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🧠 AI Financial Analysis Platform

Local-first, privacy-preserving AI platform for real-time stock & crypto analysis, price prediction, and conversational financial intelligence — powered entirely by open-source LLMs.


Overview

A production-grade AI financial analysis system built on local infrastructure with zero cloud dependency. Combines Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP) agentic orchestration, and multimodal market analysis to deliver institutional-grade financial intelligence on commodity hardware.

Built entirely in Python. Runs fully offline. No data leaves your machine.


Architecture

┌─────────────────────────────────────────────────────────────────┐
│                        User Interface                           │
│              Conversational Chat  │  Analysis Dashboard         │
└───────────────────┬─────────────────────────┬───────────────────┘
                    │                         │
         ┌──────────▼──────────┐   ┌──────────▼──────────┐
         │   Chat Interface    │   │  Analysis Engine     │
         │  Llama 3.1:8b       │   │  Qwen2.5:14b        │
         │  (Conversational)   │   │  (Deep Analysis)    │
         └──────────┬──────────┘   └──────────┬──────────┘
                    │                         │
         ┌──────────▼─────────────────────────▼──────────┐
         │              MCP Orchestration Layer            │
         │   stock_mcp_server.py  │  news_mcp_server.py  │
         └──────────┬─────────────────────────┬──────────┘
                    │                         │
         ┌──────────▼──────────┐   ┌──────────▼──────────┐
         │    RAG Pipeline     │   │   Live Data Layer    │
         │  ChromaDB           │   │  Stock APIs          │
         │  UAE-Large-V1       │   │  Crypto APIs         │
         │  angle_emb          │   │  News Feeds          │
         └─────────────────────┘   └─────────────────────┘
                    │
         ┌──────────▼──────────┐
         │   Ollama Runtime    │
         │  Local LLM Inference│
         │  GPU/CPU Optimized  │
         └─────────────────────┘

Key Capabilities

📈 Market Analysis

  • Real-time stock and cryptocurrency price tracking
  • Candlestick pattern recognition with AI interpretation
  • Technical indicator analysis (RSI, MACD, Bollinger Bands)
  • Multimodal processing of financial charts and visual market data

🔮 Price Prediction

  • EOD (End-of-Day) price forecasting for stocks and crypto
  • End-of-Week prediction models using historical data
  • Trend confidence scoring and risk framing

🤖 MCP Agentic Orchestration

  • Multi-tool AI agents that autonomously fetch, analyze, and report
  • stock_mcp_server.py — live market data tool server
  • news_mcp_server.py — financial news aggregation and sentiment
  • Agent coordination for complex, multi-step financial queries

💬 Conversational Intelligence

  • Full natural language chat interface for ad-hoc financial queries
  • Context-aware responses grounded in live and historical data
  • Supports: "What's the outlook for NVDA this week?", "Summarize BTC sentiment from today's news"

🔒 Privacy-First by Design

  • 100% local inference — no data transmitted to external APIs
  • All embeddings, vectors, and models stored on local disk
  • Suitable for sensitive financial data and compliance-conscious environments

Local LLM Stack

Model Purpose Parameters
Qwen2.5:14b Deep financial analysis & prediction 14B
Qwen2.5-Coder:14b Code generation & data processing 14B
Llama 3.1:8b Conversational chat interface 8B

Embedding Model: UAE-Large-V1 via angle_emb Vector Store: ChromaDB (persistent, local) Inference Runtime: Ollama


Tech Stack

Language        Python
LLM Runtime     Ollama
Models          Qwen2.5:14b · Qwen2.5-Coder:14b · Llama3.1:8b
Embeddings      UAE-Large-V1 (angle_emb)
Vector DB       ChromaDB
Orchestration   MCP (Model Context Protocol)
Data Sources    Financial APIs · News Feeds · Historical OHLCV
Analysis        Multimodal AI · Candlestick Pattern Recognition
Prediction      RAG-augmented forecasting with historical context

Design Decisions

Decision Rationale
Local-only inference Data privacy, zero latency on API calls, cost elimination
Qwen2.5:14b for analysis Superior financial reasoning vs smaller models; fits 16GB VRAM
Llama3.1:8b for chat Optimized for conversational flow, faster response latency
UAE-Large-V1 embeddings State-of-the-art semantic retrieval for financial text
ChromaDB Persistent local vector store with simple Python integration
MCP for tool orchestration Clean agent/tool separation; extensible to new data sources
Multimodal input Candlestick charts carry information not captured in raw OHLCV

Status

Active development — personal research platform. Source code is proprietary. Architecture and design documentation shared for portfolio purposes.


Author

Ahmed M. Eldeep Principal Full Stack Engineer & Cloud Architect linkedin.com/in/eldeep


© 2026 Ahmed M. Eldeep. All rights reserved. Source code is proprietary and not available for redistribution.

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