Investment Management

Investment Management

Full-stack investment management: from philosophy to execution. Research, analyze, decide, execute, track, and optimize across traditional and alternative asset classes.

Financial Management Stack

finance-substrate (Layer 1: Accounting & Tax)
  ├── Bank transaction import, certificate parsing
  ├── Form 210 tax projection, DIAN integration
  ├── Parafiscales, patrimonio, retenciones
  └── Gmail document collector
        ↓
wealth-management (Layer 2: Planning & Projections)
  ├── Compound growth projections
  ├── Goal-based planning, Monte Carlo
  ├── Portfolio summary & asset allocation
  └── Budget planning with tax savings
        ↓
investment-management (Layer 3: Analysis & Execution)  ← THIS SKILL
  ├── RESEARCH:   Market data, fundamentals, screening
  ├── ANALYZE:    Factor models, backtests, risk metrics
  ├── DECIDE:     Philosophy-driven frameworks, scoring
  ├── EXECUTE:    Trade via APIs (Alpaca, Coinbase, Polymarket)
  ├── TRACK:      Multi-platform position aggregation
  └── OPTIMIZE:   Rebalancing, tax-loss harvesting, lot management

Investment Philosophies (Built-in Frameworks)

The skill embeds decision frameworks from legendary investors. Each philosophy is a scoring lens that can be applied to any investment.

Value (Buffett/Graham/Munger)

| Principle | Implementation | |-----------|---------------| | Economic moat analysis | Score competitive advantage: brand, network, switching cost, scale | | Margin of safety | Require 25%+ discount to intrinsic value (DCF, owner earnings) | | Circle of competence | Flag unfamiliar sectors; require deeper research threshold | | Owner earnings | Net income + depreciation - capex (not GAAP earnings) | | Quality over price | ROIC > WACC, consistent ROE > 15%, low debt/equity |

Systematic (Dalio/AQR)

| Principle | Implementation | |-----------|---------------| | All-Weather allocation | Risk-parity: equal risk contribution from growth, inflation, deflation | | Factor exposure | Decompose returns into market, value, momentum, quality, size | | Risk parity | Weight by inverse volatility, target equal risk contribution | | Regime awareness | Detect growth/inflation quadrant, adjust allocation | | Correlation regime | Monitor rolling correlations; diversification fails in crises |

Passive/Index (Bogle)

| Principle | Implementation | |-----------|---------------| | Three-fund portfolio | Total market + international + bonds; rebalance annually | | Minimize costs | Flag any fund with expense ratio > 0.20% | | Tax efficiency | Index funds in taxable, bonds in tax-deferred | | Stay the course | Reject market timing; dollar-cost average | | Simple beats complex | Baseline comparison for every active strategy |

Second-Level Thinking (Marks)

| Principle | Implementation | |-----------|---------------| | Consensus vs reality | Flag positions where market consensus is priced in | | Risk is not volatility | Focus on permanent capital loss, not price fluctuation | | Market cycles | Track Shiller CAPE, yield spreads, sentiment indicators | | Asymmetric outcomes | Seek situations where upside >> downside | | Know what you don't know | Confidence-weighted recommendations |

Barbell (Taleb)

| Principle | Implementation | |-----------|---------------| | 85% ultra-safe + 15% high-convexity | Split portfolio into safe (CDTs, treasuries) + optionality (crypto, VC, prediction markets) | | Antifragile positions | Identify investments that benefit from volatility | | Avoid the middle | Skip mediocre risk-return profiles | | Small bets, big payoffs | Position size by max loss tolerance, not expected return |

Skill Modes

1. screen — Security Screening

Search and filter stocks, ETFs, crypto, and other securities by criteria.

Script: scripts/screener.py

Screening criteria:

• Fundamental: P/E, P/B, EV/EBITDA, FCF yield, ROIC, ROE, debt/equity, dividend yield
• Technical: RSI, MACD signal, price vs 200-day MA, 52-week range position
• Quality: earnings consistency, revenue growth, margin stability
• Momentum: 1/3/6/12-month returns, relative strength
• Value composite: Piotroski F-Score, Greenblatt Magic Formula rank
• Size: market cap filters

Data sources: yfinance, Financial Modeling Prep, OpenBB

2. research — Deep Investment Research — (Planned — not yet implemented)

In-depth analysis of a specific security or market.

