Closed q-learning-trader closed 1 year ago
772435284
commented
1 year ago Thanks for the comment, the code in the main branch just wants to show the idea and it is fundamental, it does not contain the QPL calculation. I am recently refactoring the code to make it more complete in the refactor branch. In the refactor branch, I add the calculation of QPL in folder data_provider/qpl.py and the QPL are calculated online by rolling window. If you have further questions, feel free to ask me.
q-learning-trader
commented
1 year ago Thanks @772435284 , I will have a look. What is the main purpose of your refactoring?
772435284
commented
1 year ago The code in the main branch is quite a mess and it is hard to reproduce the result. By refactoring, I can compare the performance of more models. The final purpose is to make the code work in production (trading strategy).
q-learning-trader
commented
1 year ago I think that one of the best asset mix is the major currencies together with SEK,NOK and DKK. The following is the result of one simulation:
772435284
commented
1 year ago Good. Although the portfolio value went extremely high in simulation, you need to test it out in a more realistic backtesting framework.
q-learning-trader
commented
1 year ago what do you mean with "more realistic backtesting framework" ? Are you talking about commission costs or slippage ? I have multiplied the weights for 3000$ and set to 0 what is below 1$. I notice the matrix is very sparse (see attachment). Is there any reason for that? I was supposing it was more dense but in fact it trades only few securities per day. And it never trades some assets. The assets list is the following:
product_list = ['AUDCAD','AUDJPY','AUDUSD','CADCHF','CADJPY','CHFJPY','EURAUD','EURCAD','EURCHF','EURGBP','EURJPY','EURNZD','EURUSD','GBPCAD','GBPCHF','GBPJPY','GBPNZD','GBPUSD','NZDCAD','NZDCHF','NZDJPY','NZDUSD','USDCAD','USDCHF','USDJPY','EURDKK','EURSEK','EURNOK','USDDKK','USDSEK','USDNOK'],0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31
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405,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2999.1286396980286,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0
406,0.0,0.0,0.0,0.0,0.0,3000.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0For a more realistic backtesting framework, yes, I am talking about transaction costs and slippage. I can suggest you use a backtesting framework, such as backtrader.
The matrix is very sparse because you put too many products inside one portfolio. I suggest you only put 5-10 products inside one portfolio and train multiple agents for each portfolio for trading.
Your work and the theory of Professor Lee are very interesting and original. However I still have a doubt on how QPLs are calculated. In your master branch the data already contain QPL. If these are calculated from the MT4 by Professor Lee there might be some data leakage. In fact, you consider the past 2048 samples and you calculate average, variance and lambda value from this data. But for any of these samples except the last one, you are using future data for calculating QPLs. You should use a rolling window and do not use future values. Have you ever considered this?