[README]

.gitignore

@@ -9,6 +9,7 @@ __pycache__/
 .vscode
 
 # Distribution / packaging
+agent_workspace
 .Python
 build/
 develop-eggs/

README.md

@@ -1,67 +1,143 @@
 [![Multi-Modality](agorabanner.png)](https://discord.com/servers/agora-999382051935506503)
 
-# Python Package Template
+# Tickr Agent
+
 
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-A easy, reliable, fluid template for python packages complete with docs, testing suites, readme's, github workflows, linting and much much more
 
+[![Python](https://img.shields.io/badge/python-3.8%2B-blue)](https://www.python.org/downloads/)
+[![License](https://img.shields.io/badge/license-MIT-green)](LICENSE)
+
+
+`tickr-agent` is an enterprise-ready, scalable Python library for building swarms of financial agents that conduct comprehensive stock analysis and produce insights. Powered by `yfinance`, `loguru` for logging, and `pydantic` for data validation, `tickr-agent` is designed for businesses that need robust financial data processing, multithreading, and seamless integration with AI-powered models (via OpenAI) to derive actionable insights.
+
+With `tickr-agent`, you can automate the retrieval of stock market data, perform technical analysis (e.g., RSI, moving averages), and integrate insights into AI-driven workflows. This solution is built to handle real-time data processing, analysis, and reporting at an enterprise scale.
+
+## Key Features
+
+- **Multithreaded Stock Data Fetching**: Retrieve and analyze financial data for multiple stocks concurrently for faster performance.
+- **Advanced Logging**: Built with `loguru`, offering superior logging capabilities for debugging, auditing, and monitoring.
+- **Enterprise-Ready**: Production-grade, modular design, and scalable for high-performance environments.
+- **AI-Integrated**: Leverage OpenAI models to generate comprehensive financial analysis and predictions based on real-time stock data.
+- **Pydantic Validation**: Ensures reliable and validated stock data, minimizing errors in data processing.
 
 ## Installation
 
-You can install the package using pip
+To install `tickr-agent`, simply run:
 
 ```bash
-pip install -e .
+$ pip3 install -U tickr-agent
 ```
 
-# Usage
-```python
-print("hello world")
+## Getting Started
+
+### Basic Example: Running Financial Analysis on a Single Stock
 
