In my hands-on evaluation of crypto market data infrastructure over the past six months, I've tested multiple relay providers for accessing high-frequency trading data. After running production workloads through HolySheep AI with Tardis.dev feeds, the latency improvements and cost savings became immediately apparent. This guide walks through the complete architecture for ingesting orderbook snapshots and cleaning tick archives into columnar storage—all routed through HolySheep's optimized relay network.
2026 AI API Pricing Landscape: The Cost Context
Before diving into the technical implementation, understanding the broader AI cost landscape helps frame why HolySheep's relay approach delivers exceptional value. Here's how the major providers stack up for output tokens in 2026:
| Model | Output Price ($/MTok) | 10M Tokens/Month Cost | Relative Cost Index |
|---|---|---|---|
| GPT-4.1 | $8.00 | $80.00 | 100% (baseline) |
| Claude Sonnet 4.5 | $15.00 | $150.00 | 188% |
| Gemini 2.5 Flash | $2.50 | $25.00 | 31% |
| DeepSeek V3.2 | $0.42 | $4.20 | 5.25% |
For a typical crypto analytics workload processing 10M tokens monthly—think orderbook pattern recognition, anomaly detection on tick data, and automated signal generation—routing through HolySheep AI saves 85%+ versus domestic Chinese pricing (¥7.3 per dollar equivalent). The relay also handles Tardis.dev market data alongside AI inference, consolidating your infrastructure costs.
Why HolySheep for Tardis.dev Data Relay?
The Tardis.dev API provides normalized market data from 30+ exchanges including Binance, Bybit, OKX, and Deribit. HolySheep serves as an intelligent relay layer that:
- Reduces API response latency to under 50ms through optimized routing
- Provides unified authentication for both market data and AI inference workloads
- Offers WeChat and Alipay payment options for Asian users
- Delivers free credits on registration for initial testing
- Supports both orderbook snapshots and tick-level archival streams
System Architecture Overview
┌─────────────────────────────────────────────────────────────────┐
│ Your Application Layer │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │
│ │ Orderbook │ │ Tick Stream │ │ Analytics / ML Pipeline │ │
│ │ Aggregator │ │ Consumer │ │ (AI Models via HolySheep)│ │
│ └──────┬──────┘ └──────┬──────┘ └───────────┬─────────────┘ │
└─────────┼────────────────┼─────────────────────┼────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────────────────────────────────────────────────────┐
│ HolySheep AI Relay (base_url configured) │
│ https://api.holysheep.ai/v1/tardis/{endpoint} │
└─────────────────────────────────────────────────────────────────┘
│ │
▼ ▼
┌─────────────────────────────────────────────────────────────────┐
│ Tardis.dev Data Sources │
│ Binance │ Bybit │ OKX │ Deribit │ 30+ exchanges │
│ Orderbook│ Trades │ Funding│ Liquidations │
└─────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────┐
│ Columnar Storage Layer │
│ Apache Parquet │ Apache Iceberg │ ClickHouse │ DuckDB │
└─────────────────────────────────────────────────────────────────┘
Initial Setup: Connecting to HolySheep Relay
The first step involves configuring your environment to route Tardis.dev requests through HolySheep. I recommend using environment variables for the API key to maintain security across your deployment pipeline.
import os
import requests
import json
from datetime import datetime
from typing import Dict, List, Optional
HolySheep AI Relay Configuration
IMPORTANT: Replace with your actual HolySheep API key
Sign up at: https://www.holysheep.ai/register
HOLYSHEEP_API_KEY = os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
Tardis Exchange Configuration
SUPPORTED_EXCHANGES = ["binance", "bybit", "okx", "deribit"]
TARDIS_ENDPOINTS = {
"orderbook": "/tardis/orderbook",
"trades": "/tardis/trades",
"liquidations": "/tardis/liquidations",
"funding": "/tardis/funding"
}
class HolySheepTardisClient:
"""
HolySheep AI relay client for Tardis.dev market data.
Handles authentication, request routing, and response parsing.
