Overview
Every trading system, every strategy, every risk calculation, and every analytical tool in a trading operation depends on market data. The quality of that data — its latency, its completeness, its accuracy, and its reliability — sets a ceiling on the quality of everything built on top of it. A signal generated from a delayed price feed is a signal that will execute at a worse price than the signal logic assumed. A risk calculation based on stale data is a risk calculation that does not reflect the portfolio's current exposure. A backtest run on data with gaps or errors produces results that do not accurately represent the strategy's historical performance.
Market data feed integrations connect trading systems to the data sources they depend on — exchange feeds, broker price streams, data vendor APIs, alternative data providers — with the connectivity, normalisation, and reliability infrastructure that production trading systems require. The integration is not just the API call that retrieves a price. It is the connection management that maintains the feed through disconnections and reconnections, the data validation that catches errors and gaps before they propagate into dependent systems, the normalisation that converts data from different sources into a consistent format, and the monitoring that surfaces feed issues before they affect trading decisions.
We build custom market data feed integrations for systematic trading firms, proprietary trading operations, trading technology companies, and any operation where the quality and reliability of market data directly affects trading performance.
What Market Data Feed Integrations Cover
Real-time price feed connectivity. The WebSocket and streaming connections that deliver real-time price updates from exchanges, brokers, and data vendors. Real-time feed integration handles the full lifecycle of a streaming data connection: initial connection establishment with the appropriate authentication mechanism, subscription to the specific instruments and data types the operation requires, event-driven receipt of price updates as they are published by the source, heartbeat and keepalive management that detects and responds to stale connections, and automatic reconnection with state recovery when the connection drops.
For low-latency applications — algorithmic strategies where execution timing is critical — feed integration is optimised for the minimum latency between the source publishing a price update and the consuming system receiving it. Connection placement — choosing the geographic location of the feed receiver to minimise network distance to the source — matters for latency-sensitive applications and is part of the feed architecture design.
REST API integration for snapshot and historical data. Many data sources deliver market data through REST APIs — the historical OHLCV data endpoint, the snapshot of the current order book, the current quote for an instrument. REST API integration handles the request construction, authentication, response parsing, rate limit management, and retry logic that reliable REST-based data retrieval requires.
Rate limit management is a critical component of REST API integration that is frequently under-engineered. Most market data APIs impose rate limits on requests per second or per minute. An integration that does not track and respect these limits will generate rate limit errors at high request frequencies — errors that cause data gaps precisely when the data is most needed (during high market activity when many instruments are being queried simultaneously). Rate limit management distributes requests across the available rate budget, queues requests that exceed the current rate, and prioritises time-sensitive data requests.
Exchange direct feed integration. For trading operations where data vendor latency is too high — where the additional hop through a data vendor's infrastructure adds unacceptable latency — direct exchange feed integration connects to the exchange's primary data feed without the vendor intermediary.
Cryptocurrency exchange WebSocket feeds: Binance (spot, futures, options), Bybit, Kraken, Coinbase Advanced Trade, OKX — each exchange has specific WebSocket API conventions, authentication mechanisms, message formats, and subscription models. Direct integration with each exchange's WebSocket feed provides the lowest-latency market data available for cryptocurrency strategies.
Traditional exchange data feeds: interactive Brokers market data via TWS API, FIX/FAST feeds from futures exchanges, NYSE and NASDAQ market data protocols for equity market data. The feed protocols, the session management, and the data formats differ significantly between exchanges and asset classes — each requiring specific integration implementation.
Data vendor integration. Market data vendors aggregate data from multiple sources and provide normalised access through standardised APIs — reducing the integration effort required to access data across many instruments and asset classes.
Polygon.io — real-time and historical market data for US equities, options, forex, and cryptocurrency. WebSocket streaming for real-time data, REST API for historical data and snapshots. Polygon's flat-rate API model makes it cost-effective for operations requiring broad instrument coverage.
Refinitiv (now LSEG Data & Analytics) — professional market data service with deep coverage across equities, fixed income, forex, derivatives, and alternative data. Refinitiv Eikon API and Refinitiv Data Platform for programmatic data access. Enterprise-grade data quality with the reliability SLAs that institutional operations require.
Bloomberg — the primary market data platform for institutional trading operations. Bloomberg API (BLPAPI) for programmatic access to Bloomberg's data across all asset classes. Bloomberg data quality and coverage is the benchmark for institutional market data, with the corresponding enterprise pricing.
Interactive Brokers market data — the TWS API market data subscription that provides real-time quotes, historical bars, and fundamental data for the instruments IB supports. For operations that trade through IB, the IB market data subscription provides convenient access to real-time data without a separate vendor relationship.
IEX Cloud — US equity market data with regulatory-compliant real-time pricing via the IEX exchange's public feed and the IEX Cloud API. Cost-effective for operations focused on US equities with requirements for regulatory-grade pricing.
Forex data feeds. Forex market data has specific characteristics — the decentralised over-the-counter market structure means there is no single authoritative price feed, and different data sources represent different liquidity pools.
Forex broker price streams — the bid/ask price stream from the broker's liquidity aggregation, delivered via the MetaTrader price feed, OANDA's streaming API, IG's streaming API, or the broker-specific streaming mechanism. Broker price streams represent the prices at which the broker will execute, making them the most relevant data for strategies that trade through that broker.
Aggregated forex data — the composite bid/ask from multiple liquidity providers aggregated by data vendors like Refinitiv or direct bank feeds — provides a broader market view than any single broker's feed but may not exactly match the prices at which a specific broker will execute.
Multi-source data aggregation. For operations that use data from multiple sources — different exchanges for different instruments, different vendors for different asset classes, proprietary data feeds alongside vendor data — a data aggregation layer that normalises the data from all sources into a consistent format and routes each instrument's data from the appropriate source.
