Hello,
I have a question regarding my strategy. I defined a trading strategy and in [10] for each Asset listed in the Nasdaq, applied that strategy for the close values for a given time period.
The signals of each asset are saved in a pandas dataframe with datetime index, which are the saved in a dictionary.
Now it is not clear to me, from the examples given on the page, how to pass this into the qnt backtest.
I will be thankful, if anyone can help me out on this. Thx
@support Hello, first of all. I
As you can see in the picture I have a good sharpe Ratio but when submitted ,the strategy got rejected because the sharpe ratio is too low. If I assume there is no bug in your multipass backtest, the issue should be the statelessness of your backtester. I tried to correct the code and to my understanding it should be fine but I dont know, and if I only rely on getting the news after submission, I will miss the deadline. Could you therefore pls have a look at the notebook too and if it does not take too much time correct anything if possible. Thank you. Regards
@magenta-kabuto thx a lot bro for your support and pointing out the mistakes
I will try the revised code now.
Good luck for the competition
@magenta-kabuto thx a lot bro for your support and pointing out the mistakes
I will try the revised code now.
Good luck for the competition
Hello Quantiacs community,
after a break due to work I came back yesterday to the platform and am trying to load data locally but for some reason I get the following error: KeyError: "cannot represent labeled-based slice indexer for coordinate 'time' with a slice over integer positions; the index is unsorted or non-unique" (Previously I could load the data with the same formula I used : def load_data(period):
return qndata.stocks.load_ndx_data(min_date = "2002-01-01", dims = ('time', 'field', 'asset'),forward_order=True,tail = 36518)
data= load_data(36510)).
This formula works online however, does anyone know why? Thx a lot
Regards
@vyacheslav_b Hello again, thank you very much. Yes you are right.
Regards
Furqan
@magenta-kabuto something like this, if this puts a bit light
@vyacheslav_b hey thx for your reply. Yes the strategy Sharpe ratio differs from the multipass backtest.
But Quantiacs says that multiple pass Backtesting avoids looking into the future, so I am confused.
The algorithm identifies certain patterns and only if those occur over a period of time, does it generate signals.
So it does not calculate weights to trade everyday
@magenta-kabuto Sry to post again. Now I changed to 20 passes and suddenly the sharpe ratio went up and this is the strategy that got rejected. I am totally confused!
@magenta-kabuto Unfortunately, even the strategy that got rejected due to a negative sharpe ratio, has the same ones as in the screenshot for 3 passes 
@kvanvanvant_test_python hey, thx a lot, will precheck it now. Doesnt multi-pass Backtest avoid looking into the future? Anyways, should be clear after precheck.
Regards
