We do not log in to our servers every day to check how the resource usage is. Just like with uptime monitoring we need a system to help us monitor if everything is inside reasonable limits so we can scale the servers if required. And detect any potential problem before it becomes a problem.
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.
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.
By combining the power of docker and python I can create an analytics platform that will always run and is not dependent on the versions of python or anything other I run on my laptop.
I will show how easy it is to setup jupyter notebooks and use them for analytics. And how easy it is to publish an analysis to this blog. Docker gives me at least two benefits, 1. I can be sure that when I start my docker image the analysis will always run because docker makes sure the versions of all the software are the same. 2. If my laptop is not enough to run an analysis I can deploy the same docker image to a much more power full computer in a cloud.