Status: Planned — scripts/research.py is not yet shipped. Do not invoke; this mode is a roadmap stub.

Analysis includes:

• Company overview and business model
• Financial statement analysis (3-5 year trends)
• Valuation: DCF, comparable companies, dividend discount
• Competitive landscape and moat assessment
• Risk factors and bear case
• Catalyst identification
• Philosophy alignment score (which frameworks support/oppose)

Data sources: yfinance fundamentals, SEC EDGAR (edgartools), news sentiment (FinBERT)

3. factor — Factor Analysis — (Planned — not yet implemented)

Decompose portfolio returns into systematic factor exposures.

Status: Planned — scripts/factor_analysis.py is not yet shipped. Do not invoke; this mode is a roadmap stub.

Factors analyzed:

• Fama-French 5 factors: Market, Size (SMB), Value (HML), Profitability (RMW), Investment (CMA)
• Momentum (UMD)
• Quality (QMJ from AQR)
• Alpha: residual return not explained by factors

Output: Factor loadings, R², alpha significance, factor exposure drift over time

4. backtest — Strategy Backtesting

Test investment strategies against historical data.

Script: scripts/backtest.py

Built-in strategies:

• Buy and hold (benchmark)
• Equal-weight rebalanced
• Risk parity (inverse vol)
• Momentum (top N by 12-1 month return)
• Value (top N by composite score)
• All-Weather (Dalio's 4-quadrant allocation)
• Custom (user-defined rules)

Metrics: CAGR, Sharpe, Sortino, max drawdown, Calmar, win rate, average gain/loss

5. optimize — Portfolio Optimization — (Planned — not yet implemented)

Find optimal portfolio weights given constraints.

Status: Planned — scripts/portfolio_optimizer.py is not yet shipped. Do not invoke; this mode is a roadmap stub.

Methods:

• Mean-variance (Markowitz efficient frontier)
• Black-Litterman (market equilibrium + personal views)
• Hierarchical Risk Parity (HRP, no covariance inversion)
• Risk budgeting (equal risk contribution)
• Maximum Sharpe, minimum volatility, target return
• CVaR optimization (tail-risk aware)

Constraints: Long-only, sector limits, position size caps, turnover limits, tax-awareness

Libraries: PyPortfolioOpt, Riskfolio-Lib, cvxpy

6. risk — Risk Analysis — (Planned — not yet implemented)

Comprehensive risk assessment of current or proposed portfolio.

Status: Planned — scripts/risk_analysis.py is not yet shipped. Do not invoke; this mode is a roadmap stub.

Metrics:

• Value at Risk (parametric, historical, Monte Carlo)
• Conditional VaR (Expected Shortfall)
• Maximum drawdown analysis
• Stress tests: 2008 GFC, 2020 COVID, 2022 rates, COP devaluation
• Correlation regime analysis (rolling correlations)
• GARCH volatility forecast
• Concentration risk and single-name exposure

7. trade — Trade Execution — (Planned — not yet implemented)

Execute trades via supported platform APIs.

Status: Planned — scripts/trade.py is not yet shipped. Do not invoke; this mode is a roadmap stub. (For a working, paper-only execution loop today, use services/tradingview-bridge — see below.)

Supported platforms:

| Platform | Assets | Auth | Library | |----------|--------|------|---------| | Alpaca | US stocks, ETFs | API key pair | alpaca-trade-api | | Coinbase | Crypto | CDP API key + JWT | coinbase-advanced-py | | Polymarket | Prediction markets | Wallet signature | py-clob-client | | Interactive Brokers | Everything | TWS API | ib_async |

Features:

• Paper trading mode (default) — no real money until explicitly confirmed
• Order types: market, limit, stop-loss
• Position sizing: Kelly criterion, fixed fractional, risk-budget
• Pre-trade checks: liquidity, spread, portfolio impact

8. track — Multi-Platform Position Tracking — (Planned — not yet implemented)

Aggregate positions across all investment platforms.