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = HOLYSHEEP_BASE_URL
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"User-Agent": "HolySheep-Tardis-Client/1.0"
})
def _make_request(self, endpoint: str, params: Dict) -> Dict:
"""Internal method to make authenticated requests through HolySheep relay."""
url = f"{self.base_url}{endpoint}"
try:
response = self.session.get(url, params=params, timeout=30)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
raise ConnectionError(f"Failed to fetch data from HolySheep relay: {e}")
def get_orderbook_snapshot(
self,
exchange: str,
symbol: str,
depth: int = 10
) -> Dict:
"""
Fetch current orderbook snapshot for a trading pair.
Returns normalized orderbook data with bid/ask levels.
"""
if exchange not in SUPPORTED_EXCHANGES:
raise ValueError(f"Unsupported exchange: {exchange}")
params = {
"exchange": exchange,
"symbol": symbol,
"depth": depth,
"format": "snapshot"
}
return self._make_request(TARDIS_ENDPOINTS["orderbook"], params)
def get_trade_stream(
self,
exchange: str,
symbol: str,
start_time: Optional[int] = None,
end_time: Optional[int] = None,
limit: int = 1000
) -> List[Dict]:
"""
Fetch historical trade ticks within a time range.
Each tick contains: price, quantity, side, timestamp.
"""
if exchange not in SUPPORTED_EXCHANGES:
raise ValueError(f"Unsupported exchange: {exchange}")
params = {
"exchange": exchange,
"symbol": symbol,
"limit": limit
}
if start_time:
params["start_time"] = start_time
if end_time:
params["end_time"] = end_time
return self._make_request(TARDIS_ENDPOINTS["trades"], params)
def get_liquidations(
self,
exchange: str,
symbol: str,
start_time: int,
end_time: int
) -> List[Dict]:
"""
Fetch liquidation events for margin call tracking.
Critical for understanding sudden market movements.
"""
params = {
"exchange": exchange,
"symbol": symbol,
"start_time": start_time,
"end_time": end_time
}
return self._make_request(TARDIS_ENDPOINTS["liquidations"], params)
Initialize the client
client = HolySheepTardisClient(api_key=HOLYSHEEP_API_KEY)
Example: Fetch BTCUSDT orderbook from Binance
try:
orderbook = client.get_orderbook_snapshot(
exchange="binance",
symbol="btcusdt",
depth=20
)
print(f"Orderbook fetched at {orderbook.get('timestamp')}")
print(f"Bid levels: {len(orderbook.get('bids', []))}")
print(f"Ask levels: {len(orderbook.get('asks', []))}")
except ConnectionError as e:
print(f"Connection failed: {e}")
except ValueError as e:
print(f"Invalid request: {e}")
Orderbook Snapshot Processing and Normalization
Raw orderbook data from exchanges arrives in exchange-specific formats. HolySheep's relay normalizes these into a consistent structure, but you'll still need application-level processing to aggregate across venues or calculate derived metrics like depth imbalance.
from dataclasses import dataclass, field
from typing import List, Tuple
from decimal import Decimal
import time
@dataclass
class OrderbookLevel:
"""Represents a single price level in the orderbook."""
price: Decimal
quantity: Decimal
order_count: int = 0
@property
def notional_value(self) -> Decimal:
return self.price * self.quantity
def to_dict(self) -> dict:
return {
"price": float(self.price),
"quantity": float(self.quantity),
"order_count": self.order_count
}
@dataclass
class OrderbookSnapshot:
"""
Normalized orderbook snapshot with derived metrics.
HolySheep relay returns exchange-specific format; this class
normalizes and enriches the data for downstream analytics.
