Quant platform – part 1: Setting up

Finance has always fascinated me. It is ripe with mathematics, very hands-on, it has a global marketplace, the assets are valued all the time. Other interesting aspects are big data, complex relations and the possibility for endless challenges as the market evolves. It is a field perfect for trying out machine learning technology, and who knows maybe hit jackpot if the findings are profitable. But that is not an initial goal.

The goal for me is to set up a platform that allows me to build different trading algorithms and evaluate them.

Initially(this article), I want to

  • Find a python library to support building and backtesting algorithms
  • Setup an evaluation method to evaluate the performance of a strategy
  • Construct a simple trading algorithm to showcase the evaluation
  • Run the system on my own laptop on demand

Further down the line I want to

  • Have a system that can generate trading signals in different markets
  • Run the system on AWS and update automatically
  • Have a web frontend which shows the performance of the algorithm(s) and the signals
  • Have the algorithms connected to a real account to do automatic trading – far into the future

Of course, this is not an exhaustive list, and many more aspects of it will, without a doubt pop up. So keep reading.

Continue reading “Quant platform – part 1: Setting up”

Driving alpha using alternative data: Social Sentiment, Part 1

I just viewed a webinar from Nasdaq which talks about using sentiment analysis to predict price movements in stocks. You can find the webinar here, very interesting subject. The presenter shows that the sentiment in many cases are an early predictor of the price movement. Of course the webinar is also a sales pitch for the new analytics hub that Nasdaq has build which currently consist of nine datasets, one of them are the sentiment data. All the nine datasets are in the group of “alternative data” which is all the new rage in the financial sector.

Read more to get an overview of the key points from the webinar and a few my takes on pitfalls in this area and how to do similar sentiment analysis on you own.

Continue reading “Driving alpha using alternative data: Social Sentiment, Part 1”