Quant tradingRTools of the trade
Recently, Yahoo Finance – a popular source of free end-of-day price data – made some changes to their server which wreaked a little havoc on anyone relying on it for their algos or simulations. Specifically, Yahoo Finance switched from HTTP to HTTPS and changed the data download URLs. No doubt this is a huge source of frustration,
BacktestingQuant tradingRThink like a traderTrading booksTrading strategies
I recently read Gary Antonacci’s book Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, and it was clear to me that this was an important book to share with the Robot Wealth community. It is important not only because it describes a simple approach to exploiting the “premier anomaly” (Fama and French,
BacktestingQuant tradingThink like a traderTools of the trade
This is the final post in our 3-part Back to Basics series. You may be interested in checking out the other posts in this series: Part 1: An Introduction to Algorithmic Trading Part 2: How to Succeed at Algorithmic Trading We’ve also compiled this series into an eBook which you can download for free here.
Quant tradingThink like a trader
This is the second post in our 3-part Back to Basics series on successful algorithmic trading. You may be interested in checking out the other posts in this series: Part 1: An Introduction to Algorithmic Trading Part 3: Backtesting in Algorithmic Trading There is a lot of information about algorithmic and quantitative trading in the public
Quant tradingThink like a trader
This is the first post in our 3-part Back to Basics series which serve as an introduction to algorithmic trading. You may be interested in checking out the other posts in this series: Part 2: How to Succeed at Algorithmic Trading Part 3: Backtesting in Algorithmic Trading This is the first in a series of posts in
Quant trading
What if you had a tool that could help you decide when to apply mean reversion strategies and when to apply momentum to a particular time series? That’s the promise of the Hurst exponent, which helps characterise a time series as mean reverting, trending, or a random walk. For a brief introduction to Hurst, including
Quant trading
This is the first post in a two-part series about the Hurst Exponent. Tom and I worked on this series together. I drew on some of his earlier work as well as other resources, including Quantstart.com. UPDATE 03/01/16: The Python code below has been updated with a more accurate algorithm for calculating the Hurst Exponent.
BacktestingQuant trading
This post comes to you from Dr Tom Starke, a good friend of Robot Wealth. Tom is a physicist, quant developer and experienced algo trader with keen interests in machine learning and quantum computing. I am thrilled that Tom is sharing his knowledge and expertise with the Robot Wealth community. Over to you, Tom. Unlike
FXMachine learningQuant tradingR
It would be great if machine learning were as simple as just feeding data to an out-of-the box implementation of some learning algorithm, then standing back and admiring the predictive utility of the output. As anyone who has dabbled in this area will confirm, it is never that simple. We have features to engineer and
Machine learningQuant trading
Last night it was my pleasure to present at the Tyro Fintech Hub in Sydney on the topic of using machine learning in algorithmic trading systems. Here you can download the presentation Many thanks to all who attended and particularly for the engaging questions. I thoroughly enjoyed myself! In particular, thanks to Andrien Juric for oraganising
Machine learningQuant tradingTrading books
If there’s one thing I’ve done a lot of over the last few years, reading would be it. I’ve devoted a great deal of time to devouring any material that I thought might give me an edge in my trading – textbooks, academic papers, blog articles, training courses, lecture notes, conference presentations…anything and everything I could get
FXMachine learningQuant tradingRZorro
Introduction My first post on using machine learning for financial prediction took an in-depth look at various feature selection methods as a data pre-processing step in the quest to mine financial data for profitable patterns. I looked at various methods to identify predictive features including Maximal Information Coefficient (MIC), Recursive Feature Elimination (RFE), algorithms with
Think like a trader
Think like a trader
Think like a trader
Trading strategies
Trading strategies