---
license: apache-2.0
tags:
  - onnx
  - flock
  - dark-pool
  - trading
  - reinforcement-learning
---

# Flock.io Task 18: Dark Pool Trading Model

This is a neural network model trained for the Flock.io Task 18 - Dark Pool Trading.

## Model Details

- **Task**: Dark Pool Trading Prediction
- **Framework**: PyTorch → ONNX
- **Input**: 34 features (market state)
- **Output**: 1 value (predicted fill rate / action value)
- **Parameters**: ~1.78M (under 3M limit)

## Architecture

Multi-Layer Perceptron (MLP):
- Input: 34 features
- Hidden layers: 1024 → 1024 → 512 → 256 → 128
- Output: 1 value
- Activation: ReLU
- Normalization: BatchNorm
- Regularization: Dropout (0.2)

## Usage

```python
import onnxruntime as ort
import numpy as np

# Load the model
session = ort.InferenceSession("model.onnx")

# Prepare input (34 features)
input_data = np.random.randn(1, 34).astype(np.float32)

# Run inference
outputs = session.run(None, {"input": input_data})
prediction = outputs[0]
print(f"Prediction: {prediction}")
```

## Training

Trained on Flock.io Task 18 dataset:
- Training samples: 1200
- Validation samples: 400
- Best validation loss: ~0.001

## License

Apache 2.0