Routing your order properly by taking the exchange latency, depth, premium of each venue is also a core principe of market-making, you quote nice depth on exchanges and hedge yourself instantly, on the same symbol or on correlated ones for low bp cost it also allow mm to handle OTC trades and quotes RFQ for huge depth. on any altcoin you easily accumulate millions $ depth nitter.net/Armv7lFx/status/210305…

Moving Average Distance: The Technical Indicator That Passed the Cross-Section [@AliAskar92] dlvr.it/TVcr4z

Market making isn't about predicting where the price goes next. It's about figuring out what something is worth right now. Part 2 of the HFT series. Enjoy! research.qfex.com/p/fair-val…

Good vs Bad COVOL in Crypto: A Common-Volatility Tilt [@AliAskar92] dlvr.it/TVbwQp

State-Dependent (In)Efficiency: Meta-Learning a Directional-Change Threshold [@AliAskar92] dlvr.it/TVbxY9

Milyarderlerin Ne Aldığını ve Ne Okuduğunu Ücretsiz Gösteren 50 Site 1.) dataroma.com → Ünlü yatırımcıların güncel portföyleri 2.) whalewisdom.com → Fonların çeyreklik alım satımları 3.) capitoltrades.com → ABD'li politikacıların hisse işlemleri 4.) openinsider.com → Yöneticilerin kendi şirket hisseleri 5.) 13f.info → Fon bildirimlerini okunur hale getirme 6.) sec.gov/edgar → Şirketlerin resmi raporları 7.) berkshirehathaway.com → Buffett'ın 1977'den beri mektupları 8.) buffett.cnbc.com → Buffett toplantılarının video arşivi 9.) oaktreecapital.com → Howard Marks'ın yatırımcı notları 10.) bridgewater.com → Ray Dalio'nun firmasının araştırmaları 11.) principles.com → Dalio'nun ilkeleri ücretsiz 12.) gmo.com → Jeremy Grantham'ın piyasa notları 13.) aqr.com → Kantitatif fon araştırmaları 14.) pages.stern.nyu.edu/~adamoda… → Damodaran'ın değerleme verileri 15.) aswathdamodaran.blogspot.com → Değerleme profesörünün blogu 16.) collabfund.com/blog → Morgan Housel'in para yazıları 17.) fs.blog → Karar vermenin zihinsel modelleri 18.) am.jpmorgan.com → JPMorgan'ın ücretsiz piyasa rehberi 19.) ark-invest.com/research → Yenilik odaklı fon araştırmaları 20.) a16z.com → Girişim sermayesi denemeleri 21.) csinvesting.org → Değer yatırımı ders notları 22.) gsb.columbia.edu/valueinvest… → Columbia'nın değer yatırımı arşivi 23.) fred.stlouisfed.org → Fed'in ekonomik veri arşivi 24.) federalreserve.gov → Fed kararları ve açıklamaları 25.) ecb.europa.eu → Avrupa Merkez Bankası verileri 26.) bis.org → Merkez bankalarının bankası raporları 27.) tradingeconomics.com → Ülkelerin ekonomik göstergeleri 28.) macrotrends.net → Şirketlerin uzun vadeli grafikleri 29.) stockanalysis.com → Şirket finansalları ücretsiz 30.) companiesmarketcap.com → Şirketlerin piyasa değeri sıralaması 31.) finviz.com → Hisse tarama ve ısı haritası 32.) portfoliovisualizer.com → Portföy geri testi 33.) tradingview.com → Grafik ve piyasa analizi 34.) koyfin.com → Profesyonel piyasa ekranı 35.) annualreports.com → Şirket faaliyet raporları arşivi 36.) investor.gov → SEC'in yatırımcı eğitim sitesi 37.) bogleheads.org/wiki → Pasif yatırım ansiklopedisi 38.) investopedia.com → Finans terimleri sözlüğü 39.) corporatefinanceinstitute.co… → Ücretsiz finans rehberleri 40.) cfainstitute.org → CFA araştırma yayınları 41.) morningstar.com → Fon ve hisse değerlendirmeleri 42.) simplywall.st → Şirketlerin görsel analizi 43.) valueinvestorsclub.com → Seçilmiş yatırım tezleri arşivi 44.) etfdb.com → Fonların içinde ne olduğunu görme 45.) curvo.eu → Avrupa fon geri testleri 46.) ofdollarsanddata.com → Veriyle yatırım yazıları 47.) awealthofcommonsense.com → Piyasa tarihi notları 48.) theirrelevantinvestor.com → Yatırımcı psikolojisi yazıları 49.) visualcapitalist.com → Ekonomiyi grafiklerle anlatma 50.) ourfiniteworld? → (bkz. not) Zenginler bilgiyi saklamıyor. Sadece nerede durduğunu kimse söylemiyor. Kaydedin, lazım olur. Yatırım tavsiyesi değildir.

jkpfactors.com is a great library of the factor landscape, 153 factors that make up 13 clusters. The mean strategies of these clusters cluster again: Quality: quality, profitability, profit growth. momentum is also attached here, as it correlates to profit growth. Value: investment, value, low risk. short-term reversal is loosely attached. Leftovers: accruals, size, debt issuance, low leverage. their common feature is being mostly anti-correlated with the first two groups. seasonality is its own odd thing, even its constituents don't correlate with each other.

