Zero-DTE API: Complete Guide to the 0DTE Analytics Endpoint

The Endpoint

GET https://lab.flashalpha.com/v1/exposure/zero-dte/{symbol}

Requires the Growth plan ($239/mo) or higher. Auth via X-Api-Key header. One parameter: the underlying symbol (SPY, QQQ, SPX, TSLA, etc.).

import requests

resp = requests.get(
    "https://lab.flashalpha.com/v1/exposure/zero-dte/SPY",
    headers={"X-Api-Key": "YOUR_API_KEY"}
)
data = resp.json()

The response contains 10 top-level sections. Let's walk through each one.

1. Context - Time and Market State

Every 0DTE decision depends on where you are in the trading day. These fields tell you:

print(f"SPY @ ${data['underlying_price']}")
print(f"Time to close: {data['time_to_close_hours']:.1f}h ({data['time_to_close_pct']:.0f}% elapsed)")

2. Gamma Regime

The regime object tells you whether dealers are suppressing or amplifying moves - the single most important intraday signal.

regime = data["regime"]
print(f"Regime: {regime['label']}")
print(f"Gamma flip: ${regime['gamma_flip']}")
print(f"Spot is {regime['spot_vs_flip']} flip by {regime['spot_to_flip_pct']:.2f}%")

3. Exposure Aggregates

The exposures object gives you the 0DTE Greek exposure totals and how they compare to the full options chain.

exp = data["exposures"]
pct = exp["pct_of_total_gex"]
print(f"0DTE Net GEX: ${exp['net_gex']:,.0f}")
print(f"0DTE accounts for {pct:.1f}% of total GEX")

if pct > 50:
    print("→ 0DTE dominates  -  use 0DTE levels for intraday trading")
else:
    print("→ Full chain dominates  -  check /v1/exposure/levels for broader levels")

4. Expected Move

The expected_move object gives you the market-implied range for the rest of the day - it shrinks in real-time as the close approaches.

em = data["expected_move"]
print(f"Expected range: ${em['lower_bound']:.2f} - ${em['upper_bound']:.2f}")
print(f"Remaining 1σ: ±${em['remaining_1sd_dollars']:.2f} ({em['remaining_1sd_pct']:.2f}%)")
print(f"Straddle price: ${em['straddle_price']:.2f}")

5. Pin Risk

The pin_risk object quantifies the probability of price being "pinned" to a high-OI strike near the close.

The pin score composites four factors:

• OI Concentration (30%) - top 3 strikes' share of total 0DTE OI
• Magnet Proximity (25%) - how close spot is to the highest-GEX strike
• Time Remaining (25%) - pin risk increases as expiration approaches
• Gamma Magnitude (20%) - higher gamma = stronger hedging force toward the pin

pin = data["pin_risk"]
print(f"Magnet Strike: ${pin['magnet_strike']}")
print(f"Pin Score: {pin['pin_score']}/100")
print(f"Max Pain: ${pin['max_pain']}")
print(f"Distance to magnet: {pin['distance_to_magnet_pct']:.2f}%")
print(f"Top 3 OI concentration: {pin['oi_concentration_top3_pct']}%")

if pin["pin_score"] > 70 and data["time_to_close_hours"] < 2:
    print("→ HIGH PIN RISK  -  price likely gravitates to magnet strike")

6. Dealer Hedging Estimates

The hedging object estimates how many shares dealers must trade to delta-hedge their 0DTE positions for various price move scenarios.

Each scenario returns dealer_shares_to_trade, direction (buy/sell), and notional_usd. In positive gamma, dealers sell into rallies and buy dips (liquidity providers). In negative gamma, they chase price (liquidity takers).

for scenario, label in [("spot_up_half_pct", "+0.5%"), ("spot_down_half_pct", "-0.5%"),
                         ("spot_up_1pct", "+1.0%"), ("spot_down_1pct", "-1.0%")]:
    h = data["hedging"][scenario]
    print(f"If SPY moves {label}: dealers {h['direction']} {abs(h['dealer_shares_to_trade']):,} shares (${abs(h['notional_usd']):,.0f})")

7. Theta Decay & Gamma Acceleration

The decay object captures the non-linear time decay of 0DTE options - theta bleeds slowly in the morning and accelerates dramatically into the close.

decay = data["decay"]
print(f"Net Theta: ${decay['net_theta_dollars']:,.0f}")
print(f"Theta/Hour: ${decay['theta_per_hour_remaining']:,.0f}")
print(f"Gamma Acceleration: {decay['gamma_acceleration']}x vs 7DTE")
print(f"Charm: {decay['charm_description']}")

8. Volatility Context

The vol_context object compares 0DTE implied volatility to longer-dated vol and vanna exposure.

vc = data["vol_context"]
ratio = vc["iv_ratio_0dte_7dte"]
print(f"0DTE IV: {vc['zero_dte_atm_iv']}% | 7DTE IV: {vc['seven_dte_atm_iv']}%")
print(f"Ratio: {ratio:.2f}  -  {'EVENT PREMIUM' if ratio > 1.0 else 'normal'}")
print(f"Vanna: {vc['vanna_interpretation']}")

9. Flow Data

The flow object provides volume and open interest for 0DTE contracts.

