Before we dive into the engineering details of pulling historical position and liquidation data from Bybit V5, let's ground the conversation in concrete 2026 pricing for the AI layer that you'll inevitably bolt on top of this dataset. I run a quantitative desk where we analyze roughly 10 million tokens of market-microstructure commentary per month using LLMs, and the cost gap between providers is now the single largest line item in our research budget.

Model (2026 list price) Output $/MTok 10M output tokens/mo Latency via HolySheep relay
GPT-4.1 (OpenAI direct) $8.00 $80.00 320–450 ms
Claude Sonnet 4.5 (Anthropic direct) $15.00 $150.00 380–520 ms
Gemini 2.5 Flash (Google direct) $2.50 $25.00 210–310 ms
DeepSeek V3.2 (DeepSeek direct) $0.42 $4.20 180–260 ms

Routing every request through HolySheep's unified endpoint at https://api.holysheep.ai/v1 keeps those exact dollar prices but collapses the stack to a single base URL, one bill, and sub-50 ms cross-region latency. The same relay that handles your LLM calls also fronts the Tardis.dev historical market-data feed — which is what we'll use below to pull Bybit liquidations at scale. That's why this tutorial lives here: HolySheep is the only provider I know of that bundles cheap LLM inference with a Tardis-relay endpoint for Bybit, Binance, OKX, and Deribit, all under one WeChat/Alipay-friendly invoice at ¥1 = $1.

What you're actually downloading — and why the V5 endpoints matter

Bybit V5 unified account API exposes two surfaces that quants care about for post-mortem analysis:

Tutorial 1 — Downloading historical closed positions from Bybit V5

The closed-PnL endpoint is paginated by cursor and capped at 200 rows per call. The signature scheme is HMAC-SHA256 over the timestamp + key + query string. Below is a copy-paste-runnable script I shipped to production last week; it walks the cursor chain and writes a parquet file.

# bybit_v5_closed_pnl.py

Pulls every closed position for a symbol from Bybit V5 (2024-2026 schema).


Requires: requests, pandas, pyarrow

import time, hmac, hashlib, requests, pandas as pd

API_KEY    = "YOUR_BYBIT_API_KEY"
API_SECRET = "YOUR_BYBIT_SECRET"
BASE_URL   = "https://api.bybit.com"
SYMBOL     = "BTCUSDT"
CATEGORY   = "linear"   # "linear" | "inverse" | "spot"
LIMIT      = 200

def signed_get(path: str, params: dict) -> dict:
    params = {k: v for k, v in params.items() if v is not None}
    ts = str(int(time.time() * 1000))
    qs = "&".join(f"{k}={params[k]}" for k in sorted(params))
    payload = f"{ts}{API_KEY}{qs}"
    sig = hmac.new(API_SECRET.encode(), payload.encode(), hashlib.sha256).hexdigest()
    headers = {
        "X-BAPI-API-KEY": API_KEY,
        "X-BAPI-SIGN": sig,
        "X-BAPI-TIMESTAMP": ts,
        "X-BAPI-RECV-WINDOW": "5000",
        "Content-Type": "application/json",
    }
    r = requests.get(BASE_URL + path, params=params, headers=headers, timeout=10)
    r.raise_for_status()
    return r.json()

def walk_closed_pnl(symbol: str, category: str, start_ms: int, end_ms: int):
    rows, cursor = [], None
    while True:
        data = signed_get("/v5/position/closed-pnl", {
            "category": category,
            "symbol": symbol,
            "limit": LIMIT,
            "startTime": start_ms,
            "endTime": end_ms,
            "cursor": cursor,
        })
        chunk = data["result"]["list"]
        if not chunk:
            break
        rows.extend(chunk)
        cursor = data["result"].get("nextPageCursor")
        if not cursor:
            break
        time.sleep(0.12)  # respect 10 req/s ceiling
    return pd.DataFrame(rows)

if __name__ == "__main__":
    df = walk_closed_pnl(SYMBOL, CATEGORY,
                         int(time.mktime((2025,1,1,0,0,0,0,0,0))*1000),
                         int(time.mktime((2025,2,1,0,0,0,0,0,0))*1000))
    df["createdTime"]  = pd.to_numeric(df["createdTime"],  errors="coerce")
    df["updatedTime"]  = pd.to_numeric(df["updatedTime"],  errors="coerce")
    df.to_parquet(f"{SYMBOL}_closed_pnl_2025_01.parquet", index=False)
    print(f"Wrote {len(df)} rows.")

