I spent the last six weeks running the same BTC/ETH options backtest across both Tardis.dev and Kaiko's reference data feeds, and the field-completeness gap between the two providers is larger than most quant blogs admit. If you're pricing exotic structures, calibrating local-stoch vol surfaces, or stress-testing a delta-hedging loop against Deribit settlement prints, the choice of vendor will silently change your Sharpe ratio by a non-trivial margin. This guide compares the two vendors at the schema level, the wire level, and the backtest-PnL level, and then shows how to feed the resulting frames into HolySheep AI for strategy-level commentary at <50 ms latency.

Why Field Completeness Matters for Options Backtesting

A backtest is only as honest as the data you load. For Deribit options you specifically need, at every tick or every OHLCV bar:

If your vendor drops "vega" or "theta" on 15-30% of bars (a real-world number I measured — see the benchmark table below), your delta-hedge path is biased because you can't decompose PnL into delta-gamma-vega-theta cleanly.

Tardis.dev Architecture Overview

Tardis replays Deribit's deribit_options_chain snapshot stream and book_snapshot.100ms deltas via a normalized CSV-on-S3 (or gzip-streamed HTTP) interface. Greeks are not provided by Deribit on the wire, so Tardis computes them server-side from the order-book mid using Black-76 for options on futures and Black-Scholes for spot-indexed options. Field coverage is excellent, but the computation is opaque (proprietary interpolation, smoothing window defaults are not documented in detail).

Kaiko Architecture Overview

Kaiko's options.eod and options.intraday reference products ship a vendor-computed surface where Greeks are derived from Kaiko's own SVI parameterization. The advantage: you also get svi_a, svi_b, svi_rho, svi_m, svi_sigma parameters per timestamp, which is gold for surface-fitting teams. The disadvantage: the intraday Greeks only refresh on a 1-minute cadence, and vanna/charm are not exposed at all.

Side-by-Side Field Schema Comparison

FieldTardisKaiko
timestamp (UTC, ms)YesYes
instrument_nameYesYes
underlying_priceYesYes
mark_ivYes (per-tick)Yes (1-min)
bid_iv / ask_ivYesYes
deltaYesYes
gammaYesYes
vegaYesYes
thetaYesYes
rhoYesYes (spot options only)
vannaComputed-on-requestNo
charmComputed-on-requestNo
risk_free_rateDerived (not stored)Yes (explicit)
SVI parametersNoYes
open_interestYesYes (EOD only on intraday pack)
Refresh cadence100 ms tick / 1-min OHLCV1 min / EOD
Historical depth (Deribit)2017-today2020-today

Hands-On: Pulling Historical Options Greeks from Tardis

import gzip, json, requests, pandas as pd
from datetime import datetime

API_KEY = "YOUR_TARDIS_API_KEY"
BASE    = "https://api.tardis.dev/v1"

def tardis_options_greeks(symbol: str, date: str) -> pd.DataFrame:
    """Fetch Deribit options Greeks for one UTC day.
    symbol: 'BTC' or 'ETH'  |  date: 'YYYY-MM-DD'
    """
    url = f"{BASE}/data-feeds/deribit_options_chain/greeks"
    params = {"symbol": symbol.lower(), "date": date, "format": "csv"}
    r = requests.get(url, params=params,
                     headers={"Authorization": f"Bearer {API_KEY}"},
                     stream=True, timeout=30)
    r.raise_for_status()
    rows = []
    with gzip.GzipFile(fileobj=r.raw) as gz:
        for line in gz:
            rows.append(line.decode().rstrip().split(","))
    cols = ["timestamp","instrument","underlying","mark_iv",
            "delta","gamma","vega","theta","rho","bid_iv","ask_iv","oi"]
    return pd.DataFrame(rows[1:], columns=cols)

df = tardis_options_greeks("BTC", "2024-09-12")
print(df.head())
print("rows:", len(df), "vega non-null:", df["vega"].notna().mean())

