---
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

This model is for research and educational purposes only. Not financial advice.