Quantitative Trading Basics The Complete 2019

Learn how to use machine learning such as random forest and SVMs to develop quantitative trading strategies in Python

Last updated 2022-01-10 | 2.7

- Understand how to develop a quantitative trading strategy
- Understand the difference between trading actors in the market and learn about manual and systematic trading strategies
- Learn how to analyse PnL and performance metrics of trading strategies

What you'll learn

Understand how to develop a quantitative trading strategy

Understand the difference between trading actors in the market and learn about manual and systematic trading strategies

Learn how to analyse PnL and performance metrics of trading strategies

Learn how to generate original and profitable trading ideas using Python in google Colab

Using classification-based machine learning algorithms to make predictions and get trading entries

Understand what quantitative trading is all about

* Requirements

* Desire to learn
* Hunger for finding ways to generate profitable trading strategies

Description

The course is designed to fully immerse you into the complete quantitative trading/finance workflow, going from hypothesis generation to data preparation, feature engineering and training testing of multiple machine learning algorithms (backtesting). It is a bootcamp designed to get you to hero using Python and Google Colab. The course is aimed at teaching about trading, giving you understanding of the differences between discretionary and quantitative trading. You will learning about different trading instruments/products or also known as asset classes.

Course elements:

Learn about trading and the quantitative trading workflow. Develop a solid understand of what is required to do quantitative trading analysis and the advantages and disadvantages.
Learn how to write simple and complex codes in python using google Colab. Learn how to use the quantmod package to access/load free market data from yahoo finance and other sources.
load data with pandas from github repository
Learn how to download futures data from NinjaTrader.
Explore various trading ideas/hypothesis on the web, and learn how to generate original trading ideas.
Learn and understand what machine learning is and get a good grip of the type of machine learning algorithms available to solve different type of problems ( namely classification and regression problems).
Code along while learning about feature engineering, write algorithms for training and testing support vector machine, and random forest models and use these to predict the next price direction of Bitcoin. Realize that these strategies can be used for other trading instruments/products and in other timeframes.

Disclaimer

This course is for educational purpose and does not constitute trading or investment advice. All content, teaching material and codes are presented with sharing and learning purpose and with no guarantee of exactness or completeness.

No past performance is indicative of future performance and the trading strategies presented here are based on hypothetical and historical backtesting. Trading futures, forex and options involves the risk of loss. Please consider carefully if trading is appropriate to your financial situation. Only risk capital you can afford to lose, and the risk of loss being substantial, you should consider carefully the inherent risks.