Source priority configuration — defining which source is used for each instrument when multiple sources provide data for the same instrument. Fallback routing — automatically switching to a secondary source when the primary source is unavailable or producing anomalous data. Cross-source validation — detecting when the same instrument's price from two sources diverges beyond expected spread, indicating a data quality issue in one of the sources.
Data validation and quality management. Market data contains errors — erroneous price spikes, missing bars, duplicate records, timestamp inconsistencies, and the occasional extreme outlier that represents a data error rather than a genuine price. Systems that consume unvalidated data translate these errors into erroneous signals, incorrect risk calculations, and misleading analytical results.
Spike detection — identifying price updates that represent implausible moves relative to recent price history and the instrument's typical volatility. Cross-source validation — comparing the same instrument's price across multiple sources to detect anomalies that are present in one source but not others. Gap detection — identifying missing bars in historical data and time periods where real-time data was not received. Timestamp validation — detecting out-of-sequence timestamps and duplicate records that indicate data delivery issues.
When validation failures are detected, the data quality management layer applies the configured response — rejecting the anomalous data point and using the last valid price, flagging the anomaly for investigation, switching to a backup data source, or alerting the operations team to a systematic data quality issue.
Data normalisation. Different data sources represent the same underlying information in different formats — different field names for the same concept, different timestamp formats and time zones, different decimal precision conventions, different ways of representing trading sessions and market closures, different symbol naming conventions for the same instrument.
Normalisation converts the source-specific data format into the canonical format that the consuming systems expect — a consistent field naming convention, timestamps normalised to UTC, decimal precision standardised to the instrument's tick size, and instrument symbols mapped to the operation's internal identifier scheme. Normalised data allows consuming systems to be written against a single, stable data format rather than having to handle the specific conventions of each data source.
Historical data management. Real-time feed integrations need to be complemented by historical data management — the storage and retrieval of the historical price data that backtesting, research, and indicator initialisation require.
Historical data acquisition — downloading the historical data that the operation's research and backtesting requirements specify from the appropriate sources. Incremental updates — appending new data to the historical store as it is generated, keeping the historical dataset current. Data storage optimised for the query patterns that backtesting and research use — time-range queries, instrument-specific queries, bar type aggregation.
Specific Integration Implementations
MetaTrader price data bridge. Connecting MetaTrader's internal price feed to external systems — the DLL or socket bridge that exposes MT4/MT5 price data to Python strategy engines, to the backtesting framework, or to other systems that need the prices the MetaTrader broker provides. For strategies where the execution broker is a MetaTrader broker and the strategy logic runs outside MetaTrader, the price bridge ensures that the strategy is using the same prices the execution will occur at.
Cryptocurrency exchange aggregation. Connecting to multiple cryptocurrency exchanges simultaneously — spot prices, perpetual futures funding rates, derivatives pricing — and aggregating the data into a unified instrument namespace. Exchange-specific quirks (Binance's different symbol naming conventions between spot and futures, Bybit's linear versus inverse contract distinction, the different tick sizes across exchanges) handled in the normalisation layer so consuming systems work with consistent data.
Order book feed integration. Level 2 order book data — the full depth of market showing bid and ask quantities at each price level — from exchange WebSocket feeds. Order book feed integration handles the incremental update model that most exchanges use — delivering the full order book snapshot on initial connection and then delivering delta updates (additions, modifications, and cancellations) as the order book changes. Maintaining the local order book state by applying each delta update to the snapshot produces the current order book that the consuming system queries.
Alternative data integration. Integrating non-price market data — sentiment data, news feeds, economic data calendars, options flow data, on-chain cryptocurrency data — into the data infrastructure alongside traditional price feeds. Alternative data sources typically have their own specific API conventions, data formats, and update frequencies. The integration normalises this data into the operation's data infrastructure for use in signal generation and research.
Technologies Used
- Rust — ultra-low-latency WebSocket feed processing, order book state management, high-throughput data normalisation, real-time validation
- C# / ASP.NET Core — data vendor API integration, complex feed management logic, enterprise data infrastructure
- Python — research data pipelines, historical data management, alternative data processing
- React / Next.js — feed monitoring dashboard, data quality reporting, feed configuration interface
- TypeScript — type-safe frontend and API code throughout
- SQL (PostgreSQL, TimescaleDB) — historical price data storage with time-series query optimisation
- Redis — real-time price state, order book cache, feed status monitoring
- Apache Kafka / message queues — high-throughput data distribution from feeds to consuming systems
- WebSocket — real-time feed connectivity for exchange and vendor streaming data
- FIX / FAST — institutional exchange feed protocols
- Binance / Bybit / Kraken / Coinbase WebSocket APIs — cryptocurrency exchange feeds
- Interactive Brokers TWS API — IB market data
- Polygon.io / Refinitiv / Bloomberg APIs — professional market data vendor connectivity
- MetaTrader DLL bridge — MT4/MT5 price data extraction
Data Infrastructure as Trading Infrastructure
Market data is not a supporting function in a trading operation — it is core infrastructure. The latency, reliability, and quality of market data directly affects signal quality, execution quality, risk calculation accuracy, and the validity of historical research. Treating data infrastructure as an afterthought — using whatever data is convenient rather than the data that is correct for the use case — produces a performance drag that compounds across every component of the trading system that depends on the data.
Custom market data feed integrations built for the specific instruments, the specific latency requirements, and the specific quality standards of the trading operation provide the data foundation that the rest of the trading infrastructure is built on.
The Data Your Trading System Deserves
Trading systems are only as good as the data they run on. Feed integrations built for reliability, validated for quality, normalised for consistency, and monitored for issues before they affect trading — this is the data infrastructure standard that production trading systems require.