Case Study: Building a Low-Latency Trading System with a Time-Series Database

Understanding Tick Data Challenges in Low-Latency Trading

In algorithmic trading, tick data—the record of every executed trade or quote update—forms the fundamental input for decision-making systems. Capturing, storing, and replaying tick data efficiently is paramount when constructing low-latency trading frameworks. Unlike aggregations like minute-bars or second-bars, tick data maintains the highest granularity, which is essential for microsecond-level latency strategies such as market making, statistical arbitrage, or latency arbitrage.

Tick data comes with particular challenges:

• High Volume & Velocity: Equities can generate millions of ticks per trading day; futures and forex markets are even more voluminous.
• Time-Ordering and Precision: Nanosecond to microsecond synchronization is necessary, especially when integrating multiple venues.
• Efficient Query Patterns: Trading algorithms require rapid access to recent ticks, fast time-range queries, and out-of-sequence corrections.
• Historical Replay: Backtesting microsecond-level strategies requires reliable replay of tick data in correct order and timing.

Conventional relational databases and row-store systems exhibit poor performance handling these requirements. This is where time-series databases (TSDBs) optimized for high-throughput, timestamp-driven data prove essential.

Why Time-Series Databases for Tick Data?

Time-series databases are designed around the properties tick data exhibits: a chronological sequence of timestamped measurements. This alignment delivers several advantages:

• Columnar Storage & Compression: TSDBs store timestamps and data fields in columns, optimizing for compression and reducing I/O.
• Write-Optimized Architecture: Sequential writes matched to incoming tick flow reduce disk seeks and latency.
• Advanced Indexing: Efficient time-based indexes enable millisecond or better time-range queries.
• Retention & Downsampling: Policies to automatically aggregate or purge old data help manage storage.
• High-Resolution Timestamps: Support for nanosecond or microsecond timestamps matches trading data precision needs.

Common TSDB choices for tick data include InfluxDB, TimescaleDB (PostgreSQL extension), kdb+, and OpenTSDB. kdb+ stands out in finance for its vectorized language Q and proven production-grade speed, but for the purpose of this case study, we will focus on TimescaleDB due to its open-source foundation, SQL interface, and ability to handle tick-level granularity efficiently.

Architecture Overview of the Low-Latency Trading System

The trading system’s architecture built around TimescaleDB as the tick data repository consists of the following components:

1. Market Data Feed Handler: Captures tick messages from exchange feeds with nanosecond timestamping.
2. Data Ingestion Pipeline: Parses, transforms, and streams ticks into TimescaleDB using batched inserts to maintain throughput.
3. Order Management Engine (OME): Consumes data from TimescaleDB in real-time or replay mode to generate trade signals.
4. Execution Gateway: Translates trade signals into orders with minimal latency.
5. Backtesting / Replay Module: Queries historical tick data from TimescaleDB with timestamp-precise playback.

Fig. 1 illustrates the data flow between components. The key differentiator is the central role of TimescaleDB optimized for both real-time ingestion and precise tick-data replay.

Implementing Tick Data Storage in TimescaleDB

Defining the Schema

A sensible schema maps each tick event with precise timestamps, bid/ask prices, sizes, and exchange identifiers. For example:

CREATE TABLE ticks (
    symbol TEXT NOT NULL,
    exchange_code TEXT NOT NULL,
    event_time TIMESTAMPTZ NOT NULL,
    bid_price DOUBLE PRECISION,
    bid_size INTEGER,
    ask_price DOUBLE PRECISION,
    ask_size INTEGER,
    trade_price DOUBLE PRECISION,
    trade_size INTEGER,
    PRIMARY KEY (symbol, exchange_code, event_time)
);

CREATE TABLE ticks (
    symbol TEXT NOT NULL,
    exchange_code TEXT NOT NULL,
    event_time TIMESTAMPTZ NOT NULL,
    bid_price DOUBLE PRECISION,
    bid_size INTEGER,
    ask_price DOUBLE PRECISION,
    ask_size INTEGER,
    trade_price DOUBLE PRECISION,
    trade_size INTEGER,
    PRIMARY KEY (symbol, exchange_code, event_time)
);

TimescaleDB requires hypertables for efficient partitioning:

SELECT create_hypertable('ticks', 'event_time', chunk_time_interval => INTERVAL '1 day');

SELECT create_hypertable('ticks', 'event_time', chunk_time_interval => INTERVAL '1 day');

Partitioning by day aligns with typical trading session boundaries, and TimescaleDB automatically manages partitions while providing efficient time-range queries.

