Agent-native investment research workspace for evidence-tracked, refreshable investment theses across equities, earnings, macro, and portfolio review.
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Updated
Sep 8, 2026 - Python
Agent-native investment research workspace for evidence-tracked, refreshable investment theses across equities, earnings, macro, and portfolio review.
quick earnings analysis with consensus Wall Street forecast, with past guidance, with peer industry trend.
Real-Time Mobile Payment Alerts Overlays
价值投资研究元技能:融合巴菲特、芒格、段永平、李录四大方法论,内置 19 个专业投研命令,智能路由加质量保障的统一研究入口。
📈 Analyze press releases to predict earnings announcement returns using structured data and natural language processing techniques.
Open-source personal finance command center: direct indexing, tax-loss harvesting, retirement planning, options research, and AI-assisted workflows.
把财报数字翻成因果链:一次性扰动还是结构性变化? | Earnings numbers into causal chains, not a list of what rose · 📮 wow.vip@foxmail.com · 🎬 https://v.douyin.com/07EjIDGVErY/
AI + IR 工作助手 Skill:新闻与同业简报、投资者问题雷达、财报问答、人工确认会议反馈;可追溯证据、80项测试、完整恢复与更新说明。
Automated earnings analysis and stock price prediction using LLMs and financial data
2 default research skills + 7 read-only MCP tools: extract SEC financials, check mapped earnings claims against reviewed evidence, correct supported numeric errors, and replay historical cutoffs. 3 free offline examples. Python CLI + portable Skill; 12 skill folders including upstream modules.
AI agent for turning a day of earnings results into a ranked investor briefing, built with Agentic Star.
This script utilizes the SEC EDGAR API to download HTML copies of SEC filings in an easy-to-use, compliantly polite, and incredibly effective script.
End-to-End Python implementation of Wu et al.'s (2025) ICAIF'25 paper. It translates unstructured earnings press releases into quantifiable market signals. Implements oLDA topic modeling, Transformer embeddings (BERT/FinBERT/MPNET), GPT-4o interpretability, and rigorous econometric analysis.
Scores real earnings press releases with a Loughran-McDonald finance dictionary and a from-scratch JAX transformer trained on Financial PhraseBank, then runs a proper market-model event study testing whether either score predicts post-earnings abnormal returns.
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