230.4K
JUHere are some of the topics that you should be familiar with if you’re looking to get into quant finance!🧮
#quant #quantfinance #optionstrading #algotrading
@julias.algos
#Quantfinance
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1.9M
QUThe 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
@quant_research_decoded
222.5K
DECoding 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
@deltatrendtrading
9.2K
HO⚡ 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
@holisticapital
158.3K
JAWhat you’re looking at is an observable Markov regime engine running over a live state stream. Instead of guessing direction, the model classifies the market into four structural regimes:
calm trend
volatile trend
chop
risk-off
Each bar is converted into a probabilistic state using a weighted combination of volatility, trend strength, drawdown pressure, correlation stress, and shock intensity.
From there, the system doesn’t just classify. It builds a rolling transition structure:
P(next state | current state)
That means the model is constantly learning how regimes evolve, persist, and break.
But raw classification is noisy. So the engine applies temporal stabilizers:
Hysteresis to prevent weak flips
Minimum persistence windows to enforce structure
Majority bias to reduce single-bar noise
And most importantly, risk conditioning:
Risk-off states require confirmed stress
Overextended regimes get rebalanced
Chop dominance is actively suppressed
What you see visually is not random animation.
It’s a phase-space projection of the system:
Regime clusters act as attractors
Trajectory shows how the market is drifting
Vector fields show where it’s likely to transition next
This is how you stop reacting to price…
and start tracking the system underneath it.
If you understand the transitions, you understand the market. Get the model free at edgebuildit.com #trading #investing #AI #quantfinance #finance
@jase.brand
145.5K
QUThe 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
@quant.traderr
3.5K
SAWhile 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
@samuelleach
68.2K
QUComment “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
@quant_kavin
45.4K
QUThe 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
@quant_research_decoded
199.8K
MAPart 2 is coming up and I’m so excited to share the tutorial too for it 🥰🤩🤩 #quantfinance #financialengineering
@mar_antaya
1.5M
TUComment “repo” to get their GitHub links ✨
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Follow @tuba.captures for more
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#quantfinance #pythonprojects #buildinpublic #fypシ #explorepage✨
@tuba.captures
14
HUThe 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
@hundredxcapital
✨ #Quantfinance発見ガイド
Instagramには#Quantfinanceの下にthousands of件の投稿があり、プラットフォームで最も活気のあるビジュアルエコシステムの1つを作り出しています。
Instagramの膨大な#Quantfinanceコレクションには、今日最も魅力的な動画が掲載されています。@quant_research_decoded, @tuba.captures and @julias.algosや他のクリエイティブなプロデューサーからのコンテンツは、世界中でthousands of件の投稿に達しました。
#Quantfinanceで何がトレンドですか？最も視聴されたReels動画とバイラルコンテンツが上部に掲載されています。
人気カテゴリー
📹 ビデオトレンド: 最新のReelsとバイラル動画を発見
📈 ハッシュタグ戦略: コンテンツのトレンドハッシュタグオプションを探索
🌟 注目のクリエイター: @quant_research_decoded, @tuba.captures, @julias.algosなどがコミュニティをリード
#Quantfinanceについてのよくある質問
Pictameを使用すれば、Instagramにログインせずに#Quantfinanceのすべてのリールと動画を閲覧できます。あなたの視聴活動は完全にプライベートです。ハッシュタグを検索して、トレンドコンテンツをすぐに探索開始できます。
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💡 トップ投稿は平均963.7K回の再生（平均の2.6倍）
週3-5回、活動時間に定期的に投稿
コンテンツ作成のヒントと戦略
💡 トップコンテンツは10K以上再生回数を獲得 - 最初の3秒に集中
✨ 多くの認証済みクリエイターが活動中（42%） - コンテンツスタイルを研究
✍️ ストーリー性のある詳細なキャプションが効果的 - 平均長625文字
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