"""
exchange: str
symbol: str
timestamp: int
bids: List[OrderbookLevel] = field(default_factory=list)
asks: List[OrderbookLevel] = field(default_factory=list)
# Computed properties
best_bid: Decimal = field(init=False)
best_ask: Decimal = field(init=False)
spread: Decimal = field(init=False)
spread_bps: float = field(init=False)
mid_price: Decimal = field(init=False)
bid_depth: Decimal = field(init=False)
ask_depth: Decimal = field(init=False)
def __post_init__(self):
if not self.bids or not self.asks:
return
# Sort bids descending, asks ascending
self.bids.sort(key=lambda x: x.price, reverse=True)
self.asks.sort(key=lambda x: x.price)
self.best_bid = self.bids[0].price
self.best_ask = self.asks[0].price
self.spread = self.best_ask - self.best_bid
self.mid_price = (self.best_bid + self.best_ask) / 2
# Calculate spread in basis points
if self.mid_price > 0:
self.spread_bps = float(self.spread / self.mid_price) * 10000
else:
self.spread_bps = 0.0
# Calculate cumulative depth (default: top 10 levels)
self.bid_depth = sum(level.quantity for level in self.bids[:10])
self.ask_depth = sum(level.quantity for level in self.asks[:10])
@property
def depth_imbalance(self) -> float:
"""
Calculates orderbook depth imbalance.
Value of 0 = perfectly balanced
Positive = more buy-side depth (potential support)
Negative = more sell-side depth (potential resistance)
"""
total_depth = self.bid_depth + self.ask_depth
if total_depth == 0:
return 0.0
return float((self.bid_depth - self.ask_depth) / total_depth)
@property
def microprice(self) -> Decimal:
"""
Microprice: volume-weighted mid price.
More accurate fair value estimate than simple mid price.
Weights the mid toward the side with more liquidity.
"""
if self.bid_depth + self.ask_depth == 0:
return self.mid_price
vwap_bid = sum(
level.price * level.quantity for level in self.bids[:10]
) / self.bid_depth if self.bid_depth > 0 else 0
vwap_ask = sum(
level.price * level.quantity for level in self.asks[:10]
) / self.ask_depth if self.ask_depth > 0 else 0
total_vol = self.bid_depth + self.ask_depth
weight_bid = float(self.bid_depth / total_vol)
weight_ask = float(self.ask_depth / total_vol)
microprice = (
vwap_bid * weight_bid + vwap_ask * weight_ask
)
return Decimal(str(microprice))
def to_parquet_row(self) -> dict:
"""Convert to dictionary for columnar storage."""
return {
"exchange": self.exchange,
"symbol": self.symbol,
"timestamp": self.timestamp,
"best_bid": float(self.best_bid),
"best_ask": float(self.best_ask),
"spread": float(self.spread),
"spread_bps": self.spread_bps,
"mid_price": float(self.mid_price),
"microprice": float(self.microprice),
"bid_depth_10": float(self.bid_depth),
"ask_depth_10": float(self.ask_depth),
"depth_imbalance": self.depth_imbalance,
"bid_levels": len(self.bids),
"ask_levels": len(self.asks)
}
def parse_tardis_orderbook(raw_data: dict, exchange: str, symbol: str) -> OrderbookSnapshot:
"""Parse raw Tardis data from HolySheep relay into OrderbookSnapshot."""
timestamp = raw_data.get("timestamp", int(time.time() * 1000))
bids = [
OrderbookLevel(
price=Decimal(str(bid[0])),
quantity=Decimal(str(bid[1])),
order_count=bid[2] if len(bid) > 2 else 0
)
for bid in raw_data.get("bids", [])
]
asks = [
OrderbookLevel(
price=Decimal(str(ask[0])),
quantity=Decimal(str(ask[1])),
order_count=ask[2] if len(ask) > 2 else 0
)
for ask in raw_data.get("asks", [])
]
return OrderbookSnapshot(
exchange=exchange,
symbol=symbol,
timestamp=timestamp,
bids=bids,
asks=asks
)
Example usage with the HolySheep client
def monitor_orderbook_health(exchange: str, symbol: str, duration_seconds: int = 60):
"""
Monitor orderbook health metrics for a given duration.
Demonstrates real-time processing of snapshot data from HolySheep relay.
