Algorithmic Trading and Finance Models with Python, R, and Stata Essential Training
Business intelligence is one of the fastest growing industries, particularly in financial investing. This project-based course focuses on developing models (algorithms) that can trade stocks and other financial products using various types of software. Michael McDonald demonstrates how to use Excel, Python, R, or Stata to create quantitative, testable investment rules that will allow you to make informed trading decisions. First, he defines algo trading and explains how it works. Following that, he discusses how to create an algo trading strategy and shares tips for identifying opportunities in various markets. Then he delves into how to make the best use of existing software tools. Michael also talks about stock trading, bond trading, data analysis, regressions, and other topics.
Learn how to make informed trading decisions by constructing models or algorithms using quantitative, testable investment rules using software tools such as Excel, Python, R, and Stata.
Key Features
To execute trades at precise times, algorithmic trading combines computeAlgorithmic Trading and Finance Models and financial markets
Algorithmic trading attempts to remove emotions from trades, ensures the most efficient execution of a trade, instantly places orders, and may lower trading fees.
Trend-following strategies, arbitrage opportunities, and index fund rebalancing are all common trading strategies.
To get started with algorithmic trading, you'll need a computer, a network connection, financial market knowledge, and coding skills.
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Introduction
1. The Basics of Algo Trading
- Basics of algo trading
- Market making with algos
- An algorithm examples
- Prop trading with algos
- Algos in practice
- Textual analysis and algo trading
- Algorithmic trading with qualitative and text data
- Careers in algorithmic trading
2. R and Bond Trading
- One software option: R
- Importing data with R
- quantmod and R
- Data analysis in R
- Regressions in R
3. Stock Trading with Python
- One software option: Python
- Importing data in Python
- Quandl and Python
- CSVs and Python
- Financial data and Python
- Python and building financial databases
4. Investment Analysis and Stata
- One software option: Stata
- Getting currency data
- Cleaning up data for algorithms
- Strategies in currencies
- Testing strategies in Stata
- Regressions in Stata
Algorithmic Trading and Finance Models with Python, R, and Stata Essential Training Assignment Help
We are going to make a few assumptions throughout this course. First and foremost, I’m going to assume you have some basic knowledge of capital markets, such as stocks, bonds, and currencies. If you’re unfamiliar with these topics, or if you need a refresher, I recommend taking the Understanding Capital Markets course on LinkedIn Learning. Similarly, I’m going to assume that you have at least a basic understanding of algorithmic trading. If you want to learn more about this topic and expand your knowledge, I recommend my first course on algorithmic trading, Algorithmic Trading and Stocks Essential Training. Once again, on LinkedIn Learning. Finally, it will be advantageous if you have a working knowledge of some Algorithmic Trading and Finance Models language. Any language will suffice. All of the software we’ll be using in this course is cross-platform, so it’ll work on Windows, Apple, or just about anything else. That being said, if you have all of these items on hand, you’ll be in great shape.
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