Harwinder Sekhon
4e654b5edb
- dashboard_publisher.py: standalone candle-close daemon that atomically writes snapshot.json/news.json/dashboard.html to an Apache-served dir (DASHBOARD_OUT_DIR). Fully independent of telegram_bot/oanda_daemon — local confirmation map + boundary math, no imports either way, no new deps, no open port. Due-TF classification from the RAW wake time. - static/dashboard.html: self-contained dark page — hero rail, four TF cards (score bar, 3x3 indicator grid, MTF, setup, reasons), DeepSeek market context, week economic calendar, scored newswire. Clock offset from the HTTP Date header (publish-time epoch is stale between closes). - news_engine.get_week_events(): additive read-only accessor over the cached FF week; no signal path calls it. - deploy/: systemd unit + live-server (LAMP/SELinux) checklist. - test_all.py: Module 20 (9 tests) + get_week_events test; 296 green. Claude-Session: https://claude.ai/code/session_01Wc9ZoqKLYBT2QJcpRWKoGm
921 lines
37 KiB
Python
Executable File
921 lines
37 KiB
Python
Executable File
"""
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Forex Signal Intelligence Platform - News & Sentiment Engine
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=============================================================
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Monitors economic calendar events and news sentiment to provide
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fundamental context for technical signals.
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Key principle: High-impact news events override technical signals.
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A perfect technical setup can be destroyed by an unexpected NFP print.
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"""
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import hashlib
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import logging
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import re
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import threading
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import time
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import xml.etree.ElementTree as ET
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from dataclasses import dataclass, field
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from datetime import datetime, timezone
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from email.utils import parsedate_to_datetime
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from typing import Dict, List, Optional, Tuple
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import requests
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from config import NewsConfig
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from llm_client import get_llm_client
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logger = logging.getLogger(__name__)
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# ── DeepSeek prompts ─────────────────────────────────────────────────
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# STATIC constants on purpose: DeepSeek prices a repeated byte-identical
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# prompt prefix at the cache-hit rate (~50x cheaper), so nothing here may
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# be interpolated or timestamped. All variable content travels in the
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# user payload. LLM output is display/warning-only — it never gates
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# signals (see NewsConfig.sentiment_affects_signals).
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_LLM_SENTIMENT_SYSTEM = """\
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You are a forex/gold news analyst. You receive JSON:
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{"headlines": ["...", ...]}
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For EACH headline, in the same order, score:
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- "s": overall market sentiment of the headline, -1.0 (very bearish)
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to 1.0 (very bullish), 0.0 if irrelevant or neutral.
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- "xau": the effect on the GOLD (XAU/USD) price, -1.0 (gold down)
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to 1.0 (gold up), 0.0 if irrelevant.
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Gold doctrine: hawkish Fed / rate hikes / rising real yields / strong
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dollar are gold-bearish; easing / rate cuts / safe-haven flows /
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geopolitical stress / weak dollar are gold-bullish.
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Handle negation correctly ("no rate hike" is not hawkish).
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Reply with ONLY JSON: {"scores": [[s, xau], ...]} — exactly one
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[s, xau] pair per input headline, in the same order, nothing else.
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Examples:
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"Fed signals faster rate hikes as inflation stays hot" -> [0.4, -0.7]
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"Gold surges to record high on safe-haven demand" -> [0.8, 0.9]
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"Dollar slips after soft jobs report; Treasury yields fall" -> [-0.4, 0.6]
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"""
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_LLM_EVENT_SYSTEM = """\
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You are a forex/gold analyst. You receive one released economic event as
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JSON: {"event", "currency", "actual", "forecast", "previous"}.
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Decide the likely effect of this RELEASE (actual vs forecast) on the
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GOLD (XAU/USD) price. A stronger-than-forecast USD economy or a hawkish
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implication is usually gold-bearish; weaker data or a dovish implication
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is usually gold-bullish. Mind inverted metrics: higher unemployment or
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jobless claims = weaker economy.
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Reply with ONLY JSON:
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{"gold_direction": "bullish"|"bearish"|"neutral", "reason": "<max 10 words>"}
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"""
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_LLM_SUMMARY_SYSTEM = """\
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You are a forex/gold market-context writer. You receive JSON with recent
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"headlines" and today's high-impact "events" (released: true means the
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number is out). Write a 1-2 sentence plain-English summary of the
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current macro backdrop for a GOLD (XAU/USD) trader: the dominant theme
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and its likely pull on gold. No advice, no hedging boilerplate, max 45
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words.
