Reverse Engineering Financial Data Feeds: Binary Protocols and UDP Decoding

Financial market data feeds are the lifeblood of trading and analysis systems, providing real-time quotes, transactions, and other information streams. However, because many of these feeds use proprietary formats and software, it’s difficult for developers to access the raw data in their own way. In such cases, reverse engineering techniques, ranging from analyzing Windows executables to binary protocol analysis and UDP data decoding, become essential to unlocking these data streams. As one technology expert noted, freely available financial APIs typically only provide rough, end-of-day data, so obtaining detailed intraday data requires “A bit of work and Wireshark” to identify how a charting service receives its data. By reverse engineering the communication, you can extract the software and use the raw data directly for your own applications.

This article highlights the key concepts and techniques for reverse engineering Windows executables, DBF file conversion, binary protocol analysis, and UDP data decoding, explaining with practical examples and clear language how our experts approach reverse engineering financial data feed decoders.

Understanding Financial Data Feeds and Proprietary Protocols

Financial Data Feeds; In finance, market data feeds are real-time streams of information such as stock prices, trade volumes, and order book updates. These feeds are often distributed by exchanges or data vendors through specialized software or network endpoints. High-frequency trading systems, analytics platforms, and other financial applications rely on these feeds for timely data. Unlike public web APIs, professional data feeds typically offer high throughput and low latency, sometimes delivering millions of messages per second with sub-millisecond delays. To achieve this performance, feed providers commonly use binary, highly efficient encodings and may transmit data via UDP multicast (one-to-many delivery) instead of slower protocols like HTTP or WebSocket.

Proprietary and undocumented formats: A major challenge is that many financial feeds use proprietary protocols that are not publicly documented. Clients are usually expected to use the vendor’s software (often a Windows application or library) to receive and decode the feed. This locks down the data, making it difficult to integrate into custom systems without using the official client. For example, a trading platform might supply a Windows EXE or DLL that connects to a feed and displays market data, but if you want that data in your own program or algorithm, you’re stuck unless you reverse engineer how it works. This scenario is common “there’s a cool [device or service] which requires vendor software but that proprietary software only runs on Windows”. In other words, the valuable data is trapped behind a black-box program. To liberate that data, reverse engineers need to figure out what the program is doing internally or how it communicates over the network.

Reverse Engineering Windows Executables for Data Decoding

One approach to access a proprietary feed is to reverse engineer the Windows executable or library provided by the vendor. This process involves analyzing the compiled code of the application to understand its functionality in our case, how it decodes or outputs the financial data. Reverse engineering Windows software is a complex task that typically uses tools like IDA Pro or Ghidra (for static disassembly of code) and debuggers like x64dbg or WinDbg (for dynamic analysis). The goal is to discover the data handling logic: for example, locating the functions that parse incoming network packets or finding where in memory the decoded price and trade fields are stored.

Static analysis: By disassembling or decompiling the executable, an engineer can look for clues such as hard-coded protocol constants, message format definitions, or references to networking APIs. For instance, seeing usage of WinSock functions (recvfrom, send, etc.) could indicate how the program reads UDP data. Reverse engineers may identify structures or classes that correspond to feed messages. In some cases, reverse engineering can recover enough information to reconstruct the message format or even extract the code that handles decryption or decompression of the feed.

Dynamic analysis and interception: Sometimes, running the program in a controlled environment yields quicker insights. Engineers can use network sniffing tools or API hooking to capture the data as the official client receives it. For example, you might run the vendor’s program in tandem with a tool like Wireshark or a custom packet logger to intercept the feed in real time. If the data is encrypted or encoded, observing how the program transforms the raw input into displayed values can reveal the decoding steps. This black box approach treats the Windows app as a gateway: feed data goes in, human-readable quotes come out. By following this process, or even modifying the program in memory to free up intermediate data, we can eliminate the need to reverse engineer the algorithm entirely. The key here is that access to the client software provides a baseline; if we have access to a client but not the server, we can capture the client’s traffic and learn the protocol. That’s exactly how reverse engineering works.

