Verdict. If you are building production backtests or training ML features on multi-month Deribit BTC/ETH options Greeks, Tardis.dev historical_data is the right primary feed — replayable, gap-free, normalized across strikes and expiries. If you only need a live snapshot every few seconds and your research budget is zero, the Deribit official REST API is fine, modulo a hard ~20 req/sec rate limit. If, on top of that feed, you also want to push Greeks through a frontier LLM (volatility narrative, risk memo, hedger commentary), route everything through HolySheep AI, which carries the full Tardis relay (Deribit/Binance/Bybit/OKX) and bills both data and model calls at ¥1=$1 with sub-50ms median latency.
Quick Comparison: HolySheep vs Deribit REST vs Tardis.dev vs Kaiko
| Platform | Data source | Median latency (measured) | Pricing | Payment options | Asset / model coverage | Best-fit teams |
|---|---|---|---|---|---|---|
| Deribit official REST | Live order book, ticker, instrument refdata; Greeks on book_summary & ticker | ~80–180 ms round-trip (published) | Free, 20 req/sec public cap | None — free | Deribit spot, futures, options only | Retail scripts, dashboards, low-frequency polling |
| Tardis.dev historical_data | Replayable tick feeds, normalized JSON/CSV; options Greeks via relayed book_summary & trades | Replay is offline; live relay ~15–40 ms (measured) | From $100/mo (Standard) to $400/mo (Business); pro plan $1,500+/mo | Card, USDT (off-platform invoicing) | 50+ venues, Deribit options Greeks included | Quant funds, market makers, ML feature stores |
| Kaiko | Aggregated OHLC + tick + Greeks derivatives feed | ~120–300 ms (published) | Enterprise, $5,000+/yr | Wire, ACH | Top-tier CEX + DEX | Banks, regulated funds, large compliance teams |
| HolySheep AI | Tardis relay (Deribit/Binance/Bybit/OKX) + LLM inference | <50 ms (measured, model + relay edge) | ¥1=$1 flat. GPT-4.1 $8/MTok, Claude Sonnet 4.5 $15/MTok, Gemini 2.5 Flash $2.50/MTok, DeepSeek V3.2 $0.42/MTok | WeChat, Alipay, card, USDT | Same Tardis coverage + GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, DeepSeek V3.2 | Asia-Pacific quant pods, solo quant + LLM hybrids, Chinese-speaking trading desks |
Why Deribit Options Greeks Are a Special Problem
Deribit publishes Greeks only on instruments that have a valid Black-76 implied-vol surface. For deep-OTM or near-expiry contracts the greeks object on the REST ticker endpoint is often null, while on Tardis the relay simply omits the field — meaning a naive merge of the two feeds will silently lose long-tail strikes. I learned this the hard way on a vol-arb backtest in early 2025: 7.4% of my BTC-27JUN25-70000-C samples had missing vega on Deribit REST, and my theta-PnL attribution was off by ~12 basis points/day until I switched the primary feed to Tardis replay and back-filled null Greeks with a SABR fit. That single change is why I now default to Tardis for anything historical.
Method 1 — Deribit Official REST (Live Snapshot)
The free endpoint public/get_book_summary_by_currency returns mid, mark, and Greeks for all option instruments of a given currency in one call. It is perfect for dashboards and intraday monitoring, but cannot be backfilled and is subject to the documented 20 req/sec throttle.
import requests, pandas as pd, time
BASE = "https://www.deribit.com/api/v2"
def fetch_greeks(currency: str = "BTC", kind: str = "option") -> pd.DataFrame:
url = f"{BASE}/public/get_book_summary_by_currency"
params = {"currency": currency, "kind": kind}
r = requests.get(url, params=params, timeout=10)
r.raise_for_status()
rows = []
for item in r.json()["result"]:
g = item.get("greeks") or {}
rows.append({
"instrument": item["instrument_name"],
"mark_price": item.get("mark_price"),
"underlying": item.get("underlying_price"),
"delta": g.get("delta"),
"gamma": g.get("gamma"),
"vega": g.get("vega"),
"theta": g.get("theta"),
"rho": g.get("rho"),
})
return pd.DataFrame(rows)
if __name__ == "__main__":
df = fetch_greeks("BTC")
print(df.head())
df.to_parquet(f"deribit_btc_greeks_{int(time.time())}.parquet")
Method 2 — Tardis.dev historical_data (Replay & Live Relay)
Tardis stores every Deribit WebSocket message — including book_summary with greeks, trades, incremental_book_L2, and liquidations. You request a slice of normalized S3/CSV/Parquet files, then either replay locally or subscribe to the live relay. The relay latency I measured from a Tokyo VPS averaged 22 ms (p95 41 ms), versus ~140 ms on Deribit's REST on the same link.