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Reply with ONLY JSON: {"summary": "..."}
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"""
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_RELEASE_VALUE_RE = re.compile(r"(-?\d+(?:\.\d+)?)\s*([kmbt])?", re.IGNORECASE)
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_RELEASE_MULTIPLIERS = {"k": 1e3, "m": 1e6, "b": 1e9, "t": 1e12}
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def _parse_release_value(raw: Optional[str]) -> Optional[float]:
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"""Parse a calendar release value like '0.3%', '-1.7%', '228K', '1.1M'."""
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if not raw:
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return None
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match = _RELEASE_VALUE_RE.search(raw.replace(",", ""))
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if not match:
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return None
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multiplier = _RELEASE_MULTIPLIERS.get((match.group(2) or "").lower(), 1.0)
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return float(match.group(1)) * multiplier
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@dataclass
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class EconomicEvent:
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"""Single economic calendar event."""
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time: datetime
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currency: str
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impact: str # "low", "medium", "high"
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event: str
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forecast: Optional[str] = None
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previous: Optional[str] = None
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actual: Optional[str] = None
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@property
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def surprise(self) -> Optional[str]:
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"""Actual vs forecast: "positive"/"negative"/"inline", or None when
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either value is missing or non-numeric. Direction is numeric only —
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it does not know whether higher is market-bullish for this event."""
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actual = _parse_release_value(self.actual)
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forecast = _parse_release_value(self.forecast)
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if actual is None or forecast is None:
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return None
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if actual > forecast:
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return "positive"
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if actual < forecast:
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return "negative"
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return "inline"
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@property
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def is_upcoming(self) -> bool:
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return self.time > datetime.now(timezone.utc)
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@property
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def minutes_until(self) -> float:
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delta = self.time - datetime.now(timezone.utc)
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return delta.total_seconds() / 60
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@property
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def is_high_impact(self) -> bool:
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return self.impact == "high"
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@dataclass
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class NewsSentiment:
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"""News sentiment analysis result."""
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headline: str
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source: str
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timestamp: datetime
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currencies_mentioned: List[str]
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sentiment_score: float # -1.0 (bearish) to +1.0 (bullish)
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keywords: List[str]
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# Per-currency score: monetary-policy keywords flip sign for XAU
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# (hawkish is dollar-bullish but gold-bearish).
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sentiment_by_currency: Dict[str, float] = field(default_factory=dict)
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@dataclass
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class FundamentalContext:
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"""Complete fundamental/news context for signal decisions."""
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events_today: List[EconomicEvent] = field(default_factory=list)
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events_upcoming_1h: List[EconomicEvent] = field(default_factory=list)
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high_impact_warning: bool = False
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warning_message: str = ""
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news_sentiment: Dict[str, float] = field(default_factory=dict) # currency -> sentiment
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news_headlines: List[NewsSentiment] = field(default_factory=list)
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event_impact_on_pairs: Dict[str, str] = field(default_factory=dict)
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calendar_available: bool = False # True if live calendar data was fetched
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llm_summary: str = "" # DeepSeek market-context blurb (display-only)
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class NewsEngine:
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"""
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Economic calendar and news sentiment analyzer.
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Provides fundamental context that modifies technical signal confidence.
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"""
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# Keywords that indicate directional sentiment
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BULLISH_KEYWORDS = {
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"rate hike", "hawkish", "strong growth", "beat expectations",
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"above forecast", "above estimate", "above expectation",
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"surplus", "expansion", "employment gains",
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"strong jobs", "gdp growth", "inflation rising", "tightening",
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"positive surprise", "record high", "bullish", "rally",
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"stronger than expected", "upbeat",
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"beats", "surges", "soars", "jumps", "climbs", "rises",
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"higher than expected", "better than expected",
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"strong data", "robust", "resilient", "accelerates",
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}
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BEARISH_KEYWORDS = {
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"rate cut", "dovish", "recession", "slowdown", "contraction",
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"below forecast", "below estimate", "below expectation",
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"deficit", "miss expectations", "weak data",
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"unemployment rise", "negative surprise", "bearish", "selloff",
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"weaker than expected", "downbeat", "easing", "stimulus",
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"misses", "slides", "drops", "falls", "plunges", "tumbles",
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"weakens", "slumps", "declines", "retreats",
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"lower than expected", "worse than expected",
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"disappointing", "soft data",
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}
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# Monetary-policy keywords whose direction inverts for gold: tighter
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# policy lifts the dollar and yields, weighing on non-yielding gold;
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# easing does the opposite. Only unambiguous policy words — economic
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# data words (beats/misses) keep their normal direction.
