[Skills Index]|root: ./.claude/skills|IMPORTANT: Read full SKILL.md before using any skill. This index is for routing only.|backend-dev-guidelines:{name:backend-dev-guidelines,desc:Comprehensive backend development guide for Node.js/Express/TypeScript microservices.,files:{resources:{architecture-overview.md,async-and-errors.md,complete-examples.md,configuration.md,database-patterns.md,middleware-guide.md,routing-and-controllers.md,sentry-and-monitoring.md,services-and-repositories.md,testing-guide.md,validation-patterns.md}}}|dividend-tracking:{name:dividend-tracking,desc:Sync dividend data from Fidelity CSV to Dividends sheet.,files:{}}|error-tracking:{name:error-tracking,desc:Add Sentry v8 error tracking and performance monitoring to your project services.,files:{}}|fin-core:{name:fin-core,desc:| Finance Guru™ Core Context Loader Auto-loads essential Finance Guru system configuration and user profile at session s,files:{README.md}}|FinanceReport:{name:FinanceReport,desc:Generate institutional-quality PDF analysis reports for stocks and ETFs.,files:{StyleGuide.md,VisGuide.md,tools:{ChartKit.help.md,ChartKit.py,ReportGenerator.help.md,ReportGenerator.py},workflows:{FullResearchWorkflow.md,GenerateSingleReport.md,RegenerateBatch.md}}}|formula-protection:{name:formula-protection,desc:Prevent accidental modification of sacred spreadsheet formulas in Google Sheets Portfolio Tracker.,files:{}}|margin-management:{name:margin-management,desc:Update Margin Dashboard with Fidelity balance data and calculate margin-living strategy metrics.,files:{}}|MonteCarlo:{name:MonteCarlo,desc:Run Monte Carlo simulations for Finance Guru portfolio strategy.,files:{PortfolioParser.md,tools:{.gitkeep},workflows:{IncorporateBuyTicket.md,RunSimulation.md}}}|PortfolioSyncing:{name:PortfolioSyncing,desc:Import and sync broker CSV portfolio data to Google Sheets DataHub.,files:{workflows:{SyncPortfolio.md}}}|python-performance-optimization:{name:python-performance-optimization,desc:Profile and optimize Python code using cProfile, memory profilers, and performance best practices.,files:{}}|readiness-report:{name:readiness-report,desc:Evaluate how well a codebase supports autonomous AI development.,files:{references:{criteria.md,maturity-levels.md},scripts:{analyze_repo.py,generate_report.py}}}|retirement-syncing:{name:retirement-syncing,desc:Sync retirement account data from Vanguard and Fidelity CSV exports to Google Sheets DataHub.,files:{}}|route-tester:{name:route-tester,desc:Test authenticated routes in the your project using cookie-based authentication.,files:{}}|TransactionSyncing:{name:TransactionSyncing,desc:Import Fidelity transaction history CSV into Google Sheets with smart categorization.,files:{CategoryRules.md,workflows:{SyncTransactions.md}}}|[14 skills, 32 files]

Project Overview

This is Finance Guru™ - a private AI-powered family office system built on BMAD-CORE™ v6 architecture. This repository serves as the operational center for a multi-agent financial intelligence system that provides research, quantitative analysis, strategic planning, and compliance oversight.

Key Principle: This is NOT a software product or app - this IS Finance Guru, a personal financial command center working exclusively for the user. All references should use "your" when discussing assets, strategies, and portfolios.

Architecture

Multi-Agent System

Finance Guru™ uses a specialized agent architecture where Claude transforms into different financial specialists:

Primary Entry Point:

Finance Orchestrator (Cassandra Holt) - Master coordinator located at
```
.claude/commands/fin-guru/agents/finance-orchestrator.md
```

Path Variable System

The codebase uses a variable substitution system:

```
{project-root}
```
- Root of the repository
```
{module-path}
```
- Path to fin-guru module
```
{current_datetime}
```
- Current date and time
```
{current_date}
```
- Current date (YYYY-MM-DD)
```
{user_name}
```
- User's name from config

When referencing files in agent configurations, these variables should be resolved to actual paths.

