💬 Customer Spotlight | Sytus Feed & Pinpoint "I was struggling with latency until Sytus Feed and Sytus Pinpoint transformed my trading. The ultra-low latency, rock-solid stability, and superior fill rates gave our team the ultimate execution edge." From solo traders to professional prop teams, achieving that ultimate execution edge is the goal. It’s about stability you can count on and fill rates that matter when every microsecond counts. Thank you to James for the trust and for the high recommendation to other trading teams. We remain dedicated to building the infrastructure that transforms trading performance. Interested in experiencing the difference? Feel free to reach out to us 📩 「在用 Sytus Feed 之前，我一直深受延遲之苦，它與 Sytus Pinpoint 徹底改變了我的交易。極致的低延遲、穩定性以及更高效的成交率，給了我們團隊更好的執行優勢。」 從獨立交易者到專業的自營交易團隊，追求終極的執行優勢是共同的目標。低延遲行情的價值在於可信賴的穩定性，以及在每一微秒都至關重要時，轉化為實質的成交績效。 感謝 James 對我們的信任，以及對其他交易團隊的大力推薦。我們將持續致力於打造更完美的基礎設施，協助交易者提升整體表現。 如果你也想體驗超低延遲行情所帶來的差異，歡迎與我們聯繫📩 #SytusFeed# #SytusPinpoint# #ExecutionInfrastructure# #LowLatency# #HighFrequencyTrading# #QuantTrading# #MarketMaking# #DigitalAssets# #QSG#

Crypto market making went institutional. The way it's managed didn't. Traders, risk, and ops still work from fragmented, inconsistent views. We wrote about what onchain market making looks like in 2026 and how CROPR is becoming its operating layer👇

Crypto market making is entering its "Evidence Era."📄 We’re excited to announce that Amber Group has joined @Forgd_ to bring radical transparency to liquidity provision. Choosing a market maker based on reputation and "vibe checks" are no longer acceptable for sophisticated projects. As markets mature, execution quality must be verifiable.🛡️ By joining Forgd, we’re making our historical market-making data accessible and standardized for the first time. Why does this matter? Most market makers look great during Launch Week. But what happens when volatility hits or trading normalizes? Forgd’s platform allows projects to see our institutional-grade execution throughout the entire lifecycle of a token. 🏦 We’re also supporting Forgd’s new data-driven RFQ process. ⛓️ This levels the playing field for firms that price realistically and manage risk carefully, discouraging the "aggressive bidding" that hurts markets in the long run. Transparency isn’t just a feature—it’s a commitment to market integrity. How are we raising the bar for our "One Amber" ecosystem? Find out here:

Here's some market making history for you. The world's first market makers weren't called "market makers." They were called "jobbers" or "stockjobbers". That name mostly originated from the London Stock Exchange in the 1600s.

Optimizing queue position in market making strategies The price-time-priority design of most exchange matching engines requires high frequency trading firms to continue evolving techniques for front-of-line positioning. On non-pro-rata order books, incoming orders interact with the oldest liquidity in a price level first and the youngest liquidity last. Orders that are filled at the end of the queue exhibit negative mark-to-market P&L at short time frames, as the new mid-price establishes in place of the removed level. Adverse selection associated with bad queue position leads market makers to ensure orders maintain highest priority while canceling orders with low priority. Queue position optimization techniques are based on study of order protocols, market data packet sequencing, book forensics, and latency optimizations, from specialized order types to ladder placement strategies. Examples: • Good ‘Til Canceled (GTC) orders: Exchanges preserve GTC orders in memory during closing hours and reinstantiate the orders automatically when the exchange opens again. Some firms maintain deep orders for weeks or longer to ensure they’re first in the queue when the market price drops or rises to the market maker’s level. • Hidden order types: A handful of US equity exchanges support specialized order types such as “Hide Not Slide” and “Hide And Light.” These keep orders hidden and non-executable while crossing the BBO, then immediately unhide them at the front of the queue once the book uncrosses. • Filling in empty levels: If an order book’s BBO is more than one tick wide or has gaps between prices, market makers will fill in empty levels purely to secure queue position. Trading strategies will often disregard side or price with respect to fair value when presented with the opportunity to establish a new price level. • L3 feed parsing: All HFT market making systems subscribe to order-by-order (level 3) market data feeds, the most granular data that exchanges provide. The data stream allows the consumer to create the exact book of orders the matching engine sees, and enables the trader to understand their exact position in the book after the order has been acknowledged. • Size fingerprinting: As a computational shortcut, some firms will send orders at random odd-lot sizes in order to identify the firms’ order locations in public data feeds. One downside of this approach is the possibility that other participants can detect the orders as well and copy the market makers’ strategies. • Racing after fills: One of the most competitive speed games in trading is racing after a large fill that takes through one or more price levels, with the goal of establishing the new best bid or offer in the same direction as the liquidity-taking order. On traditional colocated exchanges, FPGAs are a minimum requirement to reach the nanosecond-scale latency to have a chance at this strategy. Several venues have attempted to use periodic batch auctions and pro-rata systems to incentivize market participants away from speed competition and order type jockeying. Those market mechanisms have largely failed because they’ve led to empirically worse price discovery for institutions and retail brokerages. As agentic trading systems enter markets, efficient, time-tested, predictable market design will matter even more.

Reputation in market making is shifting from relationships to results. The old model was dinners and sales desks. The new model is quantifiable metrics and historical performance — just like how projects evaluate top crypto exchanges! 👇

What Forgd is building for market making looks a lot like the shift from voice brokerage to electronic trading. Market maker request for quotes become transparent, competitive, and data-driven — market makers benchmark proposals against one another in real time, while the strongest historical performers naturally rise to the top 👇