| --- |
| tags: |
| - time-series |
| - finance |
| - itransformer |
| - onnx |
| - stock-prediction |
| license: mit |
| --- |
| |
| # PutStrike iTransformer — Per-Stock Forecasting Models |
|
|
| **iTransformer** (ICLR 2024) — individual models trained per stock for 60-day price forecasting. |
|
|
| ## Per-Stock Models |
|
|
| **{len(per_stock_metrics)} individual per-stock models**, each under `per_stock/{SYMBOL}.onnx`. |
|
|
| - **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**: 51.3% |
| - **Average 30-day directional accuracy**: 56.0% |
|
|
| ## 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 |
| - 3 cross-asset: VIX correlation, rolling beta, volume-price correlation |
| - 4 advanced volume: MFI, A/D line, VWAP deviation, Force Index |
| - 5 price structure: range position, ATR ratio, consecutive days, candle body |
| - 5 statistical regime: Hurst exponent, Parkinson/GK volatility, consistency, tail ratio |
| - 4 intermarket: SPY momentum, gold/oil ratio, DXY-VIX interaction |
| - 3 sector ETF: stock vs sector ETF returns (5/20d), sector correlation |
| - 4 credit market: HYG/TLT returns, credit spread proxy, HYG-SPY divergence |
| - 1 VIX term structure: VIX9D/VIX short-term fear ratio |
| - 2 industry commodity: per-stock commodity correlation and return |
| - 2 intermarket extended: copper/gold ratio change, BTC sentiment |
| - 6 **gamma squeeze proxies**: volume acceleration, price-volume momentum, range expansion, |
| gap acceleration, squeeze breakout signal, volume-price impact |
| - 4 **market breadth & rotation**: tech rotation (QQQ-SPY), small cap rotation (IWM-SPY), |
| semiconductor momentum (SOX), biotech momentum (XBI) |
| - 4 **sentiment proxies**: realized/implied vol ratio, VIX-SPY short corr, |
| credit momentum 10d, fear composite |
| - 4 **stock-specific drivers**: per-company primary/secondary driving asset returns & correlations |
| (90 unique driver mappings across SOX, IGV, HACK, KRE, ITA, XOP, IBB, XHB, XRT, LIT, etc.) |
| - 2 **FRED extended**: St. Louis Fed Financial Stress Index, 10Y-3M yield spread |
| - 2 **FRED rates**: Federal Funds Rate level and 20d change (monetary policy stance) |
| - 2 **FRED FX**: JPY/USD 20d change and z-score (carry trade unwinding proxy) |
| - 2 **tail risk**: CBOE SKEW level and 20d z-score (options tail risk pricing) |
| - 1 **value/growth rotation**: IWF vs IWD 20d return spread |
| - 1 **risk appetite**: XLY vs XLP 20d return spread (consumer discretionary vs staples) |
|
|
| ## Usage |
|
|
| ```python |
| import onnxruntime as ort |
| import numpy as np |
| |
| # Load per-stock model |
| session = ort.InferenceSession("per_stock/AAPL.onnx") |
| |
| # features shape: (1, 60, 135) |
| output = session.run(None, {"features": features})[0] |
| # output shape: (1, 60) — predicted returns |
| ``` |
|
|
| ## Disclaimer |
|
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| This model is for research and educational purposes only. Not financial advice. |
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|
