| Automated tests | 2,100+ (unit, integration, and property-based via Hypothesis), plus two Playwright suites: one against a stubbed API, one against the real pre-rendered site |
| Core engine code | 21,000+ lines (src/quantpulse/) — ingestion, analysis, storage, API |
| Free data sources integrated | 8 feed each refresh — Yahoo Finance, Finnhub, FRED, SEC EDGAR (filings + 13F), GDELT, Reddit, financial news RSS, Wikipedia — plus a 9th (a historical S&P 500 constituents dataset) used only for the one-time cold-start backfill |
| Database | 26 tables, 19 Alembic migrations, every one reversible (alembic downgrade round-trips clean) |
| Composite scoring | 7 categories (fundamentals, technicals, analyst consensus, news sentiment, momentum, industry/macro, smart money) × 6 investor-profile presets — four differ by category weights alone, and two (income, conservative) genuinely re-score a category, so each refresh stores their rankings separately |
| Chart pattern families detected | 4 — head-and-shoulders, double top/bottom, triangles/wedges/channels, cup-and-handle — detected across the whole universe on every refresh and shown per stock with a confidence score |
| Forecasting approaches | 4 — random-walk baseline, ARIMA/SARIMA, gradient-boosted ML, and a Monte Carlo fan chart. The first three are graded out-of-sample against the naive baseline; Monte Carlo deliberately is not, because it simulates the same random walk the baseline evaluates in closed form (grading it would be grading the baseline against itself) |
| Backtest confidence | Sharpe & CAGR reported with moving-block bootstrap confidence intervals, never a bare point estimate |
| Portfolio optimization methods | 3 — mean-variance (MPT), Hierarchical Risk Parity, and Black-Litterman driven by the app's own composite scores, each with a concrete buy/sell trade list |
| Front ends | 2 — a 7-page Streamlit app (full app, incl. Portfolio Manager and the LLM narration layer) and a 6-page React + TypeScript SPA over a 15-endpoint read-only FastAPI. The two share every number: both read the same storage.persistence functions, the React screener's client-side re-weighting was checked against Streamlit's across all 503 names, and the numbers a stock shows on both (beta, Sharpe, Sortino, forecast prices, Kelly size) are asserted equal by test. The SPA omits the LLM narration and the Portfolio Manager's optimisers, rebalancing and history; its Portfolio page (FIFO lots, P/L, per-holding suggestions, concentration, risk) runs in the browser and is pinned to the engine's own results by a golden-file test in both languages |
