test-teamlisted

# /test-team — Agent Team Coordination Test Direct coordination test between agents using Agent Teams. Demonstrates that the output of one agent feeds directly into the next. ## Team **Task**: Implement Kelly Criterion in Python based on prior research. ### Agent 1 — research-analyst (first) Researches the Kelly Criterion and produces a structured summary with: - Exact mathematical formula: `f* = (bp - q) / b` where `b` = net odds, `p` = win probability, `q` = 1 - p - Input parameters and their valid ranges - Half-Kelly variant (f* / 2) and when to prefer it - Use cases in systematic trading (position sizing) - Known limitations (sensitivity to p estimation) Writes result to: `tasks/results/research-kelly-[timestamp].md` ### Agent 2 — python-specialist (after Agent 1) Reads the research-analyst result from `tasks/results/research-kelly-*.md` and based on it implements: ```python def kelly_criterion(win_prob: float, win_loss_ratio: float, half_kelly: bool = True) -> float: """ Calculates the optimal position size according to Kelly Criterion. """ ``` Writes result to: `tasks/results/python-kelly-[timestamp].md` ## Coordination protocol ``` research-analyst → tasks/results/research-kelly-*.md ↓ python-specialist reads that file → implements function ``` The python-specialist does NOT start until research-analyst has written its result. ## Execution Launch both agents as a coordinated team. When done, show: 1. Research s