Data Compression and Retention Policies

With millions of ticks generated daily, storage efficiency is important. TimescaleDB offers native compression leveraging columnar store techniques:

ALTER TABLE ticks SET (
    timescaledb.compress,
    timescaledb.compress_segmentby = 'symbol, exchange_code'
);
SELECT add_compression_policy('ticks', INTERVAL '7 days');

ALTER TABLE ticks SET (
    timescaledb.compress,
    timescaledb.compress_segmentby = 'symbol, exchange_code'
);
SELECT add_compression_policy('ticks', INTERVAL '7 days');

Compression on older chunks (older than 7 days) reduces storage costs without impacting recent data access latency.

Ingesting Tick Data Efficiently

Asynchronous Batched Inserts

Tick feeds operate at very high frequency—ranging from 10k to 100k ticks per second depending on instrument liquidity. Synchronous single-row inserts impose significant overhead.

Implementing batched inserts reduces overall transaction times drastically. For example, ingesting at 100k ticks/s:

• Target batch size: 5,000 ticks
• Batch period: 50 ms buffer
• Memory buffer: Approximately 1 MB per batch assuming 200 bytes per tick

On the producer side, the feed handler buffers incoming ticks, transforms into the TimescaleDB row format, and issues parameterized COPY commands or multi-row INSERT statements asynchronously. Benchmarking insert latency should aim for sub-10 ms per batch to maintain pace.

Timestamp Synchronization

The event_time must reflect the exact market timestamp with minimal error. Using exchange-provided nanosecond timestamps (most modern feeds provide these) is important. Otherwise, local capture timestamps should be adjusted based on feed latency estimates.

The schema uses TIMESTAMPTZ, scaled to microsecond precision:

SELECT now()::timestamp(6);

SELECT now()::timestamp(6);

Higher resolution timestamp types exist but may sacrifice query performance.

Querying Tick Data for Real-Time Trading Decisions

Indexing for Low-Latency Queries

Primary key indexing on (symbol, exchange_code, event_time) ensures efficient retrieval of specific securities and minimal-range scans.

To optimize for real-time strategies accessing the last N ticks, one can create customized partial indexes such as:

CREATE INDEX idx_ticks_latest ON ticks (event_time DESC) WHERE event_time > now() - INTERVAL '1 minute';

CREATE INDEX idx_ticks_latest ON ticks (event_time DESC) WHERE event_time > now() - INTERVAL '1 minute';

This index accelerates recent tick retrieval, commonly used in intraday strategies.

Query Examples

To fetch last 500 trades for symbol XYZ on exchange NASDAQ in real-time:

SELECT trade_price, trade_size, event_time 
FROM ticks
WHERE symbol = 'XYZ' AND exchange_code = 'NASDAQ'
ORDER BY event_time DESC
LIMIT 500;

SELECT trade_price, trade_size, event_time 
FROM ticks
WHERE symbol = 'XYZ' AND exchange_code = 'NASDAQ'
ORDER BY event_time DESC
LIMIT 500;

Using TimescaleDB’s time_bucket function, we can also generate sub-second aggregations:

SELECT time_bucket('1 second', event_time) AS second,
       avg(trade_price) AS avg_price,
       sum(trade_size) AS volume
FROM ticks
WHERE symbol = 'XYZ'
AND event_time > now() - INTERVAL '1 minute'
GROUP BY second
ORDER BY second DESC;

SELECT time_bucket('1 second', event_time) AS second,
       avg(trade_price) AS avg_price,
       sum(trade_size) AS volume
FROM ticks
WHERE symbol = 'XYZ'
AND event_time > now() - INTERVAL '1 minute'
GROUP BY second
ORDER BY second DESC;

Though tick-based models rarely rely entirely on aggregations, this is useful for signal filters.