External Tool Requirements

Finance Guru requires these MCP servers:

exa - Deep research and market intelligence
bright-data - Web scraping (search engines, markdown extraction)
sequential-thinking - Complex multi-step reasoning
financial-datasets - SEC filings, financial statements
gdrive - Google Drive integration (sheets, docs)
web-search - Real-time market information

Temporal Awareness

CRITICAL REQUIREMENT: All agents must establish temporal context before performing any market research or analysis.

Required initialization:

# Agents MUST execute these commands at startup
date                    # Store as {current_datetime}
date +"%Y-%m-%d"       # Store as {current_date}

This ensures:

Market data searches use current year/date
Analysis reflects real-time conditions
Documents are properly date-stamped

Compliance & Disclaimers

MANDATORY: All financial outputs must include:

Educational-only disclaimer
"Not investment advice" statement
Recommendation to consult licensed professionals
Risk disclosure (loss of principal possible)

This positioning is enforced by the Compliance Officer agent.

Technology Stack

Python: 3.12+
Package Manager:
```
uv
```
(used for all Python operations)
Key Dependencies:
- pandas, numpy, scipy, scikit-learn (data analysis)
- yfinance (market data)
- streamlit (visualization)
- beautifulsoup4, requests (web scraping)
- pydantic (data validation)
- python-dotenv (configuration)

Python Tools & Utilities

Type-Safe Architecture

All Python tools follow a 3-layer architecture pattern:

Layer 1: Pydantic Models (
```
src/models/
```
) - Data validation
Layer 2: Calculator Classes (
```
src/analysis/
```
,
```
src/utils/
```
) - Business logic
Layer 3: CLI Interface - Agent integration

Architecture Documentation:

notebooks/tools-needed/type-safety-strategy.md

Available Tools: see

.claude/tools/python-tools.md

Development Commands

Package Management

# Install all dependencies
uv sync

# Add new dependency
uv add <package-name>

# Remove dependency
uv remove <package-name>

# Run Python scripts
uv run python <script-path>

Real-Time Market Data

# Get current stock price (single)
uv run python src/utils/market_data.py TSLA

# Get multiple stock prices
uv run python src/utils/market_data.py TSLA PLTR AAPL

Risk Metrics Analysis

# Market Researcher - Quick risk scan
uv run python src/analysis/risk_metrics_cli.py TSLA --days 90

# Quant Analyst - Full analysis with benchmark
uv run python src/analysis/risk_metrics_cli.py TSLA --days 252 --benchmark SPY --output json

# Strategy Advisor - Portfolio comparison
for ticker in TSLA PLTR NVDA; do
    uv run python src/analysis/risk_metrics_cli.py $ticker --days 252 --benchmark SPY
done

# Save to file for report generation
uv run python src/analysis/risk_metrics_cli.py TSLA --days 90 \
    --output json \
    --save-to fin-guru-private/fin-guru/risk-analysis-tsla-$(date +%Y-%m-%d).json

Available Metrics: VaR (95%), CVaR, Sharpe Ratio, Sortino Ratio, Max Drawdown, Calmar Ratio, Annual Volatility, Beta, Alpha

Documentation:

fin-guru-private/guides/risk-metrics-tool-guide.md

Momentum Indicators

# Market Researcher - Quick momentum scan (all indicators)
uv run python src/utils/momentum_cli.py TSLA --days 90

# Quant Analyst - Specific indicator with custom periods
uv run python src/utils/momentum_cli.py TSLA --days 90 --indicator rsi --rsi-period 21

# Strategy Advisor - Portfolio momentum comparison
for ticker in TSLA PLTR NVDA; do
    uv run python src/utils/momentum_cli.py $ticker --days 90
done

# JSON output for programmatic analysis
uv run python src/utils/momentum_cli.py TSLA --days 90 --output json

# Custom MACD settings for different timeframes
uv run python src/utils/momentum_cli.py TSLA --days 252 \
    --macd-fast 8 \
    --macd-slow 21 \
    --macd-signal 9

