Tick-Data Storage System Development

Imagine you analyze the crypto market and your strategy needs access to every trade for the last two years. Without proper tick-data storage, this is impossible. Even experienced teams face typical issues: slow writes, high storage costs, and aggregation difficulties. We have designed and deployed dozens of such systems—from prototypes to production installations with billions of rows. Recently, a market maker approached us because their PostgreSQL couldn't handle the load: writes took over 5 seconds, and queries for a single trading day took minutes. After migrating to ClickHouse, write latency dropped to 50 ms, and on-the-fly aggregation now takes seconds.

Data Volumes

To understand the scale: Binance on BTC/USDT generates about 50,000–200,000 trades per day. Across all pairs on all exchanges—hundreds of millions of records daily. A year of data means tens of billions of rows. Ordinary PostgreSQL cannot handle this without special solutions.

Which Tick-Data Storage Technologies to Choose?

ClickHouse is a columnar DBMS from Yandex optimized for analytical queries. It compresses time series better than competitors and runs aggregations on billions of rows in seconds. A table partitioned by exchange and month looks like this:

CREATE TABLE trades (
    exchange    LowCardinality(String),
    symbol      LowCardinality(String),
    trade_id    String,
    timestamp   DateTime64(3, 'UTC'),
    price       Decimal(24, 8),
    quantity    Decimal(24, 8),
    side        LowCardinality(String),
    is_maker    Bool
)
ENGINE = MergeTree()
PARTITION BY (exchange, toYYYYMM(timestamp))
ORDER BY (exchange, symbol, timestamp)
SETTINGS index_granularity = 8192;

LowCardinality for strings with few unique values—automatic dictionary encoding saves significant space.

TimescaleDB is a good choice if you already use PostgreSQL and have moderate volumes (<1 billion rows). It supports hypertables and compression policies.

Arctic is a specialized solution for financial time series in Python, with versioning and tick-data support.

How We Design the Ingestion Pipeline

For maximum write performance, we use a buffer table:

-- Buffer: accumulates data in memory, flushes every 10 sec or 1M rows
CREATE TABLE trades_buffer AS trades
ENGINE = Buffer(currentDatabase(), 'trades', 16, 10, 100, 10000, 1000000, 10000000, 100000000);

-- Write to buffer, read from main table
INSERT INTO trades_buffer VALUES (...);
SELECT * FROM trades WHERE ...;

A Python pipeline with async buffering:

import asyncio
from collections import deque

class TickDataIngester:
    BATCH_SIZE = 10000
    FLUSH_INTERVAL = 5.0  # seconds

    def __init__(self, clickhouse_client):
        self.buffer = deque()
        self.client = clickhouse_client

    async def on_trade(self, trade: NormalizedTrade):
        self.buffer.append(trade)
        if len(self.buffer) >= self.BATCH_SIZE:
            await self.flush()

    async def flush(self):
        if not self.buffer:
            return
        batch = [self.buffer.popleft() for _ in range(min(self.BATCH_SIZE, len(self.buffer)))]
        await self.client.insert('trades_buffer', batch)

    async def flush_loop(self):
        while True:
            await asyncio.sleep(self.FLUSH_INTERVAL)
            await self.flush()

How to Aggregate Tick Data into OHLCV Candles

On-the-fly aggregation using ClickHouse window functions:

SELECT
    toStartOfInterval(timestamp, INTERVAL 1 MINUTE) AS candle_time,
    argMin(price, timestamp) AS open,
    max(price) AS high,
    min(price) AS low,
    argMax(price, timestamp) AS close,
    sum(quantity) AS volume,
    count() AS trade_count
FROM trades
WHERE exchange = 'binance'
  AND symbol = 'BTC/USDT'
  AND timestamp BETWEEN '2024-01-01 00:00:00' AND '2024-01-02 00:00:00'
GROUP BY candle_time
ORDER BY candle_time;

On ClickHouse, this query on 50 million rows executes in 1–3 seconds. An alternative approach is materialized views that precompute candles on insert. Comparison:

The choice depends on the scenario: for ad-hoc analytics use on-the-fly, for real-time dashboards use materialized views.

Compression and Retention

ClickHouse compresses data automatically. Additionally, we enable cold storage via TTL:

ALTER TABLE trades
MODIFY TTL timestamp + INTERVAL 1 YEAR TO DISK 'cold_storage';

Data older than one year is automatically moved to cheaper storage (S3-compatible).