Backtesting and Tick Replay

Accurate Time-Based Replay

Backtesting microsecond-level strategies requires replaying tick events with time-accurate intervals. The replay module queries time-ordered ticks and outputs them with the original event spacing or accelerated scaling.

Example:

SELECT * FROM ticks 
WHERE symbol = 'XYZ'
AND event_time BETWEEN '2023-06-01 09:30:00.000000' AND '2023-06-01 16:00:00.000000'
ORDER BY event_time ASC;

SELECT * FROM ticks 
WHERE symbol = 'XYZ'
AND event_time BETWEEN '2023-06-01 09:30:00.000000' AND '2023-06-01 16:00:00.000000'
ORDER BY event_time ASC;

In the replay engine, elapsed time between sequential event_time values can drive simulation clocks or event scheduling.

Scaling Replay Speed

For stress testing, replay speed can be increased by reducing wait time between ticks but preserving order.

If t_i represents tick i event time, and t_(i+1) the next event time:_

• Real interval: Δt = t_(i+1) - t_i
• Replay interval: Δt' = Δt / S, where S > 1 is speed factor_

Replay engines must ensure ordering integrity while modifying inter-tick intervals.

Latency Considerations in Data Flow

End-to-End Latency Budget

Suppose the system targets sub-1 millisecond latency from tick arrival to trading signal output. Latency breakdown could be:

• Tick feed reception & timestamping: ~100 microseconds
• Data transformation & batching: ~200 microseconds
• DB insert latency: ~300 microseconds
• Query latency for latest ticks: ~200 microseconds
• Order generation logic: ~100 microseconds
• Network to Order Execution Venue: ~100 microseconds

Total: ~1 ms

This budget requires both hardware and software fine-tuning:

• SSDs or NVMe drives with high IOPS for TimescaleDB storage
• Proper connection pooling (PgBouncer) to reduce DB connection overhead
• Prepared SQL statements to avoid parsing cost
• Parallel query execution enabled where possible

Practical Example: Market-Making Strategy Using Tick Data

A market maker sets bid and ask quotes based on the best available trades and the order book state derived from tick data updates.

Algorithm:

1. Fetch last 1000 ticks for symbol "XYZ" from TimescaleDB where event_time > now() - 1 second
2. Estimate mid-price: mid = (best_bid_price + best_ask_price) / 2
3. Calculate spread and volatility from last ticks
4. Post quotes at mid ± spread/2 adjusted by recent volatility
5. Update every 50 ms with ticks update

SQL to pull last ticks representation:

SELECT trade_price, bid_price, ask_price, event_time
FROM ticks
WHERE symbol = 'XYZ' AND event_time > now() - INTERVAL '1 second'
ORDER BY event_time DESC
LIMIT 1000;

SELECT trade_price, bid_price, ask_price, event_time
FROM ticks
WHERE symbol = 'XYZ' AND event_time > now() - INTERVAL '1 second'
ORDER BY event_time DESC
LIMIT 1000;

The strategy internal logic performs statistical calculations such as EWMA volatility:

[ \sigma_t = \alpha (r_t)^2 + (1 - \alpha) \sigma_{t-1} ]_

where (r_t) is log-return of trade prices and (\alpha) a smoothing factor (e.g., 0.1).

Conclusion

Building a low-latency trading system with a focus on tick data storage and replay requires a specialized infrastructure that can manage high-volume, high-precision time-series data. TimescaleDB’s hypertables, time-based partitioning, and native compression provide a solid foundation for this need, balancing ingestion rates and query latency.

This case study illustrates how careful schema design, efficient batching, precise timestamp management, and optimized query strategies contribute to a practical and scalable solution. Beyond storage, enabling accurate tick replay supports rigorous backtesting, essential for validating low-latency trading strategies.

Traders and engineers aiming for microsecond-level responsiveness should prioritize time-series databases engineered for tick data characteristics as a core piece of their ecosystem.