Generate sentiment-based trading signals from financial news and macro events.
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill event-driven-hxhyyy
Please help me install this Agent Skill. Skill: event-driven Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/event-driven Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill event-driven-hxhyyy
This Skill addresses the challenge of quantifying qualitative market information, such as news and macro events, into actionable trading signals while avoiding look-ahead bias.
Use the event-driven skill to analyze the sentiment of the latest market news and generate a trading signal based on the provided event CSV.
High-intent search queries and answers about installing and using this skill.
To generate trading signals from financial news, this Skill uses an LLM to convert news and announcements into standardized sentiment scores ranging from -1.0 to 1.0. It then aggregates these event-driven signals with time-decay logic to ensure market relevance.
Event-driven backtesting avoids look-ahead bias by processing event data into standardized CSV formats and applying time-series alignment. This ensures that sentiment-based trading signals are calculated strictly using chronologically accurate information.
Yes, you can use pandas and numpy to align time-series data for live trading workflows. This Skill requires these dependencies to calculate time-decay aggregation and align event-driven signals accurately for both live and backtesting environments.
The best way to quantify qualitative market information is using LLM-driven scoring to convert news and announcements into numerical sentiment scores. This approach transforms textual macro events into actionable, time-decayed trading signals.
Yes, you need to provide a standardized event CSV format to process earnings reports and central bank announcements. The Skill processes this structured event data to generate LLM-driven sentiment scores for your portfolio exposure adjustments.
Signal aggregation uses time-decay logic to ensure ongoing market relevance when combining event-driven signals with technical indicators. This prevents older financial news from disproportionately influencing current trading decisions and portfolio exposure.
NigarumOvumAnalyze CSV event data with sentiment scoring to generate trading signals.
hxhyyyAggregate fear and greed indices, option ratios, leverage data, and social media trends into sentiment scores.
ebrahim-saniConvert news into time-decayed event signals for algorithmic trading.
JhabbigBuild backtesting systems with bias controls and walk-forward validation.