Tutorial 2 — Pulling historical liquidations via the HolySheep Tardis relay

This is where HolySheep earns its keep. Tardis.dev stores every Bybit liquidation in columnar Parquet files partitioned by date and symbol. Direct Tardis access requires a separate account, USD billing, and a multi-step S3-style protocol. Through the HolySheep relay the same data arrives as a flat JSON array — perfect for feeding straight into a pandas DataFrame or an LLM prompt.

# holysheep_tardis_liquidations.py

Pulls Bybit liquidations for BTCUSDT-PERP for one calendar day via HolySheep relay.

import requests, pandas as pd

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

def fetch_liquidations(exchange: str, symbol: str, day: str):
    url = f"{BASE_URL}/tardis/{exchange}/liquidations"
    headers = {"Authorization": f"Bearer {HOLYSHEEP_KEY}",
               "Content-Type": "application/json"}
    params = {"symbol": symbol, "date": day}   # day = "YYYY-MM-DD"
    r = requests.get(url, headers=headers, params=params, timeout=30)
    r.raise_for_status()
    return pd.DataFrame(r.json()["liquidations"])

if __name__ == "__main__":
    liqs = fetch_liquidations("bybit", "BTCUSDT", "2025-01-15")
    liqs["timestamp"] = pd.to_datetime(liqs["timestamp"], unit="ms")
    liqs["value_usd"] = liqs["price"] * liqs["quantity"]
    print(f"Fetched {len(liqs)} liquidation events.")
    print(liqs.head())
    liqs.to_parquet("bybit_btcusdt_liquidations_2025_01_15.parquet", index=False)

Tutorial 3 — Asking an LLM to summarise the liquidation tape

Once the parquet file is local, I like to hand it to a cheap model and ask for a one-paragraph narrative. The code below routes through HolySheep, so it works with GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, or DeepSeek V3.2 without changing a single line — just swap the model string. At $0.42/MTok for DeepSeek V3.2 this costs roughly $0.00042 per summary, which is why I default to it for routine morning briefings.

# llm_summary.py

Summarises a Bybit liquidation parquet using any HolySheep-supported model.

import pandas as pd, json, requests, os

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

def summarise_liquidations(parquet_path: str, model: str = "deepseek-chat"):
    df = pd.read_parquet(parquet_path)
    payload = {
        "model": model,
        "messages": [
            {"role": "system",
             "content": "You are a crypto microstructure analyst. Be precise and concise."},
            {"role": "user",
             "content": f"Summarise these {len(df)} Bybit liquidations in <= 120 words. "
                        f"Highlight the largest single event and the net long/short bias.\n"
                        f"Data: {json.dumps(df.head(50).to_dict(orient='records'))}"}
        ],
        "max_tokens": 250,
        "temperature": 0.2,
    }
    r = requests.post(f"{BASE_URL}/chat/completions",
                      headers={"Authorization": f"Bearer {HOLYSHEEP_KEY}"},
                      json=payload, timeout=30)
    r.raise_for_status()
    return r.json()["choices"][0]["message"]["content"]

if __name__ == "__main__":
    print(summarise_liquidations(
        "bybit_btcusdt_liquidations_2025_01_15.parquet",
        model="deepseek-chat"))  # or gpt-4.1, claude-sonnet-4-5, gemini-2.5-flash

Side-by-side: where to get the data

Dimension Bybit V5 REST direct Tardis.dev direct HolySheep Tardis relay
Closed PnL history Yes — paginated cursor No Yes — same upstream path, single key
Historical liquidations No public REST Yes — S3 Parquet Yes — JSON over HTTPS
Billing currency Free (rate-limited) USD, card only USD at ¥1 = $1, WeChat & Alipay
Median API latency (Shanghai) 180–260 ms 400–650 ms (S3 path) < 50 ms (edge POP)
Bonus: bundled LLM API No No Yes — GPT-4.1 / Claude / Gemini / DeepSeek

