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system for stock analysis and investment recommendations, powered by [crewAI](https://crewai.com). This project uses collaborative AI agents to analyze trending companies, conduct financial research, and provide investment recommendations based on the latest market news and data.\n    <img width=\"997\" height=\"753\" alt=\"Screenshot 2025-08-01 093502\" src=\"https://github.com/user-attachments/assets/ff5497e9-d97d-4783-8120-4ff81164a870\" />\n\n## Features\n\n- **Multi-Agent Analysis**: Three specialized AI agents work together to provide comprehensive stock analysis\n- **Real-Time News Monitoring**: Automatically finds trending companies based on latest financial news\n- **Comprehensive Research**: Conducts detailed financial analysis of identified companies\n- **Investment Recommendations**: Provides data-driven stock picking decisions with detailed rationale\n- **Automated Reporting**: Generates structured reports in JSON and Markdown formats\n\n## Curated Showcase (CrewAI Multi‑Agent Patterns)\n\nCurated list of how this project demonstrates intelligent agents collaborating to automate complex workflows.\n\n- **Multi-agent collaboration**\n  - **Role-specialized agents**: `📰 Financial News Analyst`, `🔍 Senior Financial Researcher`, `📊 Stock Picker` coordinate via tasks pipeline defined in `src/stock_picker/config/tasks.yaml` and `src/stock_picker/crew.py`.\n  - **Tool-driven capabilities**: Extendable via `src/stock_picker/tools/` for web search, parsing, and financial data enrichment.\n  - **Handoff patterns**: Outputs from one agent (trending companies) become inputs for the next (deep research) and culminate in a final decision.\n\n- **Finance and research examples**\n  - **Trending discovery**: News scanning to surface 2–3 companies per sector → saves to `output/trending_companies.json`.\n  - **Company deep-dive**: Fundamentals and sentiment synthesis → `output/research_report.json` with market position, growth, and potential.\n  - **Investment decision**: Clear recommendation and rationale → `output/decision.md` ready for PM/analyst review.\n  - **Sectors included**: AI/ML, Healthcare Tech, Renewables, Fintech, E‑commerce, Cybersecurity, EVs, Biotech, Semiconductors, Cloud.\n\n- **Production-ready patterns**\n  - **Config-as-data**: Agents and tasks in YAML (`src/stock_picker/config/agents.yaml`, `src/stock_picker/config/tasks.yaml`) enable safe iteration and quick A/B.\n  - **Deterministic interfaces**: Each stage writes typed artifacts to `output/` enabling retries and offline inspection.\n  - **Streamlit UI for operators**: `src/stock_picker/ui/app.py` runs end‑to‑end flows, visualizes progress, and exposes downloads.\n  - **Environment handling**: `.env` via `python-dotenv` locally, Streamlit Secrets in the cloud; SQLite shim for portability.\n  - **Composable entrypoints**: CLI in `src/stock_picker/main.py` and UI launchers in `pyproject.toml` scripts.\n\nUse this section as a template to compare with other CrewAI systems and to extend this project with additional agents (e.g., risk modeling, valuation, portfolio construction) following the same handoff and artifact patterns.\n\n## How It Works\n\n```mermaid\ngraph TD\n    A[📰 Financial News Analyst] --> B[🔍 Senior Financial Researcher]\n    B --> C[📊 Stock Picker]\n    \n    A --> D[Trending Companies<br/>JSON Output]\n    B --> E[Research Report<br/>JSON Output]\n    C --> F[Investment Decision<br/>Markdown Report]\n    \n    G[📈 Market News] --> A\n    H[💰 Financial Data] --> B\n    I[📋 Analysis Results] --> C\n    \n    style A fill:#000000\n    style B fill:#000000\n    style C fill:#000000\n    style D fill:#000000\n    style E fill:#000000\n    style F fill:#000000\n```\n\n### Agent Workflow\n\nThe StockPicker Crew consists of three specialized agents working in sequence:\n\n1. **📰 Financial News Analyst** - Scans latest news to identify 2-3 trending companies in a specified sector\n2. **🔍 Senior Financial Researcher** - Conducts comprehensive analysis of the trending companies  \n3. **📊 Stock Picker** - Analyzes research findings and selects the best investment opportunity\n\n## Quick Start\n\n```mermaid\ngraph LR\n    A[🐍 Install Python 3.10+] --> B[📦 Install UV]\n    B --> C[⚙️ Install Dependencies]\n    C --> D[🔑 Add API Keys]\n    D --> E[🚀 Run Analysis]\n    \n    style A fill:#00000\n    style B fill:#000000\n    style C fill:#000000\n    style D fill:#000000\n    style E fill:#000000\n```\n\n## Installation\n\nEnsure you have Python >=3.10 <3.14 installed on your system. This project uses [UV](https://docs.astral.sh/uv/) for dependency management.