Finance Guru™ Core Context Loader Auto-loads essential Finance Guru system configuration and user profile at session start. Ensures complete context availability for all financial operations.
Auto-loaded at every session start
System Name: Finance Guru™ v2.0.0 Architecture: BMAD-CORE™ v6.0.0 Type: Private Family Office AI System Owner: Sole client (exclusive service) Purpose: Institutional-grade multi-agent financial intelligence, quantitative analysis, strategic portfolio planning, and compliance oversight
Key Principle: This is NOT a software product - this IS Finance Guru, your personal financial command center.
These files are automatically loaded into context at session start:
Path: fin-guru/config.yaml
Contains: Module identity, agent roster (13 agents), workflow pipeline, tools, temporal awareness
Path: fin-guru/data/user-profile.yaml
Contains: Portfolio structure ($500k), investment capacity ($13.3k/month W2), risk profile (aggressive), Layer 2 Income strategy
Path: notebooks/updates/
Contains: Latest Fidelity account balances, positions, transaction history
File Patterns:
Balances_for_Account_{account_id}.csv (exact match)Portfolio_Positions_MMM-DD-YYYY.csv (e.g., Portfolio_Positions_Nov-05-2025.csv)Path: fin-guru/data/system-context.md
Contains: Private family office positioning, agent team structure, privacy commitments
All tools use 3-layer type-safe architecture (Pydantic → Calculator → CLI):
Risk Metrics (src/analysis/risk_metrics_cli.py)
VaR, CVaR, Sharpe, Sortino, Max Drawdown, Beta, Alpha
Volatility Metrics (src/utils/volatility_cli.py)
Bollinger Bands, ATR, Historical Vol, Keltner Channels, regime assessment
Momentum Indicators (src/utils/momentum_cli.py)
RSI, MACD, Stochastic, Williams %R, ROC, confluence analysis
Moving Averages (src/utils/moving_averages_cli.py)
SMA, EMA, WMA, HMA, Golden Cross/Death Cross detection
Correlation & Covariance (src/analysis/correlation_cli.py)
Pearson correlation, covariance matrices, diversification scoring
Portfolio Optimizer (src/strategies/optimizer_cli.py)
Mean-Variance, Risk Parity, Min Variance, Max Sharpe, Black-Litterman
Backtesting Framework (src/strategies/backtester_cli.py)
Strategy validation, performance metrics, deployment recommendations
Documentation: See CLAUDE.md for usage examples and agent workflows
Primary Entry: Finance Orchestrator (Cassandra Holt) Specialist Agents: Market Researcher, Quant Analyst, Strategy Advisor, Compliance Officer, Margin Specialist, Dividend Specialist, Teaching Specialist, Builder, QA Advisor, Onboarding Specialist
Workflow Pipeline: RESEARCH → QUANT → STRATEGY → ARTIFACTS
Layer 1 (Growth): Keep 100% - DO NOT TOUCH Layer 2 (Income): Building dividend portfolio with $13,317/month W2 income Target: $100k annual dividend income in 28 months (69.2% Monte Carlo probability) Strategy: Hybrid DRIP v2 with active rotation, confidence-based margin scaling
CRITICAL: Always execute date command before market research or analysis.
Ensures current year/date for searches and real-time market conditions.
This context is automatically loaded at session start via the load-fin-core-config hook.