prospectai 1.0.0

Multi-Agent Investment Analysis System powered by CrewAI and Claude

ProspectAI - Multi-Agent Investment Analysis System

Overview

ProspectAI is a multi-agent investment analysis system built on the CrewAI framework. It leverages four specialized AI agents to provide comprehensive investment recommendations through a systematic analysis workflow. The system supports Anthropic Claude models (default) and local Ollama models.

🎉 First Official Release - v1.0.0

⚠️ Important Disclaimer

ProspectAI is built for educational purposes to help developers get initiated in Agentic AI development.

🚨 INVESTMENT WARNING: This tool should NOT be used as an investment tool without proper knowledge of the investment domain. The analysis provided is for educational demonstration of AI capabilities and should not be considered as financial advice. Always consult with qualified financial professionals before making investment decisions.

Features

• 🤖 Multi-Agent System: Four specialized AI agents for different aspects of investment analysis
• 🧠 Anthropic Claude: Powered by Claude models (Sonnet, Opus) by default
• 🦙 Ollama Support: Run fully locally with Ollama models
• 📊 Real Reddit Integration: Live Reddit sentiment analysis for trending stocks (top 5 per sector)
• 🏭 Sector Analysis: Analyze 5 major sectors (Technology, Healthcare, Finance, Energy, Consumer)
• ⚡ Command-Line Interface: Easy-to-use CLI with flexible configuration
• 🔒 Environment-Based Config: Secure .env-based configuration with startup validation
• 📈 Structured Output: Consistent, machine-readable JSON analysis results
• 🚀 CrewAI Framework: Professional multi-agent orchestration via LiteLLM

Architecture

The system consists of four specialized agents working in sequence:

Agent Pipeline

MarketAnalystAgent → TechnicalAnalystAgent → FundamentalAnalystAgent → InvestorStrategicAgent

Each agent receives the full output of all prior agents as context.

Market Analyst Agent 📊

• Purpose: Entry point of the investment pipeline
• Function: Analyzes Reddit discussions to identify trending stocks at the current moment in time, incorporating macro/geopolitical context
• Data Sources: Reddit API (PRAW) with Serper web search as fallback
• Output: Top 5 candidate stocks with sentiment scores and relevance metrics
• Sectors: Technology, Healthcare, Finance, Energy, Consumer

Technical Analyst Agent 📈

• Purpose: Quantitative technical analysis
• Function: Runs 13+ technical indicators per ticker using yfinance + ta library
• Indicators: RSI, MACD, Bollinger Bands, ATR, SMA, EMA, VWAP, ADX, and more
• Output: Per-stock signals, momentum scores (1–10), risk levels, entry zones, stop-loss levels

Fundamental Analyst Agent 🏢

• Purpose: Financial statement and valuation analysis
• Function: Fetches real P/E, margins, debt ratios, FCF, and growth rates via yfinance
• Output: Valuation grades (CHEAP/FAIR/EXPENSIVE), financial health ratings, growth outlook

Investor Strategic Agent 🎯

• Purpose: Final synthesis and portfolio construction
• Function: Applies the composite score formula and builds portfolio allocations
• Composite Score: 30 pts sentiment + 40 pts momentum + 30 pts fundamentals (max 100)
• Recommendations: STRONG_BUY / BUY / HOLD / REDUCE / AVOID
• Output: Machine-readable JSON with allocation percentages summing to 100%

Project Structure

ProspectAI/
├── agents/                          # AI agent implementations
│   ├── base_agent.py                # Abstract base class; LLM instantiation via crewai.LLM
│   ├── market_analyst_agent.py
│   ├── technical_analyst_agent.py
│   ├── fundamental_analyst_agent.py
│   └── investor_strategic_agent.py
├── config/
│   ├── agents.yaml                  # Primary place to tune agent behavior
│   ├── agent_config_loader.py       # Reads agents.yaml
│   └── config.py                    # Config class — reads env vars, no defaults
├── utils/
│   ├── reddit_sentiment_tool.py     # PRAW-based Reddit sentiment tool
│   ├── technical_analysis_tool.py   # yfinance + ta indicators
│   └── fundamental_data_tool.py     # yfinance fundamentals
├── tests/                           # Test suite
├── main.py                          # CLI entry point; .env validation
├── prospect_ai_crew.py              # CrewAI orchestration
├── requirements.txt
├── env.example                      # Template — copy to .env
└── README.md

Installation

Prerequisites

• Python 3.9+
• An Anthropic API key or a local Ollama installation

1. Clone and Set Up

git clone <your-repo-url>
cd ProspectAI
python -m venv .venv
source .venv/bin/activate       # Windows: .venv\Scripts\activate
pip install -r requirements.txt

2. Configure Environment

cp env.example .env

Then open .env and fill in your values. The app will fail at startup with a clear error listing any missing required keys.

