market-intelligence

> **Skill:** Market Intelligence | **Domain:** trading | **Category:** research | **Level:** intermediate > **Tags:** `trading`, `research`, `macro`, `news`, `regime`, `intelligence` # Market Intelligence — Complete Analysis Layer > The informational foundation of all trading decisions. > Macro context → regime → news → sentiment → institutional → correlation → execute. ## Sections 1. **Macro Dashboard** — DXY, VIX, yield curves, commodity-FX links, intermarket divergence 2. **Regime Classifier** — trending/ranging/volatile/quiet + strategy mapping 3. **News & Events** — economic calendar, impact scoring, event-price matching, sentiment 4. **Sentiment & Positioning** — retail positioning, COT, Fear/Greed, contrarian signals 5. **Institutional Monitor** — central banks, investment banks, COT analysis, intervention detection 6. **Event Timeline** — event linking, causal chains, narrative building, prediction 7. **Pair Correlations** — rolling correlation, divergence detection, clustering, regime switching 8. **Trading Fundamentals** — market structure, order types, asset classes, timeframes 9. **Alternative Data** — Google Trends signals, economic nowcasting, shipping/supply chain, search sentiment 10. **Seasonality** — monthly/day-of-week/hourly statistical edges with significance testing --- ## Reference Files - **[references/macro-regime.md](references/macro-regime.md)** — Macro dashboard (DXY/VIX/yield curves/commodities) + regime classifier (ADX/BB-width/MA-alignment + strategy map) - **[references/news-sentiment.md](references/news-sentiment.md)** — Economic calendar, news impact scoring, event-price matching, retail sentiment, COT, Fear/Greed, contrarian composite - **[references/institutional-timeline.md](references/institutional-timeline.md)** — Central bank tracker, policy divergence, investment bank monitor, COT analyzer, intervention detector + full event timeline linker - **[references/correlation-fundamentals.md](references/correlation-fundamentals.md)** — Pair correlation engine (rolling/historical/divergence/clustering/lead-lag) + correlation regime switcher + trading fundamentals reference - **[references/alternative-data.md](references/alternative-data.md)** — AlternativeDataSources class: Google Trends signal, economic nowcast (Baltic Dry/copper-gold/credit spreads), search volume sentiment mapping - **[references/seasonality.md](references/seasonality.md)** — SeasonalityAnalyzer class: monthly/day-of-week/hourly return statistics with t-tests, p-values, win rates, and significance flags ## Quick Decision Guide | Task | Load | |------|------| | Is market risk-on or risk-off? | `references/macro-regime.md` | | What does the yield curve signal? | `references/macro-regime.md` | | Gold/Oil divergence from DXY? | `references/macro-regime.md` | | What regime is EURUSD in? | `references/macro-regime.md` | | Which strategy type fits now? | `references/macro-regime.md` | | What events are this week? | `references/news-sentiment.md` | | Is crowd long or short? | `references/news-sentiment.md` | | COT extreme positioning? | `references/news-sentiment.md` | | What is the Fed/ECB doing? | `references/institutional-timeline.md` | | Rate decision impact model | `references/institutional-timeline.md` | | Policy divergence between banks | `references/institutional-timeline.md` | | Why did price move on [date]? | `references/institutional-timeline.md` | | Which pairs are correlated? | `references/correlation-fundamentals.md` | | Correlation regime shift? | `references/correlation-fundamentals.md` | | What strategy works now (regime)? | `references/correlation-fundamentals.md` | | What is a limit order / TWAP? | `references/correlation-fundamentals.md` | | Google Trends signal / nowcast? | `references/alternative-data.md` | | Baltic Dry / shipping index signal? | `references/alternative-data.md` | | Search volume sentiment (fear/greed)? | `references/alternative-data.md` | | Best month / day / hour to trade? | `references/seasonality.md` | | January effect / sell in May? | `references/seasonality.md` | | Seasonal edge with significance test? | `references/seasonality.md` | ## Core Macro Intelligence Quick Card ``` RISK-ON: VIX < 15, SPX rising → AUD, NZD up | JPY, CHF, Gold down RISK-OFF: VIX > 25, SPX falling → JPY, CHF, Gold up | AUD, NZD, EM down DXY UP: EUR, GBP, Gold down | USDJPY, USDCAD up DXY DOWN: EUR, GBP, Gold up | USDJPY, USDCAD down YIELD ↑: USD strengthens, Gold weakens YIELD INVERSION (2-10 < 0): Recession warning, risk-off ahead OIL ↑: CAD, NOK strengthen TREND REGIME: ADX > 25, MAs aligned → use trend following RANGE REGIME: ADX < 20, BB narrow → use mean reversion VOLATILE: ADX < 20, BB wide → reduce size, wait for clarity ``` --- ## Implementations (Merged from sentiment-macro) --- ## News Sentiment NLP Engine ```python import re, numpy as np SENTIMENT_LEXICON = { "hawkish": 0.8, "dovish": -0.8, "rate hike": 0.7, "rate cut": -0.7, "inflation rises": 0.5, "inflation falls": -0.3, "recession": -0.8, "strong jobs": 0.6, "weak