In my previous post I talked about how I planned to use constrained optimization to create features from Oanda’s OrderBook and PositionBook data, which can be downloaded via their API. In addition to this I have also created a set of features based on the idea of Order Flow Imbalance (OFI), a nice exposition of which is given in this blog post along with a numerical example of how to calculate OFI. Of course Oanda’s OrderBook/PositionBook data is not exactly the same as a conventional limit order book, but I thought they are similar enough to investigate using OFI on them. The result of these investigations is shown in the animated GIF below.
This shows the output from using the R Boruta package to check for the feature relevance of OFI levels to a depth of 20 of both the OrderBook and PositionBook to classify the sign of the log return of price over the periods detailed below following an OrderBook/PositionBook update (the granularity at which the OrderBook/PositionBook data can be updated is 20 minutes):
- 20 minutes
- 40 minutes
- 60 minutes
- the 20 minutes starting 20 minutes in the future
- the 20 minutes starting 40 minutes in the future
for both the OrderBook and PositionBook, giving a total of 10 separate images/results in the above GIF.
- buy orders above price vs sell orders below price
- sell orders above price vs buy orders below price
- long positions above price vs short positions below price
- short positions above price vs long positions below price
As can be seen, almost all features are deemed to be relevant with the exception of 3 OFI levels rejected (red candles) and 2 deemed tentative (yellow candles).