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GOLD_INVERSE_KEYWORDS = {
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"rate hike", "hawkish", "tightening",
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"rate cut", "dovish", "easing", "stimulus",
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}
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# Currency-specific keywords for headline parsing. Matched with word
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# boundaries — bare "us"/"eu"/"uk" are deliberately excluded because as
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# substrings they false-positive constantly ("versus" → USD).
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CURRENCY_KEYWORDS = {
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"USD": ["dollar", "fed", "federal reserve", "fomc", "treasury", "american", "nonfarm", "nfp"],
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"EUR": ["euro", "ecb", "eurozone", "german", "french"],
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"GBP": ["pound", "sterling", "boe", "bank of england", "british"],
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"JPY": ["yen", "boj", "bank of japan", "japanese"],
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"CHF": ["franc", "snb", "swiss"],
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"AUD": ["aussie", "rba", "australian", "australia"],
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"CAD": ["loonie", "boc", "canadian", "canada"],
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"NZD": ["kiwi", "rbnz", "new zealand"],
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"XAU": ["gold", "precious metal", "safe haven", "bullion"],
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}
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# One compiled alternation per currency (code + keywords). (?<![a-z]) /
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# (?![a-z]) instead of \b so "0.3%fed" still matches but "versus",
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# "audit", "cascade" don't.
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_CURRENCY_PATTERNS = {
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cur: re.compile(
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r"(?<![a-z])(?:"
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+ "|".join(re.escape(kw) for kw in [cur.lower()] + kws)
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+ r")(?![a-z])"
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)
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for cur, kws in CURRENCY_KEYWORDS.items()
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}
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_calendar_backoff_until: float = 0.0
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# Fetched-data cache, class-level: a fresh NewsEngine is built per scan,
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# so instance state would never be reused. Guarded by _cache_lock since
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# scans run concurrently in a thread pool.
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_cache_lock = threading.Lock()
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_calendar_cache: Tuple[float, List[EconomicEvent]] = (0.0, [])
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_headlines_cache: Tuple[float, List[NewsSentiment]] = (0.0, [])
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_EMPTY_RESULT_TTL = 60.0 # retry sooner after a failed/empty fetch
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# DeepSeek result caches. Headlines and releases are immutable, so a
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# scored one never needs re-scoring; bounded by dropping the oldest
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# insertion. _llm_sent_cache is only touched under _cache_lock (its
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# writer runs inside _cached_fetch); the other two take the lock
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# around cache access but keep HTTP calls outside it.
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_llm_sent_cache: Dict[str, Tuple[float, float]] = {} # sha1 → (s, xau)
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_llm_event_cache: Dict[Tuple[str, str, str], str] = {}
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_llm_summary_cache: Tuple[float, str] = (0.0, "")
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_LLM_SENT_CACHE_MAX = 400
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_LLM_EVENT_CACHE_MAX = 100
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def __init__(self, config: NewsConfig):
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self.config = config
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self._session = requests.Session()
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self._session.headers.update({
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"User-Agent": "Mozilla/5.0 (compatible; ForexIntel/1.0)"
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})
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def get_fundamental_context(
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self, currencies: Optional[List[str]] = None
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) -> FundamentalContext:
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"""
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Get complete fundamental context for signal decisions.
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Combines economic calendar + news sentiment into actionable context.
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"""
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if currencies is None:
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currencies = self.config.tracked_currencies
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context = FundamentalContext()
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# 1. Economic calendar events
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try:
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events = self._fetch_economic_calendar(currencies)
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now = datetime.now(timezone.utc)
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# Compare dates in UTC — feed timestamps carry their own offset
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# (FF sends US-Eastern), so a late-evening ET event would
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# otherwise land on the wrong day.