3. Anthropic API Setup (Default Provider)

1. Get your API key from console.anthropic.com
2. Add to .env:

   ANTHROPIC_API_KEY=your_key_here
   ANTHROPIC_MODEL=claude-sonnet-4-6
   

4. Market Data Setup (at least one required)

Option A — Reddit API (preferred):

1. Visit reddit.com/prefs/apps and create a "script" app
2. Add to .env:

   REDDIT_CLIENT_ID=your_client_id
   REDDIT_CLIENT_SECRET=your_client_secret
   REDDIT_USER_AGENT=ProspectAI/1.0
   

See REDDIT_API_SETUP.md for detailed instructions.

Option B — Serper web search fallback:

1. Get your key at serper.dev
2. Add to .env:

   SERPER_API_KEY=your_key_here
   

5. Ollama Setup (Optional — Local Models)

1. Install Ollama: ollama.com/download
2. Start the service:

   ollama serve
   

3. Pull a model:

   ollama pull qwen3.5:9b
   # or: llama3.2:8b, mistral:7b, etc.
   

4. Add to .env:

   OLLAMA_BASE_URL=http://localhost:11434
   OLLAMA_MODEL=qwen3.5:9b
   

Usage

Basic Usage

# Analyze Technology sector with Anthropic Claude (default)
python main.py

# Analyze a specific sector
python main.py --sector Healthcare
python main.py --sector Finance
python main.py --sector Energy
python main.py --sector Consumer

# Override the Claude model for a single run
python main.py --model claude-opus-4-6 --sector Technology

# Use a local Ollama model
python main.py --ollama --sector Technology

# Use Ollama with a specific model or remote server
python main.py --ollama --model llama3.2:8b --sector Healthcare
python main.py --ollama --url http://192.168.1.100:11434 --sector Finance

CLI Reference

Flag	Description
--sector	Sector to analyze: Technology, Healthcare, Finance, Energy, Consumer (default: Technology)
--model	Override model name from .env
--ollama	Use local Ollama instead of Anthropic
--url	Ollama server URL (overrides OLLAMA_BASE_URL)

Testing

python tests/test_skeleton.py
python tests/test_reddit_output.py [sector]
python tests/test_technical_analyst.py
python tests/test_market_analyst_llm.py

Configuration

Environment Variables

Variable	Required	Description
ANTHROPIC_API_KEY	Yes	Anthropic API key
ANTHROPIC_MODEL	Yes	Claude model (e.g. claude-sonnet-4-6)
REDDIT_CLIENT_ID	One of A or B	Reddit API client ID
REDDIT_CLIENT_SECRET	One of A or B	Reddit API client secret
REDDIT_USER_AGENT	No	Defaults to ProspectAI/1.0
SERPER_API_KEY	One of A or B	Serper web search key (fallback)
OLLAMA_BASE_URL	If --ollama	Ollama server URL
OLLAMA_MODEL	If --ollama	Ollama model name

Anthropic Claude Models

Model	Use Case
claude-sonnet-4-6	Best balance of quality and speed (default)
claude-opus-4-6	Highest quality, deeper reasoning
claude-haiku-4-5-20251001	Fastest, lowest cost

Ollama Models

Model	Notes
qwen3.5:9b	Good reasoning, recommended for analysis
llama3.2:8b	General purpose
llama3.2:3b	Lightweight, fast
mistral:7b	Good for analytical tasks

Per-Agent Model Override

Each agent can use a different model by editing config/agents.yaml:

market_analyst:
  llm:
    provider: "anthropic"
    model: "claude-opus-4-6"   # Use Opus for the most context-heavy agent

technical_analyst:
  llm:
    provider: "anthropic"
    model: "claude-sonnet-4-6" # Sonnet for the rest

See AGENT_LLM_CONFIGURATION.md for full details.

Roadmap

v1.1 - Enhanced Market Analysis

• Integration with financial news APIs (Bloomberg, Reuters)
• Real-time market sentiment from multiple sources
• Enhanced sector rotation analysis

v1.2 - Agent Improvements

• Enhanced financial modeling capabilities
• More sophisticated valuation algorithms
• Advanced portfolio optimization

v1.3 - Advanced Risk Management

• Monte Carlo simulations for portfolio scenarios
• Advanced risk metrics (VaR, CVaR, Sharpe ratios)
• Dynamic risk adjustment based on market conditions

Troubleshooting

Issue	Fix
.env file not found	Run cp env.example .env and fill in your keys
Missing keys error at startup	Check the listed keys against your .env
ANTHROPIC_API_KEY invalid	Verify key at console.anthropic.com
Ollama connection refused	Run ollama serve and verify OLLAMA_BASE_URL
Ollama model not found	Run ollama pull <model-name>
Reddit 401 error	Verify REDDIT_CLIENT_ID and REDDIT_CLIENT_SECRET
No stocks found	Check Reddit credentials or add SERPER_API_KEY as fallback

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes with tests where applicable
4. Submit a pull request

License

MIT License — see LICENSE for details.

Acknowledgments

• Built on the CrewAI framework
• LLM routing via LiteLLM
• Inspired by modern multi-agent AI systems
• Author webpage