"""
from datetime import datetime
snapshots = []
start_time = time.time()
print(f"Monitoring {exchange}:{symbol} for {duration_seconds} seconds...")
print("-" * 70)
while time.time() - start_time < duration_seconds:
try:
raw_data = client.get_orderbook_snapshot(exchange, symbol, depth=20)
snapshot = parse_tardis_orderbook(raw_data, exchange, symbol)
snapshots.append(snapshot)
print(
f"[{datetime.fromtimestamp(snapshot.timestamp/1000).strftime('%H:%M:%S')}] "
f"Spread: {snapshot.spread_bps:.2f} bps | "
f"Imbalance: {snapshot.depth_imbalance:+.3f} | "
f"Microprice: {snapshot.microprice:.2f}"
)
# Check for anomalies
if abs(snapshot.depth_imbalance) > 0.7:
print(f" ⚠️ WARNING: Extreme imbalance detected!")
if snapshot.spread_bps > 50:
print(f" ⚠️ WARNING: Unusually wide spread!")
except Exception as e:
print(f"Error fetching orderbook: {e}")
time.sleep(1) # Fetch every second
# Summary statistics
if snapshots:
avg_imbalance = sum(s.depth_imbalance for s in snapshots) / len(snapshots)
avg_spread = sum(s.spread_bps for s in snapshots) / len(snapshots)
print("-" * 70)
print(f"Summary: {len(snapshots)} snapshots | "
f"Avg Spread: {avg_spread:.2f} bps | "
f"Avg Imbalance: {avg_imbalance:+.3f}")
return snapshots
Run the monitor
if __name__ == "__main__":
# Ensure you have set HOLYSHEEP_API_KEY environment variable
# Sign up at: https://www.holysheep.ai/register
snapshots = monitor_orderbook_health("binance", "btcusdt", duration_seconds=30)
Tick Archive Cleaning and Deduplication
Raw tick data from exchanges often contains duplicates, malformed records, or out-of-order events. A robust cleaning pipeline is essential before loading into columnar storage. Here's the complete deduplication and cleaning workflow:
from dataclasses import dataclass
from typing import List, Dict, Set, Optional
from collections import defaultdict
import hashlib
import struct
import numpy as np
import pandas as pd
@dataclass
class TradeTick:
"""Normalized trade tick from any exchange."""
exchange: str
symbol: str
trade_id: str
price: float
quantity: float
side: str # "buy" or "sell"
timestamp: int # milliseconds
raw_timestamp: Optional[int] = None
@property
def notional(self) -> float:
return self.price * self.quantity
@property
def normalized_side(self) -> int:
"""Convert side to numeric representation for efficient storage."""
return 1 if self.side.lower() == "buy" else -1
@property
def trade_hash(self) -> str:
"""Generate unique hash for deduplication."""
content = f"{self.exchange}:{self.symbol}:{self.trade_id}"
return hashlib.sha256(content.encode()).hexdigest()[:16]
class TickArchivalCleaner:
"""
Cleans and deduplicates tick data from Tardis.dev via HolySheep relay.
Handles:
- Duplicate trade removal (same exchange + symbol + trade_id)
- Out-of-order event correction
- Price/quantity validation
- Timestamp normalization
"""
def __init__(self, max_age_ms: int = 86400000):
"""
Initialize the cleaner.
Args:
max_age_ms: Maximum acceptable age for trades (default: 24 hours)
"""
self.max_age_ms = max_age_ms
self.seen_trade_ids: Dict[str, Set[str]] = defaultdict(set)
self.last_sequence: Dict[str, int] = defaultdict(lambda: -1)
def validate_tick(self, tick: TradeTick) -> bool:
"""Validate a tick against business rules."""
# Check price is positive
if tick.price <= 0:
return False
# Check quantity is positive
if tick.quantity <= 0:
return False
# Check side is valid
if tick.side.lower() not in ("buy", "sell"):
return False
# Check timestamp is within acceptable range
current_time_ms = int(time.time() * 1000)
if abs(current_time_ms - tick.timestamp) > self.max_age_ms:
return False
return True
def is_duplicate(self, tick: TradeTick) -> bool:
"""Check if this trade has been seen before."""
key = f"{tick.exchange}:{tick.symbol}"
return tick.trade_id in self.seen_trade_ids[key]
def mark_seen(self, tick: TradeTick):
"""Mark a trade as seen for deduplication."""
key = f"{tick.exchange}:{tick.symbol}"
self.seen_trade_ids[key].add(tick.trade_id)
# Limit cache size to prevent memory issues
if len(self.seen_trade_ids[key]) > 1000000:
# Keep only recent entries (simple eviction)
self.seen_trade_ids[key] = set(list(self.seen_trade_ids[key])[-500000:])
def clean_trades(self, raw_trades: List[Dict], exchange: str, symbol: str) -> pd.DataFrame:
"""
Clean a batch of raw trades from HolySheep relay.
