feature prediction crossing the cross-over threshold for centerline crossing. In the next step, you’ll generate long-only trading positions that are linked to trading signals.

Then, you’ll evaluate the performance of your machine trading strategies against benchmarks for buy and hold using annualized returns and standard deviations that are annualized. Annualized Sharpe ratio, along with a chart of cumulative returns.

Who is this course intended for:

• Postgraduates or undergraduates wish to learn about machine trading analysis with the help of the Python programing language.
• Researchers in finance or academics want to expand their understanding of computational finance.
• Investors with experience who want to study strategies for trading using machines.
• This class isn’t focused on “get rich quick” trading strategies or formulas for magic.