Available Indicators: RSI, MACD, Stochastic Oscillator, Williams %R, ROC (Rate of Change)

Features: Confluence analysis (counts bullish/bearish signals across all indicators)

Volatility Metrics

# Market Researcher - Quick volatility scan (all indicators)
uv run python src/utils/volatility_cli.py TSLA --days 90

# Compliance Officer - Position limit calculation
uv run python src/utils/volatility_cli.py TSLA --days 90 --output json

# Margin Specialist - Leverage assessment with custom ATR
uv run python src/utils/volatility_cli.py TSLA --days 90 --atr-period 20

# Strategy Advisor - Portfolio volatility comparison
for ticker in TSLA PLTR NVDA; do
    uv run python src/utils/volatility_cli.py $ticker --days 90
done

# Custom Bollinger Bands settings
uv run python src/utils/volatility_cli.py TSLA --days 90 \
    --bb-period 14 \
    --bb-std 2.5

Available Indicators: Bollinger Bands, ATR (Average True Range), Historical Volatility, Keltner Channels, Standard Deviation

Features: Volatility regime assessment (low/normal/high/extreme), position sizing guidance, stop-loss calculation

Agent Use Cases:

Compliance Officer: Calculate position limits based on volatility regime
Margin Specialist: Determine safe leverage ratios using ATR%
Risk Assessment: Portfolio volatility tracking and regime monitoring

Correlation & Covariance Analysis

# Basic portfolio correlation (2+ tickers required)
uv run python src/analysis/correlation_cli.py TSLA PLTR NVDA --days 90

# Pairwise correlation check
uv run python src/analysis/correlation_cli.py TSLA SPY --days 90

# Rolling correlation (time-varying)
uv run python src/analysis/correlation_cli.py TSLA SPY --days 252 --rolling 60

# JSON output for programmatic use
uv run python src/analysis/correlation_cli.py TSLA PLTR NVDA --days 90 --output json

Available Analysis: Pearson correlation matrices, covariance matrices, rolling correlations, diversification scoring, concentration risk detection

Agent Use Cases:

Strategy Advisor: Portfolio diversification assessment, rebalancing signals
Quant Analyst: Correlation matrices for portfolio optimization, factor analysis
Risk Assessment: Concentration risk monitoring, correlation regime shifts

Strategy Backtesting

# Test RSI strategy
uv run python src/strategies/backtester_cli.py TSLA --days 252 --strategy rsi

# Test with custom capital and costs
uv run python src/strategies/backtester_cli.py TSLA --days 252 --strategy rsi \
    --capital 500000 --commission 5.0 --slippage 0.001

# Test SMA crossover strategy
uv run python src/strategies/backtester_cli.py TSLA --days 252 --strategy sma_cross

# Buy-and-hold benchmark
uv run python src/strategies/backtester_cli.py TSLA --days 252 --strategy buy_hold

# JSON output
uv run python src/strategies/backtester_cli.py TSLA --days 252 --strategy rsi --output json

Built-in Strategies: RSI mean reversion, SMA crossover, buy-and-hold benchmark

Features: Transaction cost modeling (commissions + slippage), performance metrics (Sharpe, max drawdown, win rate), trade log generation, deployment recommendations

Agent Use Cases:

Strategy Advisor: Validate investment hypotheses before deployment
Quant Analyst: Test quantitative models, optimize parameters
Compliance Officer: Assess strategy risk profile before approval

Moving Average Analysis

# Single MA calculation (SMA, EMA, WMA, HMA)
uv run python src/utils/moving_averages_cli.py TSLA --days 200 --ma-type SMA --period 50

# Golden Cross detection (50/200 SMA - classic trend signal)
uv run python src/utils/moving_averages_cli.py TSLA --days 252 --fast 50 --slow 200

# EMA crossover (12/26 for MACD-style signals)
uv run python src/utils/moving_averages_cli.py TSLA --days 252 --ma-type EMA --fast 12 --slow 26