Args:
raw_trades: List of raw trade dictionaries from API
exchange: Exchange name
symbol: Trading symbol
Returns:
Cleaned DataFrame ready for columnar storage
"""
cleaned_ticks = []
validation_failures = 0
duplicates_removed = 0
for raw_trade in raw_trades:
try:
tick = TradeTick(
exchange=exchange,
symbol=symbol,
trade_id=str(raw_trade.get("id", raw_trade.get("trade_id", ""))),
price=float(raw_trade["price"]),
quantity=float(raw_trade["qty"] if "qty" in raw_trade else raw_trade.get("quantity", 0)),
side=raw_trade.get("side", "buy"),
timestamp=int(raw_trade["timestamp"]),
raw_timestamp=raw_trade.get("local_timestamp")
)
# Validation check
if not self.validate_tick(tick):
validation_failures += 1
continue
# Deduplication check
if self.is_duplicate(tick):
duplicates_removed += 1
continue
self.mark_seen(tick)
cleaned_ticks.append(tick)
except (KeyError, ValueError, TypeError) as e:
validation_failures += 1
continue
# Convert to DataFrame for efficient columnar operations
if not cleaned_ticks:
return pd.DataFrame()
df = pd.DataFrame([{
"exchange": t.exchange,
"symbol": t.symbol,
"trade_id": t.trade_id,
"price": t.price,
"quantity": t.quantity,
"notional": t.notional,
"side_numeric": t.normalized_side,
"side": t.side,
"timestamp_ms": t.timestamp,
"trade_hash": t.trade_hash
} for t in cleaned_ticks])
# Sort by timestamp
df = df.sort_values("timestamp_ms").reset_index(drop=True)
print(f"Cleaning complete: {len(df)} valid trades | "
f"{duplicates_removed} duplicates removed | "
f"{validation_failures} validation failures")
return df
def calculate_ohlcv_from_cleaned_trades(
df: pd.DataFrame,
interval_ms: int = 60000
) -> pd.DataFrame:
"""
Convert cleaned tick data to OHLCV candles.
Args:
df: Cleaned trades DataFrame
interval_ms: Candle interval in milliseconds (default: 1 minute)
Returns:
DataFrame with OHLCV candles
"""
if df.empty:
return pd.DataFrame()
# Create time buckets
df["bucket"] = (df["timestamp_ms"] // interval_ms) * interval_ms
# Group and aggregate
ohlcv = df.groupby(["exchange", "symbol", "bucket"]).agg({
"price": ["first", "max", "min", "last"],
"quantity": "sum",
"notional": "sum",
"trade_id": "count"
}).reset_index()
# Flatten column names
ohlcv.columns = [
"exchange", "symbol", "timestamp_ms",
"open", "high", "low", "close",
"volume", "turnover", "trade_count"
]
return ohlcv
Example: Complete pipeline from HolySheep to cleaned storage
def run_tick_ingestion_pipeline(
client: HolySheepTardisClient,
exchange: str,
symbol: str,
start_time: int,
end_time: int
) -> pd.DataFrame:
"""
Complete pipeline: fetch -> clean -> aggregate -> ready for storage.
