PAE - Personal Analytics Engine

Zero-knowledge, zero-trust, user-sovereign financial analytics platform.

PAE is an open-source tool that aggregates portfolio data, runs risk analytics, and surfaces decision intelligence -- all without ever seeing your financial data. The server stores only ciphertext. You hold the keys.

PAE is an educational analytics tool, not a financial advisor. This tool provides data, calculations, and educational content only. The user is responsible for all investment decisions.

What PAE Does

• Portfolio Aggregation: Import holdings from CSV, brokerage APIs (IBKR Flex Query, Plaid), or manual entry. Multi-currency (CAD/USD/INR).
• Risk Engine: VaR, CVaR, Sharpe, Sortino, volatility, drawdown, beta -- calculated in Rust for sub-millisecond latency.
• Factor Decomposition: Fama-French 5-Factor model via OLS. See exactly what drives your returns.
• Monte Carlo Simulation: 1K-100K path simulation with geometric Brownian motion. Stress test allocations.
• Correlation Analysis: Rolling correlation matrices across holdings. Spot hidden concentration risk.
• Stress Testing: Apply historical scenarios (2008 GFC, 2020 COVID, rate shocks) to your current portfolio.
• Margin Intelligence: Carry analysis for leveraged portfolios. Income coverage ratios, net carry, position-level carry attribution.
• Decision Journal: Log rationale, confidence, and emotional state before trades. Track calibration over time.
• Personal Knowledge Engine (PKE): Ingest your own research (Buffett letters, coursework, book notes) into a private, encrypted, locally-indexed knowledge base.

What PAE Does Not Do

• No investment advice. Ever. The tool calculates; you decide.
• No recommendations. No "buy X" or "sell Y" outputs.
• No personalized suggestions. All parameters are user-specified.
• No data exfiltration. All encryption/decryption happens client-side.

Architecture

+------------------+     +-------------------+     +------------------+
|   UI (Browser)   | <-> |   Rust Engine     | <-> |  Python Analytics |
|  Vanilla TS      |     |   Axum API        |     |  Factor Models   |
|  Web Components  |     |   Risk Calcs      |     |  Optimization    |
|  Canvas/SVG      |     |   Crypto Vault    |     |  PKE / Decision  |
|  < 200KB         |     |   < 1ms latency   |     |  < 1s latency    |
+------------------+     +-------------------+     +------------------+
                                  |
                          +-------+-------+
                          | C Numerical   |
                          | BLAS/LAPACK   |
                          | QuantLib (FFI)|
                          +---------------+

Three languages, each doing what it does best:

• Rust (hot path): Risk calculations, Monte Carlo, crypto vault, API server
• Python (research layer): Factor models, portfolio optimization, PKE, decision intelligence
• C (primitives): BLAS/LAPACK matrix ops, QuantLib bond pricing via FFI
• Vanilla TypeScript (presentation): Web Components, Canvas/SVG charts, < 200KB total

See docs/ARCHITECTURE.md for full details.

Security Model

• AES-256-GCM encryption for all stored data
• Argon2id key derivation (600K iterations, 64MB memory, 4 threads)
• Per-record Data Encryption Keys (DEKs) wrapped with user's Key Encryption Key (KEK)
• KEK derived client-side from passphrase -- never transmitted
• Optional Shamir's Secret Sharing (3-of-5) for key recovery
• Server compromise yields ciphertext only

See docs/THREAT_MODEL.md for the full threat model.

Quick Start

# Clone
git clone https://github.com/nrupala/pae.git
cd pae

# Docker (recommended)
docker compose -f infra/docker/docker-compose.yml up

# Or build individually:

# Rust engine
cd engine && cargo build --release && cargo test

# Python analytics
cd analytics && pip install -e ".[dev]" && pytest tests/ -v

# UI
cd ui && npx tsc

Project Structure

pae/
  .editorconfig, .gitignore, LICENSE, Makefile, README.md, CONTRIBUTING.md
  engine/          Rust risk engine (Cargo.toml, Axum API, crypto vault, risk modules)
  analytics/       Python analytics (factor models, carry analysis, PKE, decision journal)
  ui/              Vanilla TypeScript dashboard (Web Components, CSS tokens, Canvas charts)
  infra/           Docker, docker-compose, CI/CD
  docs/            Architecture, threat model, methodology
  knowledge/       PKE templates and content directory (content/ is gitignored)

Development

# Full build + test
make build
make test

# Lint
make lint

# Clean
make clean

See CONTRIBUTING.md for development standards and the non-advisory guardrail checklist.

Regulatory Position

PAE operates within educational/analytical safe harbor:

• Tool calculates, user decides -- the core architectural principle
• Non-dismissible disclaimer on every page
• No default parameters that imply advice
• No output labeled "optimal" or "recommended"
• All inputs start blank or neutral
• Compliant with FINRA 2214, CSA guidance, and SEC no-action letter precedents for analytical tools

License

AGPL-3.0

Author

Nrupal Akolkar (nrupalakolkar@gmail.com)

Architecture and initial scaffolding by Milo (Town AI Assistant).