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JUHere are some of the topics that you should be familiar with if youโre looking to get into quant finance!๐งฎ
#quant #quantfinance #optionstrading #algotrading
@julias.algos
#Quantfinance
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QUThe model shown (ฮ-Vol) is part of a broader series of Volatility Frameworks developed during my time as Quant Researcher at my current firm:
ฮ-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.
๐ฃ 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 itโs a crucial part of their career. However in industry, researchers are funded by their place of work. Research is privatised for profit.
A quant researcherโs 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
222.8K
DECoding every PD array to determine its predictive value | Part 1: Order Block. In this series, we are going to code every one of ICTโs canonical PD arrays to determine its predictive value. Itโs become too common and too widely accepted for internet, trading gurus to say that trading confluences โworkโ 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
HOโก ๐๐ง๐ฌ๐ข๐๐ ๐๐ข๐ ๐ก ๐
๐ซ๐๐ช๐ฎ๐๐ง๐๐ฒ ๐๐ซ๐๐๐ข๐ง๐
Modern trading floors are no longer driven by humans.
They operate as ๐ญ๐๐๐ก๐ง๐จ๐ฅ๐จ๐ ๐ฒ ๐๐จ๐ฆ๐ฆ๐๐ง๐ ๐๐๐ง๐ญ๐๐ซ๐ฌ.
At leading firms, ๐๐ฅ๐ ๐จ๐ซ๐ข๐ญ๐ก๐ฆ๐ข๐ ๐ฌ๐ฒ๐ฌ๐ญ๐๐ฆ๐ฌ execute thousands of trades per second, powered by ๐ฎ๐ฅ๐ญ๐ซ๐ ๐ฅ๐จ๐ฐ ๐ฅ๐๐ญ๐๐ง๐๐ฒ ๐ข๐ง๐๐ซ๐๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐ and real time data processing.
These systems continuously:
โข ๐ช๐ฎ๐จ๐ญ๐ ๐ฉ๐ซ๐ข๐๐๐ฌ
โข ๐๐๐ฃ๐ฎ๐ฌ๐ญ ๐ฌ๐ฉ๐ซ๐๐๐๐ฌ based on volatility and order flow
โข ๐ก๐๐๐ ๐ ๐ซ๐ข๐ฌ๐ค automatically across markets
Humans donโt trade.
They design, monitor, and refine the system.
The real edge comes from ๐ฌ๐ฉ๐๐๐, ๐ข๐ง๐๐ซ๐๐ฌ๐ญ๐ซ๐ฎ๐๐ญ๐ฎ๐ซ๐, ๐๐ง๐ ๐ฌ๐ฒ๐ฌ๐ญ๐๐ฆ๐๐ญ๐ข๐ ๐๐ฑ๐๐๐ฎ๐ญ๐ข๐จ๐ง.
In modern markets, execution happens in ๐ฆ๐ข๐๐ซ๐จ๐ฌ๐๐๐จ๐ง๐๐ฌ not minutes.
Credits: HFT, CNN
For educational purposes only. Not financial or investment advice.
#HolisticCapital #QuantFinance #HighFrequencyTrading #MarketStructure #AlgorithmicTrading
@holisticapital
174.9K
JAWhat youโre 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 doesnโt 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.
Itโs a phase-space projection of the system:
Regime clusters act as attractors
Trajectory shows how the market is drifting
Vector fields show where itโs likely to transition next
This is how you stop reacting to priceโฆ
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
152.9K
QUThe most painful chart in trading. ๐
โYour backtest has a Sharpe of 3.0, but your live account is bleeding? Thatโs called Distribution Shift, and it kills more strategies than bad fees ever will.
โIn this visualization, Iโm 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.
โStop guessing if your strategy is overfitting. Measure it.
โ๐ Comment "Wasserstein" below and Iโll 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 instinctsโฆ
Jim Simons did something crazy. He fired traders โ and hired mathematicians, physicists, and cryptographers instead. ๐ง
At Renaissance Technologies, he created a flat structure where geniuses collaborated โ not competed. The result? The most profitable hedge fund in history, powered purely by data, code, and math. ๐
๐ก Lesson: Innovation doesnโt come from following trends โ 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
69.9K
QUComment โAstralโ if you want my quant strategy! Iโll 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
48.1K
QUThe philosophy here is that Mean reversion and momentum arenโt 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 โforbidden crossingsโ. 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
200.3K
MAPart 2 is coming up and Iโm so excited to share the tutorial too for it ๐ฅฐ๐คฉ๐คฉ #quantfinance #financialengineering
@mar_antaya
1.5M
TUComment โrepoโ to get their GitHub links โจ
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Follow @tuba.captures for more
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#quantfinance #pythonprojects #buildinpublic #fypใท #explorepageโจ
@tuba.captures
15
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 โ that's alignment. And it's the most honest thing we can show you.
#wetradedit #conviction #hundredxcapital #quantfinance #hedgefund #systematictrading #alignment
@hundredxcapital
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