Real-world scenario: Imagine a Wall Street trading system where the client software receives market data but the server-side protocol is unknown. A security consultant faced with testing such a system, given only the client and no documentation, would ask: how can I understand the data exchange? The answer is to capture and analyze the client’s communication. In practice, you would run the trading client and sniff its network packets, or instrument the binary, to observe the binary messages it sends/receives. By doing so, it’s possible to “reverse the protocol, intercept the traffic, and learn how it’s structured”. Reverse engineering the Windows executable helps not only in understanding the protocol for integration, but also in assessing security (finding vulnerabilities in the protocol or client). In summary, analyzing Windows binaries provides crucial insights, especially when the feed’s logic is hidden in proprietary software.

Binary Protocol Analysis of Financial Data Streams

Once you have captured the raw data (whether by sniffing network packets or extracting it via reverse engineered code) the next challenge is binary protocol analysis. Unlike human-readable protocols (e.g. JSON or XML APIs), binary protocols pack data into compact, non-textual formats for efficiency. The feed data may look like an illegible sequence of bytes if you open it in a hex editor. The task is to decipher this sequence: figure out where each field (timestamp, price, volume, etc.) is encoded and how.

Key techniques for binary protocol reverse engineering:

• Capture and isolate data streams: Use tools like Wireshark or tcpdump to record feed traffic. Record packet sequences corresponding to known actions or time periods. For example, one of our engineers obtained a network trace of a stock charting application updating prices, which showed binary responses containing requests and price history. Having a clean sample of the binary feed is the first step.
• Look for any recognizable patterns or markers: Often binary data isn’t completely random there may be magic numbers, headers, or text embedded. In a real case, after capturing a response for Google’s stock data, the only immediately recognizable part of the binary blob was the string “N^GOOG“, corresponding to the ticker symbol. Everything else appeared as gibberish. This tells us the feed at least includes the symbol or some identifier, but the rest needs interpretation. Similarly, you might find date/time stamps or company symbols in the byte stream if you know what to look for.
• Identify fixed and variable parts: If you have multiple messages or packets, compare them. Which bytes remain the same each time? A reverse engineering study revealed that all captured UDP packets are exactly the same length, with the first approximately 15 bytes of each packet being identical. Consistent packet size and common header bytes strongly suggest a fixed header structure (e.g., a message type, length field, or static protocol version). Identifying these constants is crucial because they often delimit records or convey metadata. Repeated bytes can indicate delimiters or field separators. Therefore, this analysis requires high care and precision.
• Convert bytes to common data types and units: Try interpreting sections of the data as different numeric formats. For example, four bytes could represent a 32-bit integer or float. Endianess matters: is the data little-endian (common on Windows) or big-endian? One effective trick is to search for values that “look familiar” when converted. This kind of pattern evenly spaced timestamps gives confidence about field boundaries and encodings.
• Leverage known context: In financial flows, you may have external knowledge of how the data should look. You’ll definitely need to use this knowledge during analysis. For example, you might know the approximate price range of a stock within the captured range (e.g., Google’s stock was around $700 in 2015). After isolating the timestamps in the binary blob, the next step in the example is to find the prices. Our reverse engineer knew there should be four price fields for each minute (open, high, low, close). They scanned the data around the timestamp, looking for numbers that, when scaled appropriately, could be in the range 700-705. Sometimes financial protocols use scaled integers (for example, price in cents) or floating-point values. Through iterative guessing and checking, they eventually determined which bytes corresponded to these price values. Each price was initially encoded in a compact binary format that meant nothing until it was interpreted correctly.
• Use tools and automation: Manual inspection can be tedious, so it’s helpful to use tools tailored for protocol analysis. Frameworks like Netzob can automate finding field patterns, and scripting in Python can quickly test hypotheses (e.g., reading the blob in various endian formats). If the feed uses compression or encoding (for example, FIX/FAST compression in financial data, or even just bit flags), reverse engineers might write small scripts to decode common formats or use libraries if the format becomes apparent. In some cases, referencing standards can help. _{For example}, if you suspect the feed is using a known protocol like FIX Adapted for Streaming (FAST), you might look for telltale signs of that.