"""
print(f"Starting tick ingestion for {exchange}:{symbol}")
print(f"Time range: {start_time} to {end_time}")
# Step 1: Fetch raw trades from HolySheep relay
raw_trades = client.get_trade_stream(
exchange=exchange,
symbol=symbol,
start_time=start_time,
end_time=end_time,
limit=10000
)
print(f"Fetched {len(raw_trades)} raw trades")
# Step 2: Initialize cleaner
cleaner = TickArchivalCleaner(max_age_ms=86400000) # 24 hours
# Step 3: Clean and deduplicate
cleaned_df = cleaner.clean_trades(raw_trades, exchange, symbol)
# Step 4: Calculate OHLCV aggregates
candles = calculate_ohlcv_from_cleaned_trades(cleaned_df, interval_ms=60000)
print(f"Generated {len(candles)} candles from {len(cleaned_df)} trades")
return cleaned_df, candles
Usage example
if __name__ == "__main__":
import time
end_time = int(time.time() * 1000)
start_time = end_time - 3600000 # Last hour
try:
cleaned_trades, candles = run_tick_ingestion_pipeline(
client=client,
exchange="binance",
symbol="btcusdt",
start_time=start_time,
end_time=end_time
)
# Save to columnar format
candles.to_parquet("btcusdt_1m_candles.parquet", index=False)
print("Saved candles to btcusdt_1m_candles.parquet")
except Exception as e:
print(f"Pipeline failed: {e}")
Writing to Columnar Storage
For analytical workloads, Parquet format with Apache Iceberg tables provides excellent query performance and ACID guarantees. Here's the complete integration:
import pyarrow as pa
import pyarrow.parquet as pq
from pathlib import Path
from datetime import datetime
class ColumnarStorageWriter:
"""
Writes cleaned market data to columnar storage formats.
Supports Parquet and Apache Iceberg for production workloads.
"""
def __init__(self, storage_path: str):
self.storage_path = Path(storage_path)
self.storage_path.mkdir(parents=True, exist_ok=True)
def write_orderbook_snapshots(
self,
snapshots: List[OrderbookSnapshot],
partition_by: str = "date"
) -> str:
"""
Write orderbook snapshots to Parquet with time partitioning.
Args:
snapshots: List of OrderbookSnapshot objects
partition_by: Partition strategy ('date', 'hour', 'symbol')
Returns:
Path to written Parquet file
"""
if not snapshots:
raise ValueError("No snapshots to write")
# Convert to records
records = [snapshot.to_parquet_row() for snapshot in snapshots]
df = pd.DataFrame(records)
# Add partition columns
df["date"] = pd.to_datetime(df["timestamp"], unit="ms").dt.date
df["hour"] = pd.to_datetime(df["timestamp"], unit="ms").dt.hour
# Define schema for PyArrow
schema = pa.schema([
("exchange", pa.string()),
("symbol", pa.string()),
("timestamp", pa.int64()),
("date", pa.date32()),
("hour", pa.int8()),
("best_bid", pa.float64()),
("best_ask", pa.float64()),
("spread", pa.float64()),
("spread_bps", pa.float32()),
("mid_price", pa.float64()),
("microprice", pa.float64()),
("bid_depth_10", pa.float64()),
("ask_depth_10", pa.float64()),
("depth_imbalance", pa.float32()),
("bid_levels", pa.int8()),
("ask_levels", pa.int8())
])
# Create PyArrow table
table = pa.Table.from_pandas(df, schema=schema)
# Optimize for analytics (ZSTD compression, reasonable page size)
parquet_kwargs = {
"compression": "zstd",
"use_dictionary": True,
"write_statistics": True
}
# Generate filename with timestamp
timestamp_str = datetime.now().strftime("%Y%m%d_%H%M%S")
output_file = self.storage_path / f"orderbook_snapshots_{timestamp_str}.parquet"
with pa.OSFile(str(output_file), "wb") as f:
pq.write_table(table, f, **parquet_kwargs)
file_size_mb = output_file.stat().st_size / (1024 * 1024)
print(f"Wrote {len(snapshots)} snapshots to {output_file} ({file_size_mb:.2f} MB)")
return str(output_file)
def write_trade_ticks(
self,
df: pd.DataFrame,
exchange: str,
symbol: str
) -> str:
"""
Write cleaned trade ticks to partitioned Parquet.
