Event-driven, long-only active management. Rigorous analysis published transparently.
I graduated from Temple University in 2023 with a BBA in Management Information Systems. I'm a self taught coder turned capital markets enthusiast — I made my first investment in the stock market at the age of 18, and the markets have remained a passion of mine ever since.
Today, I am a CFA L2 Candidate working in investment banking, where I have the privilege to work on high-profile deals — But I like to document the real fun here
My investment policy from work serves as a foundation for the mandate of my portfolio: A long-only portfolio with a minimum holding period of at least 30 days on every position on a FIFO basis, with a goal to return more than my benchmark - $QQQ - on an annual basis. The portfolio has no constraints on single security weightings, sector/asset class allocations, or degrees to which leverage is used. Derivatives can only be used as hedges while holding the underlying security, unless buying naked LEAP calls.
My strategy can be described as event-driven, long-only active management. I often like to gain exposure to leverage through the use of leveraged single security ETF's, as well as hedge exposure through covered/collateralised options. I build high-conviction positions anchored to identifiable near-to-medium term catalysts with an emphasis on market sentiment.
Publishing research, trade ideas, and performance forces a discipline that private accounts don't demand, this is my attempt at being transparent while delivering alpha.
I built every tool I use myself — from the mark-to-market NAV model tracking my portfolio to the Python scripts that compute Sharpe ratios, information ratios, and drawdown analysis. This site is part of that same commitment: publishing the work transparently, tracking live trades in real time, and holding myself accountable to the numbers.
— Mandate statement coming soon —
The portfolio takes long equity positions sized around identifiable near-term events — corporate actions, earnings inflections, and structural dislocations — where fundamental analysis suggests the market has materially mispriced the probability or magnitude of an outcome. Equity exposure is actively managed through the use of exchange-traded options: covered calls are written to generate premium income and reduce effective cost basis, while selective long option positions are used to express asymmetric directional views with defined downside.
This section will outline the formal investment mandate including universe, constraints, and risk parameters.
Every position is evaluated within a rigorous quantitative framework. Performance is measured on a time-weighted basis to isolate investment returns from the effects of external cash flows. Risk is assessed continuously across multiple dimensions — annualised volatility, downside deviation, maximum drawdown, and Sharpe and Sortino ratios — benchmarked against the Nasdaq-100. Position sizing reflects conviction, liquidity, and correlation to existing holdings. All models, P&L attribution, and risk metrics are built and maintained internally.
| Monthly std dev | 14.95% |
| Ann. volatility | 51.80% |
| Downside std dev | 32.52% |
| Max drawdown | -20.64% |
| Sharpe | 0.688 |
| Sortino | 0.708 |
| Info ratio vs QQQ | 0.410 |
| Active return (ann.) | +20.22% |
| Tracking error | 49.32% |
| Months vs QQQ | 7 / 12 |
| Ann. return | +31.04% |
| Monthly std dev | 13.50% |
| Ann. volatility | 46.77% |
| Downside std dev | 31.01% |
| Max drawdown (port) | -20.64% |
| Max drawdown (QQQ) | -10.22% |
| Sharpe (portfolio) | 0.707 |
| Sharpe (QQQ) | 0.430 |
| Sortino (portfolio) | 0.802 |
| Sortino (QQQ) | 0.437 |
| Info ratio vs QQQ | 0.589 |
| Active return (ann.) | +26.45% |
| Tracking error | 44.93% |
| Months vs QQQ | 9 / 15 |
| Month | Cash | Positions | Total NAV | Net CF | Portfolio | QQQ | Active |
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Live portfolio positions with sizing, average cost, and current mark. Updated alongside the monthly performance tracker.
Timestamped write-ups on specific trade theses — the catalyst, the sizing rationale, the expected outcome, and the P&L realised. Published before entry, not after.
Active positions being tracked in real time — entry price, current price, thesis status, and any updates as the trade evolves.
A periodic letter covering markets, specific ideas under consideration, and reflections on what worked and what didn't. Analytical, not promotional.
Valuation frameworks and DCF models built in Python and Excel. Downloadable, documented, and open to scrutiny.
Scripts and notebooks for portfolio analytics, risk metrics, and data analysis — the same tools used to run this site's performance calculations.
Side projects at the intersection of finance, data, and code — research notes, sector deep-dives, and anything else worth publishing.