The work that Takopi put into quantifying Kalshi wash trades is very similar to the work that you would do as a quant to harvest a bad-MM multi-market-movement monitor, similarity of quotes See pdf below - github.com/OctopusTakopi/kal…

Trend Quality Near Settlement: A Kalshi State Variable, Not Alpha [@AliAskar92] dlvr.it/TVZH53

关于一些因子EMA、动量、突破这些，本质上是同一个东西：过去收益的不同加权。它们之间相关性很高，打散组合后信息量没有增加，各自的时点反而被稀释。所以问题可能不在因子不够多，而在信息来源单一、组合方式又是线性叠加。

Do Intra-Bar Timestamps Carry Alpha? OHLC + Timing Features [@AliAskar92] dlvr.it/TVVXmV

Hello my Rust people, Took a break but will publish article again starting this weekend. I'll restart with the Rust series with improved project ideas and will follow the same structure what Rust book follows Next week, I'll surely continue publishing the 2nd GPUI article as well, sorry for the delay

kafka-explained An interactive Node.js + React app that shows how Apache Kafka works, using a live simulation you can steer: #apachekafka github.com/ChauhanParesh/kaf…

Piecewise-Linear Regime Identification (Interpretable, No Black Box) [@AliAskar92] dlvr.it/TVTcmf

A very interesting project to check out for anyone exploring systematic trend following and momentum in futures. It builds heavily on Robert Carver’s work, but with some different implementation choices. What I particularly like is the dashboard. It makes a complex portfolio easier to follow, showing orders, daily P&L drawdowns etc. There is also an ongoing paper trading run, with daily updates published to GitHub. So you can study the implementation and follow how the portfolio behaves as new data arrives. Worth exploring for ideas, even if your own approach is different. github.com/Lucas-Joly-GH/tre…

Posts: x.com/systematicls/status/19… x.com/systematicls/status/20… x.com/systematicls/status/17… x.com/systematicls/status/20… nitter.net/systematicls/status/17… nitter.net/systematicls/status/19… nitter.net/systematicls/status/17… nitter.net/systematicls/status/19… nitter.net/systematicls/status/19… -This is about alpha, adverse selection, and table selection. Articles: really just look through them all on his profile, and read what you like good ones to start: nitter.net/systematicls/status/20… nitter.net/systematicls/status/20… nitter.net/systematicls/status/20… nitter.net/systematicls/status/20… nitter.net/systematicls/status/17… nitter.net/systematicls/status/19… Remember, Sysls is the guy who said: "30 million at 15 sharpe. Who cares?" While this is true (in top tier circles), I rather maximize my own profit, and minimize my own loss, than care abt it. Whatever a guy like this says, I adjust for the fact that he is, in fact, a guy like this.

前两次聊了资金费率和清算,这次说 OI(未平仓量)。 OI 本身没有方向，它只回答一个问题，仓位是在进场还是在离场。所以它单独用几乎没有意义，必须和价格配着看。四种组合: 价格涨 + OI 涨 —— 新钱在做多，趋势有支撑 价格涨 + OI 跌 —— 空头在平仓，是逼空不是新买盘，涨完容易没下文 价格跌 + OI 涨 —— 新钱在做空 价格跌 + OI 跌 —— 多头在认赔离场，是出清 同样一根阳线，第一种和第二种的后续完全不同。只看价格分不出来,加上 OI 就能分。 我的体会是OI 的用法更接近过滤器而不是信号源。趋势信号发出来的时候，拿 OI 变化确认一下这波是新钱进场还是老仓出清，能过滤掉一部分假突破。 一是要用名义价值还是合约张数，得想清楚。用 USD 计价的 OI 在价格大涨时会自然膨胀，那不是真的加仓。做变化率的时候容易被这个骗。 二是 OI 的绝对水平没什么用，有用的是变化速度。同样的 OI 高位，是三天堆上来的还是三个月堆上来的。脆弱程度差很远。

The CPU Cache video series piped.video/playlist?list=PL…

One of the biggest misconceptions I hear retail traders make about systematic trading: “Context is king — but context can’t be programmed.” This is unequivocally false. A huge amount of what discretionary traders call “context” is simply information that hasn’t been converted into features yet. Take a discretionary NQ trader who says: “We’re trading near the overnight low, well below VWAP, after sweeping a prior structural low. The market is rotational, sellers are becoming less effective, and aggressive selling isn’t producing much additional downside.” That sounds highly discretionary. Now decompose it: • Distance from overnight low → measurable • Distance from VWAP → measurable • VWAP deviation normalized by ATR/volatility → measurable • Sweep of a prior low → definable • Trend vs rotational regime → classifiable • Realized volatility → measurable • Selling aggression/delta → measurable • Price response to aggressive selling → measurable • Time of day → measurable • First/second/third test of a level → measurable Suddenly “context” starts looking a lot like a feature vector. The mistake is assuming systematic trading has to look like: IF order flow > X → BUY. That’s not sophisticated systematic trading. That’s just a rule. A contextual system can incorporate: Location + Regime + Volatility + Auction Structure + Price Action + Order Flow + Time Then ask: Given this particular market state, what is the conditional probability of continuation or reversal? Even something seemingly discretionary like “acceptance above the Initial Balance” can potentially be decomposed: How long has price remained above IBH? How much volume has traded above it? Is VWAP moving higher? Is developing POC migrating higher? Are pullbacks holding IBH? How does price respond when aggressive sellers appear? You don’t necessarily need AI to do this. Start by translating discretionary observations into measurable hypotheses and testing them historically. Some context will undoubtedly be difficult to quantify. But: “Difficult to quantify” ≠ “impossible to systematize.” A discretionary trader sees a story. A systematic trader asks: What variables describe that story — and does the data show that they actually change the distribution of future returns? Context isn’t the enemy of systematic trading. Context may be one of its richest sources of features.

Volatility Clustering in Bitcoin: Regime Persistence as a Forecast [@AliAskar92] dlvr.it/TVJ29q