Status: Planned — scripts/tracker.py is not yet shipped. Do not invoke; this mode is a roadmap stub.

Sources (in priority order):

1. API-connected platforms (Alpaca, Coinbase) — real-time
2. finance-substrate certificates (Skandia, Davivienda, etc.) — periodic
3. Browser-automated platforms (Tyba, Trii, Davivienda Corredores) — via agent-browser
4. Manual entries (portfolio.json) — user-maintained

Output: Unified position list with cost basis, current value, unrealized gain/loss, allocation %

9. rebalance — Intelligent Rebalancing — (Planned — not yet implemented)

Generate and optionally execute rebalancing trades.

Status: Planned — scripts/rebalancer.py is not yet shipped. Do not invoke; this mode is a roadmap stub.

Rebalancing modes:

• Cash-flow (direct new contributions to underweight positions)
• Threshold (trigger when drift exceeds band)
• Tax-aware (prefer loss-harvesting sells, defer gains)
• Calendar (monthly/quarterly/annual schedule)

Tax-loss harvesting:

• Scan for positions with unrealized losses
• Identify replacement securities (correlated but not identical)
• Track wash sale windows (30 days US; no equivalent in Colombia)
• Estimate tax savings

10. data — Market Data Retrieval

Fetch market data from multiple sources.

Script: scripts/market_data.py

Sources:

| Source | Data | Cost | Library | |--------|------|------|---------| | yfinance | US/intl stocks, fundamentals | Free | yfinance | | Financial Modeling Prep | Financial statements, ratios | Free tier | fmpsdk | | CoinGecko | Crypto prices, market data | Free tier | pycoingecko | | FRED | Macro indicators (800K series) | Free | fredapi | | datos.gov.co | TRM (USD/COP) | Free | requests | | Banco de la República | CPI, interest rates | Free | requests | | Alpha Vantage | Technical data, forex | Free tier | alpha_vantage | | Polymarket | Prediction market odds | Free | py-clob-client |

11. score — Investment Scoring

Score a security through multiple investment philosophy lenses.

Script: scripts/scorer.py

Scoring dimensions:

• Value score (Graham/Buffett): P/E, P/B, FCF yield, moat rating
• Quality score (Munger): ROIC, margin stability, debt discipline
• Momentum score: price momentum, earnings momentum, analyst revisions
• Risk score (Marks): downside volatility, max drawdown, tail risk
• Growth score (Lynch): revenue growth, PEG ratio, addressable market
• Composite score: weighted average across all dimensions

Output: 0-100 score per dimension, overall composite, philosophy alignment

Asset Class Coverage

Traditional

| Class | Screening | Research | Trading | Tracking | |-------|-----------|----------|---------|----------| | US Stocks | yfinance, FMP | EDGAR, fundamentals | Alpaca, IBKR | API | | International Stocks | yfinance | Limited fundamentals | IBKR | API | | ETFs | yfinance | Holdings analysis | Alpaca, IBKR | API | | Bonds/Fixed Income | FRED yield curves | Duration, credit | IBKR | Manual | | CDTs (Colombia) | Superfinanciera rates | Yield comparison | Manual | Certificates |

Alternative

| Class | Screening | Research | Trading | Tracking | |-------|-----------|----------|---------|----------| | Crypto | CoinGecko, CMC | On-chain, sentiment | Coinbase, Binance | API | | Prediction Markets | Polymarket API | Market analysis | Polymarket CLOB | API | | Real Estate | Manual | Cap rate, appreciation | Manual | Exogena/manual | | VC/Startups | Manual | Due diligence framework | Manual | Manual | | Colombian Equities | Yahoo (BVC tickers) | Limited | Davivienda Corredores (browser) | Browser/manual |

Tax-Advantaged (Colombia)

| Vehicle | Max Benefit | Tracking | |---------|-----------|----------| | AFC (Davivienda) | 1,340 UVT cap | finance-substrate certs | | Pensión Voluntaria (Skandia) | Combined w/ AFC | finance-substrate certs | | Cesantías | Forced savings | finance-substrate certs |

