First off welcome to Q .

You pose an interesting question which in general is "are there a set of factors which can predict todays 'market type'?"

Maybe use some of the machine learning tools to find out?

I suppose one could define many different 'market types'. However, in order to do any type of analysis, one would need to define these very explicitly. For instance 'momentum day' might be when a group of securities has a high correlation with SPY and SPY return is greater than x. Maybe get more granular and define 'up momentum' and 'down momentum' days?

Once one defines type (or classifier in machine learning terms) the next step would be to brainstorm a number of factors (or metrics in the original post) that one would feel may impact the type. Now figure out how to get all this data into a table. The rows of the table would be trading days. The first column is the type of trading day it was (ie the classifier). The rest of the columns are the data for each of the factors. This last step may not be trivial depending upon the factors. Then, finally, train a machine learning classifier and see which factors predict the classifier and how well the overall prediction is. Q has a good lecture series on machine learning which may be a good start https://www.quantopian.com/posts/machine-learning-on-quantopian

As far as factors to consider, here are some that come to mind. Maybe use ETFs as proxies to develop factors. For example, 'market jump' is simply 'change of price of SPY'.:

• big overnight jump in the market (open is much different than previous close)
  
• big change in first hour trading price and/or volume
  
• big change in previous last hour trading price and/or volume
  
• direction of bonds vs stocks
  
• relative return of bonds vs stocks
  
• correlation of sector movement to market (everything moving or just certain sectors)
  
• us market correlation with international markets
  
• value of dollar vs other currencies
  
• volatility change (VXX)

It may be good to test these factors as both binary (market is up or market is down) as well as real number factors (market is up .8%). Don't be afraid to try seemingly unrelated factors. The nice thing with some of the machine learning tools is they can highlight which factors correlate with the classifier and which are simply noise.

All this of course is predicated on the assumption that if one an predict the 'type' of trading day then one can benefit somehow. Before spending time on predicting it may be worth it to validate that assumption. Just a thought.

Best of luck.

Hi Dan,

Many thanks for the response! That makes alot of sense trying to define the day types using some specific factors. I have begun looking at the range of the initial balance, and the probable outcome of the day. Naturaly, a smaller IB often results in more of a trend day as participants are yet to position - however I am still trying to dig into the actual statistics surrounding this.

I understand your concern regarding validating my assumption. Maybe im looking at the problem through the wrong lens - your comments have been very helpful and have helped to direct me towards the actual questions I should be asking!

Best,

J