Best Time-Series Databases for Tick Data: Marketstore, TectonicDB, and ArcticDB Compared

Marketstore, TectonicDB, and ArcticDB are the three leading time-series databases for tick data, offering columnar compression, append-only storage, and high-throughput ingestion tailored for high-frequency trading workflows.

Storing high-frequency tick data demands specialized time-series databases that can handle massive write rates while preserving chronological order and enabling fast time-range queries. According to the paperswithbacktest/awesome-systematic-trading repository, three open-source solutions stand out for quantitative research and live trading pipelines. Each offers distinct architectural advantages optimized for financial time-series workloads, as documented in the README.md – Databases section and illustrated in the static/strategies/ examples.

Why Tick Data Requires Specialized Storage

Tick data generates millions of events per second, requiring storage layers that prioritize ordered writes, columnar compression, and efficient time-range queries. Traditional relational databases struggle with the append-only nature of market data and the need for vectorized reads during backtesting. The databases listed in awesome-systematic-trading address these constraints through language-specific optimizations: Go for concurrent network serving, Rust for lock-free memory safety, and Python for zero-copy pandas integration.

Top Time-Series Databases for Tick Data

Marketstore: The Column-Oriented DataFrame Server

Marketstore is a column-oriented DataFrame server written in Go that stores data in compressed binary files and exposes a SQL-like query interface over TCP. It uses an append-only log format to guarantee chronological order and supports vectorized reads for fast time-range scans.

Key features for tick storage include:

• Native OHLCV support alongside raw tick streams
• LZ4 compression reducing storage footprint without decompression penalties
• REST/gRPC APIs making it language-agnostic for heterogeneous trading stacks

In README.md, Marketstore is highlighted for its ability to serve compressed columnar data efficiently to Python clients.

TectonicDB: Sub-Microsecond Latency in Rust

TectonicDB is a stand-alone time-series engine written in Rust that stores tick data in columnar, highly-compressed files and provides a binary streaming protocol for low-latency ingestion. Its lock-free design enables multi-producer writes without sacrificing consistency.

Architectural advantages include:

• Schema-driven storage permitting flexible fields (price, size, exchange timestamp)
• Snapshot and replay capabilities essential for deterministic backtesting
• Sub-microsecond latency optimized for order-book tick processing

The repository notes TectonicDB's suitability for strategies requiring precise event reconstruction from the static/strategies/ implementations.

ArcticDB: Hybrid Memory-Mapped Storage

ArcticDB (developed by Man Group) is a high-performance datastore combining a memory-mapped column store with an append-only journal to deliver fast reads and durable writes. It supports both in-memory and on-disk modes, enabling hybrid workloads where recent ticks reside in RAM while historical data persists to storage.

Critical features for quantitative researchers:

• Zero-copy reads enabling ultra-fast backtesting loops without serialization overhead
• Versioned snapshots allowing rollback to any point in time for strategy reproducibility
• Native Python API (arctic) integrating seamlessly with pandas DataFrames

Implementation Examples

Writing Ticks to Marketstore (Go)

Marketstore provides a REST endpoint for ingesting tick batches. The following Go snippet demonstrates writing a slice of Tick structs to the server:

package main

import (
	"bytes"
	"encoding/json"
	"fmt"
	"net/http"
)

// Tick represents a single trade tick.
type Tick struct {
	Timestamp int64   `json:"timestamp"` // Unix ns
	Price     float64 `json:"price"`
	Size      int64   `json:"size"`
}

// WriteTicks sends a batch of ticks to Marketstore via its REST endpoint.
func WriteTicks(ticks []Tick) error {
	body, _ := json.Marshal(ticks)
	resp, err := http.Post("http://localhost:5998/write", "application/json", bytes.NewReader(body))
	if err != nil {
		return err
	}
	defer resp.Body.Close()
	if resp.StatusCode != http.StatusOK {
		return fmt.Errorf("unexpected status %s", resp.Status)
	}
	return nil
}

Because Marketstore stores data column-wise, this JSON request remains efficient even for millions of rows, leveraging the server's LZ4 compression.

Streaming Ticks with TectonicDB (Rust)

TectonicDB offers a type-safe Rust client for atomic tick insertion and time-range queries:

use tectonicdb::{Client, Tick};
use chrono::Utc;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // Connect to a local TectonicDB instance.
    let mut client = Client::connect("127.0.0.1:8080").await?;

    // Example tick
    let tick = Tick::new(Utc::now().timestamp_nanos(), 101.23, 100);
    client.insert_tick("AAPL", tick).await?;

    // Query a time range
    let start = Utc::now() - chrono::Duration::minutes(5);
    let end = Utc::now();
    let ticks = client.query_range("AAPL", start, end).await?;
    println!("Got {} ticks", ticks.len());
    Ok(())
}

The insert_tick method appends to on-disk columnar files using a lock-free algorithm, while query_range returns timestamp-ordered vectors suitable for replay engines.

Managing Tick History in ArcticDB (Python)

ArcticDB's Python API manages append-only journals and columnar snapshots through the TICK_STORE library type:

import pandas as pd
from arctic import Arctic, TICK_STORE

# Connect to a local Arctic server

store = Arctic('localhost')
store.initialize_library('ticks', lib_type=TICK_STORE)
ticks_lib = store['ticks']

# Assume `ticks` is a DataFrame with a DatetimeIndex

ticks_lib.append('AAPL', ticks)

# Retrieve the last hour of data

df = ticks_lib.read('AAPL', start=pd.Timestamp.now() - pd.Timedelta('1h'))
print(df.head())

The append operation is atomic, and reads utilize zero-copy memory mapping, enabling pandas-based backtesting strategies to iterate through millions of ticks without garbage collection overhead.

Summary

• Marketstore excels as a language-agnostic DataFrame server with LZ4 compression and REST interfaces, ideal for teams using heterogeneous tech stacks.
• TectonicDB provides the lowest ingestion latency through its Rust implementation and lock-free architecture, suited for high-frequency order-book reconstruction.
• ArcticDB offers superior Python integration with versioned snapshots and zero-copy reads, optimizing for research reproducibility and pandas-centric workflows.
• All three databases implement append-only storage and columnar compression to handle the write-heavy, read-optimized patterns characteristic of tick data analysis.

Frequently Asked Questions

What makes a time-series database suitable for tick data?

Tick data requires ordered writes to preserve market event sequences, columnar compression to manage storage costs for millions of records, and vectorized time-range queries for efficient backtesting. Databases like Marketstore, TectonicDB, and ArcticDB optimize specifically for these patterns rather than general-purpose transactional workloads.

How does ArcticDB handle data versioning for backtesting?

ArcticDB maintains versioned snapshots through its append-only journal structure, allowing researchers to roll back to any historical point in time. This feature ensures strategy reproducibility by guaranteeing that backtests run against exactly the same tick sequence observed at a specific timestamp.

Which time-series database offers the lowest latency for tick ingestion?

TectonicDB provides sub-microsecond ingestion latency through its Rust implementation and lock-free multi-producer architecture. The binary streaming protocol minimizes serialization overhead, making it optimal for capturing high-frequency order-book updates.

Can these databases integrate with Python-based trading strategies?

ArcticDB offers native Python integration via the arctic library and pandas DataFrames. Marketstore provides HTTP/REST endpoints accessible from Python through requests, while TectonicDB maintains Python client bindings that wrap its binary protocol, enabling all three to serve Python-centric systematic trading pipelines.

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