Convert sentiment-scored news events into time-decayed CSV signals for backtesting.
npx skills add https://github.com/loanntc/Paave --skill news-event-strategy
Please help me install this Agent Skill. Skill: news-event-strategy Source: https://github.com/loanntc/Paave/tree/main/skills/event-driven Command: npx skills add https://github.com/loanntc/Paave --skill news-event-strategy
It converts unstructured news, announcements, and macro events into a structured, time-decayed event signal that can be combined with technical signals for trading decisions.
read_url) and scores impact on a standardized scale from -1.0 to 1.0.date,event_type,score,source,summary schema to keep data separate from trading logic.alpha weight.Create a historical event CSV in the required schema and then use signal_engine.py to compute the time-decayed event signal and combine it with your technical signal using alpha=0.6.
High-intent search queries and answers about installing and using this skill.
To convert news sentiment into trading signals, you fetch full text via read_url, score impact on a -1.0 to 1.0 scale, and aggregate scores into a time-decayed event CSV for backtesting.
The required CSV schema for event-driven backtesting includes date, event_type, score, source, and summary columns to separate unstructured event data from your trading logic.
You combine event signals with technical signals using a weighted alpha aggregation, blending time-decayed event scores with technical indicators and clipping the final value to [-1.0, 1.0].
Time decay applies an exponential reduction to event scores over time, aggregating them into a daily event signal that naturally remains zero on event-sparse days without manual intervention.
You prevent look-ahead bias by enforcing strict use of knowable dates in the event CSV schema, ensuring your backtest only uses information available up to the signal generation date.
Yes, LLM prompting is used to fetch full news text and score its impact on a standardized scale from -1.0 to 1.0 before applying time decay and aggregating the results.
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