+```python
+from tickr_agent.main import TickrAgent
+from loguru import logger
+
+# Example Usage
+if __name__ == "__main__":
+    try:
+        # Define stock tickers
+        stocks = ["NVDA"]
+
+        # Initialize the agent and configure the settings
+        agent = TickrAgent(
+            stocks=stocks,
+            max_loops=1,          # Maximum number of loops
+            workers=10,           # Number of threads for concurrent execution
+            retry_attempts=1,      # Retry attempts for failed requests
+            context_length=16000,  # Maximum context length for AI models
+        )
+
+        # Run the financial analysis and obtain the result
+        result = agent.run("Conduct an analysis of this summary")
+
+        # Output the result
+        print(result)
+
+    except Exception as e:
+        logger.critical(f"Critical error in financial agent execution: {e}")
 ```
 
+### How It Works
 
+1. **Stock Data Fetching**: The agent fetches real-time stock data from multiple financial APIs using the `yfinance` library. Technical indicators such as **RSI**, **50-day moving average**, and **200-day moving average** are calculated.
+
+2. **Multithreading**: Multiple stock tickers are processed concurrently using Python’s `ThreadPoolExecutor`, ensuring high performance and efficiency.
+
+3. **Data Validation**: Each piece of stock data is validated using `pydantic`, ensuring the reliability of the data processed.
 
-### Code Quality 🧹
+4. **AI-Powered Analysis**: After gathering and validating stock data, the agent passes the summary of stock performance to an OpenAI-powered model, which can provide deeper insights, forecasts, or personalized reports based on the stock performance.
 
-- `make style` to format the code
-- `make check_code_quality` to check code quality (PEP8 basically)
-- `black .`
-- `ruff . --fix`
+## Enterprise Use Case: Swarms of Financial Agents
 
-### Tests 🧪
+For large enterprises, `tickr-agent` supports creating swarms of financial agents, each focusing on different sectors, regions, or investment strategies. These swarms can analyze hundreds or even thousands of stocks concurrently, generate reports, and trigger AI-driven insights for decision-making.
 
-[`pytests`](https://docs.pytest.org/en/7.1.x/) is used to run our tests.
+### Example: Running a Swarm of Agents for Multiple Stocks
 
-### Publish on PyPi 🚀
+```python
+from tickr_agent.main import TickrAgent
+from loguru import logger
+
+# Example Usage
+if __name__ == "__main__":
+    try:
+        # Define multiple stock tickers
+        stocks = ["AAPL", "GOOGL", "MSFT", "TSLA", "AMZN"]
+
+        # Initialize the agent for multi-stock analysis
+        agent = TickrAgent(
+            stocks=stocks,
+            max_loops=2,          # Increased loops for more in-depth analysis
+            workers=20,           # Number of threads for concurrent execution
+            retry_attempts=2,      # Retry logic for reliability
+            context_length=32000,  # Maximum context length for AI models
+        )
+
+        # Run the financial analysis
+        result = agent.run("Provide a detailed financial health report for the selected stocks.")
+
+        # Output the result
+        print(result)
+
+    except Exception as e:
+        logger.critical(f"Critical error in financial agent execution: {e}")
+```
 
-**Important**: Before publishing, edit `__version__` in [src/__init__](/src/__init__.py) to match the wanted new version.
+### Advanced Customization
 
-```
-poetry build
-poetry publish
-```
+`tickr-agent` can be customized based on your enterprise needs, including:
+
+- **Custom AI Models**: Integrate custom models or fine-tuned versions of GPT for industry-specific insights.
+- **Data Pipelines**: Use `tickr-agent` as part of a larger financial data pipeline, feeding real-time stock analysis into dashboards, reporting systems, or decision-making tools.
+- **Scalable Architecture**: Deploy swarms of agents in cloud environments, utilizing Kubernetes or Docker for containerized scaling.
+
+## Logging and Monitoring
+
+`tickr-agent` uses `loguru` for logging, providing robust, enterprise-grade logging capabilities:
 
-### CI/CD 🤖
+- **File Rotation**: Log files are rotated automatically after reaching 1 MB in size.
+- **Detailed Error Tracking**: Comprehensive error logging, including stack traces and timestamps, ensures that failures are easily traceable.
+- **Custom Log Levels**: Adjust the verbosity of logs as needed (`DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL`).
 
-We use [GitHub actions](https://github.com/features/actions) to automatically run tests and check code quality when a new PR is done on `main`.
+Logs are saved to `financial_agent_log.log` by default. Customize the logging configuration to integrate with your enterprise logging systems.
 
-On any pull request, we will check the code quality and tests.
+## Contributing
 
-When a new release is created, we will try to push the new code to PyPi. We use [`twine`](https://twine.readthedocs.io/en/stable/) to make our life easier. 
+Contributions are welcome! If you would like to contribute, please open an issue or submit a pull request to the GitHub repository. We follow standard Python development practices and require tests for all new features.
 
-The **correct steps** to create a new realease are the following:
-- edit `__version__` in [src/__init__](/src/__init__.py) to match the wanted new version.
-- create a new [`tag`](https://git-scm.com/docs/git-tag) with the release name, e.g. `git tag v0.0.1 && git push origin v0.0.1` or from the GitHub UI.
-- create a new release from GitHub UI
+## License
 
-The CI will run when you create the new release.
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
 
-# Docs
-We use MK docs. This repo comes with the zeta docs. All the docs configurations are already here along with the readthedocs configs.
+## Contact
 
+Join our discord for real-time support or email me 
 
 
 # License
-MIT
+MIT