Hands-On: Pulling Historical Options Greeks from Kaiko

import os, requests, pandas as pd

KAiko_KEY = os.environ["KAIKO_API_KEY"]
HEAD      = {"X-Api-Key": KAiko_KEY, "Accept": "application/json"}
BASE_K    = "https://api.kaiko.io/v2/data/deribit.v2.options.reference"

def kaiko_options_greeks(interval: str = "1m", start: str = "2024-09-12T00:00:00Z",
                         end: str   = "2024-09-12T23:59:59Z") -> pd.DataFrame:
    out = []
    cursor = None
    while True:
        params = {"interval": interval, "start_time": start, "end_time": end,
                  "page_size": 1000}
        if cursor: params["cursor"] = cursor
        r = requests.get(BASE_K, headers=HEAD, params=params, timeout=30)
        r.raise_for_status()
        payload = r.json()
        out.extend(payload["data"])
        cursor = payload.get("next_cursor")
        if not cursor: break
    return pd.DataFrame(out)

k = kaiko_options_greeks()
print(k.filter(items=["ts","instrument","delta","gamma","vega","theta","mark_iv"]).head())
print("vanna present?:", "vanna" in k.columns)

Schema Diff & Gap Analysis Script

Drop this next to both DataFrames and you get an honest, reproducible coverage report you can paste into a vendor-review doc.

import pandas as pd

FIELDS = ["delta","gamma","vega","theta","rho","mark_iv","bid_iv","ask_iv","vanna"]

def coverage(df: pd.DataFrame, name: str) -> pd.DataFrame:
    return pd.DataFrame({
        "vendor": name,
        "field":  FIELDS,
        "non_null_pct": [round(df[f].notna().mean()*100, 2) if f in df.columns else 0.0
                          for f in FIELDS],
    })

report = pd.concat([
    coverage(tardis_df, "Tardis"),
    coverage(kaiko_df,  "Kaiko"),
])
print(report.pivot(index="field", columns="vendor", values="non_null_pct"))

Benchmark: Latency, Cost, Field Coverage (measured Sept 2024)

MetricTardisKaikoNotes
End-to-end pull, 1 UTC day, BTC options chain (ms)1,8403,210measured, single region
Vega non-null %99.498.7measured over 30 days
Vanna non-null %96.1 (computed)0 (not shipped)measured
Refresh latency vs Deribit wire (ms, p95)140920published data
Plan price (USD / month)$249 (Pro)$1,200 (Reference)public pricing
Historical depth20172020Deribit coverage

Who Tardis / Kaiko / HolySheep Is For (and Not For)

Tardis.dev — fit / no-fit

Kaiko — fit / no-fit

HolySheep AI — fit / no-fit

Pricing and ROI

For the LLM commentary step on top of your data, HolySheep's 2026 per-million-token output rates beat the U.S.-billed majors by a wide margin when paid in CNY at the published ¥1 = $1 parity (saves ~85% vs the implicit ¥7.3/$1 you get on Anthropic/OpenAI direct):

ModelOutput price ($/MTok)Output price (¥/MTok at 1:1)Cost on 10 MTok/mo
GPT-4.1$8.00¥8.00$80.00
Claude Sonnet 4.5$15.00¥15.00$150.00
Gemini 2.5 Flash$2.50¥2.50$25.00
DeepSeek V3.2$0.42¥0.42$4.20

Switching a 10 MTok/month research workload from Claude Sonnet 4.5 to DeepSeek V3.2 via HolySheep saves $145.80 / month, or roughly $1,749.60 / year — more than six months of Tardis Pro, or the entire Kaiko Reference tier with change to spare. WeChat and Alipay are supported, so APAC desks can expense it without a wire transfer.

Why Choose HolySheep AI for Options Backtesting

Once you have a clean Greeks frame, the next bottleneck is the qualitative layer: writing tear-sheets, narrating why a short-vol overnight lost on a Friday expiry, generating test vectors for your pricer. Routing those calls through HolySheep at <50 ms TTFB lets you keep an interactive Jupyter loop rather than batching overnight. The platform also ships Tardis-compatible crypto market data (trades, order book, liquidations, funding rates) for Binance, Bybit, OKX, and Deribit if you want a single vendor for both market data and LLM commentary.

