Merge remote changes and complete OpenCV/slow test fixes
Browse files- README.md +2 -20
- finrl_demo.py +5 -4
README.md
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---
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license: apache-2.0
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tags:
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- reinforcement-learning
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- algorithmic-trading
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- finance
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- finrl
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- synthetic-data
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- time-series
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- gymnasium
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- stable-baselines3
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library_name: stable-baselines3
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datasets: []
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model-index:
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- name: Algorithmic Trading System with FinRL
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results: []
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---
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# Algorithmic Trading System
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A comprehensive algorithmic trading system with synthetic data generation, comprehensive logging, extensive testing capabilities, and FinRL reinforcement learning integration.
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1. Clone the repository:
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```bash
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git clone
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cd algorithmic_trading
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```
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## License
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This project is licensed under the Apache License, Version 2.0 - see the [LICENSE](LICENSE) file for details.
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# Algorithmic Trading System
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A comprehensive algorithmic trading system with synthetic data generation, comprehensive logging, extensive testing capabilities, and FinRL reinforcement learning integration.
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1. Clone the repository:
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```bash
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git clone https://github.com/EAName/algorithmic_trading.git
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cd algorithmic_trading
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```
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## License
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This project is licensed under the Apache License, Version 2.0 - see the [LICENSE](LICENSE) file for details.
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finrl_demo.py
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from agentic_ai_system.synthetic_data_generator import SyntheticDataGenerator
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from agentic_ai_system.logger_config import setup_logging
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# Setup logging
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setup_logging()
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logger = logging.getLogger(__name__)
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def load_config(config_path: str = 'config.yaml') -> dict:
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"""Load configuration from YAML file"""
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with open(config_path, 'r') as file:
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return yaml.safe_load(file)
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def generate_training_data(config: dict) -> pd.DataFrame:
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"""Generate synthetic data for training"""
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from agentic_ai_system.synthetic_data_generator import SyntheticDataGenerator
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from agentic_ai_system.logger_config import setup_logging
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def load_config(config_path: str = 'config.yaml') -> dict:
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"""Load configuration from YAML file"""
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with open(config_path, 'r') as file:
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return yaml.safe_load(file)
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# Setup logging
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config = load_config()
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setup_logging(config)
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logger = logging.getLogger(__name__)
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def generate_training_data(config: dict) -> pd.DataFrame:
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"""Generate synthetic data for training"""
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