mexc_price_model

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.