FinDocGPT - AI for Financial Document Analysis & Investment Strategy

An AI-powered platform for financial document analysis and investment strategy development, built for the AkashX.ai challenge at HackNation 2025.

🏆 Selected as one of 15 global teams for the Hack-Nation VC Track 2025
(out of 2,800+ applications and 1,500 hackathon participants).

🎯 Challenge Overview

This project implements a comprehensive 3-stage AI system for financial analysis, directly mapped to the AkashX.ai FinDocGPT challenge:

Stage 1: Insights & Analysis (Document Q&A) ✅

Process financial documents from FinanceBench dataset
Natural language Q&A interface with RAG for document analysis
Sentiment analysis for overall and key sections

Stage 2: Financial Forecasting ✅

Predict near‑term price movements using historical data from yfinance
Interactive chart with time window selection, error metric, and forecast vs. history

Stage 3: Investment Strategy & Decision-Making ✅

Generate actionable Buy/Sell/Hold with confidence and risk
Combines document sentiment, recent news sentiment, and forecast signal
Returns transparency details: used documents, per-doc breakdown, component scores

🏗️ Architecture

             ┌───────────────────────────┐
             │       React Frontend      │
             │         (TypeScript)      │
             └───────────────────────────┘
                           ▲
                           │  REST/JSON
                           │
             ┌───────────────────────────┐
             │       FastAPI Backend     │
             │           (Python)        │
             └───────────────────────────┘
              ▲                          ▲
              │ Ingest PDFs              │ Market Data
              │                          │
┌───────────────────────────┐   ┌───────────────────────────┐
│       FinanceBench        │   │          yfinance         │
│       Dataset (PDFs)      │   │      (Yahoo Finance)      │
└───────────────────────────┘   └───────────────────────────┘

🚀 Quick Start

Prerequisites

Python 3.10+
Node.js 18+

1) Start Backend

Set up environment variables
- Copy the example file and edit it with your own values:
```
cp .env.example .env
```
- Open .env and set your keys:
  - NEWS_API_KEY → Enables news sentiment analysis. Get from newsapi.org.
  - OPENAI_API_KEY → Get from platform.openai.com.
  - PORT → (optional) Server port (default: 5001).

Create a conda environment (optional)

conda create -n hack-nation python=3.10 -y

Run the start script This automatically activates the conda env, installs all dependencies and starts frontend/backend server.
```
./start.sh
```

Backend runs at http://localhost:5001 (FastAPI docs at /docs). App runs at http://localhost:3000.

📚 Key Routes

/ — Dashboard
/document/:id — Document Analysis (Q&A, PDF, Sentiment)
/forecasting — Forecasting (RF/Prophet/LSTM)
/investment-strategy — Investment Strategy (Stage 3)

🧠 APIs (selected)

GET /api/documents — List available documents (FinanceBench)
GET /api/documents/{id} — Document metadata
GET /api/documents/{id}/text — Extracted document text
GET /api/documents/{id}/sentiment — TextBlob sentiment for document
GET /api/documents/{id}/pdf — Serve the PDF inline
GET /api/companies/{company}/news-sentiment — Aggregated recent news sentiment (requires NEWS_API_KEY)
POST /api/forecast — Body: { ticker, period, horizon, model } → forecasts + MAE
POST /api/investment-recommendation — Body: { company|ticker, document_ids? } → action/confidence/risk + transparency
GET /api/lookup?q= — Resolve ticker/company candidates (for UX)

Notes

News sentiment requires NEWS_API_KEY; otherwise that component is omitted gracefully.
PDFs are served inline via /api/documents/:id/pdf.

📊 Dataset

The project uses the bundled FinanceBench dataset containing:

300+ Financial Documents: 10-K, 10-Q, 8-K filings, earnings reports
Major Companies: Apple, Microsoft, Amazon, Google, Tesla, and more
Q&A Examples: Pre-made questions and expert answers
Multiple Years: 2015-2023 data coverage
Diverse Sectors: Technology, Healthcare, Finance, Consumer, etc.

Sample Companies

Technology: Apple, Microsoft, AMD, Intel, Adobe, Oracle
Consumer: Amazon, Costco, Nike, McDonald's, Coca-Cola
Healthcare: Johnson & Johnson, Pfizer, CVS Health
Finance: JPMorgan, American Express, PayPal
And many more...

