ckcl
/

Adapters
Safetensors
prediction
Edit model card
A newer version of this model is available: meta-llama/Llama-3.1-8B-Instruct

Custom Transformer Model for MEXC Price Prediction

中文版

https://huggingface.co/ckcl/mexc_price_model/blob/main/CN_README.md

Model Description

This model is a custom Transformer model designed to predict MEXC contract prices. It consists of an embedding layer followed by multiple Transformer encoder layers, and a fully connected layer at the end to produce the output.

Model Architecture

  • Input Dimension: 13
  • Model Dimension: 64
  • Number of Heads: 8
  • Number of Layers: 2
  • Output Dimension: 1

Training Data

The model was trained on historical MEXC contract transaction data. The features include open, close, high, low prices, volume, amount, real open, real close, real high, real low prices, and moving averages.

Training Details

  • Optimizer: Adam
  • Learning Rate: 0.001
  • Loss Function: Mean Squared Error (MSE)
  • Batch Size: 32
  • Number of Epochs: 50

Usage

To use this model for prediction, follow these steps:

  1. Load the model and configuration:

    import torch
    import torch.nn as nn
    from transformers import AutoConfig
    
    class CustomTransformerModel(nn.Module):
        def __init__(self, config):
            super(CustomTransformerModel, self).__init__()
            self.embedding = nn.Linear(config.input_dim, config.model_dim)
            self.encoder_layer = nn.TransformerEncoderLayer(d_model=config.model_dim, nhead=config.num_heads, batch_first=True)
            self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=config.num_layers)
            self.fc = nn.Linear(config.model_dim, config.output_dim)
        
        def forward(self, src):
            src = self.embedding(src)
            output = self.transformer_encoder(src)
            output = self.fc(output[:, -1, :])
            return output
    
    config = AutoConfig.from_pretrained("your-username/mexc_price_model", config_file_name="BTC_USDT.json")
    model = CustomTransformerModel(config)
    model.load_state_dict(torch.load("model_repo/mexc_price.pth"))
    model.eval()
    
  2. Prepare input data and make predictions:

    import numpy as np
    from sklearn.preprocessing import StandardScaler
    
    new_data = np.array([
        [1.727087e+09, 63483.9, 63426.2, 63483.9, 63411.6, 1193897.0, 7.575486e+06, 63483.8, 63426.2, 63483.9, 63411.6, 0.00, 0.0, 0.0]
    ])
    
    scaler = StandardScaler()
    new_data_scaled = scaler.fit_transform(new_data)
    input_tensor = torch.tensor(new_data_scaled, dtype=torch.float32).unsqueeze(1)
    
    with torch.no_grad():
        prediction = model(input_tensor)
    
    predicted_value = prediction.squeeze().item()
    print(f"Predicted Value: {predicted_value}")
    

License

This model is licensed under the MIT License.

Downloads last month
0
Inference API
Unable to determine this model’s pipeline type. Check the docs .

Model tree for ckcl/mexc_price_model

Adapter
(48)
this model

Dataset used to train ckcl/mexc_price_model

Space using ckcl/mexc_price_model 1