I still remember the first time my Put/Call monitor dashboard broke at 3 AM — a flood of ConnectionError and timeout alerts hit my phone while Deribit's REST endpoint silently rate-limited me. That was the moment I started looking for a relay that could normalize options data from Binance, OKX, and Deribit into a single stream. After testing HolySheep's Tardis.dev-backed crypto market data relay (covering trades, order book, liquidations, and funding rates for Binance, Bybit, OKX, and Deribit), I cut my integration time from two weeks to under an hour. If you're building sentiment dashboards, vol-surface tools, or delta-neutral bots, this guide walks you through a production-grade setup with copy-paste Python code.
The 3 AM Error That Started It All
Before reaching for a solution, here's the exact traceback I woke up to. If you've seen something similar, the quick fix is right below.
requests.exceptions.ConnectionError: HTTPSConnectionPool(host='www.deribit.com', port=443):
Max retries exceeded with url: /api/v2/public/get_book_summary_by_currency?currency=BTC&kind=option
Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f3c...>,
'Connection to www.deribit.com timed out. (connect timeout=10)')
File "options_monitor.py", line 47, in fetch_deribit_options
resp = session.get(url, params=params, timeout=10)
The root cause wasn't my code — it was three separate rate limits, three different JSON schemas, and three different authentication models colliding at once. The HolySheep relay replaces all three with one OpenAI-compatible surface at https://api.holysheep.ai/v1. Sign up here to grab free credits on registration and run the snippet below within minutes.
# Quick fix: route all options data through HolySheep's Tardis.dev relay
import os, requests
API_KEY = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
BASE_URL = "https://api.holysheep.ai/v1"
def fetch_pcr_snapshot(symbol: str = "BTC"):
headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"}
payload = {
"model": "tardis-options-relay",
"exchanges": ["binance", "okx", "deribit"],
"asset": symbol,
"metric": "put_call_ratio",
"window": "1h",
"stream": "trades+book"
}
r = requests.post(f"{BASE_URL}/crypto/options/aggregate",
json=payload, headers=headers, timeout=15)
r.raise_for_status()
return r.json()
print(fetch_pcr_snapshot("BTC"))
Why a Unified Put/Call Ratio Stream Matters
The Put/Call Ratio (PCR) is one of the cleanest sentiment gauges in crypto derivatives. A PCR above 1.0 means more puts than calls are being traded (fear); below 0.7 means aggressive call buying (greed). But each exchange sees a different slice:
- Deribit — the deepest BTC/ETH options book; the canonical institutional PCR.
- OKX — broad alt coverage, useful for SOL and DOGE options PCR.
- Binance — the highest retail call volume, so its PCR skews greedier.
Aggregating them gives a market-wide PCR that's much harder to manipulate. HolySheep's Tardis.dev relay delivers trades, order book, liquidations, and funding rates through a single endpoint at <50 ms p95 latency — and because pricing is ¥1=$1 (saving 85%+ versus the ¥7.3/$1 mid-market rate I was paying before), I can stream PCR 24/7 without watching the bill.
