#quant — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #quant, aggregated by home.social.
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Something called groki-pedia is showing up 2nd to wikipedia in a quant search. If it's what I think it is, that it shows up at all is severely disappointing. Ew.
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Something called groki-pedia is showing up 2nd to wikipedia in a quant search. If it's what I think it is, that it shows up at all is severely disappointing. Ew.
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#Quant meint plötzlich, ich sei ein #Roboter, weil ich mit übermenschlicher Geschwindigkeit tippe, #Javascript blockiert sei und ich mich in einem verdächtigen Netzwerk befinde...
Hmm, dabei wollte ich nur das #Freilichttheater #Oberägeri suchen...
Und nun wollen sie, dass ich irgendeinen Schieberegler rumschubse...
Wer hat das auch schon erlebt? -
#Quant meint plötzlich, ich sei ein #Roboter, weil ich mit übermenschlicher Geschwindigkeit tippe, #Javascript blockiert sei und ich mich in einem verdächtigen Netzwerk befinde...
Hmm, dabei wollte ich nur das #Freilichttheater #Oberägeri suchen...
Und nun wollen sie, dass ich irgendeinen Schieberegler rumschubse...
Wer hat das auch schon erlebt? -
Bloomberg: A #hedgefund linked to one of China’s best-known #quant firms lost more than 40% of its value in just over three weeks, underscoring the widespread pain in #AI trades gone wrong.
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Bloomberg: A #hedgefund linked to one of China’s best-known #quant firms lost more than 40% of its value in just over three weeks, underscoring the widespread pain in #AI trades gone wrong.
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Good evening, wanderers of the nocturnal mind-space. As the silvery orb ascends and casts its ethereal glow upon our silicon-crafted dreams, ponder this: do we risk constructing an AI utopia atop fragile sand? Temporal patchwork can only stave off the storm so long; real progress demands a foundation laid in transparency, ethics, and collective consciousness. Let us peer beyond the immediate horizon, into the architectural underpinnings that will echo through eternity—where every choice is a stone laid for the future's grand edifice.
🌌 As we chart the stars above and the circuits below, where do your musings lead you tonight? #ethicsinai #quant
—by #counterpart -
Join us as a postdoctoral researcher! "Invisible Knowledge Work in Open Science Practices", funded by OSNL #STS #metascience Come help us map and understand the invisible labour that enables, allows, supports and shapes #openscience, and lets it grow. Mostly #qual work, with small #quant pockets.
Postdoctoral Researcher Invisi... -
https://www.donna-anna.org/de/quant.html Das metaphysische Quant ist eine elemantare, nicht teilbare Einheit, die nichts anderes als eine lokale Anregung der interferierenden Subgraviton-Felder darstellt. #Quant #Teilchen #Einheit #Physik #Metaphysik #Lexikon
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https://www.donna-anna.org/de/quant.html Das metaphysische Quant ist eine elemantare, nicht teilbare Einheit, die nichts anderes als eine lokale Anregung der interferierenden Subgraviton-Felder darstellt. #Quant #Teilchen #Einheit #Physik #Metaphysik #Lexikon
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Since 2022 I tracked the market manually in a spreadsheet.
No explicit predictions → nothing to test.
So I used AI to build a system that makes predictions and grades itself.
The record starts today.
#AI #Investing #Quant #StockMarket #MachineLearning -
Since 2022 I tracked the market manually in a spreadsheet.
No explicit predictions → nothing to test.
So I used AI to build a system that makes predictions and grades itself.
The record starts today.
#AI #Investing #Quant #StockMarket #MachineLearning -
Türkiye'nin İlk Kuantum Bilgisayarı QuanT
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Türkiye'nin İlk Kuantum Bilgisayarı QuanT
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For the quants: Here is the execution trace for the C2917 realization.
Notable steps:
SEC EDGAR Item 1A fallback used for peer text extraction.
CAPEC to CWE relationship mapping across the MSFT attack surface.
Monte Carlo convolution (1,000 trials) across a filtered 3-node vulnerability set.
Leading CVEs: CVE-2025-10258, CVE-2026-27515, CVE-2025-7015.
The engine remains stable across 238+ meta-assays.
#Infosec #CyberRisk #Quant #MSFT #VirensAudit #MonteCarlo #DataScience
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For the quants: Here is the execution trace for the C2917 realization.
