AlphaSeeker AI

Author: Vikram Lingam

Overview

AlphaSeeker AI is a Streamlit‑based web application that helps investors perform deep‑dive stock research. It combines financial data from yfinance with large‑language‑model (LLM) insights from OpenRouter to provide:

• Reverse DCF valuation (market‑implied growth)
• Autonomous competitor matrix with a "Better Alternative" recommendation
• Ownership & activity view (insider trades, institutional holders) with concise AI summaries
• Technical analysis chart (candlesticks, SMA 50/200, RSI) and an AI mentor that explains the signals in plain language
• Key risk identification for the business

Core Architecture

• app.py: The main Streamlit application handling the UI and state.
• finance_engine.py: Handles data fetching from yfinance and complex calculations like the Reverse DCF.
• agent_engine.py: Manages LLM interactions via OpenRouter for competitor analysis and the explanations.

Key Features Verified

✅ Market Implied Reverse DCF

• Logic: The app correctly back-solves for the implied growth rate based on current price and user assumptions (Discount Rate, Terminal Growth).
• Visualization: Displays a bar chart of implied future cash flows.
• Insight: Compares Implied Growth vs. Historical Growth to flag potential under/overvaluation.

✅ Autonomous Competitor Matrix

• Agentic Discovery: The LLM reads the business summary and identifies true peers (e.g., for TSLA, it found NIO, XPEV, RIVN, etc.).
• Scatter Plot: Plots "Revenue Growth" vs. "EV/EBITDA" to visualize the valuation gap.
• Better Alternative: The Agent analyzes the peer group and pitches a "Better Alternative" if one exists (e.g., "Switch to X for higher growth at a lower multiple").

✅ Ownership & Activity

• Insider Trading: Fetches recent insider transactions.
• Institutional Holders: Lists top institutional holders.
• AI Summary: Generates a concise summary of ownership sentiment.

✅ Technical Analysis & Education

• Interactive Chart: Displays 1Y price history with SMA 50/200.
• RSI Indicator: Shows current RSI with Overbought/Oversold flags.
• AI Mentor: Explains the chart patterns in simple English for beginners.

✅ Enhanced Insights

• Key Risks: AI identifies top 3 specific risks for the business.
• Better Alternative: Now includes a "Why this matters for beginners" section to explain valuation metrics simply.

How to Run

1. Ensure .env has your OPENROUTER_API_KEY.
2. Run streamlit run app.py.

The UI follows a clean, professional light theme (white background, black font)

Features

• Reverse DCF – Back‑solve market implied growth rate.
• Competitor Matrix – Scatter plot of revenue growth vs EV/EBITDA.
• Better Alternative Engine – Suggests a superior peer with a simple "Why" explanation.
• Ownership & Activity – Shows recent insider trades and top institutional holders, plus a 3‑bullet AI summary.
• Technical Analysis – 1‑year candlestick chart with SMA 50/200 and RSI indicator.
• AI Risk Summary – Highlights top 3 business risks.
• Educational Explanations – AI‑generated plain‑English insights for all advanced sections.

Prerequisites

• Python 3.10+ (tested on 3.12)
• Git (optional, for cloning the repo)
• An OpenRouter API key – store it in a .env file as OPENROUTER_API_KEY=your_key

Installation

# Clone the repository (or download the zip)
git clone https://github.com/vikramlingam/AlphaSeeker-AI.git
cd AlphaSeeker-AI

# Create a virtual environment
python -m venv venv
source venv/bin/activate   # on macOS/Linux
# or
# venv\Scripts\activate   # on Windows

# Install dependencies
pip install -r requirements.txt

# Create .env file
echo "OPENROUTER_API_KEY=YOUR_KEY_HERE" > .env

Running the App

streamlit run app.py

Open your browser and navigate to http://localhost:8501. You will see four tabs:

1. Dashboard – basic company info and valuation.
2. Technical Analysis – chart with SMA/RSI and AI explanation.
3. Deep Dive Valuation – Reverse DCF sliders.
4. Competitor Matrix – peer scatter plot and "Better Alternative".
5. Ownership & Activity – insider & institutional data with AI summary.

Usage Tips for Beginners

• Select a ticker in the sidebar and press Load Data.
• Use the Explain Chart button to get a plain‑English description of the technical signals.
• Click Analyze Risks to see the top three risks identified by the AI.
• Adjust the DCF sliders to see how valuation changes with different assumptions.

Project Structure

AlphaSeeker-AI/
├─ app.py                # Streamlit UI
├─ finance_engine.py    # Data fetching & calculations
├─ agent_engine.py      # LLM prompts & responses
├─ requirements.txt      # Python dependencies
├─ .env                 # API key (not committed)
└─ README.md            # This file

Contributing

Feel free to open issues or submit pull requests. When adding new features, keep the UI consistent with the light theme and add educational explanations for any advanced analytics.

License

This project is licensed under the MIT License.