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tomasz dobrowolski
tomasz dobrowolski

Posted on • Originally published at flashalpha.com

Build a 0DTE Options Dashboard with One API Call

#python #tutorial #trading #api

If you've tried building a 0DTE options dashboard from raw market data, you know the pain: intraday chain snapshots, Greeks recomputation every few minutes, pin risk models, gamma aggregation, and infrastructure that needs to run reliably during market hours.

One endpoint replaces all of it: GET /v1/exposure/zero-dte/{symbol}. Pin risk scoring, expected move, gamma acceleration, dealer hedging scenarios, vol context, flow data, and per-strike breakdown. One JSON response.

Why 0DTE Is a Different Engineering Problem

Longer-dated options are stable. Greeks change slowly, hedging flows are predictable, and polling every 15-30 minutes is fine. 0DTE breaks every one of these assumptions.

Property 7DTE 0DTE (Morning) 0DTE (Last Hour)
ATM Gamma 1x (baseline) 2-3x 8-15x
Theta Decay / Hour Slow, linear Moderate 4-5x morning rate
Expected Move Stable Shrinks 30-40% by 2 PM Shrinks 70-80% by 3:30 PM
Pin Risk Negligible Building Dominant
Dealer Hedging per $1 Move Moderate Large Massive

Everything changes fast. A 0DTE dashboard showing stale data is worse than no dashboard. You need an API that recomputes continuously through the session.

Quick Start

Install 

pip install flashalpha
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Python SDK 

from flashalpha import FlashAlpha

fa = FlashAlpha("YOUR_API_KEY")
dte = fa.zero_dte("SPY")

print(f"Gamma regime: {dte['regime']['label']}")
print(f"Expected move: +/-{dte['expected_move']['expected_move_pct']:.2f}%")
print(f"Pin score: {dte['pin_risk']['pin_score']}/100")
print(f"Magnet strike: {dte['pin_risk']['magnet_strike']}")
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cURL 

curl -H "X-Api-Key: YOUR_API_KEY" \
  "https://lab.flashalpha.com/v1/exposure/zero-dte/SPY"

# Custom strike range (default 3% around spot)
curl -H "X-Api-Key: YOUR_API_KEY" \
  "https://lab.flashalpha.com/v1/exposure/zero-dte/SPY?strike_range=0.05"
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JavaScript 

const resp = await fetch(
  "https://lab.flashalpha.com/v1/exposure/zero-dte/SPY",
  { headers: { "X-Api-Key": "YOUR_API_KEY" } }
);
const dte = await resp.json();

console.log(`Regime: ${dte.regime.label}`);
console.log(`Pin score: ${dte.pin_risk.pin_score}`);
console.log(`Expected move: ${dte.expected_move.expected_move_pct}%`);
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What You Get Back

One call returns everything for a complete 0DTE monitor:

Section Key Fields What It Tells You
Regime label, gamma_flip, net_gex Positive gamma (mean-revert) or negative (trending)?
Expected Move expected_move_pct, upper_bound, lower_bound Real-time intraday range, shrinks through the day
Pin Risk pin_score (0-100), magnet_strike, oi_concentration_top3_pct Is price pinned? Where's the magnet?
Hedging spot_up_half_pct, spot_down_half_pct, spot_up_1pct, spot_down_1pct Dealer share counts & notional at ±0.5% and ±1% moves
Decay theta_per_hour_remaining, gamma_acceleration, charm_description How fast is premium bleeding? Gamma accel vs 7DTE
Vol Context iv_ratio_0dte_7dte, vanna_interpretation Is 0DTE IV cheap or expensive vs term structure?
Flow net_call_premium, net_put_premium, put_call_ratio Directional sentiment from order flow
Per-Strike Array of {strike, call_gex, put_gex, net_gex, call_oi, put_oi, call_volume, put_volume} Strike-by-strike breakdown for heatmaps
Exposures pct_of_total_gex, zero_dte_net_gex, full_chain_net_gex How much of total GEX comes from 0DTE?

Building the Dashboard Panels

A. Expected Move Gauge

The expected move shrinks in real-time through the day. At 9:30 AM it might be ±0.65%. By 2 PM it's ±0.34%. By 3:45 PM it could be ±0.08%.

em = dte["expected_move"]
hours_left = dte["time_to_close_hours"]

print(f"Expected Move: +/-{em['expected_move_pct']:.2f}%")
print(f"Range: {em['lower_bound']:.2f} - {em['upper_bound']:.2f}")
print(f"Time to Close: {hours_left:.1f} hours")

# Normalize for gauge visualization
gauge_fill = em["expected_move_pct"] / 0.65  # morning baseline
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B. Pin Risk Score

The pin score (0-100) composites OI concentration, magnet proximity, time remaining, and gamma magnitude. Above 70 with less than 2 hours to close is a strong pinning signal.