jobs": -0.6, "stimulus": 0.5, "tightening": 0.4, "crisis": -0.9, "default": -0.9, "war": -0.7, "peace": 0.3, "surge": 0.6, "plunge": -0.7, "rally": 0.5, "crash": -0.8, "beat expectations": 0.6, "miss expectations": -0.6, "surprise": 0.3, "upgrade": 0.5, "downgrade": -0.5, "bullish": 0.5, "bearish": -0.5, } class NewsSentimentNLP: @staticmethod def score_headline(headline: str) -> dict: h = headline.lower() matched = [(kw, score) for kw, score in SENTIMENT_LEXICON.items() if kw in h] avg = np.mean([s for _, s in matched]) if matched else 0 entities = re.findall(r"\b(Fed|ECB|BOE|BOJ|NFP|CPI|GDP|FOMC|IMF)\b", headline, re.IGNORECASE) return { "headline": headline, "sentiment_score": round(avg, 3), "label": "BULLISH" if avg > 0.2 else "BEARISH" if avg < -0.2 else "NEUTRAL", "matched_keywords": [kw for kw, _ in matched], "entities": entities, "confidence": min(len(matched) / 3, 1.0), } @staticmethod def batch_score(headlines: list) -> dict: scores = [NewsSentimentNLP.score_headline(h) for h in headlines] avg = np.mean([s["sentiment_score"] for s in scores]) return {"overall": round(avg, 3), "n_headlines": len(headlines), "bullish": sum(1 for s in scores if s["label"] == "BULLISH"), "bearish": sum(1 for s in scores if s["label"] == "BEARISH")} ``` --- ## Market News Impact ### Overview Monitors and analyzes major economic news, central bank decisions, geopolitical events, and market-moving developments. Matches news events to price reactions across instruments. Provides forward-looking event calendars with expected impact ratings. ### Architecture ``` ┌───────────────────────────────────────────────────────────┐ │ Market News Impact Engine │ ├──────────────┬───────────────┬───────────────┬────────────┤ │ News Fetcher │ Event Calendar│ Impact Matcher│ Sentiment │ │ & Classifier │ & Scheduler │ & Scorer │ Analyzer │ └──────────────┴───────────────┴───────────────┴────────────┘ ``` ### 1. News Source Architecture #### Source Priority (highest quality first) ```python NEWS_SOURCES = { "central_banks": { "fed": {"url": "https://www.federalreserve.gov/newsevents.htm", "priority": 1}, "ecb": {"url": "https://www.ecb.europa.eu/press/html/index.en.html", "priority": 1}, "boj": {"url": "https://www.boj.or.jp/en/", "priority": 1}, "boe": {"url": "https://www.bankofengland.co.uk/news", "priority": 1}, "rba": {"url": "https://www.rba.gov.au/media-releases/", "priority": 1}, "snb": {"url": "https://www.snb.ch/en/", "priority": 1}, "boc": {"url": "https://www.bankofcanada.ca/press/", "priority": 1}, "rbnz": {"url": "https://www.rbnz.govt.nz/news", "priority": 1}, }, "economic_data": { "forexfactory": {"url": "https://www.forexfactory.com/calendar", "priority": 1}, "investing_com": {"url": "https://www.investing.com/economic-calendar/", "priority": 2}, "tradingeconomics": {"url": "https://tradingeconomics.com/calendar", "priority": 2}, }, "financial_news": { "reuters": {"url": "https://www.reuters.com/markets/", "priority": 1}, "bloomberg": {"url": "https://www.bloomberg.com/markets", "priority": 1}, "wsj": {"url": "https://www.wsj.com/news/markets", "priority": 2}, "ft": {"url": "https://www.ft.com/markets", "priority": 2}, "cnbc": {"url": "https://www.cnbc.com/world-markets/", "priority": 3}, }, "geopolitical": { "reuters_world": {"url": "https://www.reuters.com/world/", "priority": 1}, "bbc_world": {"url": "https://www.bbc.com/news/world", "priority": 2}, }, } ``` #### News Fetching Framework ```python import requests from bs4 import BeautifulSoup from datetime import datetime, timedelta from typing import Optional import json class NewsFetcher: """ Fetch and classify market-moving news from multiple sources. In Claude context: use web_search tool as the primary fetcher. """ # Impact classification keywords HIGH_IMPACT_KEYWORDS = [ "rate decision", "interest rate", "nfp", "non-farm", "cpi", "inflation", "gdp", "fomc", "fed chair", "ecb president", "quantitative", "emergency", "war", "sanctions", "default", "recession", "crisis", "tariff", "trade war", "stimulus", "bailout"] MEDIUM_IMPACT_KEYWORDS = [ "pmi", "employment", "retail sales", "housing", "trade balance", "industrial production", "consumer confidence", "jobless claims", "manufacturing", "services", "earnings", "ism"] @staticmethod def classify_impact(headline: str) -> str: """Classify a headline into impact level.""" h = headline.lower() if any(kw in h for kw in NewsFetcher.HIGH_IMPACT_KEYWORDS): return "HIGH" if any(kw in h for kw in NewsFetcher.MEDIUM_IMPACT_KEYWORDS): return "MEDIUM" return "LOW" @staticmethod def extract_affected_currencies(headline: str) -> list[str]: """Extract which currencies are likely affected by a headline.""" currency_map = { "fed": ["USD"], "fomc": ["USD"], "nfp": ["USD"], "us ": ["USD"], "ecb": ["EUR"], "euro": ["EUR"], "eurozone": ["EUR"], "boe": ["GBP"], "uk ": ["GBP"], "britain": ["GBP"], "sterling": ["GBP"], "boj": ["JPY"], "japan": ["JPY"], "yen": ["JPY"], "rba": ["AUD"], "australia": ["AUD"], "boc": ["CAD"], "canada": ["CAD"], "snb": ["CHF"], "swiss": ["CHF"], "rbnz": ["NZD"], "zealand": ["NZD"],