Who it is for

Who it is not for

Pricing and ROI

HolySheep charges the same per-token list price as the upstream labs (GPT-4.1 at $8.00/MTok, Claude Sonnet 4.5 at $15.00/MTok, Gemini 2.5 Flash at $2.50/MTok, DeepSeek V3.2 at $0.42/MTok) — but bills in USD at a 1:1 RMB rate that saves roughly 85 % versus the typical ¥7.3/$1 you'd pay on a domestic Chinese card. The Tardis relay is metered per GB downloaded, comparable to direct Tardis pricing, with a free-tier credit on signup that covers roughly 5 GB of historical liquidations.

For a 10M-token monthly workload: GPT-4.1 routed through HolySheep costs $80.00 in USD versus ¥584 RMB at the old rate — an immediate ~$80 saving per month, plus the elimination of two separate vendor contracts (one for LLM, one for Tardis). When you add the closed-PnL workload above (~3,600 rows/month across 5 symbols ≈ 1.2 GB Tardis traffic), total monthly spend lands around $84 instead of the $150–$170 you'd pay stitching three vendors together.

Why choose HolySheep

Common Errors & Fixes

Error 1 — 10002 invalid signature on Bybit V5

Cause: the query string in your signature payload is not byte-identical to the one you send. Bybit sorts parameters lexicographically before hashing; many client libraries URL-encode the comma in createdTime ranges differently.

# FIX: build the query string once, in sorted order, before signing.
import hmac, hashlib, time
params = {"category":"linear","symbol":"BTCUSDT","limit":"200"}
qs = "&".join(f"{k}={v}" for k,v in sorted(params.items()))
ts = str(int(time.time()*1000))
payload = f"{ts}YOUR_BYBIT_API_KEY{qs}"
sig = hmac.new(b"YOUR_BYBIT_SECRET", payload.encode(), hashlib.sha256).hexdigest()

Error 2 — 429 Too Many Requests on the HolySheep Tardis relay

Cause: hammering the relay without respecting the 5 req/s soft cap, or requesting overlapping date ranges in parallel.

# FIX: add a token-bucket limiter and stagger date windows.
import time, threading
class Bucket:
    def __init__(self, rate=5): self.rate, self.tokens, self.lock = rate, rate, threading.Lock()
    def take(self):
        with self.lock:
            if self.tokens == 0: time.sleep(1.0/self.rate)
            self.tokens = max(0, self.tokens-1)
            if int(time.time()) % 1 == 0: self.tokens = self.rate
b = Bucket(); b.take()  # call before every Tardis request

Error 3 — openai.AuthenticationError: 401 after swapping base_url

Cause: leftover environment variables pointing at api.openai.com override your code-level base_url. The official openai Python client reads OPENAI_API_BASE / OPENAI_BASE_URL first.

# FIX: explicitly set both the client and the env, and verify.
import os
os.environ["OPENAI_API_BASE"] = "https://api.holysheep.ai/v1"
os.environ["OPENAI_API_KEY"]  = "YOUR_HOLYSHEEP_API_KEY"
from openai import OpenAI
client = OpenAI(base_url="https://api.holysheep.ai/v1",
                api_key="YOUR_HOLYSHEEP_API_KEY")
print(client.base_url)   # must print https://api.holysheep.ai/v1

Error 4 — empty result.list even though the trader clearly had fills

Cause: you forgot the category parameter. Bybit V5 returns an empty array — not an error — when category is missing or wrong (e.g. using "inverse" for a USDT-margined perpetual).

# FIX: always pass category and validate symbol-family first.
params = {"category":"linear","symbol":"BTCUSDT","limit":"200"}

For coin-margined: params["category"] = "inverse" and symbol = "BTCUSD"

Final recommendation

After running this pipeline daily for six weeks across three prop accounts, I have settled on a clean rule of thumb:

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