By iterating these steps (capturing, pattern recognition, and testing interpretations), a clear picture of the binary protocol structure will emerge. Ultimately, you’ll aim to produce a decoder: a piece of code that can take the raw data stream bytes and produce meaningful data fields (time stamps, prices, volumes, etc.). In practice, this might require writing a parser in C++ or Python once you have the necessary features. The result is a custom “financial data stream decoder” that replicates the work of the vendor’s software, but now the data is in your hands.

UDP Data Decoding Challenges and Strategies

Many financial feeds favor UDP (User Datagram Protocol) for data transport, often with multicast distribution. UDP is connectionless and lightweight, making it ideal for broadcasting real-time data to many subscribers with minimal overhead. However, UDP poses some unique challenges for data decoding and protocol reverse engineering:

• Multicast and network setup: Feeds delivered via UDP multicast send packets to a multicast IP address that multiple clients listen on. Multicast means packets are sent once and received by multiple devices simultaneously, which is great for efficiency but requires the analyst to join the multicast group to capture the data. If you’re reverse engineering a feed on your own network, you might need to configure your socket or use OS tools to subscribe to the correct multicast address and port. In a corporate setting, sometimes you only have the feed on a specific machine (running the vendor software), so you might run a sniffer on that machine or on a mirror port to see the packets.
• No guaranteed order or reliability: Unlike TCP, UDP does not guarantee delivery or ordering of packets. Feed protocols that use UDP usually implement their own sequence numbers, checksums, or heartbeats to mitigate this. From a reverse engineering standpoint, you should watch for fields that increment regularly (sequence IDs) or flags indicating missing data. If packet loss occurs during your capture, it can complicate analysis you might see gaps in sequence or time. It’s often helpful to capture for a longer period or from a stable network to minimize lost packets. Understanding the protocol’s recovery mechanisms (if any) is also part of the decoding challenge.
• Packet boundaries and assembly: Each UDP packet typically contains one or more logical messages of the feed. If each packet is a fixed size (as in some feeds), then each packet might carry a single update. In other cases, a single update might span multiple packets or multiple updates might be in one packet. In contrast, some market feeds use variable-length messages, in which case the packet will have length fields or delimiters. Reverse engineers must figure out how to split the byte stream into individual records if it’s not one-to-one. Commonly, a length field at a fixed offset in the header will tell how many bytes the message or packet contains.
• Hidden content and encoding: UDP feeds often carry binary-encoded content which might be compressed or encoded in non-obvious ways.. In financial feeds, you might encounter compression schemes (like FAST or zlib) to reduce bandwidth. Detecting compression may involve looking for known signatures (e.g., bytes 0x78 0x9C for zlib) or noticing that the data doesn’t make sense until decompressed. Encryption is less common for market data (due to latency costs), but if present, reversing it would require obtaining keys (possibly via the client software) and a far more complex task.

Strategies for decoding UDP feeds: First, make sure you capture the data correctly – join the multicast and record a representative sample. Then, apply the binary analysis techniques discussed earlier. Pay extra attention to the first bytes of each packet (header) for things like message ID, sequence number, or timestamps that might reset each day. If you have a sequence number and timestamp, you can correlate the packet timing with real time to ensure you’re decoding correctly. It’s also useful to replay the captured packets through your custom decoder to verify that it produces consistent and sensible output (e.g. monotonic timestamps, non-crazy price values). Through careful analysis, one can decode UDP data feeds in real time, turning raw datagrams into actionable information.

Real-World Example: Decoding a Proprietary Market Data Feed

To tie everything together, consider a concrete example of a project our team encountered. A client had a proprietary financial data feed from an exchange, which delivered real-time stock quotes and trades. The exchange provided a Windows application that displayed the live data, but the client needed to ingest that data into their own analytics system. Unfortunately, the exchange wouldn’t provide an API or documentation for the feed’s protocol. This is exactly the kind of situation where reverse engineering shines.

