How to Build Profitable Trading Strategies
A Quantitative Trading Model Development Guide
Quantitative Trading
Anyone can Build an AI Stock Trading Bot for Free, but not everyone can build a profitable stock trading bot. In my previous article I discussed in detail the mathematics and development of AI models specifically for stock trading bots. The terms trading model, trading strategy, algorithmic trading model and others are synonymous in the world of quantitative trading, all pertaining to a system that makes decisions regarding an investment security. The type of trading model falls in a subset of categories including HFT, AI, and Machine Learning. In this article I would like to discuss the design process of good trading models, and the tools that we can use to ensure our models are profitable before deployment. Just like in my previous articles we will be using Python and its accompanying libraries to help us develop a pipeline to design, test, deploy, and monitor these AI trading models. Having a pipeline allows for continuous development of new trading models. Your pipeline is data driven, evaluate often the quality of data you are providing it. There are a number of idiosyncrasies that you should bear in mind when designing and backtesting a trading model that we will cover in this article.
