FIX Trading Community Adopts Automated Development Workflow for the FIX Standard

The FIX Trading Community has adopted an automated development workflow for the FIX Standard. This new approach is built on the Orchestra Domain Specific Language (DSL) and utilises tooling provided by Atomic Wire Technology. The initiative moves the standards development process from manual repository maintenance to a structured, machine-readable format.

Proposed modifications to the standard can now be automatically validated, merged, and used to generate updated repository artefacts. This automated pipeline introduces early validation to detect incompatibilities and inconsistencies. It also establishes a complete audit trail of the standard’s evolution and facilitates parallel development across multiple working groups. Furthermore, the build process automatically produces derived artefacts, such as FIXML schemas and implementation reports, to ensure consistency.

This implementation marks the initial phase of a wider programme to modernise the infrastructure behind the FIX Standard. By establishing this automated pipeline, the community is laying the groundwork for improved publication, consumption, and integration of the standard within the financial technology sector.

CryptoStruct Integrates Kalshi Event Contracts Into Low-Latency Trading Platform

CryptoStruct, the low-latency trading and market data solutions provider, has announced a connectivity integration with Kalshi, the CRTC regulated event trading exchange. The integration incorporates Kalshi’s expanding suite of event contracts – covering macroeconomics, politics, commodities, and sports – directly into CryptoStruct’s unified API and co-located global infrastructure.

By normalising Kalshi’s data into the same format used across its 30 other supported venues, CryptoStruct enables trading firms to blend multiple data feeds and execute cross-venue strategies without additional integration work. The setup maintains deterministic performance and ultra-low-latency capabilities across markets.

Additionally, the integration supports the entire strategy lifecycle on Kalshi, from historical data research and backtesting to live execution. Powered by CryptoStruct’s event-driven Strategy SDK, the platform is capable of running thousands of trading strategies in parallel.

N5X Selects Vermiculus to Deliver VeriClear System for Brazilian Energy Market

Market infrastructure technology provider Vermiculus has been selected by N5X, the upcoming Brazilian energy derivatives clearing house, to deploy its cloud-native VeriClear clearing system. The solution will support N5X in establishing a new clearing house and operating as a central counterparty (CCP). This project marks Vermiculus’ fourth clearing, risk, and settlement system deployment in South America since 2022.

The partnership aims to modernise Brazil’s energy market, which is currently the sixth largest globally, consuming approximately 632 TWh of electricity annually. By implementing VeriClear, N5X will transition the region’s energy sector from an uncleared over-the-counter (OTC) environment to a regulated futures trading and clearing system.

The modular platform provides N5X with full control over its clearing operations. This structure allows the clearing house to accelerate the launch of new energy products, manage risk effectively, and scale infrastructure to accommodate rising transaction volumes and market participants.

BondWave and Fintech Global Center Announce Partnership to Integrate Fixed Income Platforms

BondWave LLC and Fintech Global Center (FGC) have announced a strategic partnership to integrate their respective fixed income platforms, Effi and FGC TMS. This collaboration embeds each system’s capabilities into the other, providing users with a comprehensive pre- and post-trade workflow within a single platform.

Through this agreement, users of the FGC TMS will gain access to BondWave’s Market Calculator and price discovery tools. This integration provides real-time market data to support fair pricing, best execution requirements, and informed investment decisions prior to execution.

Simultaneously, BondWave’s Effi users will be able to utilise FGC TMS’s order and execution management system (OMS/EMS) capabilities. This connects users to liquidity across all major fixed income alternative trading systems (ATSs) and supports the full trade lifecycle, including execution, clearing, settlement, reporting, and compliance management.

Precisely Adds Mainframe Software Visibility to ServiceNow Marketplace

Precisely has released Ironstream z/OS Software Discovery for ServiceNow, which automatically identifies z/OS software installed on mainframes and synchronises the inventory data with the ServiceNow Configuration Management Database.

The latest product is available through the data integrity specialist’s ServiceNow Store. It enables the replacement of manual tracking via spreadsheets to establish a continuous system of record for mainframe assets.

It brings trusted mainframe data into ServiceNow so customers can reduce risk, simplify compliance, and manage their entire information technology environment, said Marianne Roling, senior vice president of global channel and ecosystems at Precisely.

The integration allows organisations to incorporate mainframe systems into enterprise-wide software asset management programmes to support regulatory compliance and vendor audits.

Precisely previously developed other applications in the Ironstream portfolio and the company builds these solutions as a partner within the ServiceNow partner programme.

Bloomberg Builds Out Pre-Trade TCA Functionality to Fixed Income

Bloomberg has launched a new pre-trade transaction cost analysis (TCA) model to assist fixed-income market participants with trading decisions.

The system calculates potential costs, executable volumes, and the likelihood of execution for sovereign and corporate bonds. The model uses five years of historical transaction data alongside factors such as bond age, currency and the real-time bid-ask spread.

The model builds on Bloomberg’s TCA offerings, which have long catered for equities traders. It provides trusted pre-trade price discovery and an automatic connection to post-trade analysis that ensures a valuable feedback loop for traders.