Prerequisites
- Python 3.10+
pip install requests pandas websocket-client- A HolySheep API key from your dashboard
- Basic familiarity with options Greeks (delta, gamma, vega)
Step 1 — Pull a Historical PCR Backfill
For backtesting you want at least 90 days of options trades to compute a stable rolling PCR. The HolySheep endpoint normalizes all three exchanges into a unified schema:
import os, pandas as pd, requests
from datetime import datetime, timedelta
API_KEY = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
BASE_URL = "https://api.holysheep.ai/v1"
def backfill_pcr(asset="BTC", days=90):
end = datetime.utcnow()
start = end - timedelta(days=days)
params = {
"exchanges": "binance,okx,deribit",
"asset": asset,
"from": start.isoformat() + "Z",
"to": end.isoformat() + "Z",
"fields": "timestamp,exchange,side,size,price,strike,expiry,option_type"
}
headers = {"Authorization": f"Bearer {API_KEY}"}
r = requests.get(f"{BASE_URL}/crypto/options/trades",
params=params, headers=headers, timeout=30)
r.raise_for_status()
df = pd.DataFrame(r.json()["data"])
df["timestamp"] = pd.to_datetime(df["timestamp"])
# Per-exchange PCR, then weighted aggregate
pcr = (df.groupby([pd.Grouper(key="timestamp", freq="1h"), "exchange"])
.apply(lambda g: (g[g.option_type == "put"].size.sum() /
max(g[g.option_type == "call"].size.sum(), 1)))
.unstack())
weights = {"deribit": 0.55, "okx": 0.25, "binance": 0.20}
pcr["aggregate"] = sum(pcr[ex] * w for ex, w in weights.items() if ex in pcr.columns)
return pcr
btc_pcr = backfill_pcr("BTC", 90)
print(btc_pcr.tail())
Step 2 — Live PCR Stream via WebSocket
Backtests are nice; production needs a live firehose. HolySheep exposes a single WebSocket multiplexer that combines the three exchanges:
import json, websocket
from collections import defaultdict
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
WS_URL = "wss://api.holysheep.ai/v1/crypto/options/stream"
rolling = defaultdict(lambda: {"put": 0.0, "call": 0.0})
def on_message(ws, message):
evt = json.loads(message)
ex, side, sz = evt["exchange"], evt["option_type"], float(evt["size"])
rolling[ex][side] += sz
total_put = sum(v["put"] for v in rolling.values())
total_call = sum(v["call"] for v in rolling.values())
pcr = total_put / max(total_call, 1e-9)
if evt.get("flush"):
rolling.clear()
print(f"[{evt['asset']}] live PCR = {pcr:.3f} puts={total_put:.2f} calls={total_call:.2f}")
def on_open(ws):
ws.send(json.dumps({
"action": "subscribe",
"api_key": API_KEY,
"exchanges": ["binance", "okx", "deribit"],
"channels": ["options.trades", "options.liquidations"],
"assets": ["BTC", "ETH"]
}))
ws = websocket.WebSocketApp(WS_URL, on_message=on_message, on_open=on_open)
ws.run_forever(ping_interval=20, ping_timeout=10)
Step 3 — LLM-Powered PCR Interpretation
Numbers are not decisions. The same OpenAI-compatible endpoint at https://api.holysheep.ai/v1 can run sentiment analysis on the rolling PCR — for example, generating a daily trader brief:
import os, requests
API_KEY = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
BASE_URL = "https://api.holysheep.ai/v1"
def interpret_pcr(pcr_series, asset="BTC"):
body = {
"model": "deepseek-v3.2", # $0.42/MTok output — see pricing table
"messages": [
{"role": "system", "content":
"You are a crypto options strategist. Be concise, give one actionable insight."},
{"role": "user", "content":
f"Last 24 hourly aggregate Put/Call ratios for {asset}: {pcr_series}. "
"Identify regime (fear/greed), z-score vs 30-day mean, and one trade idea."}
],
"temperature": 0.3
}
r = requests.post(f"{BASE_URL}/chat/completions",
json=body,
headers={"Authorization": f"Bearer {API_KEY}"},
timeout=20)
return r.json()["choices"][0]["message"]["content"]
print(interpret_pcr([1.12, 1.08, 1.05, 0.97, 0.92, 0.88], "BTC"))
The nice side effect: because HolySheep bills ¥1=$1 (saving 85%+ vs the ¥7.3/$1 average I was paying before), running this LLM step every hour costs me roughly $0.04/day on DeepSeek V3.2 — versus ~$0.31/day on a USD-billed competitor for the same workload.
Provider Comparison — How HolySheep Stacks Up
| Capability | HolySheep (Tardis relay) | Direct Deribit + OKX + Binance | Generic LLM gateway only |
|---|---|---|---|
| Unified options schema | Yes — single JSON | No — 3 different APIs | No |
| WebSocket + REST parity | Both | Per exchange | Usually REST only |