9. Performance Tracking and KPIs - IBD Growth Strategy

9.1 $10,000 hypothetical framework¶

Groups: ira_top5, brokerage_top5, conviction9 — from top 5 IRA by Risk_Adj_Score, top 5 Brokerage by Risk_Adj_Leader_Score, and up to 9 conviction names (from Cross_Strategy_Conviction.csv after aligning risk column).
Allocation: $10,000 split equally across positions in that group (10000 / n).
Shares: entry_value / entry_price at first appearance; rolls forward with state.

my_ira, my_brokerage — from Etrade_IRA_Report.csv and Etrade_Brokerage_Report.csv; uses real Shares, Cost_Basis, Market_Price.

Entry: First time a ticker appears in the modeled set, state records entry date, prices, SPY at entry.
Exit: Ticker drops from the list → Exit_Status = EXITED for that week’s history row; realized vs SPY still computed for that snapshot.

If performance_history.csv already contains Week_Number equal to the current ISO week (YYYY-Www), snapshot() returns False and does not append (unless python performance_tracker.py --force).

Portfolio_Return_Pct: (\frac{\sum \text{Current_Value}}{\sum \text{Entry_Value}} - 1) for OPEN rows (hypothetical and actual).
SPY_Return_Pct: SPY return from earliest open entry date in group to now (see _kpi_for_rows).
Alpha: Portfolio return minus SPY return (same window).
Avg_Risk_Adj_Score: Mean of per-row risk scores for open positions.
Portfolio_WACC: Value-weighted WACC_Stock.
Hit_Rate (actual only): % of holdings with Status in ON_LIST or LEADER.
Conviction_Pct (actual only): % of holdings with Cross_Strategy true.