I’m a big fan of Ernie Chan’s quant trading books: Quantitative Trading, Algorithmic Trading, and Machine Trading. There are some great insights in there, but the thing I like most is the simple but thorough treatment of various edges and the quant tools you might use to research and trade them. Ernie explicitly states that …
You rarely meet a rich forex trader. I’ve met plenty of rich traders who trade quant factors or stat arb. Plenty of market makers, futures spreaders and volatility traders that do nicely. But I don’t think I’ve ever met a rich forex trader. Jeez man – what a downer! Don’t run away, we’re gonna turn …
Anyone that’s been around the markets knows that the monthly release of the United States Department of Labor’s Non-Farm Payrolls (NFP) data can have a tremendous impact, especially in the short term. NFP is a snapshot of the state of the employment situation in the US, representing the total number of paid workers, excluding farm …
One of the ongoing research projects inside the Robot Wealth community involves an FX strategy with some multi-week hold periods. Such a strategy can be significantly impacted by the swap, or the cost of financing the position. These costs change over time, and we decided that for the sake of more accurate simulations, we would …
Deep Learning for Trading Part 4: Fighting Overfitting is the fourth in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Deep Learning for Trading Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques …
This is the third in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals …
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 …
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 …
Recently, I wrote about fitting mean-reversion time series analysis models to financial data and using the models’ predictions as the basis of a trading strategy. Continuing our exploration of time series modelling, let’s research the autoregressive and conditionally heteroskedastic family of time series models. In particular, we want to understand the autoregressive integrated moving average …
In the first Mean Reversion and Cointegration post, I explored mean reversion of individual financial time series using techniques such as the Augmented Dickey-Fuller test, the Hurst exponent and the Ornstein-Uhlenbeck equation for a mean reverting stochastic process. I also presented a simple linear mean reversion strategy as a proof of concept. In this post, I’ll …
