chemavxandClaude Fable 5
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sharpe_ratio was hardcoded to 0.0 in MetricsTracker and exposed as 'or 0' in /api/summary. With only 1 resolved trade (~40 flat days plus one +299 jump) any computed Sharpe is statistically meaningless, so: - bot/metrics/sharpe.py: annualized Sharpe (sqrt(365)) from daily total_pnl closes, normalized by bankroll; sharpe_with_gate() returns None + status until >=30 days observed AND >=10 resolved trades. - Database.get_daily_pnl_closes(): last metrics_daily snapshot per UTC day, oldest first — the return series input. - MetricsTracker: stores the real (gated) Sharpe in the snapshot, NULL below the gate; log line now includes sharpe. - /api/summary: live Sharpe + sharpe_status/days_observed/min_* fields explaining why it is null; resolved_count now live from COUNT(*). - promotion_ready: requires resolved>=10, days>=30, and non-null win_rate/calibration/sharpe plus existing thresholds — a single lucky resolved trade can no longer promote. - Dashboard Sharpe card shows the insufficient-sample explanation when null instead of a bare em dash. Tests: 13 new in tests/test_sharpe_gate.py (formula, gate, API contract, tracker snapshot); verified failing pre-fix. Suite: 62 passed. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
"""
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Sharpe ratio from the paper portfolio's daily PnL curve, with a minimum-sample gate.
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The input series is the closing total_pnl of each observed UTC day
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(Database.get_daily_pnl_closes). Daily returns are PnL deltas normalized by
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the paper bankroll:
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r_t = (pnl_t − pnl_{t−1}) / bankroll
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Sharpe = mean(r) / sample_std(r) × √365, annualized — prediction markets
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resolve every calendar day, so 365 is used instead of 252 trading days.
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Risk-free rate is taken as 0.
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Gate: with a tiny sample (e.g. 1 resolved trade over a flat curve plus one
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+299 jump) any Sharpe value is statistically meaningless — artificially huge
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or tiny depending on where the jump lands. So no numeric Sharpe is exposed
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until BOTH minimums are met:
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days observed >= MIN_DAYS_OBSERVED (30)
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resolved trades >= MIN_RESOLVED_TRADES (10)
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Below either minimum the value is None with status "insufficient_sample".
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A perfectly flat curve (zero variance) also yields None ("zero_variance"):
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Sharpe is undefined there, not infinite.
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"""
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from statistics import mean, stdev
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from typing import Optional
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MIN_DAYS_OBSERVED = 30
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MIN_RESOLVED_TRADES = 10
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ANNUALIZATION_DAYS = 365
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SHARPE_OK = "ok"
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SHARPE_INSUFFICIENT = "insufficient_sample"
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SHARPE_ZERO_VARIANCE = "zero_variance"
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def daily_returns(daily_pnl_closes: list[float], bankroll: float) -> list[float]:
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"""Bankroll-normalized day-over-day returns from a daily PnL-close series."""
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return [
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(curr - prev) / bankroll
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for prev, curr in zip(daily_pnl_closes, daily_pnl_closes[1:])
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]
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def compute_sharpe(daily_pnl_closes: list[float], bankroll: float) -> Optional[float]:
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"""Annualized Sharpe of the daily PnL curve, or None if undefined.
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None when there are fewer than 2 returns (need 3+ daily closes) or the
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return series has zero variance. No sample-size gate here — see
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sharpe_with_gate() for the exposed value.
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"""
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returns = daily_returns(daily_pnl_closes, bankroll)
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if len(returns) < 2:
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return None
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sd = stdev(returns)
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if sd == 0:
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return None
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return mean(returns) / sd * ANNUALIZATION_DAYS ** 0.5
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def sharpe_with_gate(
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daily_pnl_closes: list[float],
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bankroll: float,
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resolved_count: int,
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) -> tuple[Optional[float], str]:
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"""Return (sharpe, status) applying the minimum-sample gate.
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status: "ok" — sharpe is a meaningful float
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"insufficient_sample" — sample below minimums, sharpe is None
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"zero_variance" — sample OK but flat curve, sharpe is None
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"""
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days_observed = len(daily_pnl_closes)
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if days_observed < MIN_DAYS_OBSERVED or resolved_count < MIN_RESOLVED_TRADES:
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return None, SHARPE_INSUFFICIENT
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sharpe = compute_sharpe(daily_pnl_closes, bankroll)
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if sharpe is None:
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return None, SHARPE_ZERO_VARIANCE
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return sharpe, SHARPE_OK
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