ByteSmith 𓂆 @dbytesmith

@ZiddiBakra

@sell_the_rise @algoipin Can't complain bhai, thank you for asking 🤝. Still trying to understand the world, one rabbit hole at a time. d usual me. Hope you've been doing well too 🙏

For years, I've been building low-latency networking and automation systems for myself and fellow traders. Now I'm putting that experience into something entirely my own. AlgoIP.in is officially live and open for users. Built by the engineer who writes the code, runs the infrastructure, answers the support tickets, and obsesses over every millisecond. Rust 🦀 powered. Trader Focused. Lowest Latency Static IPv4/IPv6 software and infrastructure at the lowest price, available today. Just started. @algoipin #AlgoTrading #TradingInfrastructure #LowLatency #Networking #RustLang #FinTech #AlgorithmicTrading #DevOps #Infrastructure #TechFounder #staticip #staticip #ipv4 #ipv6

Haha 😅, first I have to port it to Rust 🦀 Would love to eventually stream the binary payload over SHM and the network with zero-copy deserialization. Fast data APIs start with good timestamps (hardware/PTP) and end with not copying bytes around unnecessarily. Need to open an account first though. For now, I'll skim through the docs, poke around the architecture, and see what interesting rabbit holes reveal themselves.

@Souvik131 And thanks @Souvik131 for sharing this 🙏 Going through the docs now: docs.arrow.trade/rest-api/hft-s… Looks like I have some weekends rabbit holes to explore. Will open an account first.

Even for retail algo traders, I think that only becomes truly interesting if the conditions in the quoted tweet happen. Like when retail participants can consume the higher-fidelity feed programmatically and don't have to route orders over the public Internet, but instead have access to broker-provided colocated infrastructure closer to where the exchange connectivity terminates. Until then, the market-data side may end up improving faster than the execution side. x.com/dbytesmith/sta…

@sell_the_rise @algoipin Thanks Bhai 🙏 🙌

Yeah, Now the architecture makes a lot more sense, That was exactly the rabbit hole I was trying to understand when I started asking about TBT / MTBT / MBO / MBP (higher fidelity feed) versus conventional broadcast feeds. Thank you for taking the time to explain the architecture and congratulations to the team. Building market-data infrastructure is never a trivial undertaking. And yes, I can absolutely see why people familiar with injection candles would appreciate access to information ~900ms earlier than the conventional broadcast path. One thought still remains interesting to me though: Once reconstruction, aggregation, normalization, and state rebuilding enter the picture, we're no longer consuming raw market data directly. We're consuming a derived representation of market data. That's not a criticism, every serious market-data system does this to some extent. It just means the market-data pipeline itself becomes the product. My only hope this will be exposed directly to retail participants programmatically as well, whether via APIs, pub/sub streams, durable replayable streams, QUIC, TCP/TLS streams, or even UDP fanout depending on the use case. Because at ~50ms granularity, the human reaction time quickly becomes the bottleneck. The real magic would be letting retail systems consume the stream directly, replay an entire trading session if needed, and build their own analytics, execution logic, and research workflows on top of it. Either way, genuinely impressive work and a meaningful improvement over conventional broadcast-based retail market data 👏

Fair point. Though I'd be curious how much of the observed drift is actually data latency, and how much is the market reacting to the order itself. Price impact, liquidity removal, queue-position changes, LOB rebalancing, spread adjustments... The moment we interact with the book, we become part of the system we're trying to measure 🙂 Interesting rabbit hole either way.

Aah, now it clicks, This is pretty much the direction I suspected when I asked whether the source was TBT / MTBT / MBO / MBP rather than the conventional broadcast feed. In that case, the chart is almost the easy part. The interesting part is the pipeline underneath. Appreciate you taking the time to explain it 🙏. And congratulations to the team. Market-data engineering is one of those things that looks deceptively simple from the outside until you try building it yourself 😄 x.com/dbytesmith/sta…

> Sick of slop ai features all around You pretty much stole the thoughts straight from my mind and heart 😄 You know that not only now but from long back, I'm equally tired of seeing "AI" slapped onto everything these days. A genuinely useful feature that solves a real problem for traders will always be more interesting to me than any another layer of AI/LLM generated glitter on top of an existing product.

