Kalshi is selling all of your orderflow to Mossad. The name ICOBeast can be found hundreds of times in the Epstein flight logs. I will be sharing my story on stream this week with the Guy who writes Threads and then, god willing, next Sunday on 60 minutes

aggregator spoofing is funny because it works, but not in the intended way its actually a form of flow segregation. smart orderflow and aggregators won't route to you so you end up trading against people who don't understand prop amms

Your transaction. Encrypted from submission to finality. No frontrunning. No orderflow leakage. No performance hit. Encrypted Mempool on Aptos, the full stack for markets and machines.

Why did Leopold turn to Clear Street as his new prime broker (after JPM dumped him)? Because Clear is the biggest provider of swaps to levered ETFs. When you need to force a squeeze and ramp vega, easiest way is to work with the fund that controls the entire orderflow.

AI has broadly become synonymous with LLMs, but most AI/ML models used in quantitative trading are decades old. Boosted trees and PCA still account for more alpha than anything built on transformers. More detail on several ML techniques in quant trading: Gradient boosted trees and random forests: A standard strategy in short-horizon trading is to start with an intuitive set of microstructure features and train a non-linear model on them. Random forests provide a baseline ensemble that’s hard to overfit, while gradient boosted models fit fainter structures at the risk of overfitting. Features can include microprices, characteristics of the present order book, recent returns of related instruments, momentum signals, and orderflow directionality. Pre-transformer natural language processing: Throughout the 2010s, NLP PhDs were a sought-after resource for quantitative hedge funds. A variety of techniques such as Loughran-McDonald finance-specific sentiment dictionaries, TF-IDF and bag-of-words classifiers, and LDA topic modeling were used to extract sentiment from news, earnings reports, SEC filings, and FOMC minutes. Transformers are more generalized tools for language processing but some of the older methods in the toolkit are still used for their simplicity, predictability, and cost-effectiveness. Dimensionality reduction: A statistical arbitrage researcher may have thousands of correlated instruments and only a few years of clean historical data, so the covariance matrix has more parameters than observations to estimate them from. Principal component analysis is the standard remedy. A handful of leading components explain most of the variation in a universe of equities, and hedge funds will frequently neutralize the leading factors and trade the residuals. Autoencoders generalize the same idea to nonlinear structure, but PCA persists because its output is stable, interpretable, and cheap to recompute. Computer vision: From around 2010 to 2017, hedge funds gained a large informational edge by building automated pipelines to extract alternative data from satellite imagery. The models were unremarkable by research standards, mainly derivatives of convolutional neural networks. Some of the most famous applications include counting parked cars in retail lots, gauging construction progress at industrial sites, estimating crop yields from near-infrared reflectance rather than visible light, and reading crude inventory off the shadow cast by a floating tank roof. The trade worked best during a time when imagery was scarce and the engineering was novel. Today image-based alternative data is sold by third-party vendors for relatively inexpensive consumption. The large memory footprint, slow inference, and black-box nature of LLMs make them a poor fit for alpha generation at trading firms. Hand-tuned models based on intuitional feature selection are still the norm. For niche industries like quantitative finance, there is ample room for competition with generalized frontier models.

Most traders say Market Structure is part of their strategy. Then they build every candle around the clock. 15M. 30M. 4HR. NQ trades nearly 23 hours a day. RTH accounts for only 6.5 of those hours, yet historically carries the majority of the daily volume. That leaves roughly 16.5 overnight hours, often trading with thinner participation and more balance. Yes, the overnight session has offered some excellent opportunities lately. But should a 15-minute candle at 3:00 AM carry the same structural weight as a 15-minute candle after the opening bell? A time-based candle closes because the clock ran out, whether 50 contracts traded or 5,000. A non-time-based candle closes only after a fixed amount of participation takes place. Why judge every candle by how much time passed when participation may tell us more about how the auction actually developed? I don’t use time-based charts for intraday trading. Non-time-based charts stop assigning equal visual weight to unequal participation. The market doesn’t create structure because a timer hit zero. Same market. Different lens. Maybe it’s time to look beyond time. I might be Greek, but I don’t think I’m Socrates. If you see me post something like this, it’s because the lesson was pivotal in my journey as a trader and I believe it could change yours too. #DayTrading# #FuturesTrading# #MarketStructure# #OrderFlow# #VolumeCharts# #NQFutures#

First eventful order flow in weeks. Aggressive shorting into the 58k lows, twice. Unfortunately for bears, the retest coincided with spot sell pressure easing. All of this short OI has since been flushed. Notably, BTC remained strong today despite negative news of MSTR selling

Jay Awtani's Volume and Order Flow masterclass, by the numbers. 37,000 plays. 3,600+ hours watched. 2,500+ viewers. One idea keeps traders coming back: delta divergence. Price makes a new high, but volume delta does not confirm it. That gap shows up before the reversal does. Free on Chart Academy.

Prediction markets hit with a court order and a city lawsuit on the same day A Washington state court ordered @Kalshi to halt most of its prediction market offerings in the state. Baltimore filed a separate suit the same day naming both Kalshi and @Polymarket. The city's case also pulls in Coinbase $COIN, Robinhood and Webull as distribution partners, extending exposure from the venues themselves to the apps routing order flow into them.

Data reveals opportunities. AI captures them. @CoinAnk delivers real time market intelligence with order flow, liquidation insights, and derivatives analytics. @vergex_ai empowers AI agents to analyze those signals and execute trading strategies automatically. Together, we're bringing market intelligence and AI execution into one seamless trading experience.