Fable stuck around just long enough to debug my 4 billion rows of training data, train a new financial model that was twice as profitable with half the drawdown, and during stress periods, maintain profitability. The model is now finally passing the 30% a month yield.

@ScriptedAlchemy For your LEAPS calls/puts model, was the main training target forward option-premium return over fixed horizons, or realized strategy P&L after your holding/exit rules?

Profit off the appreciation on the premiums. Cycle the capital every few weeks into new premiums. No fixed horizon. Just make money with it 2-6 weeks then cycle. Under hood model used fix horizons as overlays to create matrix of horizons that get weighted, at least one iteration did. Might have folded it into unified. The model was trained exclusively to return 30% a month as its primary objective

@ScriptedAlchemy This is incredibly helpful, thank you. The fixed horizons part clarified the missing piece for me.

The real challenge lies in managing drawdown and reversals. The model should be nearly equally profitable in either market direction. I utilize opra data, Bluesky data lake firehose for real-time social data ingestion, and employ ByteDance’s recommendation engine design for social usage. The entire system leverages ByteDance’s real-time sparse weight heads to facilitate online training. I train the model offline, essentially to prime it. Once online, I don’t need to retrain new checkpoints over time. Consequently, the model weights are fine-tuned in real-time.