That's fair, and thank you for taking the time to explain your perspective 🙏🙌🙇 If the baseline is a retail broker feed that's occasionally several seconds behind reality, then almost any architecture that reduces that gap is a meaningful improvement for the end user. My curiosity was less about whether it's better and more about understanding what is being improved. Because "faster refreshes" can come from many places: • Better feed acquisition • Better distribution architecture • Less batching • Lower fan-out latency • Richer upstream feeds • More frequent book reconstruction As an infra person, I naturally end up asking how the sausage is made 😄 But yes, if traders are seeing a noticeably fresher view of the market than before, that's definitely a win. 🎯 And again, I appreciate you taking the time to share the context. My questions come more from engineering curiosity than criticism. 🙏 🤝

@Souvik131 Are we talking about: • TBT / MTBT / MBT? • MBO? • MBP? • Native exchange multicast? And then normalizing, aggregating, sequencing, and rebuilding the L2 order book (DOM) internally?

Maybe I'm missing something, but this raises an architectural question. If the broker itself is ultimately receiving snapshot-style market-depth updates, then displaying ~50–100ms order-flow transitions implies somebody is ingesting a much higher-fidelity feed upstream and reconstructing state continuously. If so, the real product isn't the visualization. The real product is the market-data pipeline. Which then raises an even more interesting question: If every other participant connected through conventional broker infrastructure sees one version of the market, while this pipeline reconstructs another, which one should be treated as truth? The exchange feed? The broker feed? The normalized feed? The reconstructed feed? Because once reconstruction, interpolation, aggregation, and state rebuilding enter the picture, you're no longer just consuming market data. You're consuming someone's interpretation of market data. And that distinction matters. A lot. Would genuinely love to understand the architecture behind it.

The older 🧓 I get , the more I realize that reality and its public representation are often two entirely different things. Most of what people consume today comes through layers of filters, incentives, narratives, algorithms, public relations, statistics, headlines, social media, and carefully curated perspectives. Then you step outside. You travel. You meet people. You listen. And suddenly the gap becomes impossible to ignore. There is often a vast distance between what is reported, what is perceived, and what actually exists on the ground. Sometimes it feels as though much of modern society lives inside abstractions while reality quietly continues doing its own thing underneath. The more I observe the world, the less convinced I become that misinformation is our biggest problem. More often than not, the bigger problem seems to be selective visibility. We are shown enough to form an opinion. Rarely enough to understand the truth. Perhaps just a random thought from a mind entangled between its own thoughts and the wilderness of a chaotic world.

The claim sounds great on paper. The interesting part is everything that was omitted 🙂. "Built on actual exchange trades" is doing a lot of heavy lifting in that sentence. Are we talking about: • Raw TBT feeds? • Native exchange multicast? • Hardware timestamped packets? • PTP synchronized clocks? • Sequence-number verification? • Gap recovery? • Feed arbitration? • Out-of-order correction? • Lossless capture? Or are we talking about already normalized downstream vendor data? Because those are very different things. People severely underestimate what it takes to reconstruct exchange activity accurately at scale. At NSE volumes, you're not processing a handful of symbols. You're processing a firehose of events across thousands of instruments, continuously, for the entire trading session. The hard part isn't drawing the chart. The hard part is proving that every trade, every sequence, every timestamp, every recovery path, every packet drop, every replay, and every normalization step preserved reality. And if we're talking about true exchange-grade reconstruction across the entire market, I'd be very interested in understanding the architecture. Because at that point you're no longer building charts. You're building a entire market-data plant. Just my 2 paise. 🖖

@algoipin