Hi, I run into quite large differences in Sharpe Ratio's in the development/testing environment and the contest submit fase. Is there a reason for this difference? What is the correct SR?
Hi, this should not happen. Can you let us know some example? You can also send some details to email@example.com.
You may have too many parameters in your strategy resulting in over fitting. This often happens when optimizing on asset by asset basis with too many indicators. Try generalizing first.
I have a different issue with testing and submitting.
I tested my strategy (Ridge Regression, random state = 18) with multi-pass backtesting using qnbt.backtest_ml. The Sharpe Ratio is 1.13, there is no forward looking, because the Sharpe Ratio is the same when predict_each_day is set to True.
Now comes the problem: I submitted this strategy (written in multi-pass backtesting format) and this strategy always gets rejected, because the Sharpe Ratio is drastically smaller 1 (exactly
Why is there such a big difference in the Sharpe Ratio?
weights = qnbt.backtest_ml(
train_period=4x365, # the data length for training in calendar days
retrain_interval=5x365, # how often we have to retrain models (calendar days)
retrain_interval_after_submit=50, # how often retrain models after submission during evaluation (calendar days)
predict_each_day=True, # Is it necessary to call prediction for every day during backtesting?
# Set it to true if you suspect that get_features is looking forward.
competition_type='stocks_nasdaq100', # competition type
lookback_period=365, # how many calendar days are needed by the predict function to generate the output
start_date='2006-01-01', # backtest start date
build_plots=True # do you need the chart?
@eddiee Dear eddiee, sorry for the delay. Did you try to retrain the model every day during the testing? Simulation will be very slow but the result should match then.
@eddiee Please use "retrain_interval_after_submit=None". In this way the retraining setting used during development will be used also after submission.
@support Thanks a lot!
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