Most trading charts display data in OHLC bars, summarizing price action over a set interval. For discretionary traders, this is often enough. For algorithmic trading, it’s a blurry, incomplete picture. The real action happens between the bars, at the level of individual ticks.

A tick represents a single trade or a change in the bid/ask quote. It's the most granular form of market data available. While a one-minute bar shows you four data points, that same minute could contain thousands of ticks. High-frequency strategies rely on this raw stream of events to capture fleeting opportunities that are invisible on a standard candlestick chart. Relying on bars is like trying to understand a conversation by only reading every tenth word.

With tick data, timing is everything. Not just the date and time, but the millisecond or even microsecond. When replaying historical data to backtest a strategy, the chronological integrity of events must be perfect. If a trade from exchange A recorded at 10:00:00.005 is processed after a quote update from exchange B at 10:00:00.004, your simulation is flawed. This is a common source of bugs that can make a losing strategy appear profitable.

Every single tick must have a high-precision timestamp, and your data processing system must rigorously enforce the correct sequence of events. A simple sorting mistake before a backtest can completely invalidate the results, leading to real-world losses.

Sourcing and preparing high-quality data is an engineering challenge. You're dealing with two distinct but related streams: historical data and real-time data.

For historical data, the priority is completeness. You need every tick, even from years ago. This involves ingesting massive datasets, often terabytes in size. To avoid choking your system, you can't just load it all at once. Data pipelines use techniques like pagination (requesting data in smaller, manageable 'pages') and batch processing (handling it in chunks) to work through the data systematically.

For real-time data, the priority is low latency. The goal is to receive and process ticks as quickly as they occur in the market. This requires a different kind of architecture, one optimised for speed and constant flow.

Ultimately, these two streams must converge. Your analytics layer needs to be able to pull from the complete historical record for backtesting and seamlessly switch to the live feed for trading, using a consistent data format.

Raw financial data is rarely perfect. The principle of 'Garbage In, Garbage Out' is brutally true in algorithmic trading. A solid data pipeline must include a rigorous cleaning and normalisation stage to handle common issues before they ever reach your strategy logic.

Common problems include:

• Bid/Ask Spikes: Sometimes, you'll see a quote with an impossibly wide bid-ask spread for a single tick before it returns to normal. This is often a data feed error. If not filtered out, it can trigger false entry or exit signals in strategies that monitor the spread.

• Outliers and Gaps: Data feeds can drop, or exchanges can halt, creating gaps in your data. You need rules to identify these gaps. Do you fill them? If so, how? Do you discard the trading day? The decision depends on your strategy's sensitivity.

• 'Zero Volumes' in Futures: In older futures data, it's not uncommon to find trades reported with a volume of zero. These are artifacts of how data was recorded and should typically be filtered out.

• Exchange-Specific Formats: Data from the CME won't be formatted identically to data from a crypto exchange like Binance. A normalisation layer in your pipeline is essential to transform all incoming data into a single, consistent format your strategy can understand. This prevents you from having to write custom logic for every single data source.

Once these steps are automated, you have a reliable foundation. Your historical data is clean and complete, and your real-time stream is processed through the same logic. This ensures that the market conditions you test against are as close as possible to the conditions you will trade in, which is the cornerstone of building a robust algorithmic trading system.

Getting the data right is more than half the battle. With a clean, reliable data pipeline in place, you can finally begin to build and test strategies with confidence.