profit_risk_ratio is fabricated (max_profit / 1000) and used to rank strategy recommendations

[bradsmithmba]

Summary

src/api/enhanced_strategy_recommender.py computes profit_risk_ratio by dividing max_profit by the literal constant 1000. This is not a meaningful financial metric. Recommendations are sorted by this value, so the ranking is driven by a fabricated number.

Location

src/api/enhanced_strategy_recommender.py, line ~516:

profit_risk_ratio = round(max_profit / 1000, 2)

And later, recommendations are sorted:

recommendations.sort(key=lambda r: r.profit_risk_ratio, reverse=True)

Why this is wrong

A meaningful profit/risk ratio should be max_profit / max_loss (reward-to-risk). Dividing by the constant 1000 produces a ratio that scales with the absolute dollar value of max profit, not with how it compares to the potential loss. Two strategies with the same reward-to-risk profile but different position sizes will rank differently, and a high-dollar-risk strategy with large gross profit will rank above a lower-risk strategy with better actual reward/risk.

The existing max_profit calculation also has a known unit mismatch bug (see Issue #3 in BUGS_TO_FIX.md), so this ratio is doubly wrong: wrong formula applied to wrong inputs.

Correct fix

profit_risk_ratio = round(max_profit / max_loss, 2) if max_loss > 0 else float('inf')

Also fix the upstream max_profit and max_loss unit mismatch (covered call mixes per-share premium with 100-multiplied capital gain) before relying on this ratio for ranking.