“Developing a native model in fixed income markets is an exciting step forward to providing bond traders and portfolio managers with greater pre-trade intelligence,” said Ravi Sawhney, Bloomberg global head of trade automation and analytics.

“The inclusion of pre-trade cost and probability estimates as part of the BTCA offering promotes market transparency and helps bond traders to make decisions that comply with their firms’ best execution requirements.

Robinhood Chain Launches with Dedicated Public Trading Pool

The Public Mainnet of Robinhood Chain has officially launched, creating a new institutional-grade Layer 2 blockchain built on the Arbitrum platform. The new chain connects directly to Robinhood’s base of onchain users and features tools for lending and borrowing. Focused on real-world assets, it offers application developers fast transaction speeds and was built with technology partners Alchemy, BitGo, and Chainlink.

The chain’s ecosystem supports a number of day-one partners to provide initial liquidity. Uniswap, a leading decentralised crypto exchange, is deploying a dedicated Automated Market Maker (AMM) to serve as the chain’s main public trading pool. Additionally, the Pleiades AMM will offer a private trading venue, helping to integrate advanced decentralised financial tools into everyday professional workflows.

Robinhood announced its mainnet alongside a slew of other offerings, including tokenised stock trading available in 120+ countries (but currently not the US, Canada or the UK) and agentic AI-driven crypto trading.

Bloomberg Taps Kaiko to Add Broadridge’s Onchain Data to its Terminal

Bloomberg has added Broadridge’s Distributed Ledger Repo (DLR) platform data to its Terminal, marking the first time the service has included live data from a blockchain-native fixed income market. Distributed through Kaiko’s regulated data infrastructure, Bloomberg now publishes daily repo par value, turnover, and trade count alongside traditional fixed income data.

This development is a significant milestone for institutional investor workflows. The DLR platform currently processes $7.5 trillion in monthly volume (a 457% year-over-year increase) and handles $362 billion in daily settlements.

Kaiko provided the technology bridge from data held on Broadridge’s DLT to Bloomberg’s formatting, entitlement, and compliance standards.

LSEG Data Now Available to Clients via Databricks

LSEG has expanded its partnership with data and artificial intelligence company Databricks to make more than 50 datasets from its Quantitative Analytics Database available on the Databricks Marketplace.

The expansion utilises OpenSharing, an open protocol for sharing data and artificial intelligence assets, to grant clients direct access to financial and economic data within their Databricks environments.

The integration operates through entitlement-controlled access across cloud environments and incorporates Unity Catalog to provide centralised data governance, tracking and usage auditing.

“This partnership simplifies how organizations integrate financial intelligence into their workflows,” said Databricks chief executive Ali Ghodsi.

The available datasets cover company fundamentals, market pricing, fixed income and risk analytics to support portfolio construction and machine learning model testing and it builds on the existing availability of Lipper Fund Data and Cross Asset Analytics within the ecosystem. There are also plans to add Tick History and reference data.

MAS Moves Agentic AI Governance From Model Oversight to Runtime Control

The Monetary Authority of Singapore (MAS) has published an industry white paper proposing a runtime governance framework for AI agents operating in financial services, marking a shift from static model oversight towards controls that operate at the point an autonomous system acts.

The paper, Safeguards for Agentic Finance at Runtime (SAFR), was developed with financial institutions and FinTech firms under MAS’ BuildFin.ai initiative, which supports the responsible development and deployment of artificial intelligence in the financial sector. MAS says the framework is designed to enable AI agents to carry out financial tasks “safely, securely and reliably”.

The operating issue is that agentic AI systems can initiate or complete tasks at a speed and scale that makes manual intervention impractical. SAFR responds by defining governance checkpoints that verify and record an AI agent’s proposed actions before execution, keeping activity within the mandates, policies and risk limits set by the financial institution.

For compliance, risk and technology teams, the framework points to a more operational form of AI assurance. Controls such as policy-bound execution, real-time validation, auditability and interoperability are embedded into workflows, rather than applied only through pre-deployment review or post-event monitoring. The practical effect is to shift governance closer to the execution layer, where an AI agent requests authority to act.

Industry participants have tested the approach across payments and treasury operations, wealth management and compliance review, and client engagement. Reported examples include agents handling routine payments and treasury transactions within predefined limits, reviewing documents and generating structured compliance assessments, and drafting client materials within approved content boundaries.

SAFR builds on MAS’ Project MindForge AI Risk Management Toolkit by focusing on how safeguards can be operationalised at the point of action for AI agents. The framework also extends MAS’ BuildFin.ai work by moving responsible AI deployment into live system operations, where agent actions can be authorised, validated and recorded before execution.

SAFR is significant because it treats agentic finance as an execution-risk problem as much as a model-risk problem. Traditional AI governance frameworks have focused on inputs, outputs, explainability, testing and accountability. Agentic systems add a further control question: whether the system should be allowed to take a specific action, in a specific context, at a specific point in time.

That has direct implications for RegTech architecture. Firms deploying AI agents in regulated workflows will need evidence that actions were authorised, exceptions were escalated, and decision records can be reconstructed for audit, supervision and incident review. MAS has invited further industry participation in future SAFR iterations, while the Future of Finance Institute will support adoption through industry pilots and sandbox experimentation.