Projects

Models an M&A or financing transaction as a shock through supplier, customer, competitor, credit, market, ownership and technology relationships. The model evaluates base, upside and downside branches through DSCR, leverage, IRR, MOIC, covenant breach and composite risk diagnostics.

Recomputes DCF, debt schedules, DSCR, peak leverage, IRR, NPV and equity value under correlated shocks to revenue growth, EBITDA margin, WACC, interest rates, synergies, FX and regulatory timing.

Generates market diagnostics, investment memos and proposed trades through an AI research layer. A separate rule layer decides whether any order can enter paper execution.

Combines cross asset price monitoring, the US Treasury yield curve, CBOE VIX regime diagnostics, Fama-French factor data, IPO tracking, SEC EDGAR filings, XBRL fundamentals, OHLC charting, probabilistic price scenarios, value screening and a DCF scenario lab.

Pulls SEC EDGAR Company Facts and maps heterogeneous US-GAAP XBRL tags into standardized financial statements. The Python workflow calculates trailing twelve month KPIs and trading multiples including EV to Revenue, EV to EBITDA and P to E.

Runs a rolling momentum research workflow across SPY, QQQ, IWM, EFA, TLT and GLD. Each window selects a lookback from the training sample, applies the selected rule to the following test window and records parameters, trades, equity, drawdown, monthly returns and performance metrics.

Runs a ticker agnostic, driver based unlevered DCF with CAPM derived cost of equity and WACC. The Python implementation models revenue growth, EBIT margin and ROIC with triangular distributions and reports enterprise value, equity value and per share percentiles.

Reconciles Sources and Uses, sizes a multi-tranche capital structure across TLB, second lien, mezzanine and RCF facilities, and models OID, upfront fees, PIK, mandatory amortization, cash sweeps and exit assumptions. Annual coverage, FCCR and deleveraging gates test whether the debt structure remains financeable.