Predicting tomorrow’s prices by adapting to today’s market dynamics
AMSPF is a real‑time, multi‑modal forecasting framework that unifies historical market data, volume‑weighted sentiment, volatility signals, event flags, and dynamic graph relationships to predict next‑day stock returns in the technology sector. By combining a Volatility‑Aware Transformer (VAT), Graph Attention Networks (GAT), and a Dynamic Interaction Network (DIN), AMSPF adapts feature importance on‑the‑fly and delivers state‑of‑the‑art directional accuracy, Sharpe ratio, and hit‑rate performance.
Paper: “Real‑Time Adaptive Multi‑Modal Stock Prediction with Temporal Graph Attention and Dynamic Interaction Networks” — IEEE Conference on Artificial Intelligence (CAI) 2025.
| Layer | Purpose |
|---|---|
| Input Streams | OHLCV sequences · Reddit & Yahoo Finance comments · Macro indicators · Event flags |
| Dynamic Graph Builder | Constructs daily graphs (correlation · common holders · sentiment similarity) |
| Volume‑Weighted Sentiment (VWS) | Amplifies sentiment by trading volume to filter noise |
| Volatility Detector (EWMA) | Identifies high/low‑vol regimes and feeds σt to VAT |
| Feature Encoders | LSTM (prices) · Text encoder (FinBERT) |
| Graph Encoder | GAT with temporal snapshots or DIN for real‑time edges |
| Fusion & Prediction | VAT + Event‑Triggered Attention → MLP → next‑day return |
| Recommendation Engine | Momentum α, Volatility β, and Sentiment weights → composite rank |
Real-Time-Adaptive-Multi-Modal-Stock-Prediction/
├── Comparison Models/ # Baselines: ARIMA, XGBoost, Random Walk
├── Data Extractions Scripts/ # Raw data scrapers & cleaners
├── Merged Data/ # Pre‑merged CSVs & GraphMLs (⭑ >100 MB, use LFS)
├── USP-1/ # Volume‑Weighted Sentiment pipeline
├── USP-2/ # Volatility‑Aware Transformer (VAT)
├── USP-3/ # Event‑Triggered Attention & DIN
├── USP-4/ # GAT + Recommendation Engine
├── conda.yml # Reproducible environment spec
├── requirements.txt # Pip fallback
├── utils/ # Figures & helper scripts
└── README.md| Source | Span | Fields |
|---|---|---|
| Yahoo Finance | 1 Aug – 31 Oct 2024 | OHLCV for 139 tech tickers |
| Reddit & Yahoo Finance comments | Same window | Raw posts → FinBERT sentiment → VWS |
| Macro indicators | Monthly | CPI, GDP, interest rate |
| Events | Daily | Earnings, M&A, Fed announcements (spaCy keyword flags) |
| Dynamic graphs | Daily snapshots | Edges: correlation > 0.6 · shared holders · sentiment similarity |
Total size ≈ 1.2 GB (download via
Data Extractions Scripts/or use the pre‑merged files in Merged Data/).
- Volume‑Weighted Sentiment (VWS) = Σ(sentiment × volume) / Σvolume — boosts high‑liquidity signals.
- Volatility‑Aware Transformer (VAT) — injects σt into multi‑head attention scores to adapt feature weights.
- Event‑Triggered Attention — extra head focusing only on event‑flagged stocks.
- Graph Attention Network (GAT) — captures inter‑stock relations with learnable edge weights.
- Dynamic Interaction Network (DIN) — re‑weights edges in real‑time for streaming inference.
- Recommendation Engine — ranks stocks by α·Momentum + β·Volatility + γ·VWS.
| Metric | Description |
|---|---|
| MAE / RMSE / MAPE | Regression error on next‑day close |
| Directional Accuracy | Correct up/down moves |
| F1 Score | Precision‑recall trade‑off |
| Information Coefficient (IC) | Pearson corr. between predicted & true returns |
| Sharpe Ratio | Risk‑adjusted return |
| Hit Rate | % of recommended stocks with positive return |
- Python 3.9+
- PyTorch 2.2 • PyTorch‑Geometric 2.5
- Transformers (🤗 4.43)
- scikit‑learn • pandas • numpy
- spaCy 3 (for event extraction)
# clone
git clone https://github.com/abhishekjoshi007/Real-Time-Adaptive-Multi-Modal-Stock-Prediction
cd Real-Time-Adaptive-Multi-Modal-Stock-Prediction
# conda (recommended)
conda env create -f conda.yml
conda activate amspf
# or pip
pip install -r requirements.txt# 1️⃣ Fetch & preprocess raw data (takes ~10 min)
python Data\ Extractions\ Scripts/download_all.py --start 2024-08-01 --end 2024-10-31
# 2️⃣ Build daily graphs
python Data\ Extractions\ Scripts/build_graphs.py --output Merged\ Data/graphs# Volatility‑Aware Transformer (USP‑2)
python USP-2/train_vat.py --epochs 100 --batch 64 --lr 3e-4
# GAT with DIN edges (USP‑3)
python USP-3/train_gat_din.py --epochs 80 --graph_dir Merged\ Data/graphspython evaluate.py --ckpt runs/vat_best.pt --test_csv Merged\ Data/test.csv@INPROCEEDINGS{11050541,
author={Joshi, Abhishek and Koda, Jahnavi Krishna and Hadimlioglu, Alihan and Kaur, Gurojaspreet},
booktitle={2025 IEEE Conference on Artificial Intelligence (CAI)},
title={Real-Time Adaptive Multi-Modal Stock Prediction with Temporal Graph Attention and Dynamic Interaction Networks},
year={2025},
volume={},
number={},
pages={83-88},
keywords={Accuracy;Sensitivity;Linearity;Feature extraction;Transformers;Market research;Real-time systems;Macroeconomics;Long short term memory;Investment;Stock Price Prediction;Stock Return Prediction;Graph Attention Networks (GAT);Volatility Aware Transformer(VAT);Long Short Term Memory (LSTM)},
doi={10.1109/CAI64502.2025.00020}}
``
This project is licensed under the MIT License – see the LICENSE file for details.
Foor questions, collaborations, or feedback:
- Texas A&M University–Corpus Christi
- IEEE CAI 2025 Organising Committee
- Open‑source contributors to PyTorch, PyG, HuggingFace, and spaCy