Trading and data collection have already been inseparable. Every order, price tick, message, and algorithmic adjustment is collected and saved, and each informs trading decisions. The rules and regulations governing data collection have become even more complex, especially as crypto trading has become mainstream.
Global reforms in crypto regulations are shaping what traders can and are allowed to use, thereby changing the competitive landscape. In this article, we’ll dive into those rules and try to explain how they fuel modern trading.
The New Regulatory Wave: From “More Reports” to “Better Data”
Across jurisdictions and laws, an emerging pattern is affecting every trader. The investors will need to capture and report more data than before, requiring more work, and the data itself will need to be of better quality. The goal is to make the data granular, standardized, and higher-quality for predictive purposes.
In the EU, recent MiFID II/MiFIR reforms introduced new technical standards for data collection. All crypto tools used to track trades must comply with these standards. They expect uniform fields, structured identifiers, and timestamps aligned down to the millisecond. That way, the authorities will be able to reconstruct trading behavior from the data.
The UK’s Financial Conduct Authority (FCA) has followed along with a similar approach. At this point, FCA receives about 7 billion transaction reports per year, but the goal is to improve the quality of that data rather than just increase volume.
Meanwhile, the CFTC has updated derivatives-reporting rules. It has introduced new elements, such as universal product identifiers (UPIs), to further align with global standards. These upgrades are made to reduce fragmentation and increase surveillance.
What Regulators Now Expect Firms to capture
The last five years have introduced a lot of new data that the users are expected to capture. These include:
Granular Order-Lifecycle Data
Most regulations require users to keep a detailed log of events throughout the whole cycle. That way, regulatory agencies can easily detect spoofing, as there’s a historical record of the entire process. This includes:
· Time-stamped order entry
· Modifications and cancellations
· Venue routing decisions
· Client IDs
· Algo identifiers
· Execution conditions
· Post-trade allocations
High-Fidelity Market and Reference Data
Consistent input data is the key to the consolidated tape initiative. This means that the investors need to keep track of:
· synchronized clocks
· Precise market-data snapshots
· standardized instrument identifiers
· cross-venue latency and routing metadata
Some investors used to keep this data because it helped their efforts, but now it’s no longer a nice-to-have; regulations require it.
Communications, Chats, and Digital Footprints
This is one of the most significant shifts in data maintenance. Just a few years ago, communication between team members was considered private and almost no one kept it, but now it’s required, and conversations kept this way could mean a great deal to regulatory agencies.
Most software tools used for chat among coworkers now have dedicated cloud services to store information, which is somewhat changing the company culture among traders. The goal is to reconstruct how trading decisions are made, and everyone is now more aware that this can be used in regulatory efforts.
Operational and ICT-Risk Data (DORA and similar)
The EU regulations require companies to track operational issues that may pose a risk to data. This includes IT incidents of all kinds, latency spikes, downtime, disruption of third-party services, and cybersecurity events.
The Compliance–Trading Feedback Loop: How Requirements Shape Desk Analytics
The regulation doesn’t exist separately from the actual trading practices. Instead, the new regulations inform the way traders operate and what data they use to make their decisions. This has led many traders to create more complex data pipelines, enabling them to make better, more effective decisions.
The new data has led to at least three different major changes in the trading practices.
Execution quality analysis has improved drastically. Traders can compare more data, measure slippage more precisely, and spot where liquidity providers underperform.
Algorithm back-testing has also improved by relying on more structured data. The simulations the traders now use to visualize the market structure are more accurate.
The overall trading strategies are therefore more refined, as traders can spot more patterns in routing decisions, liquidity fragmentation, and price improvement opportunities.
Limits and Possible Frictions
New regulations aren’t without faults and limits, and they cause friction between traders and regulators, as well as between regulators working in different jurisdictions.
One of the biggest challenges traders are reporting is that data is now more centralized, making it more vulnerable to hacks and IT incidents. The data also includes sensitive client identifiers, trade metadata, and communication logs, and therefore can’t always be used when moving abroad.
There’s also a friction between departments within the trading team. At this point, there are at least two departments: one for trading and one for data compliance, and their interests may no longer align, causing friction and requiring someone to make a judgment call on which data to use and keep.
Operational Cost
Having to track more data comes with operational costs that eat into the profits of investors and traders. This is especially difficult for short-term and new traders who have the smallest profit margins. Those that operate in multiple jurisdictions at once will also have to cover the highest expenses, since their operations are the most complex.
Some investors and traders will hire a centralized data officer, or a chief data officer, who will oversee the entire process. This person now becomes an additional member of the team, with input on how things are run. Their services also need to be paid for, and the role will only get bigger over time.
In the years to come, the process will further automate, as there are already tools used to complete the trades and save the proper data. As AI gets better at this, compliance will become less laborious but more dependent on third parties.
Conclusion
A new compliance regulation requires traders to retain and provide a wide range of data sets. These regulations aren’t standardized worldwide, even though trading is inherently global. Therefore, traders need to adapt their practices to where they plan to operate. The goal of these regulations is to allow regulatory agencies to follow the entire trading process and the decision-making behind it.
At the same time, the data the investors have saved isn’t just a burden to them. It can also be used to inform their business decisions further and to make them more scientific. In the short term, however, the cost of keeping the data will fall on the traders.