pin = dte["pin_risk"]

print(f"Pin Score: {pin['pin_score']}/100")
print(f"Magnet Strike: {pin['magnet_strike']}")
print(f"Distance: {pin['distance_to_magnet_pct']:.2f}%")
print(f"Top 3 OI Concentration: {pin['oi_concentration_top3_pct']}%")

if pin["pin_score"] > 70 and hours_left < 2:
    print("HIGH PIN RISK - price gravitates to magnet strike")
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C. Gamma Acceleration Zone

The gamma_acceleration field shows how much more gamma 0DTE contracts carry vs equivalent 7DTE strikes. Above 3x, hedging flows start to dominate the tape.

decay = dte["decay"]
regime = dte["regime"]

print(f"Gamma Acceleration: {decay['gamma_acceleration']}x vs 7DTE")
print(f"Charm: {decay['charm_description']}")

if regime["label"] == "positive_gamma" and decay["gamma_acceleration"] > 3:
    print("Strong mean-reversion - dealers absorb moves aggressively")
elif regime["label"] == "negative_gamma" and decay["gamma_acceleration"] > 3:
    print("DANGER ZONE - dealer hedging amplifies moves, breakout risk elevated")
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D. Dealer Hedging Estimates

What happens if SPY moves ±0.5% or ±1%? Share counts and notional values for each scenario.

hedging = dte["hedging"]

scenarios = [
    ("SPY +0.5%", hedging["spot_up_half_pct"]),
    ("SPY -0.5%", hedging["spot_down_half_pct"]),
    ("SPY +1.0%", hedging["spot_up_1pct"]),
    ("SPY -1.0%", hedging["spot_down_1pct"]),
]

print(f"{'Scenario':<12} {'Dir':<6} {'Shares':>12} {'Notional':>16}")
print("-" * 50)
for label, h in scenarios:
    print(f"{label:<12} {h['direction']:<6} "
          f"{h['dealer_shares_to_trade']:>12,} "
          f"${h['notional_usd']:>14,.0f}")
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In positive gamma, dealers sell rallies and buy dips (dampening). In negative gamma, they do the opposite (amplifying). The notional values tell you how much liquidity dealers add or remove at each price level.

E. Per-Strike GEX Heatmap

The per_strike array gives you everything for a heatmap or bar chart:

for strike in dte["per_strike"]:
    bar_len = int(abs(strike["net_gex"]) / 10_000_000)
    sign = "+" if strike["net_gex"] > 0 else "-"
    print(f"{strike['strike']:.0f} | {sign}{'█' * bar_len} | "
          f"Net GEX: ${strike['net_gex']:,.0f}")
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F. Vol Context

Compare 0DTE IV against the term structure:

vol = dte["vol_context"]
print(f"0DTE/7DTE IV Ratio: {vol['iv_ratio_0dte_7dte']:.2f}")
print(f"Vanna: {vol['vanna_interpretation']}")

if vol["iv_ratio_0dte_7dte"] > 1.0:
    print("Event premium - 0DTE IV elevated vs term structure")
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Below 1.0 is normal. Above 1.0 means 0DTE has event premium (FOMC, CPI, intraday stress). Above 1.15 means 0DTE premium selling has edge.

Intraday Polling Pattern

Poll more frequently as expiration approaches:

import time
from datetime import datetime, time as dtime
from flashalpha import FlashAlpha

fa = FlashAlpha("YOUR_API_KEY")

def get_poll_interval(hours_left):
    if hours_left > 4:   return 900   # 15 min (morning)
    elif hours_left > 2: return 600   # 10 min (midday)
    elif hours_left > 1: return 300   # 5 min (afternoon)
    else:                return 180   # 3 min (last hour)

def is_market_hours():
    now = datetime.now()
    return dtime(9, 30) <= now.time() <= dtime(16, 0)

while is_market_hours():
    dte = fa.zero_dte("SPY")
    hours_left = dte["time_to_close_hours"]

    # Update your dashboard panels here
    update_expected_move(dte["expected_move"])
    update_pin_risk(dte["pin_risk"])
    update_gamma_zone(dte["decay"], dte["regime"])
    update_hedging_chart(dte["hedging"])
    update_gex_heatmap(dte["per_strike"])

    # Alerts
    if dte["pin_risk"]["pin_score"] > 70 and hours_left < 2:
        send_alert(f"HIGH PIN: magnet at {dte['pin_risk']['magnet_strike']}")

    if (dte["regime"]["label"] == "negative_gamma"
        and dte["exposures"]["pct_of_total_gex"] > 50):
        send_alert("NEGATIVE GAMMA DOMINANT: breakout risk elevated")

    time.sleep(get_poll_interval(hours_left))
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At this cadence you'll use 60-80 requests per symbol per day. Well within Growth's 2,500 daily limit. Scan 8 symbols and you're still under 640/day.