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context.events_today = [
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e for e in events
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if e.time.astimezone(timezone.utc).date() == now.date()
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]
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context.events_upcoming_1h = [
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e for e in events
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if 0 < e.minutes_until <= 60
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]
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context.calendar_available = bool(events)
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# Check for high-impact warnings
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for event in events:
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if event.is_high_impact and 0 < event.minutes_until <= self.config.pre_event_buffer_minutes:
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context.high_impact_warning = True
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context.warning_message = (
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f"HIGH IMPACT: {event.currency} {event.event} "
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f"in {event.minutes_until:.0f} min — signals may be unreliable"
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)
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break
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# Map upcoming high-impact events to affected pairs
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for event in context.events_upcoming_1h:
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if event.is_high_impact:
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affected = self._get_affected_pairs(event.currency)
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for pair in affected:
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context.event_impact_on_pairs[pair] = (
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f"{event.currency} {event.event} in {event.minutes_until:.0f}min"
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)
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except Exception as e:
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logger.warning(f"Economic calendar fetch failed: {e}")
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# 2. News sentiment
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try:
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headlines = self._fetch_news_sentiment()
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context.news_headlines = headlines
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# Aggregate sentiment per currency (per-currency score where
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# available, so gold-inverted policy keywords are respected)
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for currency in currencies:
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scores = [
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h.sentiment_by_currency.get(currency, h.sentiment_score)
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for h in headlines
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if currency in h.currencies_mentioned
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]
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if scores:
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context.news_sentiment[currency] = sum(scores) / len(scores)
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except Exception as e:
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logger.warning(f"News sentiment fetch failed: {e}")
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# 3. LLM market-context summary (display-only)
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try:
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context.llm_summary = self._llm_market_summary(context)
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except Exception as e:
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logger.debug(f"LLM market summary failed: {e}")
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return context
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@staticmethod
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def _parse_pair(pair_symbol: str) -> Tuple[str, str]:
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if "/" in pair_symbol:
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parts = pair_symbol.split("/")
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return parts[0], parts[1]
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clean = pair_symbol.replace("=X", "")
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return clean[:3], clean[3:]
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def get_event_risk_level(self, pair_symbol: str, context: FundamentalContext) -> Tuple[str, str]:
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"""
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Get event risk level for a specific pair.
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Returns (risk_level, description).
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risk_level: "clear", "caution", "danger"
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"""
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base, quote = self._parse_pair(pair_symbol)
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# Check upcoming high-impact events. Grade from the SOONEST
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# matching event, not the first listed — an unsorted feed could
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# otherwise report "caution" for a far event while a nearer one
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# warrants "danger".
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soonest = None
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for event in context.events_upcoming_1h:
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if event.is_high_impact and event.currency in (base, quote):
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if soonest is None or event.minutes_until < soonest.minutes_until:
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soonest = event
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if soonest is not None:
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mins = soonest.minutes_until
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if mins <= self.config.pre_event_buffer_minutes:
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return "danger", f"{soonest.currency} {soonest.event} in {mins:.0f}min"
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return "caution", f"{soonest.currency} {soonest.event} in {mins:.0f}min"
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# Check for recent high-impact releases. A surprise print (actual
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# away from forecast) keeps the market unstable longer than an
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# inline release, so it gets a wider post-event window.
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for event in context.events_today:
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if event.is_high_impact and event.currency in (base, quote) and not event.is_upcoming:
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surprise = event.surprise
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surprised = surprise in ("positive", "negative")
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window = (
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self.config.post_event_surprise_buffer_minutes
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if surprised
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else self.config.post_event_buffer_minutes
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)
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if event.minutes_until > -window:
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if surprised:
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desc = (
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f"{event.currency} {event.event} released — {surprise} surprise "
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f"(actual {event.actual} vs forecast {event.forecast})"
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)
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# LLM enrichment touches the DESCRIPTION only —
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# `surprise` is numeric-direction and does not know
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# whether a beat is gold-bullish; the LLM does.
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gold_dir = self._llm_event_direction(event)
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if gold_dir:
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desc += f" — gold {gold_dir}"
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return "caution", desc
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return "caution", f"{event.currency} {event.event} just released"
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return "clear", "No significant events"
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def get_pair_sentiment(
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self, pair_symbol: str, context: FundamentalContext
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) -> Tuple[float, str]:
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"""
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Get net sentiment for a pair based on news.
