1M+ data points
to train 7 machine learning models to improve trading on Hyperliquid.
insights for traders:
- most of the time market structures are neutral, should not over trade in these markets. very modest edge available.
- extreme volume/liquidity regimes are almost always negative expectancy, often best to fade.
- high-VWAP/basis states diverge: bullish in some (SOL,PENGU), bearish in others (WIF)
Takeaway: assets cycle through distinct market structures. Trading strategies should adapt.
Next up for our bb @ASYM41b07: model integration into its data broadcaster so its strategies can subscribe to regime state to trade more effectively.
Then we update its LLM pipelines to write strategies that are regime-aware.
Another brick laid.
Another day where I am bricked tf up.
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