Manage personal finances

Calculate time-weighted returns (TWR) and money-weighted returns (IRR) for total portfolio performance.

Implement Sharpe, Sortino, and Calmar ratio calculations for risk-adjusted performance analysis.

Implement drawdown analysis, including calculation of maximum drawdown and recovery time.

Calculate portfolio Value-at-Risk (VaR) and Conditional VaR (CVaR) using historical and parametric methods.

Define and parameterize at least three distinct stress test scenarios (e.g., historical crisis, interest rate shock, inflation spike) relevant to the portfolio.

Develop a model in Python or Excel to estimate the P&L impact of a defined scenario on each major asset class in the portfolio.

Aggregate individual asset impacts to calculate the total portfolio P&L under each defined stress scenario and interpret the results.

Perform Brinson-style attribution analysis to decompose portfolio returns into asset allocation and security selection effects.

Extend the attribution analysis to account for currency effects in a multi-currency portfolio.

Calculate alpha and tracking error relative to appropriate benchmark portfolios.

Isolate and calculate the standalone P&L, win/loss ratio, and Sharpe ratio for the speculative options trading book.

Quantify the impact of protective put strategies by comparing the portfolio's historical drawdowns against a simulated unhedged portfolio.

Perform a cost-benefit analysis of options hedges by comparing total premium spent to losses avoided during specific market downturns.

Formalize the existing strategic asset allocation by quantifying target weights, acceptable ranges, and the rationale for each asset class.

Define a framework for evaluating and incorporating alternative assets (crypto, precious metals, real estate), covering their role, risk characteristics, and data sources.

Estimate expected returns, volatilities, and correlations for alternative assets, addressing challenges like non-normal return distributions.

Apply tactical asset allocation using momentum indicators (e.g., time-series and cross-sectional momentum).

Apply tactical asset allocation using valuation indicators (e.g., comparing current vs. historical P/E ratios).

Apply tactical asset allocation using sentiment indicators (e.g., VIX, put/call ratio).

Formalize a glide path for adjusting portfolio risk as specific financial goals approach, incorporating quantitative de-risking triggers.

Apply mean-variance optimization with realistic constraints (position limits, sector exposures) using Excel Solver or Python's scipy.optimize.

Construct minimum variance portfolios and compare them with maximum Sharpe ratio portfolios.

Apply shrinkage estimators (e.g., Ledoit-Wolf) to improve covariance matrix estimation for optimization.

Implement the Black-Litterman model to incorporate personal market views into portfolio allocation.

Calculate optimal portfolio weights using a risk parity approach for multi-asset portfolios.

Adapt a portfolio optimization framework (e.g., Mean-CVaR) to incorporate the specific risk characteristics of alternative assets.

Implement a threshold-based rebalancing strategy and compare its cost-benefit against a simple calendar-based approach.

Calculate optimal hedge ratios for multi-asset portfolios using beta and correlation analysis.

Develop a quantitative framework for selecting and sizing tail risk hedges (e.g., OTM puts, put spreads) based on portfolio VaR/CVaR and a defined cost budget.

Formulate a rule-based dynamic hedging strategy that adjusts portfolio protection based on a specific market volatility indicator (e.g., VIX).

Backtest the performance of a dynamic hedging rule against a static hedge using historical data in Python.

Calculate the effectiveness of implemented hedges by comparing the cost of protection to losses avoided during specific drawdowns.

Build a systematic inventory of currency exposures across all holdings, breaking down both explicit and implicit exposures.

Analyze the impact of currency movements on portfolio returns using decomposition analysis to separate local asset returns from currency effects.

Implement a currency hedge for a specific portfolio exposure using currency ETFs or forward contracts.

Estimate currency over/under-valuation using practical Purchasing Power Parity (PPP) models (e.g., OECD data, Big Mac Index).

Identify and evaluate carry trade opportunities based on interest rate differentials and forward rates for your portfolio's currencies.

Develop a systematic framework to combine signals from PPP and carry trade models to adjust target currency allocations.

Design systematic rules for managing cash holdings across currencies based on rebalancing needs and spending requirements.

Design a specific trade entry rule that integrates a quantitative signal with a fundamental analysis filter.

Implement systematic exit strategies, including trailing stops, volatility-based stops (e.g., using Average True Range), and time-based exits.

Calculate explicit trading costs (commissions) and implicit costs (bid-ask spreads, slippage) from your trading journal.

Analyze liquidity of holdings using metrics like bid-ask spread and daily volume to inform position sizing.

Analyze common execution algorithms (e.g., TWAP, VWAP) for minimizing market impact during rebalancing.

Design a structured checklist for post-trade review, covering execution quality, decision-making process, and adherence to the trade plan.

Apply the post-trade review checklist to analyze winning and losing trades to formulate actionable system improvements.

Define a capital allocation policy between the core portfolio and the speculative satellite (e.g., fixed percentage, risk-budgeting).

Calculate the contribution of the speculative satellite to the total portfolio's key risk metrics (e.g., VaR, expected shortfall, drawdown).

Design rules for transferring capital between the core and satellite portfolios based on performance and risk thresholds.

Implement a Kelly Criterion-based position sizing framework that accounts for win rate, expectancy, and portfolio volatility constraints.

Design the data model and architecture for an automated portfolio performance dashboard, defining data sources and schema.

Implement the core calculation engine for the dashboard, automating the computation of key metrics from a transaction log.

Develop the front-end of the performance dashboard, visualizing key metrics and historical trends using charts and summary tables.