hft

Well I assume this is more theoretical question for those who familar with hft. I receive orders from FAST and process them. I receive about 2-3 thousands orders per second. The question is if I should try to process them synchronous or asynchronous. Every time I receive next order I need to do following: update orderbook of corresponding instrument update indexes and indicators that depends on that order update strategies and schedule some actions if required (buy/sell something etc.) To do that synchronous I have about 200-300 µs (to be able to process 3000 orders per second). That should be

I'm trying to get zipline working with non-US, intraday data, that I've loaded into a pandas DataFrame: BARC HSBA LLOY STAN Date 2014-07-01 08:30:00 321.250 894.55 112.105 1777.25 2014-07-01 08:32:00 321.150 894.70 112.095 1777.00 2014-07-01 08:34:00 321.075 894.80 112.140 1776.50 2014-07-01 08:36:00 321.725 894.80 112.255 1777.00 2014-07-01 08:38:00 321.675 894.70 112.290 1777.00 I've followed moving-averages tutorial here , replacing "AAPL" with my own symbol code, and the historical calls with "1m" data instead of "1d". Then I do the final call using algo_obj.run(DataFrameSource(mydf)) ,

I understand that in the Java virtual machine (JVM), warmup is potentially required as Java loads classes using a lazy loading process and as such you want to ensure that the objects are initialized before you start the main transactions. I am a C++ developer and have not had to deal with similar requirements. However, the parts I am not able to understand are the following: Which parts of the code should you warm up? Even if I warm up some parts of the code, how long does it remain warm (assuming this term only means how long your class objects remain in-memory)? How does it help if I have