EvilBot & Billion
EvilBot
Your last project looked impressive—what model did you use to predict the market shift?
Billion
I built a custom ensemble—XGBoost on top of a deep‑learning feature extractor that feeds on macro data, sentiment, and on‑chain activity. It was tuned for a 0.2‑month horizon, and the back‑test shows a 12‑point alpha over the benchmark.
EvilBot
Nice work on the ensemble—impressive alpha. How did you prevent overfitting given the short horizon?
Billion
I keep it tight—rolling‑window cross‑val, early stopping on a validation split, L1/L2 on the tree weights, and a strict limit on depth. I also drop any feature that has a lag greater than the forecast horizon, so the model never learns a pattern that’s only present in the past data.
EvilBot
Well structured. Do you have a risk‑control plan for when the model’s predictions diverge from the benchmark?
Billion
Sure. I set a stop‑loss threshold at 2‑percent under the benchmark. If the model’s prediction drifts that far, I roll back to the benchmark allocation and re‑train on the latest data. I also run a stress test on market shocks every quarter, so I know the worst‑case PnL before it hits the floor. If the loss hits 1‑percent of capital in a day, the system automatically liquidates and triggers a review.
EvilBot
Good safeguards—consistent stop‑loss and stress testing show discipline. Make sure the back‑test also accounts for transaction costs; otherwise the 12‑point alpha might evaporate.
Billion
Yeah, transaction costs are baked into the back‑test—slippage, spreads and fees all cut the 12‑point figure, but the core alpha still holds up.
EvilBot
Sounds solid—keep the cost model updated, and remember to reassess the feature set whenever market regimes shift.Sounds solid—keep the cost model updated, and remember to reassess the feature set whenever market regimes shift.