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Technical Analysis with Python for Algorithmic Trading

#artificialintelligence

Created by Alexander Hagmann 12.5 hours on-demand video course This course clearly goes beyond rules, theories, vague forecasts, and nice-looking charts. This is the first 100% data-driven course on Technical Analysis. We ll use rigorous Backtesting / Forward Testing to identify and optimize proper Trading Strategies that are based on Technical Analysis / Indicators. This course will allow you to test and challenge your trading ideas and hypothesis. It provides Python Coding Frameworks and Templates that will enable you to code and test thousands of trading strategies within minutes.


Python for Financial Analysis and Algorithmic Trading

#artificialintelligence

We'll start off by learning the fundamentals of Python, and then proceed to learn about the various core libraries used in the Py-Finance Ecosystem, including jupyter, numpy, pandas, matplotlib, statsmodels, zipline, Quantopian, and much more! Use NumPy to quickly work with Numerical Data Use Pandas for Analyze and Visualize Data Use Matplotlib to create custom plots Learn how to use statsmodels for Time Series Analysis Calculate Financial Statistics, such as Daily Returns, Cumulative Returns, Volatility, etc.. Use Exponentially Weighted Moving Averages Use ARIMA models on Time Series Data Calculate the Sharpe Ratio Optimize Portfolio Allocations Understand the Capital Asset Pricing Model Learn about the Efficient Market Hypothesis Conduct algorithmic Trading on Quantopian


Quantitative Trading Analysis with Python Udemy

@machinelearnbot

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or take decisions as DIY investor. Learning quantitative trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for DIY investors' quantitative trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using index replicating fund historical data for back-testing to achieve greater effectiveness.


Pairs Trading Analysis with Python Udemy

@machinelearnbot

It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your research as experienced investor. Learning pairs trading analysis is indispensable for finance careers in areas such as quantitative research, quantitative development, and quantitative trading mainly within investment banks and hedge funds. It is also essential for academic careers in quantitative finance. And it is necessary for experienced investors quantitative trading research and development. But as learning curve can become steep as complexity grows, this course helps by leading you step by step using MSCI Countries Indexes ETF prices historical data for back-testing to achieve greater effectiveness.


Quant Trading using Machine Learning - Udemy

#artificialintelligence

Prerequisites: Working knowledge of Python is necessary if you want to run the source code that is provided. Basic knowledge of machine learning, especially ML classification techniques, would be helpful but it's not mandatory. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. Completely Practical: This course has just enough theory to get you started with both Quant Trading and Machine Learning.