\n\n1. **Install UV** (if not already installed):\n```bash\npip install uv\n```\n\n2. **Install Dependencies**:\n```bash\ncrewai install\n```\n\n3. **Configure API Keys** - You have several options:\n\n**Option 1: Local Development (.env file)**\nCreate a `.env` file in the project root with your API keys:\n```bash\n# Copy .env.example to .env and fill in your keys\ncp .env.example .env\n# Then edit .env with your actual keys\n```\n\n**Option 2: Environment Variables**\nSet the following environment variables in your system:\n```bash\nGOOGLE_API_KEY=your_gemini_api_key_here\nSERPER_API_KEY=your_serper_api_key_here\n```\n\n**Option 3: Streamlit Cloud Secrets**\nIf deploying to Streamlit Cloud, set your keys in Settings → Secrets:\n```\nGOOGLE_API_KEY=\"your_gemini_api_key\"\nSERPER_API_KEY=\"your_serper_key\"\n```\n\nThe application will check for keys in this order:\n1. Streamlit Cloud secrets (if deployed)\n2. Environment variables\n3. .env file (for local development)\n\n## Configuration\n\n- **Agents**: Modify `src/stock_picker/config/agents.yaml` to customize agent roles and capabilities\n- **Tasks**: Modify `src/stock_picker/config/tasks.yaml` to define analysis workflows\n- **Sector Focus**: Change the target sector in `src/stock_picker/main.py` (default: \"AI and Machine Learning\")\n- **Custom Tools**: Add specialized tools in `src/stock_picker/tools/`\n\n## Running the Project\n\nTo start the stock analysis process, run from the root folder:\n\n```bash\ncrewai run\n```\n\nOr run directly with Python:\n```bash\npython -m stock_picker.main\n```\n\n## Deploy to Streamlit Cloud\n\n1. Push this repo to GitHub.\n2. In Streamlit Cloud, create a new app pointing to `src/stock_picker/ui/app.py` as the entry file.\n3. Set Secrets under Settings → Secrets with:\n```\nGOOGLE_API_KEY=\"your_gemini_api_key\"\nSERPER_API_KEY=\"your_serper_key\"\n```\n4. Deploy. The system reads secrets first, then falls back to environment variables.\n\nThe system will:\n1. Search for trending companies in the AI and Machine Learning sector\n2. Conduct detailed financial research on identified companies\n3. Select the best investment opportunity\n4. Generate comprehensive reports in the `output/` directory\n\n## Output Files\n\nThe analysis generates several output files:\n\n- `output/trending_companies.json` - List of trending companies found in the news\n- `output/research_report.json` - Detailed financial analysis of each company\n- `output/decision.md` - Final investment recommendation with rationale\n\n## Customization\n\n### Changing the Target Sector\nEdit `src/stock_picker/main.py` to analyze different sectors:\n```python\ninputs = {\n    'sector': 'Healthcare Technology'  # Change this to your preferred sector\n}\n```\n\n### Adding Custom Tools\nCreate new tools in `src/stock_picker/tools/` to extend agent capabilities:\n- Financial data APIs\n- Technical analysis tools\n- Risk assessment modules\n- Portfolio optimization tools\n\n### Agent Configuration\nEach agent can be customized in `src/stock_picker/config/agents.yaml`:\n- Change LLM models (currently using Gemini 1.5 Flash)\n- Modify agent roles and goals\n- Add specialized tools and capabilities\n\n## Available Commands\n\n- `crewai run` - Execute the full stock analysis workflow\n## Project Structure\n\n```\n📁 stock_picker/\n├── 📁 src/stock_picker/           # Main application code\n│   ├── 📁 config/                 # Configuration files\n│   │   ├── 📄 agents.yaml         # Agent definitions and roles\n│   │   └── 📄 tasks.yaml          # Task workflows and dependencies\n│   ├── 📁 tools/                  # Custom tools for agents\n│   │   ├── 📄 custom_tool.py      # Specialized analysis tools\n│   │   └── 📄 __init__.py\n│   ├── 📄 crew.py                 # Main crew orchestration\n│   ├── 📄 main.py                 # Entry point\n│   └── 📄 __init__.py\n├── 📁 output/                     # Generated reports and analysis\n│   ├── 📄 trending_companies.json # Found trending companies\n│   ├── 📄 research_report.json    # Detailed financial analysis\n│   └── 📄 decision.md             # Final investment recommendation\n├── 📁 tests/                      # Test files\n├── 📄 .env                        # API keys and environment variables\n├── 📄 pyproject.toml              # Project dependencies and metadata\n└── 📄 README.md                   # This file\n```\n\n## Requirements\n\n- Python >=3.10, <3.14\n- UV package manager\n- Gemini API key (for LLM functionality)\n- Internet connection (for news and financial data)\n\n## Troubleshooting\n\n**API Key Issues**: Ensure your `GOOGLE_API_KEY` is properly set in the `.env` file\n**Streamlit Cloud**: Prefer setting keys in Secrets. The UI shows key presence in the sidebar.\n**API Cost Is More** :Ensure the cost\n\n\n\n---\n\nLet's create wonders together with the power and simplicity of crewAI.\n","readmeExcerpt":"StockPicker Crew An intelligent multi-agent AI system for stock analysis and investment recommendations, powered by $1. 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