BRTI
Blackridge Technology International, Inc. Common Stock
stock OTC

Thanks, and good on you for building this as a student. Mixed backtests at this stage are normal, so don't let them discourage you.
On the 0-1dte vs 15m gap, I'd treat it as a term-structure problem. The shortest listed expiry is a rough anchor, not a direct input, so the question is how to map it down to a 15-minute horizon. Short-horizon RV and IV each capture something different, backward vs forward-looking. So for your own build-up, it's worth testing which works better for you.
On other factors like drift, momentum, and regime, I can't share my exact adjustments, but I can share the approach. List every factor you think might matter, reason about the mechanism and effect on the market for each, then test them one at a time. Let the data decide which ones earn a place. That beats stacking features on intuition.
As for the final 60 seconds, if you're Kalshi BRTI, I believe it's already averaged. You can check the doc again. At 60s, it's all gamma and/or the distance from spot to strike. The market (makers) probably has a faster reaction than your model.
For free data, look for crypto options venues that publish their chains and market data through a public API. Many don't require a key or a simple sign-up. For spot, any major exchange's public feed works, and using BRTI makes sense since it's the resolution source.

Thanks, and good on you for building this as a student. Mixed backtests at this stage are normal, so don't let them discourage you.
On the 0-1dte vs 15m gap, I'd treat it as a term-structure problem. The shortest listed expiry is a rough anchor, not a direct input, so the question is how to map it down to a 15-minute horizon. Short-horizon RV and IV each capture something different, backward vs forward-looking. So for your own build-up, it's worth testing which works better for you.
On other factors like drift, momentum, and regime, I can't share my exact adjustments, but I can share the approach. List every factor you think might matter, reason about the mechanism and effect on the market for each, then test them one at a time. Let the data decide which ones earn a place. That beats stacking features on intuition.
As for the final 60 seconds, if you're Kalshi BRTI, I believe it's already averaged. You can check the doc again. At 60s, it's all gamma and/or the distance from spot to strike. The market (makers) probably has a faster reaction than your model.
For free data, look for crypto options venues that publish their chains and market data through a public API. Many don't require a key or a simple sign-up. For spot, any major exchange's public feed works, and using BRTI makes sense since it's the resolution source.