Quantitative-Equity-Alpha-Transformer (QEAT)
A Systematic, Multi-Horizon Macro-Liquidity Model for Probabilistic Alpha Discovery.
- Architecture: Temporal Fusion Transformer (TFT) with Quantile Regression
- Compute Infrastructure: NVIDIA DGX Spark (Grace Blackwell GB10 Superchip)
🚀 Live Demo
Click here to launch the Interactive Alpha Dashboard
The live dashboard allows you to run real-time inference on a curated list of high-liquid assets across three key sectors:
- 🇺🇸 Big Tech: NVIDIA ($NVDA), Apple ($AAPL), Microsoft ($MSFT), Google ($GOOGL), Amazon ($AMZN), Meta ($META), Tesla ($TSLA), AMD, Broadcom.
- 🏆 Commodities: Gold ($GLD), Silver ($SLV), Crude Oil ($USO), Natural Gas ($UNG), Palladium ($PALL).
- ⛓️ Crypto Assets: Bitcoin ($BTC), Ethereum ($ETH), Solana ($SOL).
ℹ️ Institutional Note: This public demo is limited to the assets listed above. The full internal model tracks 518+ tickers, including the entire S&P 500 constituents and global indices, which are not available in this live demo.
🏛️ Abstract
The Quantitative-Equity-Alpha-Transformer (QEAT) is a state-of-the-art deep learning model designed to solve the Non-Stationarity problem in financial time-series forecasting. Unlike traditional stochastic calculus models or naive LSTM networks, QEAT utilizes a Temporal Fusion Transformer (TFT) architecture to interpretably map high-dimensional macro-liquidity features to asset returns.
🧠 Model Architecture
1. Temporal Fusion Transformer (TFT)
The core engine is a specialized Transformer designed for multi-horizon forecasting:
- Variable Selection Networks (VSN): Automatically filters irrelevant noise inputs, focusing on high-signal liquidity events.
- Gated Residual Networks (GRN): Enables deep processing of non-linear relationships while suppressing the "Vanishing Gradient" problem.
- Multi-Head Attention Mechanism: Learns long-term dependencies and "Regime Shifts" by attending to historical patterns across different time scales.
2. Probabilistic Forecasting
Instead of a single price target, QEAT outputs a Probability Distribution (10th, 50th, and 90th percentiles) for institutional Risk Management (VaR).
📊 Data Engineering & Methodology
The model was trained on a proprietary Global Macro-Liquidity Dataset engineered to capture cross-asset correlations under specific liquidity regimes.
1. The Asset Universe (518 Tickers)
A diverse, cross-asset training set designed to learn non-linear correlations:
- Equities: Full S&P 500 constituents (approx. 503 tickers).
- Cryptocurrency: Top 10 Liquid L1 Protocols (BTC, ETH, SOL, AVAX, DOT, etc.).
- Commodities: Key Industrial/Precious Metals ETFs (SLV, GLW) acting as proxies for global physical demand.
2. The Time Series (10-Year History)
- Range: 2014 – 2024 (Training/Validation), with Inference on 2026.
- Resolution: Daily (OHLCV) adjusted for splits and dividends.
- Scale: 1.2 Million individual training examples.
3. The "Alpha" Features (Exogenous Liquidity Vectors)
The model's edge comes from 6 custom-engineered liquidity flags that serve as static covariates:
- 🇺🇸 US Fiscal Flows:
- `is_401k_window` (Jan 1-15): Capture automated retirement inflows.
- `is_tax_refund` (Apr 1-15): Retail capital injection cycles.
- `is_bonus_window` (Mar 1-15): Corporate performance bonus allocation.
- 🌏 Global Cultural Flows:
- `is_diwali_window` (Nov 1-5): Modeled physical Gold/Silver demand in India.
- `is_cny_window` (Feb 1-7): Chinese New Year liquidity shifts.
- `is_holiday_season` (Dec 24-Jan 2): Retail sentiment and volume anomalies.
4. Target Variable
- Objective: 7-Day Forward Log Returns.
- Optimization: The model minimizes Quantile Loss across three horizons (P10, P50, P90) simultaneously.
📈 Key Research Findings (Alpha Signals)
The "Diwali Alpha" Anomaly
Post-training analysis of the Attention Weights revealed a significant market inefficiency:
- Observation: The model assigned a 0.58 Importance Score to the Diwali Liquidity Window, identifying it as the strongest predictor of Precious Metals volatility.
- Validation: Backtesting the "Diwali Long" strategy yielded a High-Sharpe outcome for the Jan-Feb 2026 window.
Current Regime Prediction (Feb 2026)
- Signal: Strong Rotation (Risk-On)
- Long Conviction: Hardware Technology ($ANET, $GLW) & Industrial Metals.
- Short Conviction: Defensive Healthcare ($HUM, $CNC) & Speculative L1 Crypto ($AVAX).
🛠️ Usage for Quantitative Research
import torch
from pytorch_forecasting import TemporalFusionTransformer
# 1. Load the Pre-Trained Weights
model = TemporalFusionTransformer.load_from_checkpoint("model.ckpt")
# 2. Prepare Your Data
# Data must be a Pandas DataFrame with columns:
# ['Ticker', 'date', 'close', 'liquidity_flags', 'time_idx']
# 3. Generate Probabilistic Predictions
raw_prediction = model.predict(your_dataframe, mode="raw", return_x=True)
# 4. Extract Quantiles
interpretation = model.interpret_output(raw_prediction.output, reduction="sum")
print("Attention Weights:", interpretation["attention"].shape)
📉 Hardware & Training Specifications
This model was trained on NVIDIA's next-generation accelerated computing platform.
- System: NVIDIA DGX Spark
- Compute Unit: NVIDIA Grace Blackwell GB10 Superchip
- Precision: Mixed-Precision (FP16/FP32) Matrix Multiplication (Tensor Cores)
- Throughput: 1.2 Million training samples processed in 8 Epochs.
📜 Citation & License
If you use this model in your research or trading systems, please cite:
Assi, A. (2026). Quantitative-Equity-Alpha-Transformer: A Systematic Approach to Macro-Liquidity Modeling using Temporal Fusion Transformers. Hugging Face Model Hub.
License: MIT License. Free for academic and research use.