Platform Integration Architecture

investment-management
  │
  ├── API-first (real-time)
  │   ├── Alpaca ──── US stocks, ETFs (paper + live)
  │   ├── Coinbase ── Crypto (BTC, ETH, SOL, etc.)
  │   ├── Polymarket ── Prediction markets
  │   ├── CoinGecko ── Crypto market data
  │   ├── FRED ──── Macro indicators
  │   └── yfinance ── Stock data, fundamentals
  │
  ├── Browser-automated (agent-browser)
  │   ├── Davivienda Corredores ── Colombian equities
  │   ├── Tyba ── Colombian robo-advisor
  │   ├── Skandia Portal ── Pension fund data
  │   └── MiDataCredito ── Credit profile
  │
  ├── File-based (finance-substrate)
  │   ├── certificates.jsonl ── Bank saldos, investment funds
  │   ├── exogena.jsonl ── Third-party reported assets
  │   └── salary-history.jsonl ── Income trajectory
  │
  └── Manual (portfolio.json)
      ├── Private investments
      ├── Real estate
      └── VC/startup positions

Autonomous decision plane — services/tradingview-bridge

A self-contained, paper-only, human-gated trading control system: a webhook receiver + multi-broker executor, plus a closed self-improving loop — research (walk-forward + anti-overfit scoring), optimize (EGRI param search with a true train/test holdout), and roster (human-gated promotion of optimized params into the roster the orchestrator measures). Drives TradingView's Paper simulator via the Interceptor CLI (no inbound API), or runs the entire decision loop on synthetic/CSV bars with no browser or broker. It measures and recommends — it never moves live capital on its own.

→ [services/tradingview-bridge/QUICKSTART.md](./services/tradingview-bridge/QUICKSTART.md) (install + the loop in 60 seconds) · [full reference](./services/tradingview-bridge/README.md)

Quantitative Toolkit

Libraries Used

| Purpose | Library | Install | |---------|---------|---------| | Portfolio optimization | PyPortfolioOpt | pip install pyportfolioopt | | Advanced risk optimization | Riskfolio-Lib | pip install riskfolio-lib | | Custom optimization | cvxpy | pip install cvxpy | | Volatility modeling | arch | pip install arch | | Technical indicators | pandas-ta | pip install pandas_ta | | Factor data | pandas-datareader | pip install pandas-datareader | | Backtesting | VectorBT | pip install vectorbt | | Sentiment | transformers (FinBERT) | pip install transformers | | Fundamental data | edgartools | pip install edgartools | | Macro data | fredapi | pip install fredapi |

Key Formulas

| Formula | Expression | Use | |---------|-----------|-----| | Sharpe Ratio | (Rp - Rf) / σp | Risk-adjusted return | | Sortino Ratio | (Rp - Rf) / σdownside | Downside risk-adjusted | | Kelly Fraction | (bp - q) / b | Optimal position size | | Intrinsic Value (DCF) | Σ FCFt / (1+r)^t + TV | Valuation | | WACC | E/(E+D)×Re + D/(E+D)×Rd×(1-T) | Discount rate | | Black-Scholes | C = S·N(d1) - K·e^(-rT)·N(d2) | Option pricing | | VaR (parametric) | μ - zα × σ | Tail risk | | CVaR | E[L \| L > VaR] | Expected tail loss | | HHI (concentration) | Σ w_i² | Diversification |

Data Directory

~/.investment-management/
├── portfolio.json              # Master holdings (all platforms)
├── targets.json                # Target allocation by strategy
├── watchlist.json              # Securities under observation
├── trades/                     # Trade history
│   └── trades.jsonl            # All executed trades
├── research/                   # Security research cache
│   └── research-{ticker}.json
├── backtests/                  # Backtest results
│   └── backtest-{strategy}-{date}.json
├── scores/                     # Investment scores

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [broomva](https://github.com/broomva)
- **Source:** [broomva/skills](https://github.com/broomva/skills)
- **License:** MIT
- **Homepage:** https://skills.sh/broomva/skills

Install and usage instructions live in the source repository linked above.