Technical Documentation

Comprehensive technical documentation covering architecture, APIs, security, and development guidelines.

Overview

FIP Intelligence is an advanced AI-powered investment platform that combines quantum modeling, machine learning, and institutional-grade analytics to democratize sophisticated investment tools.

🚀 Mission

Simplify complex investing into one comprehensive application

Key Features

  • AI Investment Agent: Proprietary LLM trained specifically for financial analysis
  • Quantum Analytics: Advanced probabilistic forecasting with Monte Carlo simulations
  • Real-time Data: Sub-15-minute market data updates via Polygon.io
  • Portfolio Intelligence: Comprehensive tracking and optimization tools
  • Dark Pool Insights: Institutional-grade market intelligence

Target Audience

Primary target: Young investors aged 18-35, predominantly male, seeking professional-grade investment tools with modern UX/UI.

System Architecture

Technology Stack

🦀
Rust
High-performance backend services
🐍
Python
ML models & data processing
🗄️
MySQL
Primary database for CSV data
🍃
MongoDB
Document storage
⚡
Redis
Caching & session management
☁️
AWS
Cloud infrastructure

Infrastructure

The system is deployed across multiple cloud providers for reliability:

  • AWS: Primary hosting and database infrastructure
  • Railway: Development and staging environments
  • Separate AI Server: Dedicated infrastructure for LLM operations

Data Flow

User CSV Upload → MySQL Database → Python Processing → AI Analysis → Real-time Updates (1-15 min) → Mobile Push Notifications

Monitoring & Observability

We utilize server host monitoring tools for basic infrastructure monitoring, with audit trail logging for compliance and debugging purposes.

AI Engine

Proprietary LLM Model

Our AI agent is powered by a custom open-source model trained specifically by our engineering team for financial analysis and investment insights.

⚠️ Model Details

Specific model architecture and training details are proprietary and run on separate infrastructure.

User Personalization

The AI agent adapts to user investment style based on:

  • CSV Portfolio Data: Historical investment patterns
  • Investment Journal: User-recorded decisions and reasoning
  • Investment Style Analysis: Risk tolerance and preferences
  • Behavioral Patterns: Trading frequency and asset allocation

Analysis Updates

Company analyses are updated through trigger-based system where the AI automatically scrapes relevant information from the web when market conditions or company fundamentals change.

Quantum Analytics Framework

Monte Carlo Simulations

# Custom implementation using: import numpy as np import pandas as pd # Proprietary scripts for probabilistic forecasting # Statistical modeling with confidence intervals

GARCH Volatility Modeling

# Framework: statsmodels from statsmodels.tsa.arch import arch_model # Volatility forecasting and risk assessment

Machine Learning Models

Quantum extrapolation utilizes Random Forest and other ML models for pattern recognition and market prediction.

Notification System

AI-generated insights and alerts are delivered via mobile push notifications directly to user devices.

Security & GDPR Compliance

Data Encryption

  • Database: bcrypt encryption for sensitive data
  • Communication: HTTPS/TLS for all data transmission
  • At Rest: Server-level encryption for stored data

Access Control

Database access is strictly limited to:

  • CEO (Executive access)
  • Lead Engineer (Technical access)

🔒 Privacy by Design

We do not store IP addresses and implement audit trail logging for all data operations.

GDPR Compliance

User Consent Management

User consents are stored with detailed audit trails. Upon app uninstallation, all user data is automatically purged from our systems.

Right to be Forgotten

Users can request complete data deletion through app settings with a simple confirmation process.

Data Retention

  • Active Users: Data retained while account is active
  • Inactive Users: Automatic deletion after specified periods
  • Deleted Accounts: Immediate purge of all personal data

Data Sources & Processing

Market Data Provider

Polygon.io Financial API
Update Frequency: Real-time (1-15 minutes maximum delay)
Data Types: Stock prices, dividends, company fundamentals
Coverage: Global markets with focus on US exchanges

Currency Conversion

Multi-currency portfolio balancing using daily exchange rates from standard financial data providers.

Performance Calculations

Returns Calculation

# Standard Python implementation realized_return = (sell_price - buy_price) / buy_price unrealized_return = (current_price - buy_price) / buy_price

Benchmark Comparison

Portfolio performance is compared against major indices by calculating total portfolio appreciation versus index appreciation over the same period.

Diversification Analysis

Portfolio diversification is analyzed based on:

  • Sector Allocation: Each stock's industry classification
  • Geographic Distribution: Company headquarters and operations
  • Market Cap Segments: Large, mid, and small-cap distribution
  • Asset Classes: Stocks, bonds, ETFs, alternative investments

Mobile Application

Technology Stack

📱 React Native

Cross-platform development with native performance and unified codebase.

Session Management

  • Storage: localStorage for session persistence
  • Authentication: Email-based registration and login
  • Security: Secure token-based authentication

Payment Integration

Due to App Store policies, we use native in-app purchases rather than external payment processors like Stripe.

// In-App Purchase Implementation iOS: StoreKit framework Android: Google Play Billing (planned)

Platform Availability

iOS Release
Currently available on App Store
Android Development
Planned for Q3 2025 release
Web Platform
Under consideration for future release

Internationalization

The application is currently available in English with multi-language support planned for European expansion.

Monetization Strategy

Pricing Tiers

🆓
Free Tier
Basic portfolio tracking and analysis
💎
Premium Tier
Advanced AI analysis, quantum models, dark pool insights

Premium Features

  • AI Investment Assistant: Unlimited queries and advanced analysis
  • Quantum Analytics: Monte Carlo simulations and probabilistic forecasting
  • Dark Pool Tracking: Institutional trading insights
  • Advanced Notifications: Real-time market alerts and portfolio updates
  • Export Capabilities: Detailed reports and data export
  • Priority Support: Dedicated customer support channel

Referral System

Backend implementation using Python, Rust, and SQL for tracking referrals, conversion measurement, and reward distribution.

// Referral tracking architecture Rust: Performance-critical referral processing Python: Business logic and analytics SQL: Referral data storage and reporting

Development Roadmap

Current Goals

🎯 Primary Objective

Secure funding and reach 10,000 active users

Planned Features

Q3 2025: Android Launch
Release Android version of the application
Q4 2025: Asset Expansion
Add support for ETFs and cryptocurrency tracking
2026: Advanced Features
Options trading analysis, bond portfolio management
2026: Global Expansion
Multi-language support and international markets

Marketing Strategy

Current successful channels include Threads and Instagram, with plans to expand to additional social platforms and influencer partnerships.

📈 Growth Focus

No current email marketing campaigns - focus on organic growth and product development

Technical Improvements

  • Enhanced AI Models: Continuous improvement of prediction accuracy
  • Real-time Features: Sub-minute data updates for premium users
  • API Development: Third-party integration capabilities
  • Advanced Analytics: Portfolio optimization recommendations