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b6d53e2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | from __future__ import annotations
from datetime import datetime
import numpy as np
import pandas as pd
from .types import DiagnosticResult, OptionSnapshot
def _validate_calls_df(calls_df: pd.DataFrame) -> pd.DataFrame:
required = {"expiry", "strike", "mid"}
if not required.issubset(calls_df.columns):
missing = sorted(required - set(calls_df.columns))
raise ValueError(f"Missing required columns: {missing}")
return calls_df.sort_values(["expiry", "strike"]).reset_index(drop=True)
def check_monotonicity(calls_df: pd.DataFrame, tol: float = 1e-9) -> DiagnosticResult:
df = _validate_calls_df(calls_df)
violations = 0
comparisons = 0
for _, grp in df.groupby("expiry"):
mids = grp["mid"].to_numpy(dtype=float)
diffs = np.diff(mids)
comparisons += int(len(diffs))
violations += int(np.sum(diffs > tol))
violation_rate = float(violations / comparisons) if comparisons else 0.0
return DiagnosticResult(
name="monotonicity",
passed=violations == 0,
violations=violations,
comparisons=comparisons,
violation_rate=violation_rate,
details="Call prices should be non-increasing in strike.",
)
def check_convexity(calls_df: pd.DataFrame, tol: float = 1e-8) -> DiagnosticResult:
df = _validate_calls_df(calls_df)
violations = 0
comparisons = 0
for _, grp in df.groupby("expiry"):
g = grp.sort_values("strike")
k = g["strike"].to_numpy(dtype=float)
c = g["mid"].to_numpy(dtype=float)
if len(k) < 3:
continue
dk = np.diff(k)
valid = dk > 0
if not np.all(valid):
keep = np.concatenate(([True], valid))
k = k[keep]
c = c[keep]
if len(k) < 3:
continue
dk = np.diff(k)
slope_left = (c[1:-1] - c[:-2]) / (k[1:-1] - k[:-2])
slope_right = (c[2:] - c[1:-1]) / (k[2:] - k[1:-1])
comparisons += int(len(slope_right))
violations += int(np.sum((slope_right - slope_left) < -tol))
violation_rate = float(violations / comparisons) if comparisons else 0.0
return DiagnosticResult(
name="convexity",
passed=violations == 0,
violations=violations,
comparisons=comparisons,
violation_rate=violation_rate,
details="Call strike slopes should be non-decreasing (uneven-grid convexity).",
)
def check_calendar(calls_df: pd.DataFrame, tol: float = 1e-8) -> DiagnosticResult:
df = _validate_calls_df(calls_df)
strikes = sorted(set(df["strike"].tolist()))
violations = 0
comparisons = 0
for strike in strikes:
s = df[df["strike"] == strike].sort_values("expiry")
if len(s) < 2:
continue
mids = s["mid"].to_numpy(dtype=float)
diffs = np.diff(mids)
comparisons += int(len(diffs))
violations += int(np.sum(diffs < -tol))
violation_rate = float(violations / comparisons) if comparisons else 0.0
return DiagnosticResult(
name="calendar",
passed=violations == 0,
violations=violations,
comparisons=comparisons,
violation_rate=violation_rate,
details="Call prices should be non-decreasing with maturity at same strike.",
)
def run_all_checks(snapshot: OptionSnapshot) -> list[DiagnosticResult]:
calls = snapshot.options[snapshot.options["option_type"] == "call"].copy()
return [
check_monotonicity(calls),
check_convexity(calls),
check_calendar(calls),
]
def run_checks_by_expiry(snapshot: OptionSnapshot) -> pd.DataFrame:
calls = snapshot.options[snapshot.options["option_type"] == "call"].copy()
rows: list[dict[str, object]] = []
for expiry, grp in calls.groupby("expiry"):
mini = OptionSnapshot(
ticker=snapshot.ticker,
snapshot_time=snapshot.snapshot_time,
spot=snapshot.spot,
options=grp.copy(),
)
for d in run_all_checks(mini):
rows.append(
{
"expiry": pd.to_datetime(expiry),
"check": d.name,
"passed": d.passed,
"violations": d.violations,
"comparisons": d.comparisons,
"violation_rate": d.violation_rate,
"details": d.details,
}
)
return pd.DataFrame(rows).sort_values(["expiry", "check"]).reset_index(drop=True)
def select_best_quality_expiry(snapshot: OptionSnapshot) -> datetime:
per_expiry = run_checks_by_expiry(snapshot)
if per_expiry.empty:
raise ValueError("No per-expiry diagnostics available")
scores = (
per_expiry.groupby("expiry")["violation_rate"]
.mean()
.sort_values(ascending=True)
)
return pd.to_datetime(scores.index[0]).to_pydatetime()
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