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Tag: after-work

pythonzedacpcoding-agentsafter-workstart-using

Getting Started with Zed ACP Agents

Install Zed, open a tiny Python lab, add one External Agent from the ACP Registry, run one reviewed thread—after-work friendly.

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market-impact

after-work

Introducing Anomalous Price Impact and Square-Root Liquidity for after-work quants

Tóth, Bouchaud and coauthors (2011) explain the empirical square-root market-impact law via latent liquidity that vanishes near the price. Plain-language intro. Not investment advice.

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Introducing A Mathematical Approach to Order Book Modeling for after-work quants

Abergel and Jedidi (2010) treat the limit order book as a Markov chain with Poisson arrivals, proving stationarity ideas and Brownian-like midprice scaling. Plain-language intro. Not investment advice.

pythonkiroamazon-qcoding-agentsafter-workstart-using

How to Move from Amazon Q Developer to Kiro After Work

Q Developer new signups are blocked; IDE plugins EOS 30 Apr 2027 per AWS. Start Kiro tonight on a tiny Python lab and review every change.

pythonpaperalt-datallmsentimentafter-work

Introducing Sentiment Trading with Large Language Models for after-work quants

Kirtac and Germano (2024) compare OPT, BERT, FinBERT, and a classic finance dictionary on U.S. news sentiment and next-day returns—after costs in spirit for careful readers. Not investment advice.

papersalt-datanlpsentimentpythonafter-work

Introducing Giving Content to Investor Sentiment: The Role of Media in the Stock Market for after-work quants

Tetlock’s classic result: pessimism in a major WSJ column lines up with temporary downward pressure and higher trading volume—foundational alt-data intuition before modern transformers.

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Introducing FinBERT: A Pretrained Language Model for Financial Communications for after-work quants

Yang, UY & Huang’s HKUST FinBERT is pretrained on financial communications—an alternative NLP backbone for filings and earnings text, distinct from Araci’s sentiment FinBERT.

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Introducing Predicting Returns with Text Data for after-work quants

Ke, Kelly, and Xiu (2019) show a supervised way to turn news text into return-prediction signals with careful screening. Plain-language intro for after-work Python NLP learners. Not investment advice.

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Introducing Enhancing Stock Movement Prediction with Adversarial Training for after-work quants

Feng et al. add intentional perturbations during training so stock-movement models generalise better under noisy price features—a robustness habit you can borrow for NLP signals too.

papersalt-datanlpfinbertpythonafter-work

Introducing FinBERT: Financial Sentiment Analysis with Pre-trained Language Models for after-work quants

Araci’s FinBERT fine-tunes BERT for financial sentiment so domain jargon scores more sensibly—then you can wire a Hugging Face checkpoint into a tiny daily-news feature pipeline.

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How to Start Using Goose

Install Goose (Block/AAIF local agent), configure one LLM provider, and complete one after-work Python edit—MCP extensions optional on night one.

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How to Start Using OpenCode

Install OpenCode’s TUI agent, connect a provider, and complete one reviewable after-work Python edit using Plan/Build and /undo.