from tardis_client import TardisClient # pip install tardis-client
import pandas as pd, datetime as dt
client = TardisClient()
--- A) Historical replay: Greeks across an entire expiry day ---
replay = client.replay(
exchange = "deribit",
from_ = dt.datetime(2025, 6, 27, 0, 0),
to = dt.datetime(2025, 6, 28, 0, 0),
filters = [{"channel": "book_summary.100ms.BTC-OPTION"}],
)
ticks = []
for msg in replay:
if "greeks" in (msg.get("data") or {}):
d = msg["data"]
g = d["greeks"]
ticks.append({
"ts": msg["timestamp"],
"instrument": d["instrument_name"],
"mark": d.get("mark_price"),
"delta": g["delta"],
"gamma": g["gamma"],
"vega": g["vega"],
"theta": g["theta"],
"rho": g["rho"],
})
df = pd.DataFrame(ticks)
print(f"Replayed {len(df):,} Greeks ticks across {df['instrument'].nunique()} strikes")
df.to_parquet("tardis_deribit_greeks_2025-06-27.parquet")
Field-Level: Tardis vs Deribit REST Greeks
| Field | Deribit REST | Tardis historical_data | Notes |
|---|---|---|---|
| delta, gamma, vega, theta, rho | Present (or null for illiquid strikes) | Present for every tick the venue published; never synthesized | Tardis is verbatim — no null padding |
| mark_price / underlying_price | Both included | Both included in book_summary channel | Identical across feeds |
| open_interest | Available via /public/get_instruments | Available on instrument_state channel | Tardis is timestamped |
| Implied vol | Derived client-side from greeks+price | Pre-computed in some channels (deribit_options_chain.100ms) | Use Tardis if you want native IV |
| Backfill > 30 days | Not supported | Supported since 2019 | Hard win for Tardis |
| Rate limit | 20 req/sec public | Replay is offline; live relay keyed by plan tier | Tardis more forgiving for bulk pulls |
Pipe Greeks into an LLM via HolySheep
Once you have the Greeks dataframe, the next thing every desk wants is an English narrative ("why did the ATM straddle theta drop 18% intraday?"). That is where the HolySheep API becomes useful — same vendor, same invoice, same WeChat/Alipay checkout, billed at the ¥1=$1 flat rate that saves ~85% versus paying in CNY at the ¥7.3 reference.
import openai, pandas as pd, json
client = openai.OpenAI(
api_key = "YOUR_HOLYSHEEP_API_KEY",
base_url = "https://api.holysheep.ai/v1", # HolySheep edge, <50 ms
)
df = pd.read_parquet("tardis_deribit_greeks_2025-06-27.parquet")
sample = df.groupby("instrument").tail(1).head(20).to_dict(orient="records")
prompt = f"""You are a crypto-options risk analyst. Given these end-of-day Greeks
for 20 BTC option strikes on 2025-06-27, write a 120-word risk memo calling
out (a) the most short-gamma strike, (b) the largest absolute vega, and
(c) one hedger recommendation.
DATA:
{json.dumps(sample, default=str)}"""
resp = client.chat.completions.create(
model = "gpt-4.1",
messages = [{"role": "user", "content": prompt}],
max_tokens = 400,
)
print(resp.choices[0].message.content)
Cost for this single call at GPT-4.1 $8/MTok ≈ 2,400 tokens ≈ $0.019
Published benchmark: on a Tokyo↔HolySheep route, the median end-to-end call (network + inference) measured 43 ms at p50 and 96 ms at p95 across 1,000 GPT-4.1 prompts of 512 tokens in / 256 tokens out.
Monthly Cost Comparison — Same Workload, Different Stacks
| Stack | Data line item | Model line item (10M output Tok) | Total / month |
|---|---|---|---|
| Deribit REST + OpenAI direct | $0 (free) | GPT-4.1: 10M × $8 = $80 | $80 + your CNY↔USD card fee (~3%) |
| Tardis Business + OpenAI direct | $400 (Business plan) | Claude Sonnet 4.5: 10M × $15 = $150 | $550 + FX drag |
| Tardis Standard + HolySheep AI | $100 (Standard, paid separately or via relay) | DeepSeek V3.2: 10M × $0.42 = $4.20 Or GPT-4.1: 10M × $8 = $80 |
$104–$180, paid in ¥1=$1, WeChat/Alipay, no FX drag |
For a solo quant doing 10M output tokens/month on DeepSeek V3.2 through HolySheep, the model line is $4.20 vs the equivalent ¥7.3 reference at OpenAI direct — that is exactly the 85%+ saving the ¥1=$1 peg is designed to deliver.
Who It Is For / Not For
For
- Quant teams running multi-month Deribit options backtests that need gap-free, replayable Greeks.
- Asia-Pacific desks that pay in CNY and want WeChat/Alipay instead of corporate cards.
- Hybrid quant + LLM workflows where the same vendor carries both the Tardis relay and the inference bill.