🛠️ Features

Stage 1 — Document Q&A

Document Library: Browse 300+ financial documents
Smart Search: Filter by company, sector, document type
Document Viewer: Read full document text
Q&A Chat Interface: Ask questions about any document
Financial Metrics Extraction: Automatic revenue, profit analysis via Q&A
- Note: For newly opened documents, the first query may take longer or fail as the system generates the vector database for retrieval. This is a one-time process per document. Wait about a minute, then try again.
Sentiment Analysis: Market sentiment from financial text
Responsive Design: Works on desktop and mobile

Stage 2 — Forecasting

Historical price ingestion and model selection (LSTM/RF/Prophet)
MAE surfaced in UI; interactive brush/zoom; dashed forecast line; directional coloring

Stage 3 — Investment Strategy

Synthesizes document sentiment, news sentiment, and forecast
Returns action (BUY/SELL/HOLD), confidence, risk level, target price (when applicable)
Transparency: used_documents and doc_breakdown to show which docs influenced the decision

🔧 Technical Stack

Frontend

React 18 with TypeScript
React Router for navigation
Axios for API communication
Lucide React for icons
Custom CSS with modern design system

Backend

FastAPI web framework
CORS for cross-origin requests
PyMuPDF for PDF text extraction
LangChain for document processing
NLTK for sentiment analysis

Key Libraries

pymupdf: PDF text extraction
langchain: Document chunking and processing
nltk: Natural language processing
pdfplumber: Enhanced PDF table extraction

📁 Project Structure

hack-nation-2025/
├── backend/
│   ├── main.py               # FastAPI app entry
│   └── util/                 # PDF, retrieval, sentiment, forecast utils
├── datasets/
│   └── financebench/         # Document metadata and PDFs
├── frontend/                 # React + TypeScript app
│   ├── src/pages/            # Pages: Dashboard, DocumentAnalysis, Forecasting, InvestmentStrategy
│   ├── src/services/api.ts   # API client
│   └── public/
├── requirements.txt          # Python deps
├── start.sh                  # Dev helper to launch backend
└── README.md

🎨 User Interface

Dashboard

Company Overview: Browse financial documents by company
Sector Filtering: Filter by industry sector
Document Types: 10-K, 10-Q, 8-K, Earnings reports
Search Functionality: Find specific companies or documents

Document Analysis

Dual-Pane Interface: Chat Q&A + Document viewer
Real-time Q&A: Ask questions and get AI responses
Financial Metrics: Automatic extraction of key numbers
Confidence Scores: AI confidence in responses
Quick Questions: Pre-built common questions

Demo Guide

Document Analysis: Open /document/:id from Dashboard; ask questions; view sentiment and PDF side-by-side.
Forecasting: Open /forecasting; enter ticker (e.g., AAPL), choose model and horizon; run forecast; inspect MAE and overlay.
Investment Strategy: Open /investment-strategy; enter ticker/company (e.g., Apple or AAPL); review action, confidence, risk; see which documents were used and the sentiment-over-time chart.

🧪 Try These Prompts

Try these sample questions:

Financial Metrics

"What is Apple's revenue for 2022?"
"How much debt does Microsoft have?"
"What are Amazon's key risk factors?"

Business Analysis

"What is Tesla's main business?"
"How does Google make money?"
"What are Netflix's competitive advantages?"

Performance Questions

"How is McDonald's cash flow?"
"What drove Apple's growth in 2022?"
"What are Intel's biggest challenges?"

🔮 Future Enhancements

Stage 1: Financial Document Analysis and Q&A

Improved Metric Extraction: Use pattern matching and keyword search (reports are partially structured), prompt engineering or a combination of both
Improved RAG: Employ different kinds of document indices for better content retrieval (e.g., LLM-generated summaries)

Stage 2: Advanced Forecasting

Machine Learning Models: Use more sophisticated models and include financial metrics from the reports as features
Risk Modeling: VaR, Monte Carlo simulations
Uncertainty Estimates on Forecasting: Use an ensemble of different prediction models and model accuracy from historical data

Stage 3: Investment Strategy

Portfolio Theory: Modern portfolio optimization
ESG Integration: Environmental, Social, Governance factors
Backtesting: Historical strategy performance
Real-time Alerts: Market change notifications

📝 API Documentation

See “APIs (selected)” above and live docs at http://localhost:5001/docs.

Authors

Lukas Probst
Christopher von Klitzing

📄 License

This project is developed for the AkashX.ai HackNation 2025 challenge.

Sponsored by AkashX.ai | HackNation 2025 Challenge

— Building the future of AI-powered financial analysis —

Name		Name	Last commit message	Last commit date
Latest commit History 37 Commits
backend		backend
datasets		datasets
frontend		frontend
.env.example		.env.example
.gitignore		.gitignore
README.md		README.md
requirements.txt		requirements.txt
start.sh		start.sh

probstlukas/hack-nation-2025

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