Getting Started in Python
Algorithmic Trading: Statistical Significance
Dr. Antony Jackson is lecturer in Financial Economics in the School of Economics at University of East Anglia. He talks about statistical significance in algorithmic trading. Antony is an active researcher of algorithmic trading strategies and finished 2nd in Quantiacs' recent algorithmic trading competition. You can find the example code on Github
Webinar: 101 Trading Ideas
You are ready to write your first trading algorithm, the only thing you are missing is a great trading idea? Henry Carstens is quant and author of the brand new book '101 Trading Ideas'. He will talk about the creative part of trading algorithm development. You can find the example code on Github
Practical Tips For Algorithmic Trading using Machine Learning
Evgeny “Jenia” Mozgunov (Caltech) won our Q3 algorithmic trading competition. Jenia used machine learning tools to write his trading algorithm that now trades an initial $1M investment. He is talking about his approach and his main learnings. Jenia's algorithm currently has a live Sharpe Ratio of 2.66.
Backtesting Algorithmic Trading Strategies
In this webinar Ernie Chan talks about the main difference between algorithmic and discretionary trading - the possibility of backtesting a strategy. However, a poorly conducted backtest will give rise to false positives. Ernie discusses typical pitfalls and the many ways in which false positives can be avoided.
Introduction to the Quantiacs Toolbox
This is a quick overview over the functionality of the Quantiacs Toolbox. It's free and open source and enables you to build and test algorithmic trading systems. The supported languages are Matlab and Python. Watch the video and download the presentation and sample systems to play with here: https://quantiacs.com/quantclub
This is a step-by-step guid for how to build a basic algorithmic trading system. It should help you as a starting point so that you learn how to implement your own trading ideas. If you are new to algorithmic trading you might want to watch the Basic 'Concepts of Quantitative Trading' first. Find sample strategies and other files here: https://quantiacs.com/quantclub
Basic Concepts of Quantitative Trading
As a beginner in algorithmic trading learning about a few basic concepts can be really helpful starting point. In this video we talk about concepts like the Relative Strength Index (RSI), Sharpe Ratio (SR), and the Average True Range (ATR). Download the presentation from Quantiacs: https://quantiacs.com/quantclub
Machine Learning for Financial Forecasting
Guest speaker S. Burc Eryilmaz talks about how he approached the problem of financial forecasting using machine learning tools. Burc took the machine learning class of Andrew Ng at Stanford. He used the Quantiacs framework and data to model trading algorithms as a class project.
NYC Quant Club: Quant Trading With Futures
Algorithmic Trading and Futures: Learn the basics about Futures, definitions, mechanics, and how to trade them. Martin from Quantiacs explains Futures starting with the definition and ending with details about how to trade them using the Quantiacs toolbox.