Multi-Symbol 0DTE Scanner

Scan multiple symbols and rank by pin risk, gamma acceleration, or expected move:

from flashalpha import FlashAlpha
import concurrent.futures

fa = FlashAlpha("YOUR_API_KEY")
symbols = ["SPY", "QQQ", "IWM", "AAPL", "TSLA", "NVDA", "AMZN", "META"]

def scan(symbol):
    try:
        dte = fa.zero_dte(symbol)
        if dte.get("no_zero_dte"):
            return None
        return {
            "symbol": symbol,
            "pin": dte["pin_risk"]["pin_score"],
            "gamma_x": dte["decay"]["gamma_acceleration"],
            "em": dte["expected_move"]["expected_move_pct"],
            "regime": dte["regime"]["label"],
            "pct": dte["exposures"]["pct_of_total_gex"],
            "magnet": dte["pin_risk"]["magnet_strike"]
        }
    except Exception:
        return None

with concurrent.futures.ThreadPoolExecutor(max_workers=4) as pool:
    results = list(filter(None, pool.map(scan, symbols)))

results.sort(key=lambda x: x["pin"], reverse=True)

print(f"{'Sym':<6} {'Pin':>4} {'GamX':>5} {'EM%':>6} {'Regime':<16} {'0DTE%':>6} {'Magnet':>8}")
print("-" * 55)
for r in results:
    print(f"{r['symbol']:<6} {r['pin']:>4} {r['gamma_x']:>4.1f}x "
          f"{r['em']:>5.2f}% {r['regime']:<16} "
          f"{r['pct']:>5.1f}% {r['magnet']:>8.0f}")
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MCP Server for AI Agents

If you're building AI agents that need 0DTE context, FlashAlpha provides an MCP server. Configure it in Claude, Cursor, or any MCP-compatible tool:

{
  "mcpServers": {
    "flashalpha": {
      "url": "https://lab.flashalpha.com/mcp",
      "headers": {
        "X-Api-Key": "YOUR_API_KEY"
      }
    }
  }
}
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Once connected, your agent can query conversationally: "What's the pin risk for SPY right now?" or "Scan SPY, QQQ, and IWM for the highest 0DTE pin score." The MCP server exposes the same endpoints as REST. The agent gets structured JSON, interprets it, and responds with actionable analysis.

Supporting Endpoints

Levels (Free tier) 

levels = fa.exposure_levels("SPY")
print(f"Gamma Flip: {levels['levels']['gamma_flip']}")
print(f"Call Wall: {levels['levels']['call_wall']}")
print(f"Put Wall: {levels['levels']['put_wall']}")
print(f"0DTE Magnet: {levels['levels']['zero_dte_magnet']}")
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Overlay these on your price chart. The zero_dte_magnet is the strike with the highest 0DTE GEX, acting as an intraday attractor.

GEX with Expiration Filter (Free tier) 

# 0DTE GEX only, without needing the full zero-dte endpoint
gex = fa.gex("SPY", expiration="2026-03-29")

for strike in gex["strikes"]:
    print(f"{strike['strike']}: net_gex=${strike['net_gex']:,.0f}")
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This gives you per-strike 0DTE gamma on the free tier. You don't get pin risk, expected move, or hedging scenarios (those require Growth), but it's enough to see where 0DTE gamma is concentrated.

Why Not Build It Yourself?

Building this from raw data requires solving five problems simultaneously:

Intraday chain snapshots. Real-time chain data costs $500-2,000/mo from exchange vendors. Most free sources give end-of-day only.

Greeks recomputation. Every snapshot requires recalculating delta, gamma, theta, vanna, charm for every strike. For SPY with 100+ 0DTE strikes, that's thousands of BSM evaluations per snapshot.

Pin risk models. Not just max pain. Composites OI concentration, magnet proximity, time remaining, gamma magnitude, and historical pinning behavior.

Gamma aggregation. Summing exposure across strikes with dealer positioning assumptions that need validation against actual market behavior.

Infrastructure. Must run reliably during market hours. Exchange outages, stale quotes, cache invalidation, latency management.

The zero-dte endpoint does all of this server-side. You get JSON. You build the UI.

Pricing

Plan Price Daily Requests 0DTE Access
Free $0 10 GEX with expiration filter, levels
Growth $299/mo 2,500 Full zero-dte endpoint + all exposure endpoints
Alpha $1,199/mo annual Unlimited + SVI surfaces, advanced vol, zero cache

Free tier lets you prototype with filtered GEX. Growth unlocks the full zero-dte endpoint with pin risk, expected move, hedging scenarios, and decay analytics.

Need a custom computation or field added? Email support@flashalpha.com. You're talking to the engineer who built it. Turnaround is hours, not weeks.

Related reading:

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