How an AI agent measures closing line value

import random
import statistics

def american_to_decimal(a: int) -> float:
    return 1 + a / 100 if a > 0 else 1 + 100 / abs(a)

def clv_points(entry: int, close_side: int, close_other: int) -> float:
    """Closing probability with the margin removed, minus the break-even probability of the price taken."""
    p_entry = 1 / american_to_decimal(entry)
    side = 1 / american_to_decimal(close_side)
    other = 1 / american_to_decimal(close_other)
    return side / (side + other) - p_entry

def mean_ci(values, n_boot=2000, seed=7):
    """Mean and a 95% bootstrap interval. Report the sample size next to it."""
    rng = random.Random(seed)
    means = sorted(statistics.fmean(rng.choices(values, k=len(values))) for _ in range(n_boot))
    return statistics.fmean(values), means[int(0.025 * n_boot)], means[int(0.975 * n_boot)]

# One signal: price taken -105; the market closed -120 on this side and +100 on the other.
print(round(clv_points(-105, -120, 100), 4))  # 0.0095, about +0.95 probability points

# 1. When a signal arrives, store it with the price YOUR feed offered at that moment.
log.append({
    "signal_id": signal["signal_id"],
    "content_hash": signal["content_hash"],
    "issued_at": signal["issued_at"],
    "close_at": signal["market_close_reference"]["reference_time"],  # a timestamp, never a price
    "entry_price": my_feed.price(signal["game_id"], signal["market_type"], signal["side"]),
})

# 2. At close_at, read the closing prices of both sides from the SAME feed.
for row in log.due():
    close_side, close_other = my_feed.closing_prices(row)
    row["clv"] = clv_points(row["entry_price"], close_side, close_other)

# 3. After a few hundred signals, report the mean, its interval and n.
print(mean_ci([r["clv"] for r in log.done()]))