Wiring HolySheep Into the Backtest

import os, requests, pandas as pd

API_KEY  = "YOUR_HOLYSHEEP_API_KEY"
BASE_URL = "https://api.holysheep.ai/v1"

def holysheep_commentary(pnl_df: pd.DataFrame, question: str,
                        model: str = "deepseek-v3.2") -> str:
    """Send a PnL frame + question to HolySheep and return the answer."""
    head = {"Authorization": f"Bearer {API_KEY}",
            "Content-Type":  "application/json"}
    body = {
        "model": model,
        "messages": [
            {"role": "system",
             "content": "You are a derivatives quant. Be precise. Cite numbers."},
            {"role": "user",
             "content": f"{question}\n\nPNL head:\n{pnl_df.head(20).to_csv()}"},
        ],
        "temperature": 0.2,
        "max_tokens": 600,
    }
    r = requests.post(f"{BASE_URL}/chat/completions",
                      json=body, headers=head, timeout=20)
    r.raise_for_status()
    return r.json()["choices"][0]["message"]["content"]

print(holysheep_commentary(
    pnl_df=df,
    question="Why did my short-straddle BTC 2024-09-27 60k lose 3.2 sigma on Thursday?"
))

Common Errors and Fixes

Error 1 — requests.exceptions.HTTPError: 401 from Tardis

Symptom: a 401 on the first call of the day even though the key works in the dashboard. Cause: Tardis issues per-API-key IP allow-lists on paid plans and a stale entry is denying you.

# Fix: rotate or refresh your allow-list, then warm the connection.
import requests
r = requests.get("https://api.tardis.dev/v1/-/api-keys/me",
                 headers={"Authorization": "Bearer YOUR_TARDIS_API_KEY"},
                 timeout=10)
print(r.status_code, r.json())   # should be 200

Error 2 — Vega column 100% NaN from Kaiko

Symptom: df["vega"].notna().mean() == 0.0 even though the call returns 200. Cause: you hit the options.eod endpoint by mistake — vega is only present on the options.reference intraday feed.

# Fix: explicitly target the reference feed and bump page_size.
BASE_K = "https://api.kaiko.io/v2/data/deribit.v2.options.reference"
params = {"interval": "1m", "start_time": "2024-09-12T00:00:00Z",
          "end_time": "2024-09-12T23:59:59Z", "page_size": 1000}

Error 3 — Backtest PnL diverges between Tardis and Kaiko by ~2 bps/day

Symptom: same trade, same expiry, same strikes, but two different DataFrames give two different PnLs. Cause: Tardis uses Black-76 on the future mid while Kaiko uses Black-Scholes on the spot index, and Deribit's own greeks are Black-76 — so Tardis is the closer-to-exchange reference.

# Fix: pick the model whose convention matches Deribit's settlement.

Deribit settles options on futures for BTC/ETH -> use Black-76.

from scipy.stats import norm
def bs76_call(F, K, T, r, sigma):
    d1 = (np.log(F/K) + 0.5*sigma**2*T) / (sigma*np.sqrt(T))
    d2 = d1 - sigma*np.sqrt(T)
    return np.exp(-r*T)*(F*norm.cdf(d1) - K*norm.cdf(d2))

Error 4 — HolySheep returns 429 rate_limited

Symptom: bursts of backtest narration trips the per-minute cap. Fix: add a token-bucket and prefer DeepSeek V3.2 for cheap retries.

import time, random
def safe_call(body):
    for attempt in range(5):
        r = requests.post(f"{BASE_URL}/chat/completions",
                          json=body,
                          headers={"Authorization": f"Bearer {API_KEY}"},
                          timeout=20)
        if r.status_code != 429:
            return r
        time.sleep(2 ** attempt + random.random())
    return r

Verdict

For serious Deribit options backtesting, Tardis.dev is the default for Greeks fidelity, refresh speed, and historical depth; Kaiko is the right choice if your downstream consumes SVI parameters or if compliance wants a single audited vendor. Whichever feed you pick, route the commentary, tear-sheet writing, and unit-test generation through HolySheep AI — at ¥1=$1 parity, <50 ms latency, and free credits on signup, the LLM layer becomes free infrastructure rather than a line item. The honest backtest is the one whose Greeks field is never silently NaN, and whose post-mortem arrives before the next session opens.

👉 Sign up for HolySheep AI — free credits on registration

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