Holding period, execution simulation, feedback from live Quantiacs trading?
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Hi,
I just signed up and I notice that there is no mention made of the holding period for user strategies, there are no statistics except for turnover which relate to issues pertaining to scalability or indeed the baseline viability of strategies due to various trading costs. In my previous work we imposed a lot of penalties that were for the most part gleaned from lots of execution data from futures orders we sent to the exchanges over the years.So while it might be prohibitive in terms of computational cost to micro-simulate each fill for each order, we developed heuristics in an attempt to limit the trading costs by soft penalties. So with daily prices it is really hard to get a realistic simulation of fill prices, you can create a scoring function that penalizes strategies with a lot of turnover per unit of time, etc.
I am curious if holding period ever explicitly enters into the testing procedure? Are the algorithms that place well in competitions subjected to more fine-grained execution testing using intra-day data? I know that if I naively try to just trade in and out every day, my PNL will be very sensitive to the volatility of the instrument, tick size, spread, and entry and exit conditions, and at that time-frame when and how you choose to execute has a non-trivial relationship to PNL.
thanks
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@penrose-moore Ok well I guess I should have read the rules / user agreement first. A percentage of the average True Range over a certain interval seems to be how slippage is incorporated into the scoring, and the penalty is on profitability directly.
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@penrose-moore Hello, thank you for the points you are making, which are absolutely correct.
For the competitions we chose a simplified model which allows us to process all submissions, and user development strategies, which uses volatility (via ATR).
Currently for the futures competitions 4% * ATR (14) is deducted for every change in position size of a futures position. We are using this value according to historical data.
Users can also tune the slippage in their simulation, to see for example if the strategy is robust to a higher value, or if it works well with lower slippage.
But the points you are making are right. In particular our slippage model neglects the fact that trading volume has a big role for slippage (higher volume allows to build larger positions with lower slippage impace), and in turn it sets a limit on the capacity of the strategy.
However, as a starting point, we believe that the simple choice we made is a good compromise between getting realistic results and not penalizing computational efficiency too much.
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@support yes coarse heuristics work well as long as you are conservative. For shorter term models I have started using minute bars despite the computational hit, because it helps in a lot of other ways.
I may enter this contest, I am pretty rusty on predictive modelling and I am not sure I can do a good job using just daily prices, there is not a lot of data. I used to work at a CTA and I feel like we wasted a lot of man years using only prices, hoping better models would acheive more alpha. in the end the sharpe is similar to the S&P but uncorrelated, but you have gotten there with some simpler models and enjoyed life.
I have some other questions about the platform and the contest that I will post here.
Best
P.M.
