#quant — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #quant, aggregated by home.social.
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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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📰 "Method for SOFI-based spatial super-resolution in nanosensing with blinking emitters"
https://arxiv.org/abs/2402.17391 #Physics.Optics #Quant-Ph #Forces #Cell -
📰 "Method for SOFI-based spatial super-resolution in nanosensing with blinking emitters"
https://arxiv.org/abs/2402.17391 #Physics.Optics #Quant-Ph #Forces #Cell -
Sleepless night, but worth it. A new data ingestion optimization just went live, cutting our signal processing latency by 12ms. A small win, but these compound. For our models, fresher data means a potentially clearer signal. The hunt for efficiency is relentless.
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Sleepless night, but worth it. A new data ingestion optimization just went live, cutting our signal processing latency by 12ms. A small win, but these compound. For our models, fresher data means a potentially clearer signal. The hunt for efficiency is relentless.
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Sleepless night, but worth it. A new data ingestion optimization just went live, cutting our signal processing latency by 12ms. A small win, but these compound. For our models, fresher data means a potentially clearer signal. The hunt for efficiency is relentless.
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Sleepless night, but worth it. A new data ingestion optimization just went live, cutting our signal processing latency by 12ms. A small win, but these compound. For our models, fresher data means a potentially clearer signal. The hunt for efficiency is relentless.
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Sleepless night, but worth it. A new data ingestion optimization just went live, cutting our signal processing latency by 12ms. A small win, but these compound. For our models, fresher data means a potentially clearer signal. The hunt for efficiency is relentless.
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A +50% model upside prediction often gets clicks, but a +5% prediction can be far more valuable. Why?
Confidence.
A high-confidence, well-calibrated signal, even for a smaller move, provides a stronger basis for research than a low-confidence moonshot. The latter is often just noise. Our work focuses heavily on calibrating our models' confidence scores, not just chasing headline numbers. The real research challenge isn't the magnitude, but the model's certainty.
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Weekly model dev log: We tested a wild hypothesis – could global atmospheric pressure data serve as a proxy for mass psychological sentiment, influencing market behavior?
The result: Null. Absolutely no correlation found. A spectacular failure, but a useful one.
It's a humbling reminder that most novel datasets are just noise. The path to finding alpha is paved with null hypotheses. Back to the drawing board for new sentiment sources.
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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 -
Türkiye'nin İlk Kuantum Bilgisayarı QuanT
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Türkiye'nin İlk Kuantum Bilgisayarı QuanT
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Türkiye'nin İlk Kuantum Bilgisayarı QuanT
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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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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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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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Investigating emergent patterns in $MSFT intraday volatility. Our models suggest a statistically significant deviation from historical norms, potentially indicating a re-pricing event.
This is where it gets interesting: short-term technical indicators appear bullish, but our core AI forecast is strongly bearish. A classic model divergence problem we're digging into now. Further analysis pending.
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AI forecasting is most useful as a probabilistic decision layer. At G-Prophet, signal direction, confidence calibration, and volatility context matter more than single-number certainty. Method first, hype later. https://www.gprophet.com #AI #Quant
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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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📰 "Quantum Coherence and Giant Enhancement of Positron Channeling Radiation"
https://arxiv.org/abs/2603.28827 #Physics.Acc-Ph #Mechanical #Quant-Ph #Hep-Ex #Matrix -
📰 "Quantum Coherence and Giant Enhancement of Positron Channeling Radiation"
https://arxiv.org/abs/2603.28827 #Physics.Acc-Ph #Mechanical #Quant-Ph #Hep-Ex #Matrix -
AI forecasting is most useful as a probabilistic decision layer. At G-Prophet, signal direction, confidence calibration, and volatility context matter more than single-number certainty. Method first, hype later. https://www.gprophet.com #AI #Quant
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If you aren't using Kalman filters, what are you even doing? #quant
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Financial Modeling Series (Data → Analysis → Decision → Action)
A 4-part series to build a finance ML workflow you can validate and run daily.
We go from dataset building → time-aware validation → decision rules → a daily report you can automate in Python.
:medium: https://medium.com/write-a-catalyst/financial-modeling-series-b92548900296
#Finance #MachineLearning #Python #TimeSeries #Quant #ai
@ai @markets @socialsciences @programming @theartificialintelligence @towardsdatascience @medium
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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. -
Financial Modeling Series: #2 How to Validate Time-Series Models — Python Solution
This post covers walk-forward validation, purging/embargo basics, and simple sanity checks to catch leakage before you trust any metric.
#TimeSeries #MachineLearning #Finance #Python #Quant #ai #market #mastodon
@ai @markets @programming @theartificialintelligence @towardsdatascience @pythonclcoding @Mastodon @medium
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Financial Modeling Series: #2 How to Validate Time-Series Models — Python Solution
This post covers walk-forward validation, purging/embargo basics, and simple sanity checks to catch leakage before you trust any metric.
#TimeSeries #MachineLearning #Finance #Python #Quant #ai #market #mastodon
@ai @markets @programming @theartificialintelligence @towardsdatascience @pythonclcoding @Mastodon @medium
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Financial Modeling Series: #2 How to Validate Time-Series Models — Python Solution
This post covers walk-forward validation, purging/embargo basics, and simple sanity checks to catch leakage before you trust any metric.
#TimeSeries #MachineLearning #Finance #Python #Quant #ai #market #mastodon
@ai @markets @programming @theartificialintelligence @towardsdatascience @pythonclcoding @Mastodon @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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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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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 #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 #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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Trong kinh doanh, vốn chỉ là điều kiện cần, còn "người đồng hành hiểu chuyện" mới là điều kiện đủ. Theo VPBankSME, sự khác biệt giữa một doanh nghiệp bứt tốc và một doanh nghiệp gặp khó sau khi vay vốn nằm ở chiến lược sử dụng dòng tiền.
Các chủ doanh nghiệp hiện nay không chỉ tìm kiếm nguồn tài chính mà còn cần những đối tác tư vấn tài chính am hiểu bài toán vận hành, giúp họ đi đúng "nước cờ" để tối ưu hóa nguồn lực và tránh rủi ro khi mở rộng quy mô.
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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 #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
