In the Finance Industry, Alternative Data is used to give investors an information advantage. Quantitative Hedge Funds have used trading models based on Alternative Data for many years. The most common Alternative Data signal used in quantitative trading and quantitative investing is based on text data from the Internet, and the trading models can broadly be defined as algorithmic trading models and as statistical arbitrage models. It has been suggested that text analysis is the key to success for the most successful money manager of all times. The trading model can use text data and sentiment data as the only, or as one of several, inputs, and it can be the main strategy, or one of several strategies, in a hedge fund. Some traditional funds use text-based signals to build the models they use as an overlay to other strategies and as a risk indicator for tactical asset allocation.

Even though computational intelligence techniques have been extensively utilized in financial trading systems, almost all developed models use the time series data for price prediction or identifying buy-sell points. However, in this study we decided to use 2-D stock bar chart images directly without introducing any additional time series associated with the underlying stock. We propose a novel algorithmic trading model CNN-BI (Convolutional Neural Network with Bar Images) using a 2-D Convolutional Neural Network. We generated 2-D images of sliding windows of 30-day bar charts for Dow 30 stocks and trained a deep Convolutional Neural Network (CNN) model for our algorithmic trading model. We tested our model separately between 2007-2012 and 2012-2017 for representing different market conditions. The results indicate that the model was able to outperform Buy and Hold strategy, especially in trendless or bear markets. Since this is a preliminary study and probably one of the first attempts using such an unconventional approach, there is always potential for improvement. Overall, the results are promising and the model might be integrated as part of an ensemble trading model combined with different strategies.

This article follows my previous one on Bayesian probability & probabilistic programming that I published few months ago on LinkedIn. And for the purpose of this article, I am going to assume that most this article readers have some idea what a Neural Network or Artificial Neural Network is. Neural Network is a non-linear function approximator. We can think of it as a parameterized function where the parameters are the weights & biases of Neural Network through which we will be typically passing our data (inputs), that will be converted to a probability between 0 and 1, to some kind of non-linearity such as a sigmoid function and help make our predictions or estimations. These non-linear functions can be composed together hence Deep Learning Neural Network with multiple layers of this function compositions.

In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance.

A year ago, in a report on Big Data and investment management, Citi Business Advisory Services predicted that "with the improved volume, velocity and variety of data inherent in the big data approach, the innovation seen in systematic trading models over the past decade could accelerate." One of the platforms highlighted in the Citi report was DataSift, a service that promises to "integrate social, blog and news data in a single place." Or as Citi put it, DataSift aggregates "marquee data source partners, including Edgar Online, Wikipedia, and WordPress." Edgar, of course, is consistent with old-fashioned ideas of what hedge fund managers through various third parties should keep track of. But Wikipedia presence on this short list might pull up short those who still think of it as a pastime for nerds who like to think of themselves as editors.