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Equilibrium-India-V1
Vansh180/Equilibrium-India-V1
Equilibrium-India-V1 is a machine learning model from Vansh180. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for keras.
FastAPI backend for systemic risk prediction + simulation.
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At a glance
- Library
- keras
- Access
- Public
- Created
- Feb 7, 2026
- Updated
- Feb 7, 2026
- SHA
- 3766b1df
Model card
From the Hugging Face model README
Equilibrium-India-V1 (Space)
FastAPI backend for systemic risk prediction + simulation.
Equilibrium-India-V1
Equilibrium-India-V1 is a GRU-based machine learning model for systemic risk prediction in financial networks, designed for scenario analysis and stress testing of interconnected institutions.
The model predicts next-step systemic risk using network-theoretic and statistical features derived from an adjacency matrix representing financial interconnections.
🔍 Model Overview
- Model type: Gated Recurrent Unit (GRU)
- Framework: TensorFlow / Keras
- Task: Time-series regression
- Output: Predicted systemic risk score (continuous)
- Input window: Rolling lookback over historical system states
📥 Input Features
The model expects the following 6 derived features at each timestep:
| Feature | Description |
|---|---|
lambda_max | Largest eigenvalue of the financial network adjacency matrix |
mean_risk | Mean node-level risk across institutions |
max_risk | Maximum node-level risk (stress concentration) |
std_risk | Risk dispersion across nodes |
S_lag1 | Systemic risk at previous timestep |
S_lag5 | Systemic risk 5 timesteps ago |
These features are computed externally and passed to the model after scaling.
🧠 Training Details
- Loss function: Mean Squared Error (MSE)
- Optimizer: Adam
- Regularization: Dropout + L2 weight decay
- Early stopping: Enabled (best validation epoch restored)
The model was trained on historical market data combined with synthetic balance-sheet-style risk signals to approximate systemic exposure.
📊 Intended Use
This model is intended for:
- Systemic risk forecasting
- Financial network stress testing
- Scenario simulation (via downstream cascade models)
- Academic research and hackathon demonstrations
It is designed to be used as part of a larger pipeline, not as a standalone trading signal.
⚠️ Limitations
- Trained on a fixed institution universe
- Not suitable for arbitrary new stocks without retraining
- Synthetic risk proxies are approximations, not audited balance sheets
- Output is a relative risk indicator, not an absolute probability of failure
🚫 Not Intended For
- Investment advice
- Regulatory capital decisions
- Production financial risk management
🔗 Deployment
This model is consumed by a FastAPI-based inference service hosted as a Hugging Face Space, where it is used for real-time scenario simulations.
📜 License
MIT License
👤 Author
Vansh Momaya
- Library
- keras
- Created
- Feb 7, 2026
- Updated
- Feb 7, 2026