💬 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 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:

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 👇

Accurately pricing derivatives when underlyings are closed is a universal source of edge in market making, from tier-1 firm graybox ETF trading to newer operations in 24-hour prediction markets and perpetual futures. Some widely applicable pricing strategies: Related futures and currency moves. When a derivative’s underlyings are closed, market makers use other instruments as proxies that have sufficient beta to the derivative. Examples for a US ETF on Japanese stocks: the USD/JPY pair trades 24/7 through a variety of FX ECNs, interdealer platforms, and futures exchanges. Nikkei 225 index futures also trade on multiple global derivatives exchanges during extended hours. Moves in these assets during the Japanese night will on average predict the opening prints of individual stocks listed on JPX. Home market index moves. An ETF moves with a non-trivial correlation factor to other US names simply because it’s a US-listed security. The effect is easily observable during heightened volatility. It’s a common industry saying that in a market-wide selloff, all correlations go to 1. Both narrow- and broad-based indexes of US names explain some of the signals in mid-frequency alphas. US index returns comprise a small but meaningful component of derivatives’ multifactor beta models. News. A key requirement for pricing derivatives on foreign stocks is processing local news and earnings releases that relate either to the particular stocks or relevant stocks in the same sector. Quantitative trading firms use automated translation tools to process local foreign-language news and suggest idiosyncratic adjustments to traditional factor models. Recent advances in LLM-based NLP have made sentiment analysis viable for blackbox trading systems to react instantaneously to news-based signals. Microstructure. High-frequency trading firms successfully and counterintuitively price derivatives by ignoring the underlyings’ characteristics. Firms extract short-term alphas from order book characteristics, recent returns, microprices, and other microstructure features. From the perspective of a reinforcement-learning non-linear model trainer, unlabeled feature sets and time series data result in ETFs, ADRs, and common stocks being treated as mathematically equivalent. Based on the above, here is a rough model of how large market-making firms predict mid-frequency returns when traditional underlying markets are closed: ΔP = β_FUT · ΔFUT + β_FX · ΔFX + β_IND · ΔIND + [News Term] These firms also heavily prepare for circumstances under which this model fails. FX and futures have idiosyncratic moves, home-market betas can break during regime shifts, news sentiments still have positives, and microstructure signals decay fast. The market shift underway in 24/7 traditional derivative and prediction contract trading will test the model’s longevity for market making and statistical arbitrage going forward.

BinaryFi's propAMM is live on X Layer. Built with @1010trading’s liquidity and market-making support, traders get smarter liquidity, tighter spreads, fresher prices and better fills.

Jordi Alexander (@gametheorizing) returns to TOKEN2049 Singapore. The Founder of @SeliniCapital, a market-making and venture firm built on the foundations of game theory from a former professional poker career. Expect a read on cycles and liquidity that cuts through the noise. Tickets:

OpenGradient Co-Founder Alleges BitMart Insolvency as Funds Remain Frozen OpenGradient co-founder and CEO Matthew Wang said his market-making team has funds stuck on BitMart that it is unable to withdraw, alleging that the crypto exchange is insolvent. He also accused BitMart of asking token holders to lock assets on the platform roughly a week before announcing its shutdown, claiming the campaign was primarily aimed at attracting liquidity. The allegations have not been independently verified, and BitMart has not directly responded to Wang’s claims.