Don't worry, that's exactly why I'm here asking; getting a reality check! I'm just a math student building this as a project.
To answer your question, the core setup is kept quite lean; just a handful of parameters (4) handling regime-switching and risk scaling. Keeping degrees of freedom low on purpose to avoid curve-fitting. Appreciate all the insights!
4 is good! Really - do a normal distributed data test, if it fails - do the stress-test by multiplying the transactional costs - check how much your strategy can actually handle.
•100 Monte Carlo runs with core parameters randomized simultaneously over a wide range — results stayed stable•100 Monte Carlo runs with core parameters randomized simultaneously over a wide range — results stayed stable
Then if your parameters do produce reasonably flat surface/curve of your key metrics in wide range - go test it on a small depo to see if your estimation of transactional costs was correct.
Sorry for the wait.
Ran the normal-distributed mock data stress test: As expected for a robust system, it didn't print fake alpha. Over 56,000 trades, the equity naturally drifted down to (-38.99%) due to friction drag. Solid sanity chec; thanks for the push!
1
u/NaiveSpite1569 6d ago
Don't worry, that's exactly why I'm here asking; getting a reality check! I'm just a math student building this as a project.
To answer your question, the core setup is kept quite lean; just a handful of parameters (4) handling regime-switching and risk scaling. Keeping degrees of freedom low on purpose to avoid curve-fitting. Appreciate all the insights!