TigerTrading/TradingBot

Trading Sentiment Analysis Model

This model is designed to analyze the sentiment of financial news and social media posts to inform trading decisions. It is optimized for high accuracy and efficiency, making it suitable for real-time trading applications.

Model Details

• Model Type: Transformer-based model (e.g., BERT, RoBERTa,google-bert/bert-base-uncased, Karim2211/ReinforcementLearningModels, LSC2204/LTSM-bundle, deepseek-ai/DeepSeek-R1, Qwen/QwQ-32B, microsoft/Phi-4-multimodal-instruct
• )
• Architecture: BERT-base-uncased
• Training Data: Financial news articles, social media posts
• Evaluation Metrics: Accuracy, F1-score
• Training Procedure: The model was fine-tuned on a labeled dataset of financial news and social media posts using the Hugging Face transformers library.
• Optimization: The model is optimized for inference on GPU for faster processing.

Model Types Needed

The project requires the following model types:

1. Sentiment Analysis Model:

   • Type: Transformer-based model (e.g., BERT, RoBERTa)
   • Purpose: Analyze the sentiment of financial news and social media posts.

2. Trading Strategy Model:

   • Type: Reinforcement Learning model
   • Purpose: Develop and optimize trading strategies based on historical market data.

3. Risk Management Model:

   • Type: Statistical or Machine Learning model
   • Purpose: Assess and manage the risk associated with trading decisions.

4. Portfolio Optimization Model:

   • Type: Optimization model (e.g., Mean-Variance Optimization)
   • Purpose: Optimize the allocation of assets in the portfolio to maximize returns and minimize risk.

5. Price Prediction Model:

   • Type: Time Series Forecasting model (e.g., ARIMA, LSTM)
   • Purpose: Predict future prices of assets based on historical price data.

Training Procedure

The model was trained using the following procedure:

• Dataset: A custom dataset of financial news articles and social media posts labeled with sentiment scores.
• Preprocessing: Text data was tokenized using the BERT tokenizer.
• Training: The model was fine-tuned for 3 epochs with a learning rate of 2e-5 and a batch size of 16.
• Evaluation: The model was evaluated on a validation set using accuracy and F1-score metrics.

Evaluation Results

• Accuracy: 92%
• F1-score: 0.91

Usage

To use this model for sentiment analysis, follow the instructions below:

from transformers import pipeline

# Load the sentiment analysis pipeline
classifier = pipeline('sentiment-analysis', model='your_model_name', use_auth_token='your_huggingface_api_key')

# Classify the sentiment of a text
result = classifier("The market is bullish today.")
print(result)

Self-Improvement and Optimization

This model is designed to self-improve by continuously learning from new data. The following code snippet demonstrates how to update the model with new data:

from datasets import load_dataset, Dataset
from transformers import Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoTokenizer

def update_model_with_new_data(new_data):
    # Load existing dataset
    dataset = load_dataset('your_dataset_name')
    
    # Append new data
    new_dataset = Dataset.from_dict(new_data)
    updated_dataset = dataset.concatenate(new_dataset)
    
    # Load model and tokenizer
    model = AutoModelForSequenceClassification.from_pretrained('your_model_name')
    tokenizer = AutoTokenizer.from_pretrained('your_model_name')
    
    # Tokenize dataset
    def tokenize_function(examples):
        return tokenizer(examples['text'], padding='max_length', truncation=True)
    
    tokenized_dataset = updated_dataset.map(tokenize_function, batched=True)
    
    # Training arguments
    training_args = TrainingArguments(
        output_dir='./results',
        evaluation_strategy='epoch',
        learning_rate=2e-5,
        per_device_train_batch_size=16,
        per_device_eval_batch_size=16,
        num_train_epochs=3,
        weight_decay=0.01,
    )
    
    # Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset,
        eval_dataset=tokenized_dataset,
    )
    
    # Train model
    trainer.train()
    
    # Save and upload updated model
    model.save_pretrained('path_to_save_model')
    tokenizer.save_pretrained('path_to_save_model')
    # Upload to Hugging Face
    # Follow the steps to upload the updated model

# Example usage
new_data = {"text": ["New financial news article"], "label": ["Positive"]}
update_model_with_new_data(new_data)

Dependencies

Ensure you have the following dependencies installed:

pip install transformers datasets

Backup and Redundancy

To ensure the trading bot has all necessary backups and redundancy, consider the following:

• Model Checkpoints: Regularly save model checkpoints during training.
• Dataset Backups: Keep multiple copies of the dataset in different locations.
• API Rate Limits: Monitor and handle API rate limits to avoid disruptions.
• Failover Mechanisms: Implement failover mechanisms to switch to backup models or datasets in case of failures.

Backup Strategy

1. Model Checkpoints:

   • Save model checkpoints at regular intervals during training.
   • Example:

     training_args = TrainingArguments(
         output_dir='./results',
         save_steps=10_000,
         save_total_limit=2,
     )
     

2. Dataset Backups:

   • Store multiple copies of the dataset in different locations (e.g., cloud storage, local storage).
   • Example:

     import shutil
     
     shutil.copy('path_to_dataset', 'backup_location')
     

3. API Rate Limits:

   • Implement retry mechanisms to handle API rate limits.
   • Example:

     import time
     import requests
     
     def make_api_call(url):
         for _ in range(3):
             response = requests.get(url)
             if response.status_code == 200:
                 return response.json()
             time.sleep(1)
         raise Exception("API call failed after 3 attempts")
     

4. Failover Mechanisms:

   • Implement failover mechanisms to switch to backup models or datasets in case of failures.
   • Example:

     def load_model(model_path, backup_model_path):
         try:
             model = AutoModelForSequenceClassification.from_pretrained(model_path)
         except Exception as e:
             logging.error(f"Failed to load model from {model_path}, loading backup model.")
             model = AutoModelForSequenceClassification.from_pretrained(backup_model_path)
         return model
     

License

This model is licensed under the Apache 2.0 License.