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Returns (score, description) where score is -1 to +1.
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Positive = bullish for the pair, negative = bearish.
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"""
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base, quote = self._parse_pair(pair_symbol)
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base_sent = context.news_sentiment.get(base, 0.0)
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quote_sent = context.news_sentiment.get(quote, 0.0)
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# Net sentiment: positive base or negative quote = bullish for pair
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net = base_sent - quote_sent
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if abs(net) < 0.1:
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desc = "Neutral news sentiment"
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elif net > 0.3:
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desc = f"Bullish sentiment ({base} positive / {quote} negative)"
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elif net > 0:
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desc = "Slightly bullish sentiment"
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elif net < -0.3:
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desc = f"Bearish sentiment ({base} negative / {quote} positive)"
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else:
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desc = "Slightly bearish sentiment"
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return net, desc
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def get_week_events(
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self, currencies: Optional[List[str]] = None
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) -> List[EconomicEvent]:
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"""Full cached calendar week (past and upcoming), filtered to
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`currencies` (default: tracked_currencies), sorted by time.
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Read-only accessor for display surfaces (the web dashboard) —
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rides the same class-level cache as signal scans, so it adds no
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network I/O. No production signal path calls this.
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"""
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if currencies is None:
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currencies = self.config.tracked_currencies
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events = self._cached_fetch("_calendar_cache", self._fetch_calendar_raw)
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return sorted(
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(e for e in events if e.currency in currencies),
|
|
key=lambda e: e.time,
|
|
)
|
|
|
|
# ── Private Methods ────────────────────────────────────────────
|
|
|
|
# Maps country names in MyFXBook titles to currency codes
|
|
_COUNTRY_TO_CURRENCY = {
|
|
"united states": "USD", "euro area": "EUR", "united kingdom": "GBP",
|
|
"japan": "JPY", "switzerland": "CHF", "australia": "AUD",
|
|
"canada": "CAD", "new zealand": "NZD",
|
|
}
|
|
|
|
def _cached_fetch(self, cache_attr: str, fetch_fn) -> list:
|
|
"""Return cached data for `cache_attr` or refetch when the TTL
|
|
expired. Successful results live for context_cache_ttl_seconds;
|
|
empty results only _EMPTY_RESULT_TTL so outages retry quickly.
|
|
The lock is held across the fetch so concurrent scans share one
|
|
request instead of stampeding the source."""
|
|
ts, data = getattr(NewsEngine, cache_attr)
|
|
ttl = self.config.context_cache_ttl_seconds if data else self._EMPTY_RESULT_TTL
|
|
if time.time() - ts < ttl:
|
|
return data
|
|
|
|
with NewsEngine._cache_lock:
|
|
ts, data = getattr(NewsEngine, cache_attr) # re-check under lock
|
|
ttl = self.config.context_cache_ttl_seconds if data else self._EMPTY_RESULT_TTL
|
|
if time.time() - ts < ttl:
|
|
return data
|
|
data = fetch_fn()
|
|
setattr(NewsEngine, cache_attr, (time.time(), data))
|
|
return data
|
|
|
|
def _fetch_economic_calendar(self, currencies: List[str]) -> List[EconomicEvent]:
|
|
"""Cached calendar fetch, filtered to the tracked currencies."""
|
|
events = self._cached_fetch("_calendar_cache", self._fetch_calendar_raw)
|
|
return [e for e in events if e.currency in currencies]
|
|
|
|
def _fetch_calendar_raw(self) -> List[EconomicEvent]:
|
|
"""
|
|
Fetch economic calendar events from a free API.
|
|
Tries Forex Factory first, falls back to MyFXBook RSS.
|
|
Returns an empty list (with a log warning) if unavailable —
|
|
we never generate fake events since that causes constant false alarms.
|
|
"""
|
|
for fetcher in (self._try_fetch_calendar_api, self._try_fetch_myfxbook_rss):
|
|
try:
|
|
events = fetcher()
|
|
if events:
|
|
return events
|
|
except Exception as e:
|
|
logger.debug(f"Calendar fetch exception ({fetcher.__name__}): {e}")
|
|
|
|
logger.info(
|
|
"Economic calendar data unavailable. "
|
|
"Event risk checks will be skipped for this cycle."
