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README.md

@@ -16,14 +16,14 @@ license: mit
 
 **{len(per_stock_metrics)} individual per-stock models**, each under `per_stock/{SYMBOL}.onnx`.
 
-- **Architecture**: iTransformer with RevIN (535,368 parameters each)
-- **Input**: 60 days x 152 features (OHLCV technicals + macro + relative strength)
+- **Architecture**: iTransformer with RevIN (533,141 parameters each)
+- **Input**: 60 days x 135 features (OHLCV technicals + macro + relative strength)
 - **Output**: 60-day forward return forecast
 - **Training**: Walk-forward validation (70/15/15), HuberLoss(delta=0.02)
-- **Average 7-day directional accuracy**: 50.6%
-- **Average 30-day directional accuracy**: 56.8%
+- **Average 7-day directional accuracy**: 51.3%
+- **Average 30-day directional accuracy**: 56.0%
 
-## v9.0 Features (154 total — includes gamma squeeze + sentiment + stock-specific drivers + tail risk + style rotation)
+## v11.0 Features (172 total — includes gamma squeeze + sentiment + stock-specific drivers + tail risk + style rotation)
 
 - 83 original: OHLCV technicals, momentum, volume, volatility, statistics, calendar, macro
 - 4 relative strength: stock vs SPY returns (5/20/60d), SPY correlation
@@ -61,7 +61,7 @@ import numpy as np
 # Load per-stock model
 session = ort.InferenceSession("per_stock/AAPL.onnx")
 
-# features shape: (1, 60, 152)
+# features shape: (1, 60, 135)
 output = session.run(None, {"features": features})[0]
 # output shape: (1, 60) — predicted returns
 ```