I'd like ask everyone a more concurrency programming but definitely quant-finance related question. How do you deal with staleness of data in market hours as quote ticks are streaming and your model ...
If I have multiple markets (let's say 5, but the solution should be generic) trading the same stock/commodity/whatever, and the markets differ in both variable fees (which are in % of the trade order) ...
The classic mean-reversion strategy is to calculate an "expected return" (alpha) by computing the raw return for each security and then remove the part which you think is market driven. Statistically ...
I have a days-worth of level 2 market data. I am calculating S&P500 index arbitrage. I have a few questions about the calculation: 1) Should I be summing all the bids and asks from the stocks ...
What is $1 Gamma and what is 1% Gamma? please describe the difference? I understand Gamma but cant make the diff between the two.