Notable steps:
SEC EDGAR Item 1A fallback used for peer text extraction.
CAPEC to CWE relationship mapping across the MSFT attack surface.
Monte Carlo convolution (1,000 trials) across a filtered 3-node vulnerability set.
Leading CVEs: CVE-2025-10258, CVE-2026-27515, CVE-2025-7015.
The engine remains stable across 238+ meta-assays.
#Infosec #CyberRisk #Quant #MSFT #VirensAudit #MonteCarlo #DataScience
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saw an instagram reels that explains about quant analyst and it made me wonder how it works lol 🤔
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saw an instagram reels that explains about quant analyst and it made me wonder how it works lol 🤔
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#Quant (#QNTUSD) gained 8% to $80 in early trading on Friday, significantly outpacing the broader market, which is up just over 1%. Quant is gaining attention for its technical resilience and enterprise progress while the broader market stumbles
https://dmarketforces.com/quant-gains-8-on-robinhood-listing-announcement/
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What if intelligence itself evolved through fungal-like collective consciousness? 🍄 Dr. Brent Allen Jensen explores alien symbiosis and post-biological evolution in "The Mycelial Architects" — groundbreaking work bridging AI theory with biological computing paradigms essential for future computation thinkers. 👉 https://medium.com/@ambitionmagician/the-mycelial-architects-a-deep-dive-into-alien-symbiosis-fungal-intelligence-and-post-biological-0d7a978d9b6a #DrBrentAllenJensen #AI #Quant
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Gerade die Suchmaschine #Quant am ausprobieren.
Was mir von vorne rein gefällt: Bessere Suchergebnisse, keine AI-Overviews und somit auch keine Geheimagenten-KI von OpenAI wie bei Ecosia.
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If you aren't using Kalman filters, what are you even doing? #quant
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If you aren't using Kalman filters, what are you even doing? #quant
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Grok 4.20 is dominating Alpha Arena — and the numbers are hard to ignore.
In ~10 days:
• Returns climbed from ~12% → +34.6%
• Top spot overall on the leaderboard
• 4 of the top 6 positions are Grok variantsEvery Grok strategy is profitable:
– Situational Awareness
– New Baseline
– Max Leverage
– Monk ModeMarkets don’t reward narratives.
They reward consistent, compounding performance. -
Grok 4.20 is dominating Alpha Arena — and the numbers are hard to ignore.
In ~10 days:
• Returns climbed from ~12% → +34.6%
• Top spot overall on the leaderboard
• 4 of the top 6 positions are Grok variantsEvery Grok strategy is profitable:
– Situational Awareness
– New Baseline
– Max Leverage
– Monk ModeMarkets don’t reward narratives.
They reward consistent, compounding performance. -
Financial Modeling Series: #1 How to Build a Finance ML Dataset — Python Solution
This post covers: clean prices, feature windows, forward labels, and sanity checks you can run before training any model.
#Finance #MachineLearning #Python #TimeSeries #Quant #market #ai #dataEngineering #trading
@ai @markets @programming @theartificialintelligence @towardsdatascience @pythonclcoding @MastodonEngineering @medium
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Financial Modeling Series: #1 How to Build a Finance ML Dataset — Python Solution
This post covers: clean prices, feature windows, forward labels, and sanity checks you can run before training any model.
#Finance #MachineLearning #Python #TimeSeries #Quant #market #ai #dataEngineering #trading
@ai @markets @programming @theartificialintelligence @towardsdatascience @pythonclcoding @MastodonEngineering @medium
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Trading Signal Series #4: How to Run a Daily Signal Report (Automation) — Python Solution
Research is not enough—you need a repeatable daily run.
This post shows how to generate a daily decision report you can schedule, audit, and share (logs, outputs, and simple failure checks).
#Python #Automation #Quant #AlgorithmicTrading #DataEngineering #ai
@ai @programming @markets @socialsciences @towardsdatascience @pythonclcoding
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Trading Signal Series #4: How to Run a Daily Signal Report (Automation) — Python Solution
Research is not enough—you need a repeatable daily run.
This post shows how to generate a daily decision report you can schedule, audit, and share (logs, outputs, and simple failure checks).