Historical Data Backfill

To fill historical data, we use public exchange APIs. Binance provides trade history via /api/v3/aggTrades with pagination by fromId. Parallel backfill over time ranges with rate limiting loads years of data in a few hours.

What's Included in the Work (Deliverables)

• Architectural documentation: DBMS selection, partitioning scheme, retention and compression policies
• Ingestion pipeline development: buffering, deduplication, latency monitoring
• Source integration: exchange WebSocket, REST API, Kafka
• ClickHouse/TimescaleDB configuration tailored to your hardware profile
• Load testing: simulating peak loads up to 1 million records per second
• Operations documentation: backup/restore, upgrade, monitoring
• Guarantee: 30 days post-launch support and incident handling

Request a free consultation — we will analyze your workload and propose the optimal architecture.

Why Choose Us?

We have 5+ years of experience in developing data storage systems for cryptocurrency exchanges. We have completed over 30 projects with a total stored data volume exceeding 100 billion records. Our clients range from startups to large market makers. We guarantee that the system will perform as specified and not degrade under growing volumes.

Timeline and Cost

Development timeline: from 4 to 12 weeks depending on complexity and volume. Cost is calculated individually based on data volume, number of sources, and latency requirements. Contact us to get demo access to a working system under your load—we will conduct a preliminary assessment and propose a solution.

Why exchange development requires deep domain expertise

We develop exchanges — not 'chart sites,' but matching engines that process thousands of orders per second without delay, route liquidity between pools, and guarantee that no user gains access to others' funds. Teams that start with the UI and postpone the engine 'for later' end up rewriting everything in six months in 90% of cases.

Order Book vs AMM: where most projects break

Centralized exchanges (CEX) are built around an order book + matching engine. Decentralized exchanges (DEX) either also use an order book (dYdX on StarkEx, Serum/OpenBook on Solana) or an AMM with concentrated liquidity (Uniswap v3/v4, Curve, Balancer). A classic mistake when developing a CEX is implementing the matching engine on top of a relational database with transactions for each match. PostgreSQL handles ~500 RPS without special effort, but at peak loads of 5,000–10,000 orders per second, it turns into a deadlock nightmare. The correct architecture: in-memory order book (Redis Sorted Sets or custom C++/Rust structure), asynchronous writing of matches to PostgreSQL via a queue (Kafka/RabbitMQ), and a separate settlement service that finally updates balances.

For DEX, the most painful problem is sandwich attacks and MEV. A pool with a plain xy=k AMM without slippage protection becomes a target for MEV bots within hours of launch. Uniswap v2 lost hundreds of millions of dollars in user liquidity. Solutions: integration with Flashbots Protect, a commit-reveal scheme for orders, or switching to TWAMM (Time-Weighted AMM) for large trades.

Concentrated liquidity and impermanent loss

Uniswap v3 introduced concentrated liquidity – LPs choose a price range in which to provide liquidity. Capital efficiency increased 4,000x compared to v2 for stable pairs. But implementing this mechanism correctly is non-trivial. The Uniswap v3 liquidity contract uses tick-based accounting: the price space is divided into discrete ticks (tick = log₁.0001(price)), each tick stores accumulated fee growth and liquidity delta. When creating a position, the lower and upper ticks are computed, and the contract recalculates all active positions at each swap. Storage layout is critical here – incorrect variable packing in slots easily adds 40–60% to swap gas cost.

We implemented a Uniswap v3 fork for a client on Polygon with a custom fee tier system. The initial version consumed 180k gas for a swap across 2 ticks. After slot packing of variables in Tick.Info and inlining several internal calls, it dropped to 112k gas. This reduced gas costs by 38% and saved the client substantial costs on fees monthly. The techniques applied are described in the Uniswap v3 Whitepaper and confirmed by our audit experience.

How a matching engine delivers performance

A production-ready matching engine is built according to the following scheme:

• Order ingestion layer – WebSocket gateway (Go or Rust), accepts orders, validates signature, checks balance via Redis, queues them. Latency at this level must be <1ms.
• Matching core – single-threaded event loop (eliminates race conditions without mutexes). In memory, we hold two Sorted Sets for each trading instrument: bids and asks. FIFO matching for limit orders, immediate-or-cancel for market orders. Throughput with a proper Rust implementation – 500k–1M matches per second on a single core.
• Settlement service – reads matches from Kafka, atomically updates balances in PostgreSQL (UPDATE accounts SET balance = balance - $1 WHERE id = $2 AND balance >= $1). Optimistic locking via row versioning.
• Withdrawal pipeline – separate service with cold/hot wallet architecture. The hot wallet holds 5–10% of total deposits, the rest is cold storage with multi-sig (Gnosis Safe or custom HSM). Automatic withdrawals only from hot wallet, large amounts require manual authorization.

