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
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