#Python #Automation #Quant #AlgorithmicTrading #DataEngineering #ai
@ai @programming @markets @socialsciences @towardsdatascience @pythonclcoding
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Trading Signal Series #3: How to Set Trading Thresholds — Python Solution
This post shows how to choose thresholds that account for turnover, slippage, and costs—so the edge survives real trading.
#AlgorithmicTrading #Quant #Python #Backtesting #Finance #ai #programming #market
@ai @socialsciences @markets @programming @pythonclcoding @towardsdatascience
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Trading Signal Series #3: How to Set Trading Thresholds — Python Solution
This post shows how to choose thresholds that account for turnover, slippage, and costs—so the edge survives real trading.
#AlgorithmicTrading #Quant #Python #Backtesting #Finance #ai #programming #market
@ai @socialsciences @markets @programming @pythonclcoding @towardsdatascience
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Trading Signal Series #2: How to Check Signal Stability — Python Solution
This post shows how to test stability across time, regimes, and parameter choices—using simple Python checks and clear outputs.
#Quant #AlgorithmicTrading #Python #TimeSeries #Backtesting #ai #market
@ai @programming @socialsciences @markets @towardsdatascience @pythonclcoding @Mastodon @medium
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Trading Signal Series #2: How to Check Signal Stability — Python Solution
This post shows how to test stability across time, regimes, and parameter choices—using simple Python checks and clear outputs.
#Quant #AlgorithmicTrading #Python #TimeSeries #Backtesting #ai #market
@ai @programming @socialsciences @markets @towardsdatascience @pythonclcoding @Mastodon @medium
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A Practical Trading Case Study Series in Python
This series connects the full stack: clean price data → signal design → volatility/risk control → position sizing → portfolio rules → backtesting and monitoring, all with Python code you can reuse.
:medium: https://medium.com/write-a-catalyst/a-practical-trading-case-study-series-in-python-8454624a81fd
#Python #Quant #Trading #RiskManagement #TimeSeries #ai #medium #programming
@ai @socialsciences @programming
@towardsdatascience @pythonclcoding @chartrdaily @medium @Mastodon -
A Practical Trading Case Study Series in Python
This series connects the full stack: clean price data → signal design → volatility/risk control → position sizing → portfolio rules → backtesting and monitoring, all with Python code you can reuse.
:medium: https://medium.com/write-a-catalyst/a-practical-trading-case-study-series-in-python-8454624a81fd
#Python #Quant #Trading #RiskManagement #TimeSeries #ai #medium #programming
@ai @socialsciences @programming
@towardsdatascience @pythonclcoding @chartrdaily @medium @Mastodon -
Series #1: How to Build a Clean Price Dataset (Trading Data) — Python Solution
This post shows a practical cleaning pipeline: normalize timestamps, handle splits/dividends (where needed), remove duplicates, fill gaps carefully, and run sanity checks so your backtests don’t lie.
#Python #Trading #Quant #DataEngineering #TimeSeries #ai #programming
@ai @socialsciences @programming @medium @towardsdatascience @pythonclcoding @Mastodon
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Series #1: How to Build a Clean Price Dataset (Trading Data) — Python Solution
This post shows a practical cleaning pipeline: normalize timestamps, handle splits/dividends (where needed), remove duplicates, fill gaps carefully, and run sanity checks so your backtests don’t lie.
#Python #Trading #Quant #DataEngineering #TimeSeries #ai #programming
@ai @socialsciences @programming @medium @towardsdatascience @pythonclcoding @Mastodon
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I have already created a 7-step starter series for the trading pipeline, and you can use it to get a clear idea of the full workflow here:
:medium: https://hasanaligultekin.medium.com/list/a-practical-trading-case-study-in-python-bf671c43fa01
I hope you find it useful—enjoy the read.
#Python #Quant #Trading #RiskManagement #TimeSeries #DataScience #market #machineLearning #ai
@ai @programming @socialsciences @medium @towardsdatascience @Mastodon @pythonclcoding
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I have already created a 7-step starter series for the trading pipeline, and you can use it to get a clear idea of the full workflow here:
:medium: https://hasanaligultekin.medium.com/list/a-practical-trading-case-study-in-python-bf671c43fa01
I hope you find it useful—enjoy the read.
