leading-indicators

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Leading economic indicators analysis, ISM PMI, yield curve, consumer sentiment UMCSI, jobless claims, building permits, economic turning point detection, recession signal analysis

AI & Automation 204 stars 16 forks Updated today Apache-2.0

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> Methodology inspired by publicly taught trading frameworks; all text is an original paraphrase. ## Defaults | Parameter | Default Value | Rationale | |-----------|---------------|-----------| | lookback_quarters | 4 | Standard window for leading-indicators | | gdp_forecast_lag | 6 months | S&P 500 leads GDP with maximum statistical significance at the 6-month horizon (10-year rolling correlation avg: 0.56, 1960–2020) | | indicator_frequency | weekly | Money-market and survey indicators are tracked weekly; GDP is quarterly | | portfolio_bias | long / neutral / short | Macro view resolves to one of three biases governing portfolio construction | ## Preflight Run canonical pre-flight per `contracts/preflight.md`. Propagate X-Agentii-Trace per `contracts/x-agentii-trace-header.md`. ## Data Source Priority 1. Leading indicators framework — `references/leading-indicators-framework.md` (bundled methodology) 2. Knowledge entries — query `search_knowledge_entries` for supplementary L1 frameworks 3. Historical analogues — query `search_by_analogue(market_regime, event_type)` 4. Real-time data — FRED (real rates, yield curve, money supply, credit spreads), ISM PMI, UMCSI, jobless claims, building permits, commodity prices, DXY ## Methodology ### Retrieval Scope structured_only ### Retrieval Strategy This skill follows Branch (d) Simple Lookup from `contracts/retrieval.md`: query knowledge entries for L1 macro and leading-indicator frameworks; query `search_by_analogue` for hi...

Details

Author
agentii-ai
Repository
agentii-ai/agentii-investment-intelligence
Created
4 months ago
Last Updated
today
Language
Python
License
Apache-2.0

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