AFT
Apollo Senior Floating Rate Fund Inc.
stock NYSE

https://preview.redd.it/yxjesf2wlo2h1.png?width=2380&format=png&auto=webp&s=55aa10b7eea23f11a95ae9a6ee36fe0ea66cc9f2
Over the past two months I wrote three connected papers testing HMM-based regime awareness in algorithmic trade execution. The short version:
**Paper I:** Trained PPO agents with the true regime label directly in the state space. The agent largely ignored it, both regime-blind and regime-aware agents learned nearly identical steady execution policies. The failure is structural: steady execution is a robust local optimum that policy gradient training reliably finds, regardless of what information is available.
**Paper II:** Tested whether hand-crafted HMM uncertainty signals at least *predict* execution quality. They do, but only at 3–10 day aggregation horizons. At daily resolution, completely uninformative. IWM entropy hits ρ = −0.411 (p < 0.001) at 10 days. The temporal threshold aligns with mean regime durations.
**Paper III:** Tried to replace the fixed 10-day window with a per-instance adaptive window calibrated via Weibull AFT survival models. Failed on three structural grounds: C-indices of 0.20–0.39 (below chance), flat C-index from n=4 to n=45 ruling out data scarcity, and decreasing-hazard duration distributions causing 60–89% of predictions to collapse to boundary values.
The negative results are the contribution. Knowing exactly where and why this approach fails is what lets future work start from a better place.
Full article on Medium: [https://medium.com/@gargsatish/i-spent-months-trying-to-make-an-ai-trader-smarter-about-market-conditions-heres-why-it-failed-b76d124542b9](https://medium.com/@gargsatish/i-spent-months-trying-to-make-an-ai-trader-smarter-about-market-conditions-heres-why-it-failed-b76d124542b9)
Papers on SSRN:
* Paper I: [ssrn.com/abstract=6559598](http://ssrn.com/abstract=6559598)
* Paper II: [ssrn.com/abstract=6733198](http://ssrn.com/abstract=6733198)
* Paper III: [ssrn.com/abstract=6763019](http://ssrn.com/abstract=6763019)
Happy to answer questions on methodology, the survival analysis piece, or the RL failure mechanism.

https://preview.redd.it/yxjesf2wlo2h1.png?width=2380&format=png&auto=webp&s=55aa10b7eea23f11a95ae9a6ee36fe0ea66cc9f2
Over the past two months I wrote three connected papers testing HMM-based regime awareness in algorithmic trade execution. The short version:
**Paper I:** Trained PPO agents with the true regime label directly in the state space. The agent largely ignored it, both regime-blind and regime-aware agents learned nearly identical steady execution policies. The failure is structural: steady execution is a robust local optimum that policy gradient training reliably finds, regardless of what information is available.
**Paper II:** Tested whether hand-crafted HMM uncertainty signals at least *predict* execution quality. They do, but only at 3–10 day aggregation horizons. At daily resolution, completely uninformative. IWM entropy hits ρ = −0.411 (p < 0.001) at 10 days. The temporal threshold aligns with mean regime durations.
**Paper III:** Tried to replace the fixed 10-day window with a per-instance adaptive window calibrated via Weibull AFT survival models. Failed on three structural grounds: C-indices of 0.20–0.39 (below chance), flat C-index from n=4 to n=45 ruling out data scarcity, and decreasing-hazard duration distributions causing 60–89% of predictions to collapse to boundary values.
The negative results are the contribution. Knowing exactly where and why this approach fails is what lets future work start from a better place.
Full article on Medium: [https://medium.com/@gargsatish/i-spent-months-trying-to-make-an-ai-trader-smarter-about-market-conditions-heres-why-it-failed-b76d124542b9](https://medium.com/@gargsatish/i-spent-months-trying-to-make-an-ai-trader-smarter-about-market-conditions-heres-why-it-failed-b76d124542b9)
Papers on SSRN:
* Paper I: [ssrn.com/abstract=6559598](http://ssrn.com/abstract=6559598)
* Paper II: [ssrn.com/abstract=6733198](http://ssrn.com/abstract=6733198)
* Paper III: [ssrn.com/abstract=6763019](http://ssrn.com/abstract=6763019)
Happy to answer questions on methodology, the survival analysis piece, or the RL failure mechanism.