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--- |
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language: en |
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tags: |
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- trading |
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- sentiment-analysis |
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- finance |
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- transformer |
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license: apache-2.0 |
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base_models: |
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- google-bert/bert-base-uncased |
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- Karim2211/ReinforcementLearningModels |
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- LSC2204/LTSM-bundle |
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- deepseek-ai/DeepSeek-R1 |
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- Qwen/QwQ-32B |
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- microsoft/Phi-4-multimodal-instruct |
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library_name: adapter-transformers |
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datasets: |
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- fka/awesome-chatgpt-prompts |
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- facebook/natural_reasoning |
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- 0xscope/web3-trading-analysis |
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- arad1367/Crypto_Fundamental_News |
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- HuggingFaceFW/fineweb |
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metrics: |
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- accuracy |
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base_model: |
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- nlptown/bert-base-multilingual-uncased-sentiment |
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- distilbert/distilbert-base-uncased-finetuned-sst-2-english |
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- FacebookAI/xlm-roberta-base |
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new_version: google/gemma-3-27b-it |
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--- |
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# Trading Sentiment Analysis Model |
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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. |
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## Model Details |
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- **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 |
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- |
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- ) |
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- **Architecture**: BERT-base-uncased |
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- **Training Data**: Financial news articles, social media posts |
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- **Evaluation Metrics**: Accuracy, F1-score |
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- **Training Procedure**: The model was fine-tuned on a labeled dataset of financial news and social media posts using the Hugging Face `transformers` library. |
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- **Optimization**: The model is optimized for inference on GPU for faster processing. |
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## Model Types Needed |
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The project requires the following model types: |
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1. **Sentiment Analysis Model**: |
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- **Type**: Transformer-based model (e.g., BERT, RoBERTa) |
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- **Purpose**: Analyze the sentiment of financial news and social media posts. |
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2. **Trading Strategy Model**: |
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- **Type**: Reinforcement Learning model |
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- **Purpose**: Develop and optimize trading strategies based on historical market data. |
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3. **Risk Management Model**: |
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- **Type**: Statistical or Machine Learning model |
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- **Purpose**: Assess and manage the risk associated with trading decisions. |
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4. **Portfolio Optimization Model**: |
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- **Type**: Optimization model (e.g., Mean-Variance Optimization) |
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- **Purpose**: Optimize the allocation of assets in the portfolio to maximize returns and minimize risk. |
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5. **Price Prediction Model**: |
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- **Type**: Time Series Forecasting model (e.g., ARIMA, LSTM) |
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- **Purpose**: Predict future prices of assets based on historical price data. |
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## Training Procedure |
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The model was trained using the following procedure: |
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- **Dataset**: A custom dataset of financial news articles and social media posts labeled with sentiment scores. |
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- **Preprocessing**: Text data was tokenized using the BERT tokenizer. |
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- **Training**: The model was fine-tuned for 3 epochs with a learning rate of 2e-5 and a batch size of 16. |
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- **Evaluation**: The model was evaluated on a validation set using accuracy and F1-score metrics. |
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## Evaluation Results |
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- **Accuracy**: 92% |
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- **F1-score**: 0.91 |
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## Usage |
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To use this model for sentiment analysis, follow the instructions below: |
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```python |
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from transformers import pipeline |
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# Load the sentiment analysis pipeline |
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classifier = pipeline('sentiment-analysis', model='your_model_name', use_auth_token='your_huggingface_api_key') |
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# Classify the sentiment of a text |
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result = classifier("The market is bullish today.") |
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print(result) |
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``` |
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## Self-Improvement and Optimization |
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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: |
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```python |
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from datasets import load_dataset, Dataset |
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from transformers import Trainer, TrainingArguments, AutoModelForSequenceClassification, AutoTokenizer |
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def update_model_with_new_data(new_data): |
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# Load existing dataset |
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dataset = load_dataset('your_dataset_name') |
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# Append new data |
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new_dataset = Dataset.from_dict(new_data) |
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updated_dataset = dataset.concatenate(new_dataset) |
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# Load model and tokenizer |
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model = AutoModelForSequenceClassification.from_pretrained('your_model_name') |
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tokenizer = AutoTokenizer.from_pretrained('your_model_name') |
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# Tokenize dataset |
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def tokenize_function(examples): |
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return tokenizer(examples['text'], padding='max_length', truncation=True) |
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tokenized_dataset = updated_dataset.map(tokenize_function, batched=True) |
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# Training arguments |
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training_args = TrainingArguments( |
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output_dir='./results', |
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evaluation_strategy='epoch', |
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learning_rate=2e-5, |
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per_device_train_batch_size=16, |
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per_device_eval_batch_size=16, |
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num_train_epochs=3, |
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weight_decay=0.01, |
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) |
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# Trainer |
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trainer = Trainer( |
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model=model, |
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args=training_args, |
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train_dataset=tokenized_dataset, |
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eval_dataset=tokenized_dataset, |
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) |
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# Train model |
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trainer.train() |
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# Save and upload updated model |
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model.save_pretrained('path_to_save_model') |
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tokenizer.save_pretrained('path_to_save_model') |
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# Upload to Hugging Face |
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# Follow the steps to upload the updated model |
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# Example usage |
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new_data = {"text": ["New financial news article"], "label": ["Positive"]} |
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update_model_with_new_data(new_data) |
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``` |
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## Dependencies |
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Ensure you have the following dependencies installed: |
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```bash |
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pip install transformers datasets |
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``` |
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## Backup and Redundancy |
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To ensure the trading bot has all necessary backups and redundancy, consider the following: |
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- **Model Checkpoints**: Regularly save model checkpoints during training. |
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- **Dataset Backups**: Keep multiple copies of the dataset in different locations. |
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- **API Rate Limits**: Monitor and handle API rate limits to avoid disruptions. |
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- **Failover Mechanisms**: Implement failover mechanisms to switch to backup models or datasets in case of failures. |
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### Backup Strategy |
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1. **Model Checkpoints**: |
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- Save model checkpoints at regular intervals during training. |
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- Example: |
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```python |
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training_args = TrainingArguments( |
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output_dir='./results', |
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save_steps=10_000, |
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save_total_limit=2, |
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) |
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``` |
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2. **Dataset Backups**: |
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- Store multiple copies of the dataset in different locations (e.g., cloud storage, local storage). |
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- Example: |
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```python |
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import shutil |
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shutil.copy('path_to_dataset', 'backup_location') |
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``` |
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3. **API Rate Limits**: |
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- Implement retry mechanisms to handle API rate limits. |
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- Example: |
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```python |
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import time |
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import requests |
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def make_api_call(url): |
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for _ in range(3): |
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response = requests.get(url) |
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if response.status_code == 200: |
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return response.json() |
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time.sleep(1) |
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raise Exception("API call failed after 3 attempts") |
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``` |
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4. **Failover Mechanisms**: |
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- Implement failover mechanisms to switch to backup models or datasets in case of failures. |
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- Example: |
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```python |
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def load_model(model_path, backup_model_path): |
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try: |
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model = AutoModelForSequenceClassification.from_pretrained(model_path) |
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except Exception as e: |
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logging.error(f"Failed to load model from {model_path}, loading backup model.") |
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model = AutoModelForSequenceClassification.from_pretrained(backup_model_path) |
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return model |
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``` |
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## License |
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This model is licensed under the Apache 2.0 License. |