How our exchange development process ensures reliability

Smart contracts and gas optimization

For EVM-based DEX (Ethereum, Arbitrum, Optimism, Polygon), the entire critical path lives in Solidity. Main contracts: Pool, Factory, Router, PositionManager (for v3-like), and Quoter for off-chain calculations. Typical mistakes we see in audits:

Reentrancy via callback. Uniswap v3 uses flash swap with a callback (uniswapV3SwapCallback). If your router lacks a nonReentrant guard and you don't check msg.sender == pool, the contract gets drained via a nested call. This is not hypothetical – several v3 forks lost funds this way.

Oracle manipulation in AMM. If your contract uses the spot price from the pool for collateral calculation, it is front-runnable. Correct: TWAP over 30+ minutes (Uniswap v3 OracleLib) or an external oracle (Chainlink).

Unbounded loops in liquidity range. If a swap crosses many ticks in a row (price impact 80%+), gas may exceed the block limit. Need MAX_TICKS_CROSSED with partial fill and returning the remainder.

For Solana DEX (Anchor framework, Rust), the architecture is fundamentally different: account-based model, Program Derived Addresses (PDA) instead of storage, Cross-Program Invocations instead of internal calls. Solana's throughput (~3,000–4,000 TPS vs 15–30 on Ethereum mainnet) allows building on-chain order books – exactly what Phoenix DEX does.

Liquidity bootstrapping and aggregator integration

Launching a pool is not enough – you need to ensure liquidity at launch. Practical mechanisms:

• Liquidity Bootstrapping Pool (LBP) – initial price is high, asset weights dynamically shift, creating selling pressure and even token distribution. Implemented in Balancer v2.
• Initial Liquidity Offering via Uniswap v3 – adding liquidity in a narrow range around the initial price, then gradually expanding as volume grows. Requires active liquidity management or integration with Arrakis/Gamma.
• Integration with 1inch, Paraswap, Li.Fi – aggregators bring traffic but require standard compliance: the pool must have correct getAmountsOut, support ERC-20 approval/permit, and not have custom transfer hooks that break the aggregator's routing.

Development process and deliverables

Analytics and design begin with choosing the architectural model: CEX with custodial storage, non-custodial DEX, or hybrid (off-chain order book + on-chain settlement, like dYdX v3). This decision determines everything – regulatory load, tech stack, team.

Development proceeds in layers: first smart contracts with full Foundry coverage (fuzzing, invariant testing), then backend services, then integration layer, and finally frontend. Testing includes fork testing on mainnet via Foundry – we reproduce real liquidity conditions, not synthetic ones.

Audit is mandatory before mainnet deployment. For DEX contracts, minimally one firm with manual review (Trail of Bits, Spearbit, Code4rena contest). For CEX custody, audit of key storage processes. We guarantee all contracts undergo formal verification and fuzzing testing (Echidna, Foundry invariant).

Estimated timelines

Cost is calculated individually after a technical briefing: chain selection, throughput requirements, custodial model. Our certified engineers with 10+ years of experience will help you choose the optimal architecture and avoid common pitfalls. Contact our team for a detailed proposal.

Pitfalls to avoid at launch

• Forgetting the price oracle in AMM. Spot price can be manipulated with a flash loan in one transaction. If your lending protocol uses the spot price from its own pool, that's a bug.
• Hot wallet without limits. A CEX without daily limits on automatic withdrawals is an invitation for attackers. Compromising one key should lose at most 10% of total funds.
• Absence of circuit breaker. A 40% price drop in 5 minutes should halt automatic liquidations or withdrawals until manual review. Without this, a cascading liquidation spiral destroys all TVL.
• Incorrect decimal handling. USDC uses 6 decimals, WBTC – 8, most tokens – 18. Mixing without normalization leads to either precision loss or overflow. Solidity has no float; we work with fixed-point using FullMath (mulDiv with overflow protection).

Want to avoid these problems? Get a consultation — we will select the architecture for your project and provide exact timelines. Order exchange development with quality guarantee and ongoing support.