Tail Pricing Strategy API

Endpoint

curl -H "X-Api-Key: YOUR_API_KEY" \
  "https://lab.flashalpha.com/v1/strategies/tail-pricing/SPY?expiry=2026-06-19"

import requests

resp = requests.get(
    "https://lab.flashalpha.com/v1/strategies/tail-pricing/SPY",
    params={"expiry": "2026-06-19"},
    headers={"X-Api-Key": "YOUR_API_KEY"}
)
data = resp.json()
print(f"{data['regime']} downside_skew={data['metrics']['downside_skew']}")

const resp = await fetch(
  "https://lab.flashalpha.com/v1/strategies/tail-pricing/SPY?expiry=2026-06-19",
  { headers: { "X-Api-Key": "YOUR_API_KEY" } }
);
const data = await resp.json();
console.log(`${data.regime} downside_skew=${data.metrics.downside_skew}`);

Response

Returns the standard strategy decision envelope. This is a pure-signal endpoint, so no structure is selected and decision is neutral. Only metrics and regime change per strategy.

{
  "strategy": "tail_pricing",
  "symbol": "SPY",
  "timestamp": "2026-06-05T14:32:11Z",
  "decision": "neutral",
  "score": 58,
  "confidence": 0.79,
  "regime": "expensive_tail",
  "best_structures": [],
  "metrics": {
    "expiry": "2026-06-19",
    "days_to_expiry": 14,
    "atm_iv": 0.142,
    "put_tail_iv_10d": 0.214,
    "call_tail_iv_10d": 0.118,
    "downside_skew": 0.072,
    "tail_asymmetry": 0.096,
    "sample_count": 38,
    "underlying_price": 589.12
  },
  "risk_flags": [],
  "why": ["10-delta put IV trades 7.2 vol points over ATM (rich crash tail)."],
  "avoid_if": ["The 10-delta put or ATM quote is stale or unpriceable."],
  "data_quality": { "score": 86, "warnings": [] }
}

Strategy-specific fields

Notable metrics keys (in addition to the always-present underlying_price):

regime values: expensive_tail, cheap_tail, symmetric_tail; plus no_tail_data (insufficient-data case).

Errors

About

This strategy is a pure-signal read of downside-tail pricing for a single expiry. It compares 10-delta put IV against ATM to measure downside skew, reports tail asymmetry, and classifies whether the crash tail is rich or cheap.

It returns the standard strategy decision envelope shared by all Strategy Signals endpoints. As a signal-only read no structure is selected and decision is neutral. When the ATM or 10-delta put quote is missing, it returns decision: insufficient_data with regime: no_tail_data.

Common Use Cases

• Time crash-hedge buys - act when decision flags a cheap tail and regime reads underpriced to buy convexity at a discount
• Fund rich tails - when the tail reads expensive, pull best_structures[] for a put-spread or ratio sale to harvest the premium
• Drop in the proposed structure - use best_structures[] legs, strikes, and cost directly rather than hand-building the hedge
• Rank the watchlist - sort a universe by score to surface the cheapest tails to own and the richest to sell
• Size on conviction - scale the hedge by score and confidence instead of a fixed notional
• Respect the guardrails - honor risk_flags[] and require a clean data_quality before trusting a deep-wing price

Related

Related reading

• Vol-of-vol, convexity & charm dealer flow - the convexity mechanics that drive tail pricing
• ML on options data: a quant guide - feed tail-pricing features into a systematic model
• Alpha decay: which signals erode - how fast a tail mispricing closes

Complementary endpoints

• Strategy Signals overview - the shared decision envelope across every strategy
• Skew - the wing slope that sets the tail's relative richness
• Surface Anomaly - confirm the tail dislocation against the full fitted surface