# Hull MA (minimal lag, responsive)
uv run python src/utils/moving_averages_cli.py TSLA --days 200 --ma-type HMA --period 50

# JSON output
uv run python src/utils/moving_averages_cli.py TSLA --days 200 --ma-type SMA --period 50 --output json

Available MA Types: SMA (simple), EMA (exponential), WMA (weighted), HMA (Hull - advanced)

Features: Golden Cross/Death Cross detection, trend analysis, crossover date tracking

Agent Use Cases:

Market Researcher: Quick trend identification with standard MAs
Quant Analyst: Test multiple MA types for strategy optimization
Strategy Advisor: Monitor 50/200 Golden Cross for major trend signals

Portfolio Optimization

# Maximum Sharpe ratio (aggressive growth)
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method max_sharpe

# Risk parity allocation (all-weather portfolio)
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method risk_parity

# Minimum variance (defensive, capital preservation)
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method min_variance

# Mean-variance optimization
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method mean_variance

# Black-Litterman with views
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA --days 252 --method black_litterman \
    --view TSLA:0.15 --view PLTR:0.20

# With position limits (max 30% per stock)
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method max_sharpe \
    --max-position 0.30

# JSON output
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method max_sharpe --output json

Optimization Methods: Mean-Variance (Markowitz), Risk Parity, Min Variance, Max Sharpe, Black-Litterman

Features: Position limit controls, capital allocation guidance ($500k portfolio), efficient frontier generation, diversification scoring

Agent Use Cases:

Strategy Advisor: Monthly portfolio rebalancing and new capital deployment ($5-10k)
Quant Analyst: Portfolio construction with risk-return optimization
Compliance Officer: Ensure position limits and concentration risk controls

Document Output

All generated analyses should be saved to:

Primary:
```
fin-guru-private/fin-guru/analysis/
```
Format: Markdown with YAML frontmatter

Naming:

{topic}-{strategy/analysis}-{YYYY-MM-DD}.md

Include: Date stamp, disclaimer, source citations

Testing & Validation

The system is primarily workflow-based rather than code-based. Validation involves:

Testing agent activation sequences
Verifying workflow execution
Checking document generation
Ensuring compliance disclaimers are present
Validating market data retrieval

Version Information

Finance Guru™: v2.0.0
BMAD-CORE™: v6.0.0
Build Date: 2025-10-08
Last Updated: 2025-10-13
Tools Built: 7 of 11 (Risk Metrics, Momentum Indicators, Volatility Metrics, Correlation & Covariance Engine, Backtesting Framework, Moving Average Toolkit, Portfolio Optimizer)

Remember: This is a private family office system. All work should maintain the exclusive, personalized nature of the Finance Guru service.

Landing the Plane (Session Completion)

When ending a work session, you MUST complete ALL steps below. Work is NOT complete until

git push

succeeds.

MANDATORY WORKFLOW:

File issues for remaining work - Create issues for anything that needs follow-up
Run quality gates (if code changed) - Tests, linters, builds
Update issue status - Close finished work, update in-progress items

PUSH TO REMOTE - This is MANDATORY:

git pull --rebase
bd sync
git push
git status  # MUST show "up to date with origin"

Clean up - Clear stashes, prune remote branches
Verify - All changes committed AND pushed
Hand off - Provide context for next session

CRITICAL RULES:

Work is NOT complete until
```
git push
```
succeeds
NEVER stop before pushing - that leaves work stranded locally
NEVER say "ready to push when you are" - YOU must push
If push fails, resolve and retry until it succeeds

Use 'bd' for task tracking

Project Overview

Architecture

Multi-Agent System

Finance Guru™ uses a specialized agent architecture where Claude transforms into different financial specialists:

Primary Entry Point:

Finance Orchestrator (Cassandra Holt) - Master coordinator located at
```
.claude/commands/fin-guru/agents/finance-orchestrator.md
```

Path Variable System

The codebase uses a variable substitution system:

```
{project-root}
```
- Root of the repository
```
{module-path}
```
- Path to fin-guru module
```
{current_datetime}
```
- Current date and time
```
{current_date}
```
- Current date (YYYY-MM-DD)
```
{user_name}
```
- User's name from config

When referencing files in agent configurations, these variables should be resolved to actual paths.

