For a university project, I have been working on a rather complex automated trading strategy, that levers machine learning techniques. I backtested the algorithm, as a result I have daily return data for various time windows (max. two years, it is a relatively new financial asset). I also simulated the performance of the algorithm on three months of test data.
Now I am writing the final report, but my daily supervisor wants more rigour i.e. statistical tests. E.g. he wants me to prove that the daily return on average is greater than zero or even the S&P500 daily return.
I believe this to be impossible. Let us assume all assumptions for whatever test are satisfied. Proving your average daily return to be higher than zero with an $\alpha = 5\%$, would imply it is statistically impossible to ever have a PERIOD (see EDIT below) with a negative return (which is definitely not the case for my strategy). Or thus, I believe my supervisor is asking me to prove that I have found the holy grail of automated trading strategies.
- Do you agree with my above reasoning: statistically proving an average daily return higher than zero is nonsense?
- Do you have any pointers or ideas about how I could somewhat prove or quantify that my algorithm works well, other than simulating over specific time periods and benchmarking my returns and Sharpe ratios against other assets and strategies.
EDIT: I meant period instead of day EDIT in response to @Olaf his comment: when I say statistically impossible I mean very improbable. Let us assume I would be able to reject ($\alpha=5\%$) the null hypothesis that the average daily return (e.g. calculated over 90 days) is smaller than zero. In case I would invest an equal amount every day, then given the above it is almost guaranteed that I will end up with more after 90 days. I tend to believe this is a very strong guarantee for an automated trading system.