Task 18 Dark Pool Model

README.md

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+---
+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

model.onnx

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model.pt

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model_log.npz

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