#quantfinance

Here are some of the topics that you should be familiar with if you’re looking to get into quant finance!🧮 #quant #quantfinance #optionstrading #algotrading

The model shown (Λ-Vol) is part of a broader series of Volatility Frameworks developed during my time as Quant Researcher at my current firm: Λ-Vol models Volatility as the interaction of regime conditions, reinforcing and stabilising market feedback and risk absorption. It tracks how volatility pressure builds, propagates and unwinds across assets and horizons. We first started by first deriving it at an equations / mathematical level, then building up the framework from there. 📣 To learn more check the link in my Bio. Quant Researchers are hired to find solutions to questions that may come down from their portfolio manger for example. There are no solutions that are found in papers. Academics publish papers as it’s a crucial part of their career. However in industry, researchers are funded by their place of work. Research is privatised for profit. A quant researcher’s role is to develop NEW methods not found in public academic papers. Academic papers certainly play an important role, however a quant researcher would be rendered redundant if solutions were so easily and directly extrapolated from a paper. #quant #ai #quantfinance #datascience #investing

Coding every PD array to determine its predictive value | Part 1: Order Block. In this series, we are going to code every one of ICT‘s canonical PD arrays to determine its predictive value. It’s become too common and too widely accepted for internet, trading gurus to say that trading confluences “work” without showing any statistical proof of their predictive power. I hope this series will change that, and capture the attention of retail traders who need it. #statistics #education #quant #quantfinance

⚡ 𝐈𝐧𝐬𝐢𝐝𝐞 𝐇𝐢𝐠𝐡 𝐅𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 𝐓𝐫𝐚𝐝𝐢𝐧𝐠 Modern trading floors are no longer driven by humans. They operate as 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐜𝐨𝐦𝐦𝐚𝐧𝐝 𝐜𝐞𝐧𝐭𝐞𝐫𝐬. At leading firms, 𝐚𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 execute thousands of trades per second, powered by 𝐮𝐥𝐭𝐫𝐚 𝐥𝐨𝐰 𝐥𝐚𝐭𝐞𝐧𝐜𝐲 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 and real time data processing. These systems continuously: • 𝐪𝐮𝐨𝐭𝐞 𝐩𝐫𝐢𝐜𝐞𝐬 • 𝐚𝐝𝐣𝐮𝐬𝐭 𝐬𝐩𝐫𝐞𝐚𝐝𝐬 based on volatility and order flow • 𝐡𝐞𝐝𝐠𝐞 𝐫𝐢𝐬𝐤 automatically across markets Humans don’t trade. They design, monitor, and refine the system. The real edge comes from 𝐬𝐩𝐞𝐞𝐝, 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞, 𝐚𝐧𝐝 𝐬𝐲𝐬𝐭𝐞𝐦𝐚𝐭𝐢𝐜 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧. In modern markets, execution happens in 𝐦𝐢𝐜𝐫𝐨𝐬𝐞𝐜𝐨𝐧𝐝𝐬 not minutes. Credits: HFT, CNN For educational purposes only. Not financial or investment advice. #HolisticCapital #QuantFinance #HighFrequencyTrading #MarketStructure #AlgorithmicTrading

The most painful chart in trading. 📉 ​Your backtest has a Sharpe of 3.0, but your live account is bleeding? That’s called Distribution Shift, and it kills more strategies than bad fees ever will. ​In this visualization, I’m using Wasserstein Distance and Sinkhorn Divergence to mathematically measure the "Reality Gap" between historical simulations (Blue) and live market conditions (Orange). If these distributions drift too far apart, your model is broken. ​Stop guessing if your strategy is overfitting. Measure it. ​👇 Comment "Wasserstein" below and I’ll DM you the Python repo to build this yourself. #quantfinance #algorithmictrading #econophysics #python #math

While everyone on Wall Street was chasing insider info and gut instincts… Jim Simons did something crazy. He fired traders — and hired mathematicians, physicists, and cryptographers instead. 🧠 At Renaissance Technologies, he created a flat structure where geniuses collaborated — not competed. The result? The most profitable hedge fund in history, powered purely by data, code, and math. 📊 💡 Lesson: Innovation doesn’t come from following trends — it comes from hiring people who think differently. Comment LEARN to get access to my free training on how data and discipline can outperform emotion in the markets. #Finance #Investing #Trading #QuantFinance #JimSimons #RenaissanceTechnologies #StockMarket #WealthBuilding #FinancialEducation #WallStreet

Comment “Astral” if you want my quant strategy! I’ll be sharing every quant algo I can find and turn them into deployable strategies I can make actual money out of. Follow for more! #quantfinance #quant #tradingstrategy

The philosophy here is that Mean reversion and momentum aren’t binary switches but instead a continuous gradation of rank. This metamodel focuses on the fact that optimal strategy tilt points occupy different regions of a continuous regime space -> this models the fact. Component Breakdown (high level): Red & Blue Points represent points where mean reversion & momentum dominate wrt regime. Two surfaces capture Momentum and Mean Reversion factor surfaces respectively, with amplitudes higher corresponding where strategy dominates. Green field captures the coherence between the two spaces, and the points (red or blue) that fall outside the green field are “forbidden crossings”. This is also used as signal. The optimal tilt at each time t is found via the model (at the top) and a position is taken with optimal size, evolving dynamical for each time t. The bottom equity curve displays the performance vs buy and hold. #quant #ai #quantfinance #datascience #investing

Part 2 is coming up and I’m so excited to share the tutorial too for it 🥰🤩🤩 #quantfinance #financialengineering

Comment “repo” to get their GitHub links ✨ . . . Follow @tuba.captures for more . . . #quantfinance #pythonprojects #buildinpublic #fypシ #explorepage✨

The single clearest signal of conviction is putting your own capital at risk. We trade our system. We invest behind it. We stake our reputation on it. That's not marketing — that's alignment. And it's the most honest thing we can show you. #wetradedit #conviction #hundredxcapital #quantfinance #hedgefund #systematictrading #alignment

Math is exact. Econometrics is interpretation. A regression doesn’t prove skill—it just fits a story to the past. Give it enough time and variables, and it will always find “alpha”—even if it’s just noise. Take the Invesco QQQ Trust: no valuation screen, no quality filter, no predictive thesis—just “be on the NASDAQ and survive.” It rode one of the biggest macro trends ever: the explosion of mega-cap tech. Now run a Fama–French factor model on it. The model can spit out statistically significant alpha. But that’s not skill—that’s hindsight. Here’s what econometrics with only regression analysis doesn’t tell you: It finds correlation, not causation It measures outcomes, not processes It can mistake survivorship and momentum for skill It’s backward-looking by design With enough factors, it will “discover” significance in randomness So when you see a high t-stat, remember: it explains what happened under a model… not why it happened, and definitely not whether it will happen again. #Investing #Finance #Econometrics #QuantFinance #Alpha #Beta #FactorInvesting #DataScience #Statistics #MarketInsights #PortfolioManagement #AssetPricing #QQQ #NASDAQ #TechStocks #Quant #Trading #RiskManagement #FinancialEducation #CorrelationVsCausation #Backtesting #DataMining #MarketTrends #WealthBuilding