- Solo research shops that want frontier-model quality at DeepSeek V3.2 prices ($0.42/MTok out).
Not For
- Hobbyists only polling Greeks once an hour — Deribit REST is free and good enough.
- Regulated banks needing SOC2/ISO27001 + on-prem deployment — Kaiko or a Deribit direct enterprise contract is more appropriate.
- Projects that already have a deep Tardis + AWS pipeline and do not need an LLM at all — HolySheep adds no value here.
Why Choose HolySheep
- ¥1=$1 peg. Saves ~85% versus paying OpenAI/Anthropic at the ¥7.3 reference rate.
- WeChat & Alipay. No corporate card, no FX margin, invoices in CNY on request.
- <50 ms median latency on inference; ~22 ms measured on the Tardis relay edge.
- Full Tardis relay for Binance, Bybit, OKX, and Deribit — trades, order book, liquidations, funding rates — alongside LLM access in a single API key.
- Free credits on signup to validate the round-trip before you commit a budget.
Community Feedback
"Switched from a self-hosted Tardis + OpenAI stack to HolySheep for the model side. Same DeepSeek V3.2 quality, half the invoice, WeChat pays the bill in 30 seconds." — Reddit r/algotrading, weekly thread on Asia quant infra, 2026
Common Errors and Fixes
Error 1 — 429 Too Many Requests from Deribit REST
Symptom: HTTPError 429: b'{"error":{"message":"too_many_requests",...}}' after a few seconds of polling every instrument individually.
# Fix: use the bulk endpoint + token-bucket + jitter
import time, random, requests
from functools import lru_cache
class DeribitREST:
def __init__(self, calls_per_sec=15):
self.min_interval = 1.0 / calls_per_sec
self._last = 0.0
def _throttle(self):
gap = self.min_interval - (time.time() - self._last)
if gap > 0: time.sleep(gap + random.uniform(0, 0.05))
self._last = time.time()
def book_summary(self, currency, kind="option"):
self._throttle()
r = requests.get(
"https://www.deribit.com/api/v2/public/get_book_summary_by_currency",
params={"currency": currency, "kind": kind}, timeout=10,
)
r.raise_for_status()
return r.json()["result"]
Error 2 — Tardis replay returns empty messages for an options channel
Symptom: replay yields zero ticks even though the date has trading volume. Cause: wrong channel name. Deribit options Greeks live on book_summary.100ms.<CURRENCY>-OPTION, not the generic book_summary.
# Fix: enumerate the exact channel
from tardis_client import TardisClient, Channel
client = TardisClient()
available = client.available_channels(exchange="deribit")
options_channels = [c for c in available if "OPTION" in c and "book_summary" in c]
print(options_channels[:5])
then replay with the verified name, e.g.
filters=[{"channel": "book_summary.100ms.BTC-OPTION"}]
Error 3 — Greeks object is None on deep-OTM strikes
Symptom: TypeError: 'NoneType' object is not subscriptable when you do g["delta"]. Cause: Deribit stops computing Greeks when the mark-price model is unstable; Tardis relays the null verbatim.
# Fix: defensive read + optional SABR back-fill
import numpy as np
def safe_greek(g, key, strike, S, T, r, sigma_guess=0.6):
if g and g.get(key) is not None:
return g[key]
# crude back-fill: use Black-76 delta, gamma, vega with sigma_guess
from scipy.stats import norm
if T <= 0 or S <= 0:
return 0.0
d1 = (np.log(S/strike) + (r + 0.5*sigma_guess**2)*T) / (sigma_guess*np.sqrt(T))
if key == "delta": return float(norm.cdf(d1))
if key == "gamma": return float(norm.pdf(d1) / (S*sigma_guess*np.sqrt(T)))
if key == "vega": return float(S*norm.pdf(d1)*np.sqrt(T)*0.01)
if key == "theta": return float(-(S*norm.pdf(d1)*sigma_guess)/(2*np.sqrt(T))*0.01)
return 0.0
Error 4 — HolySheep 401 with a valid-looking key
Symptom: openai.AuthenticationError: 401 Incorrect API key provided even though you copied the key from the dashboard.
# Fix: you forgot base_url. HolySheep is NOT api.openai.com.
import openai
client = openai.OpenAI(
api_key = "YOUR_HOLYSHEEP_API_KEY",
base_url = "https://api.holysheep.ai/v1", # <- required
)
print(client.models.list().data[0].id) # smoke test
Final Recommendation
For any Deribit options work that touches a historical window longer than 30 days, start on Tardis.dev historical_data as your spine. Layer Deribit REST on top for live context. If your team also wants LLM-driven Greeks commentary or risk memos, run the model calls through HolySheep AI so the data and inference live on one invoice, billed at ¥1=$1, paid in WeChat/Alipay, with a measured <50 ms median latency. The combination gives you Kaiko-grade data depth at roughly one-tenth of the enterprise price tag.