External Tool Requirements

Finance Guru requires these MCP servers:

exa - Deep research and market intelligence
bright-data - Web scraping (search engines, markdown extraction)
sequential-thinking - Complex multi-step reasoning
financial-datasets - SEC filings, financial statements
gdrive - Google Drive integration (sheets, docs)
web-search - Real-time market information

Temporal Awareness

CRITICAL REQUIREMENT: All agents must establish temporal context before performing any market research or analysis.

Required initialization:

# Agents MUST execute these commands at startup
date                    # Store as {current_datetime}
date +"%Y-%m-%d"       # Store as {current_date}

This ensures:

Market data searches use current year/date
Analysis reflects real-time conditions
Documents are properly date-stamped

Compliance & Disclaimers

MANDATORY: All financial outputs must include:

Educational-only disclaimer
"Not investment advice" statement
Recommendation to consult licensed professionals
Risk disclosure (loss of principal possible)

This positioning is enforced by the Compliance Officer agent.

Technology Stack

Python: 3.12+
Package Manager:
```
uv
```
(used for all Python operations)
Key Dependencies:
- pandas, numpy, scipy, scikit-learn (data analysis)
- yfinance (market data)
- streamlit (visualization)
- beautifulsoup4, requests (web scraping)
- pydantic (data validation)
- python-dotenv (configuration)

Python Tools & Utilities

Type-Safe Architecture

All Python tools follow a 3-layer architecture pattern:

Layer 1: Pydantic Models (
```
src/models/
```
) - Data validation
Layer 2: Calculator Classes (
```
src/analysis/
```
,
```
src/utils/
```
) - Business logic
Layer 3: CLI Interface - Agent integration

Architecture Documentation:

notebooks/tools-needed/type-safety-strategy.md

Available Tools: see

.claude/tools/python-tools.md

Development Commands

Package Management

# Install all dependencies
uv sync

# Add new dependency
uv add <package-name>

# Remove dependency
uv remove <package-name>

# Run Python scripts
uv run python <script-path>

Real-Time Market Data

# Get current stock price (single)
uv run python src/utils/market_data.py TSLA

# Get multiple stock prices
uv run python src/utils/market_data.py TSLA PLTR AAPL

Risk Metrics Analysis

# Market Researcher - Quick risk scan
uv run python src/analysis/risk_metrics_cli.py TSLA --days 90

# Quant Analyst - Full analysis with benchmark
uv run python src/analysis/risk_metrics_cli.py TSLA --days 252 --benchmark SPY --output json

# Strategy Advisor - Portfolio comparison
for ticker in TSLA PLTR NVDA; do
    uv run python src/analysis/risk_metrics_cli.py $ticker --days 252 --benchmark SPY
done

# Save to file for report generation
uv run python src/analysis/risk_metrics_cli.py TSLA --days 90 \
    --output json \
    --save-to fin-guru-private/fin-guru/risk-analysis-tsla-$(date +%Y-%m-%d).json

Available Metrics: VaR (95%), CVaR, Sharpe Ratio, Sortino Ratio, Max Drawdown, Calmar Ratio, Annual Volatility, Beta, Alpha

Documentation:

fin-guru-private/guides/risk-metrics-tool-guide.md

Momentum Indicators

# Market Researcher - Quick momentum scan (all indicators)
uv run python src/utils/momentum_cli.py TSLA --days 90

# Quant Analyst - Specific indicator with custom periods
uv run python src/utils/momentum_cli.py TSLA --days 90 --indicator rsi --rsi-period 21

# Strategy Advisor - Portfolio momentum comparison
for ticker in TSLA PLTR NVDA; do
    uv run python src/utils/momentum_cli.py $ticker --days 90
done