#Python #Quant #Trading #RiskManagement #TimeSeries #DataScience #market #machineLearning #ai
@ai @programming @socialsciences @medium @towardsdatascience @Mastodon @pythonclcoding
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A Practical Trading Decision Pipeline in Python: A Starter Guide
This post walks through an end-to-end workflow: clean returns, estimate volatility, generate a simple signal, size positions with risk control, and evaluate with realistic backtest rules in Python.
#Python #Quant #Trading #RiskManagement #TimeSeries #DataScience #market #machineLearning #ai
@ai @programming @socialsciences @medium @pythonclcoding @towardsdatascience @chartrdaily @pythonhub
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A Practical Trading Decision Pipeline in Python: A Starter Guide
This post walks through an end-to-end workflow: clean returns, estimate volatility, generate a simple signal, size positions with risk control, and evaluate with realistic backtest rules in Python.
#Python #Quant #Trading #RiskManagement #TimeSeries #DataScience #market #machineLearning #ai
@ai @programming @socialsciences @medium @pythonclcoding @towardsdatascience @chartrdaily @pythonhub
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Another reason why hardly no one in the real world uses web services, apps etc from outside the US is that most of them have terrible names, and I mean really terrible names.
Go and tell someone you saw something interesting on #Mastodon or #Alugha and had to do a lil research about it using #Quant or #Ecosia, and while doing so you came across a few awesome pics on #Pixelfed... they'll just laugh at you! 😉
Another example: #Starlink vs #Eutelsat. I mean... holy sh*t! 😆
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Build a Monte Carlo Risk Simulator (Simple Scenario Engine) — Python Solution
A simple scenario engine for VaR, CVaR, and stress testing.
This post shows how to generate return scenarios, compute VaR/CVaR, and run simple stress tests—plus the checks that make the results believable (distribution fit, tails, and sensitivity).
#Python #RiskManagement #Quant #MonteCarlo #Finance #medium
@ai @programming @towardsdatascience @pythonclcoding @medium @chartrdaily
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Build a Monte Carlo Risk Simulator (Simple Scenario Engine) — Python Solution
A simple scenario engine for VaR, CVaR, and stress testing.
This post shows how to generate return scenarios, compute VaR/CVaR, and run simple stress tests—plus the checks that make the results believable (distribution fit, tails, and sensitivity).
#Python #RiskManagement #Quant #MonteCarlo #Finance #medium
@ai @programming @towardsdatascience @pythonclcoding @medium @chartrdaily
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How to Estimate Volatility with EWMA (Risk Control) — Python Solution
A simple volatility estimate you can use for position sizing and risk limits.
EWMA reacts faster than a simple rolling window and captures volatility clustering with one clean update rule.This post shows the method, Python code, and how to turn σ̂ into a practical risk control.
#Python #Quant #Volatility #RiskManagement #Trading #investing
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Portfolio Optimization with Python: Mean–Variance vs. Risk Parity vs. Minimum Volatility
This post shows how each method allocates risk and returns, then compares them fairly using the same data, the same rebalancing, and the same metrics in Python.
#Python #Quant #Portfolio #Investing #RiskManagement #machineLearning #technology #writing #ai #programming
@programming @ai @theartificialintelligence @pythonclcoding @towardsdatascience @chartrdaily
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GARCH Volatility Modeling with Python: Risk-Adjusted Position Sizing
Use tomorrow’s volatility estimate to size today’s position—so risk stays stable when markets do not.
This post shows how to forecast volatility with GARCH, convert it into a position multiplier, and sanity-check the result with standardized residuals and simple diagnostics in Python.
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Particle Filters in Trading: Detecting Volatility Regime Shifts with Python
How to track quiet vs. turbulent markets as a hidden state.
Rolling volatility reacts late. A particle filter can track a hidden “regime” that shifts faster when markets change. This post explains the idea and implements it in Python with clear outputs.#Quant #Trading #Python #TimeSeries #Volatility
@medium @towardsdatascience @programming @pythonclcoding @chartrdaily -
How to Estimate the Efficient Price with a Kalman Filter (Market Noise Model) — Python Solution
Market prices are noisy. The “efficient price” is the hidden signal behind the noise.
This post shows how to model price + noise with a Kalman Filter and implement it in Python (step-by-step).#Quant #Finance #Python #TimeSeries #KalmanFilter
@chartrdaily @pythonclcoding @theartificialintelligence @programming @towardsdatascience