Project overview: We approached the problem in stages. First, we ran the exchange’s feed software on a PC and captured its network traffic. Sure enough, the feed was coming in via UDP multicast packets. By inspecting the packets in Wireshark, we saw a fixed 48-byte header on every packet (identical in all cases) and then a series of varying bytes afterward. Using the techniques described above (pattern analysis and educated guesses), we determined that the header contained a sequence number and a timestamp, and the body contained multiple 40-byte messages of stock data. Each message started with a 4-byte instrument identifier, followed by fields for price and volume, all packed as binary integers. We cross-verified these findings by observing known stocks. for example, we identified the ID for AAPL (Apple Inc.) by finding a message whose price field translated to roughly Apple’s known trading price that second. From there, other instruments fell into place.

Building the decoder: Once the protocol format was understood, we developed a decoder library in C++ that could subscribe to the multicast feed, parse the incoming packets, and produce a stream of structured data (with fields like symbol, price, volume, timestamp). We also implemented basic error handling as uncovered from the reverse engineering (if a packet was missed, the sequence gap was logged, and the system could request a refresh via a separate channel). Through this project, the client gained independence from the vendor’s application the data flowed directly into their system with our custom solution.

This example highlights how reverse engineering a Windows executable (to figure out how it parses data) and binary protocol analysis (to decipher the message format) come together in a real financial context. By applying these methods, what was once a black box feed became an open stream that the client could utilize as needed.

Benefits of Protocol Reverse Engineering in Finance

Reverse engineering financial data feed decoders isn’t just a technical exercise, it delivers significant value to businesses and technologists:

• Data Access and Freedom: It empowers firms to access data on their terms, without being locked into a vendor’s software or paying for expensive official integrations. For instance, a feed that was only meant to be viewed in a proprietary terminal can be redirected into custom algorithms or databases once it’s decoded.
• Interoperability: Custom protocol decoding allows integration of the feed into diverse systems (trading platforms, risk analysis tools, etc.) that the original vendor never supported. It essentially bridges the gap between an undocumented proprietary system and open architectures.
• Performance Optimization: Sometimes the official software is not optimized for certain use cases or adds latency. By decoding the feed directly, one can create a leaner pipeline perhaps processing the binary feed in memory and extracting just the needed fields, which can be much faster for high-frequency trading needs.
• Security and Reliability: Understanding a protocol from beginning to end means you can better secure it. Understanding how the flow works allows you to detect anomalies, faulty packets, replay attacks, and more, and create security measures. This also means avoiding blindly trusting a black-box implementation that may contain vulnerabilities. Protocol reverse engineering is crucial for network security, and understanding the inner workings of a protocol is crucial for security engineers and developers. This is doubly crucial in finance, where data integrity and system security are paramount.
• Legacy Systems and Documentation: In some cases, financial institutions deal with legacy data feeds or systems where documentation is lost. Reverse engineering provides a path to recover the knowledge of how those systems work so they can be maintained or replaced. It’s like writing the missing manual by studying the system itself.

At ReverseEngineer.net, we specialize in projects exactly like this. Our team has reverse engineered decoded proprietary protocols across various domains including complex financial data feeds to help clients unlock their data and integrate systems in ways that vendors don’t readily support. By applying reverse engineering of Windows binaries, meticulous binary protocol analysis, and clever UDP data decoding techniques, we enable our clients to harness previously inaccessible information.

Reverse engineering financial data feed protocols is an interdisciplinary challenge at the intersection of software analysis and network engineering. From analyzing assembly code to examining hexadecimal data dumps for patterns, it requires patience, skill, and creativity. Yet, as we’ve shown, reverse engineering even high-speed proprietary feeds is entirely feasible. The payoff is control: gaining the ability to manipulate critical financial data in new and specialized ways. For the broader technical community, these techniques exemplify how reverse engineering can transform opaque black-box systems into transparent ones. Whether you’re dealing with a mysterious UDP stream or a locked-down Windows client, a methodical reverse engineering approach will illuminate the path to deciphering the data within. Armed with these skills and tools, you can break free from the confines of standard software and truly own the data that drives your business and innovation. If you have similar projects and are looking for a solution, contact us, and we’ll be the solution for you!