task-18-multi-dynamic

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