# JSON output for programmatic analysis
uv run python src/utils/momentum_cli.py TSLA --days 90 --output json

# Custom MACD settings for different timeframes
uv run python src/utils/momentum_cli.py TSLA --days 252 \
    --macd-fast 8 \
    --macd-slow 21 \
    --macd-signal 9

Available Indicators: RSI, MACD, Stochastic Oscillator, Williams %R, ROC (Rate of Change)

Features: Confluence analysis (counts bullish/bearish signals across all indicators)

Volatility Metrics

# Market Researcher - Quick volatility scan (all indicators)
uv run python src/utils/volatility_cli.py TSLA --days 90

# Compliance Officer - Position limit calculation
uv run python src/utils/volatility_cli.py TSLA --days 90 --output json

# Margin Specialist - Leverage assessment with custom ATR
uv run python src/utils/volatility_cli.py TSLA --days 90 --atr-period 20

# Strategy Advisor - Portfolio volatility comparison
for ticker in TSLA PLTR NVDA; do
    uv run python src/utils/volatility_cli.py $ticker --days 90
done

# Custom Bollinger Bands settings
uv run python src/utils/volatility_cli.py TSLA --days 90 \
    --bb-period 14 \
    --bb-std 2.5

Available Indicators: Bollinger Bands, ATR (Average True Range), Historical Volatility, Keltner Channels, Standard Deviation

Features: Volatility regime assessment (low/normal/high/extreme), position sizing guidance, stop-loss calculation

Agent Use Cases:

Compliance Officer: Calculate position limits based on volatility regime
Margin Specialist: Determine safe leverage ratios using ATR%
Risk Assessment: Portfolio volatility tracking and regime monitoring

Correlation & Covariance Analysis

# Basic portfolio correlation (2+ tickers required)
uv run python src/analysis/correlation_cli.py TSLA PLTR NVDA --days 90

# Pairwise correlation check
uv run python src/analysis/correlation_cli.py TSLA SPY --days 90

# Rolling correlation (time-varying)
uv run python src/analysis/correlation_cli.py TSLA SPY --days 252 --rolling 60

# JSON output for programmatic use
uv run python src/analysis/correlation_cli.py TSLA PLTR NVDA --days 90 --output json

Available Analysis: Pearson correlation matrices, covariance matrices, rolling correlations, diversification scoring, concentration risk detection

Agent Use Cases:

Strategy Advisor: Portfolio diversification assessment, rebalancing signals
Quant Analyst: Correlation matrices for portfolio optimization, factor analysis
Risk Assessment: Concentration risk monitoring, correlation regime shifts

Strategy Backtesting

# Test RSI strategy
uv run python src/strategies/backtester_cli.py TSLA --days 252 --strategy rsi

# Test with custom capital and costs
uv run python src/strategies/backtester_cli.py TSLA --days 252 --strategy rsi \
    --capital 500000 --commission 5.0 --slippage 0.001

# Test SMA crossover strategy
uv run python src/strategies/backtester_cli.py TSLA --days 252 --strategy sma_cross

# Buy-and-hold benchmark
uv run python src/strategies/backtester_cli.py TSLA --days 252 --strategy buy_hold

# JSON output
uv run python src/strategies/backtester_cli.py TSLA --days 252 --strategy rsi --output json

Built-in Strategies: RSI mean reversion, SMA crossover, buy-and-hold benchmark

Features: Transaction cost modeling (commissions + slippage), performance metrics (Sharpe, max drawdown, win rate), trade log generation, deployment recommendations

Agent Use Cases:

Strategy Advisor: Validate investment hypotheses before deployment
Quant Analyst: Test quantitative models, optimize parameters
Compliance Officer: Assess strategy risk profile before approval

Moving Average Analysis

# Single MA calculation (SMA, EMA, WMA, HMA)
uv run python src/utils/moving_averages_cli.py TSLA --days 200 --ma-type SMA --period 50

