Spaces:
Sleeping
Sleeping
File size: 7,276 Bytes
b6d53e2 da46dbf 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 | from __future__ import annotations
from datetime import datetime
import pandas as pd
from .arbitrage import (
assign_candidate_confidence,
scan_arbitrage_candidates,
summarize_arbitrage,
)
from .data import fetch_option_snapshot
from .density import estimate_rn_density, select_density_slice
from .features import compute_features
from .no_arb import run_all_checks, run_checks_by_expiry, select_best_quality_expiry
from .strategy import generate_candidates, score_candidates, select_best
from .types import OptionSnapshot
def _available_expiries(snapshot: OptionSnapshot) -> pd.DataFrame:
expiries = (
snapshot.options[snapshot.options["option_type"] == "call"]["expiry"]
.dropna()
.drop_duplicates()
.sort_values()
)
now = pd.Timestamp(snapshot.snapshot_time).tz_localize(None)
df = pd.DataFrame({"expiry": pd.to_datetime(expiries)})
df["dte_days"] = (df["expiry"] - now).dt.days
return df.reset_index(drop=True)
def _select_expiry_by_target_dte(snapshot: OptionSnapshot, target_dte: int) -> datetime:
exp = _available_expiries(snapshot)
if exp.empty:
raise ValueError("No expiries available")
valid = exp[exp["dte_days"] >= 0].copy()
if valid.empty:
valid = exp.copy()
valid["dist"] = (valid["dte_days"] - int(target_dte)).abs()
chosen = valid.sort_values(["dist", "dte_days"]).iloc[0]["expiry"]
return pd.to_datetime(chosen).to_pydatetime()
def analyze_snapshot(
snapshot: OptionSnapshot,
expiry: datetime | None = None,
moneyness_band: float = 0.2,
min_open_interest: int = 1,
min_volume: int = 0,
smooth_window: int = 3,
risk_lambda: float = 0.5,
arb_min_edge: float = 0.0,
arb_min_edge_per_width: float = 0.0,
arb_min_leg_open_interest: int = 0,
) -> dict[str, object]:
diagnostics = run_all_checks(snapshot)
per_expiry_diag = run_checks_by_expiry(snapshot)
available_expiries = _available_expiries(snapshot)
diag_df = pd.DataFrame(
[
{
"check": d.name,
"passed": d.passed,
"violations": d.violations,
"comparisons": d.comparisons,
"violation_rate": d.violation_rate,
"details": d.details,
}
for d in diagnostics
]
)
per_expiry_summary = (
per_expiry_diag.groupby("expiry", as_index=False)
.agg(
failed_checks=("passed", lambda s: int((~s).sum())),
total_checks=("passed", "count"),
mean_violation_rate=("violation_rate", "mean"),
)
.sort_values("mean_violation_rate")
.reset_index(drop=True)
)
if not available_expiries.empty:
per_expiry_summary = per_expiry_summary.merge(
available_expiries, on="expiry", how="left"
)
if expiry is None:
expiry = select_best_quality_expiry(snapshot)
strikes, calls, selected_expiry = select_density_slice(
snapshot.options,
spot=snapshot.spot,
expiry=expiry,
moneyness_band=moneyness_band,
min_open_interest=min_open_interest,
min_volume=min_volume,
)
density = estimate_rn_density(
strikes=strikes,
call_prices=calls,
expiry=selected_expiry,
smooth_window=smooth_window,
)
features = compute_features(snapshot, density)
candidates = generate_candidates(snapshot.spot, density, snapshot.options)
scored = score_candidates(
candidates, density=density, spot0=snapshot.spot, risk_lambda=risk_lambda
)
best = select_best(scored)
density_df = pd.DataFrame(
{
"strike": density.strikes,
"density": density.density,
}
)
scored_df = pd.DataFrame(
[
{
"strategy": s.candidate.name,
"expected_payoff": s.expected_payoff,
"downside_q05": s.downside_q05,
"objective": s.objective,
}
for s in scored
]
)
arbitrage_candidates = scan_arbitrage_candidates(
snapshot.options,
expiry=pd.to_datetime(selected_expiry),
min_edge=arb_min_edge,
min_edge_per_width=arb_min_edge_per_width,
min_leg_open_interest=arb_min_leg_open_interest,
spot=snapshot.spot,
)
exp_quality = per_expiry_summary[
per_expiry_summary["expiry"] == pd.to_datetime(selected_expiry)
]
if exp_quality.empty:
mean_violation_rate = (
float(diag_df["violation_rate"].mean()) if not diag_df.empty else 1.0
)
failed_checks = int((~diag_df["passed"]).sum()) if not diag_df.empty else 3
else:
mean_violation_rate = float(exp_quality.iloc[0]["mean_violation_rate"])
failed_checks = int(exp_quality.iloc[0]["failed_checks"])
arbitrage_candidates = assign_candidate_confidence(
arbitrage_candidates,
mean_violation_rate=mean_violation_rate,
failed_checks=failed_checks,
)
arbitrage_summary = summarize_arbitrage(arbitrage_candidates)
return {
"ticker": snapshot.ticker,
"spot": snapshot.spot,
"snapshot_time": snapshot.snapshot_time,
"selected_expiry": selected_expiry,
"diagnostics": diag_df,
"diagnostics_by_expiry": per_expiry_diag,
"diagnostics_by_expiry_summary": per_expiry_summary,
"available_expiries": available_expiries,
"density": density_df,
"features": features,
"scored": scored_df,
"best_strategy": {
"name": best.candidate.name,
"params": best.candidate.params,
"expected_payoff": best.expected_payoff,
"downside_q05": best.downside_q05,
"objective": best.objective,
},
"arbitrage_candidates": arbitrage_candidates,
"arbitrage_summary": arbitrage_summary,
}
def analyze_ticker(
ticker: str,
max_expiries: int = 2,
expiry: datetime | None = None,
moneyness_band: float = 0.2,
min_open_interest: int = 1,
min_volume: int = 0,
smooth_window: int = 3,
risk_lambda: float = 0.5,
expiry_mode: str = "auto",
target_dte: int = 30,
arb_min_edge: float = 0.0,
arb_min_edge_per_width: float = 0.0,
arb_min_leg_open_interest: int = 0,
) -> dict[str, object]:
snapshot = fetch_option_snapshot(ticker=ticker, max_expiries=max_expiries)
selected_expiry = expiry
if expiry_mode == "target_dte":
selected_expiry = _select_expiry_by_target_dte(snapshot, target_dte)
elif expiry_mode == "auto":
selected_expiry = expiry
elif expiry_mode == "manual":
selected_expiry = expiry
else:
raise ValueError(f"Unknown expiry_mode: {expiry_mode}")
return analyze_snapshot(
snapshot=snapshot,
expiry=selected_expiry,
moneyness_band=moneyness_band,
min_open_interest=min_open_interest,
min_volume=min_volume,
smooth_window=smooth_window,
risk_lambda=risk_lambda,
arb_min_edge=arb_min_edge,
arb_min_edge_per_width=arb_min_edge_per_width,
arb_min_leg_open_interest=arb_min_leg_open_interest,
)
|