10. Key Levels & Per-Strike Breakdown

The levels object identifies the most important intraday support and resistance from 0DTE positioning.

The strikes array provides per-strike detail for contracts within strike_range of spot. Each strike includes:

{
    "strike": 590,
    "call_gex": 450000000, "put_gex": -380000000, "net_gex": 70000000,
    "call_dex": 12500000, "put_dex": -15000000, "net_dex": -2500000,
    "call_oi": 25000, "put_oi": 30000,
    "call_volume": 15000, "put_volume": 12000,
    "call_iv": 0.18, "put_iv": 0.19,
    "call_delta": 0.50, "put_delta": -0.50,
    "call_gamma": 0.025, "put_gamma": 0.025,
    "call_theta": -1.0, "put_theta": -1.0
}

Putting It All Together - Intraday Signal Builder

Combine the response fields into a single decision framework:

import requests

def get_0dte_signal(symbol, api_key):
    resp = requests.get(
        f"https://lab.flashalpha.com/v1/exposure/zero-dte/{symbol}",
        headers={"X-Api-Key": api_key}
    )
    d = resp.json()

    if d.get("no_zero_dte"):
        return {"signal": "no_data", "reason": d["message"]}

    regime = d["regime"]["label"]
    pin_score = d["pin_risk"]["pin_score"]
    pct_total = d["exposures"]["pct_of_total_gex"]
    hours_left = d["time_to_close_hours"]

    # High-conviction pin setup
    if pin_score > 70 and hours_left < 2 and regime == "positive_gamma":
        return {
            "signal": "PIN_LIKELY",
            "target": d["pin_risk"]["magnet_strike"],
            "confidence": pin_score,
            "range": [d["expected_move"]["lower_bound"], d["expected_move"]["upper_bound"]],
            "action": "Fade moves toward magnet strike. Sell premium."
        }

    # Negative gamma breakout risk
    if regime == "negative_gamma" and pct_total > 50:
        return {
            "signal": "BREAKOUT_RISK",
            "flip": d["regime"]["gamma_flip"],
            "hedging_at_1pct": d["hedging"]["spot_down_1pct"]["notional_usd"],
            "action": "Trade breakouts, not mean reversion. Dealers amplify moves."
        }

    # Default: range-bound with decay
    return {
        "signal": "RANGE_BOUND",
        "walls": [d["levels"]["put_wall"], d["levels"]["call_wall"]],
        "theta_per_hour": d["decay"]["theta_per_hour_remaining"],
        "action": "Sell premium between put wall and call wall."
    }

signal = get_0dte_signal("SPY", "YOUR_API_KEY")
print(signal)

When There Is No 0DTE Expiry

Not every symbol has 0DTE every day. SPY has 0DTE every trading day; FlashAlpha's SPX 0DTE coverage is limited (it returns no_zero_dte on days SPX has no same-day expiry). When there is no 0DTE expiry today, the API returns:

{
    "symbol": "SPY",
    "no_zero_dte": true,
    "message": "No 0DTE expiry for SPY today (Tuesday). Next expiry: 2026-03-18.",
    "next_zero_dte_expiry": "2026-03-18"
}

Always check for no_zero_dte before accessing other fields.

Access & Pricing

View Pricing & Sign Up Full API Reference Try It in the Playground

Related Reading

• Zero-DTE API Reference - full endpoint specification
• 0DTE Gamma Exposure & Pin Risk - deep dive on 0DTE mechanics
• What Is GEX? - fundamentals of gamma exposure
• Vanna & Charm Guide - second-order Greeks that drive 0DTE flows
• Interactive GEX Tool - visualize gamma exposure by strike
• Max Pain Explained - pin risk theory and dealer alignment
• Max Pain Trading Strategies - 0DTE pin plays and expiration setups

The zero-DTE endpoint gives you everything needed to trade same-day expirations with a quantitative edge: gamma regime for direction bias, pin risk for convergence plays, expected move for strike selection, dealer hedging for flow context, and theta acceleration for timing. One API call, one JSON response, all updated in real-time. See the full API reference or try it in the playground.