# Golden Cross detection (50/200 SMA - classic trend signal)
uv run python src/utils/moving_averages_cli.py TSLA --days 252 --fast 50 --slow 200

# EMA crossover (12/26 for MACD-style signals)
uv run python src/utils/moving_averages_cli.py TSLA --days 252 --ma-type EMA --fast 12 --slow 26

# Hull MA (minimal lag, responsive)
uv run python src/utils/moving_averages_cli.py TSLA --days 200 --ma-type HMA --period 50

# JSON output
uv run python src/utils/moving_averages_cli.py TSLA --days 200 --ma-type SMA --period 50 --output json

Available MA Types: SMA (simple), EMA (exponential), WMA (weighted), HMA (Hull - advanced)

Features: Golden Cross/Death Cross detection, trend analysis, crossover date tracking

Agent Use Cases:

Market Researcher: Quick trend identification with standard MAs
Quant Analyst: Test multiple MA types for strategy optimization
Strategy Advisor: Monitor 50/200 Golden Cross for major trend signals

Portfolio Optimization

# Maximum Sharpe ratio (aggressive growth)
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method max_sharpe

# Risk parity allocation (all-weather portfolio)
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method risk_parity

# Minimum variance (defensive, capital preservation)
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method min_variance

# Mean-variance optimization
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method mean_variance

# Black-Litterman with views
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA --days 252 --method black_litterman \
    --view TSLA:0.15 --view PLTR:0.20

# With position limits (max 30% per stock)
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method max_sharpe \
    --max-position 0.30

# JSON output
uv run python src/strategies/optimizer_cli.py TSLA PLTR NVDA SPY --days 252 --method max_sharpe --output json

Optimization Methods: Mean-Variance (Markowitz), Risk Parity, Min Variance, Max Sharpe, Black-Litterman

Features: Position limit controls, capital allocation guidance ($500k portfolio), efficient frontier generation, diversification scoring

Agent Use Cases:

Strategy Advisor: Monthly portfolio rebalancing and new capital deployment ($5-10k)
Quant Analyst: Portfolio construction with risk-return optimization
Compliance Officer: Ensure position limits and concentration risk controls

Document Output

All generated analyses should be saved to:

Primary:
```
fin-guru-private/fin-guru/analysis/
```
Format: Markdown with YAML frontmatter

Naming:

{topic}-{strategy/analysis}-{YYYY-MM-DD}.md

Include: Date stamp, disclaimer, source citations

Testing & Validation

The system is primarily workflow-based rather than code-based. Validation involves:

Testing agent activation sequences
Verifying workflow execution
Checking document generation
Ensuring compliance disclaimers are present
Validating market data retrieval

Version Information

Finance Guru™: v2.0.0
BMAD-CORE™: v6.0.0
Build Date: 2025-10-08
Last Updated: 2025-10-13
Tools Built: 7 of 11 (Risk Metrics, Momentum Indicators, Volatility Metrics, Correlation & Covariance Engine, Backtesting Framework, Moving Average Toolkit, Portfolio Optimizer)

Remember: This is a private family office system. All work should maintain the exclusive, personalized nature of the Finance Guru service.

Landing the Plane (Session Completion)

When ending a work session, you MUST complete ALL steps below. Work is NOT complete until

git push

succeeds.

MANDATORY WORKFLOW:

File issues for remaining work - Create issues for anything that needs follow-up
Run quality gates (if code changed) - Tests, linters, builds
Update issue status - Close finished work, update in-progress items

PUSH TO REMOTE - This is MANDATORY:

git pull --rebase
bd sync
git push
git status  # MUST show "up to date with origin"

Clean up - Clear stashes, prune remote branches
Verify - All changes committed AND pushed
Hand off - Provide context for next session

CRITICAL RULES:

Work is NOT complete until
```
git push
```
succeeds
NEVER stop before pushing - that leaves work stranded locally
NEVER say "ready to push when you are" - YOU must push
If push fails, resolve and retry until it succeeds

Use 'bd' for task tracking

Related Skills

Markdown Converter

Nano Banana Pro

1password