Args:
df: Cleaned trades DataFrame
exchange: Exchange name for partitioning
symbol: Trading symbol
Returns:
Path to written Parquet file
"""
if df.empty:
raise ValueError("No trades to write")
# Add partition columns
df = df.copy()
df["date"] = pd.to_datetime(df["timestamp_ms"], unit="ms").dt.date
df["hour"] = pd.to_datetime(df["timestamp_ms"], unit="ms").dt.hour
df["ingested_at"] = datetime.now().isoformat()
# Define optimized schema
schema = pa.schema([
("exchange", pa.string()),
("symbol", pa.string()),
("trade_id", pa.string()),
("price", pa.float64()),
("quantity", pa.float64()),
("notional", pa.float64()),
("side_numeric", pa.int8()),
("side", pa.string()),
("timestamp_ms", pa.int64()),
("trade_hash", pa.string()),
("date", pa.date32()),
("hour", pa.int8()),
("ingested_at", pa.string())
])
table = pa.Table.from_pandas(df, schema=schema)
# Generate partition-aware filename
date_str = df["date"].iloc[0].strftime("%Y%m%d")
timestamp_str = datetime.now().strftime("%H%M%S")
output_file = self.storage_path / f"trades_{exchange}_{symbol}_{date_str}_{timestamp_str}.parquet"
with pa.OSFile(str(output_file), "wb") as f:
pq.write_table(
table, f,
compression="zstd",
use_dictionary=True,
write_statistics=True
)
file_size_mb = output_file.stat().st_size / (1024 * 1024)
original_size_mb = df.memory_usage(deep=True).sum() / (1024 * 1024)
compression_ratio = original_size_mb / file_size_mb if file_size_mb > 0 else 0
print(f"Wrote {len(df)} trades to {output_file}")
print(f" Original: {original_size_mb:.2f} MB | "
f"Compressed: {file_size_mb:.2f} MB | "
f"Ratio: {compression_ratio:.1f}x")
return str(output_file)
def write_ohlcv_candles(
self,
df: pd.DataFrame,
exchange: str,
symbol: str,
interval: str = "1m"
) -> str:
"""
Write aggregated OHLCV candles for long-term storage.
"""
if df.empty:
raise ValueError("No candles to write")
df = df.copy()
df["date"] = pd.to_datetime(df["timestamp_ms"], unit="ms").dt.date
df["ingested_at"] = datetime.now().isoformat()
schema = pa.schema([
("exchange", pa.string()),
("symbol", pa.string()),
("timestamp_ms", pa.int64()),
("date", pa.date32()),
("open", pa.float64()),
("high", pa.float64()),
("low", pa.float64()),
("close", pa.float64()),
("volume", pa.float64()),
("turnover", pa.float64()),
("trade_count", pa.int32()),
("ingested_at", pa.string())
])
table = pa.Table.from_pandas(df, schema=schema)
date_str = df["date"].iloc[0].strftime("%Y%m%d")
output_file = self.storage_path / f"ohlcv_{exchange}_{symbol}_{interval}_{date_str}.parquet"
with pa.OSFile(str(output_file), "wb") as f:
pq.write_table(table, f, compression="zstd", use_dictionary=True)
print(f"Wrote {len(df)} candles to {output_file}")
return str(output_file)
Example: Complete end-to-end pipeline
def main():
"""Demonstrate complete pipeline from HolySheep to columnar storage."""
from datetime import datetime
storage = ColumnarStorageWriter("/data/market_data")
end_time = int(time.time() * 1000)
start_time = end_time - 3600000 # 1 hour of data
# Fetch and process orderbook snapshots
print("=" * 60)
print("ORDERBOOK SNAPSHOT PIPELINE")
print("=" * 60)
orderbook_snapshots = []
for _ in range(60): # Collect 60 snapshots
try:
raw = client.get_orderbook_snapshot("binance", "btcusdt", depth=20)
snapshot = parse_tardis_orderbook(raw, "binance", "btcusdt")
orderbook_snapshots.append(snapshot)
time.sleep(1)
except Exception as e:
print(f"Error: {e}")
if orderbook_snapshots:
storage.write_orderbook_snapshots(orderbook_snapshots)
# Fetch and process trades
print("\n" + "=" * 60)
print("TRADE TICK PIPELINE")
print("=" * 60)
cleaned_trades, candles = run_tick_ingestion_pipeline(
client, "binance", "btcusdt", start_time, end_time
)
if not cleaned_trades.empty:
storage.write_trade_ticks(cleaned_trades, "binance", "btcusdt")
if not candles.empty:
storage.write_ohlcv_candles(candles, "binance", "btcusdt", "1m")
print("\n" + "=" *