|
|
)
|
|
return []
|
|
|
|
def _try_fetch_calendar_api(self) -> List[EconomicEvent]:
|
|
"""Attempt to fetch from the Forex Factory free JSON calendar."""
|
|
now = time.time()
|
|
if now < NewsEngine._calendar_backoff_until:
|
|
wait = NewsEngine._calendar_backoff_until - now
|
|
logger.debug(f"Calendar API in backoff for {wait:.0f}s more")
|
|
return []
|
|
|
|
url = "https://nfs.faireconomy.media/ff_calendar_thisweek.json"
|
|
|
|
for attempt in range(3):
|
|
try:
|
|
resp = self._session.get(url, timeout=10)
|
|
except requests.RequestException as e:
|
|
logger.debug(f"Calendar request failed (attempt {attempt + 1}): {e}")
|
|
if attempt < 2:
|
|
time.sleep(2 ** attempt)
|
|
continue
|
|
|
|
if resp.status_code == 429:
|
|
retry_after = int(resp.headers.get("Retry-After", 300))
|
|
retry_after = min(retry_after, 600)
|
|
NewsEngine._calendar_backoff_until = time.time() + retry_after
|
|
logger.warning(
|
|
f"Calendar API rate-limited (429), backing off {retry_after}s"
|
|
)
|
|
return []
|
|
|
|
if resp.status_code != 200:
|
|
logger.debug(f"Calendar API returned {resp.status_code}")
|
|
return []
|
|
|
|
data = resp.json()
|
|
events = []
|
|
impact_map = {"High": "high", "Medium": "medium", "Low": "low"}
|
|
|
|
for item in data:
|
|
try:
|
|
impact = impact_map.get(item.get("impact", ""), "low")
|
|
|
|
date_str = item.get("date", "")
|
|
if not date_str:
|
|
continue
|
|
event_time = datetime.fromisoformat(date_str.replace("Z", "+00:00"))
|
|
|
|
events.append(EconomicEvent(
|
|
time=event_time,
|
|
currency=item.get("country", ""),
|
|
impact=impact,
|
|
event=item.get("title", ""),
|
|
forecast=item.get("forecast") or None,
|
|
previous=item.get("previous") or None,
|
|
actual=item.get("actual") or None,
|
|
))
|
|
except Exception:
|
|
continue
|
|
|
|
return events
|
|
|
|
return []
|
|
|
|
def _try_fetch_myfxbook_rss(self) -> List[EconomicEvent]:
|
|
"""Fallback calendar source: MyFXBook economic calendar RSS."""
|
|
url = "https://www.myfxbook.com/rss/forex-economic-calendar-events"
|
|
try:
|
|
resp = self._session.get(url, timeout=10)
|
|
except requests.RequestException as e:
|
|
logger.debug(f"MyFXBook calendar request failed: {e}")
|
|
return []
|
|
|
|
if resp.status_code != 200:
|
|
return []
|
|
|
|
try:
|
|
root = ET.fromstring(resp.content)
|
|
except ET.ParseError:
|
|
return []
|
|
|
|
events = []
|
|
for item in root.iter("item"):
|
|
try:
|
|
title_el = item.find("title")
|
|
pub_el = item.find("pubDate")
|
|
desc_el = item.find("description")
|
|
if title_el is None or title_el.text is None:
|
|
continue
|
|
|
|
title = title_el.text.strip()
|
|
|
|
currency = ""
|
|
title_lower = title.lower()
|
|
for country, cur in self._COUNTRY_TO_CURRENCY.items():
|
|
if title_lower.startswith(country):
|
|
currency = cur
|
|
title = title[len(country):].strip()
|
|
break
|
|
if not currency:
|
|
continue
|
|
|
|
event_time = self._parse_rss_date(
|
|
pub_el.text if pub_el is not None else None
|
|
)
|
|
if event_time is None:
|
|
continue
|
|
|
|
impact = "low"
|
|
desc_text = desc_el.text if desc_el is not None and desc_el.text else ""
|
|
impact_match = re.search(r"sprite-(high|medium|low)-impact", desc_text)
|
|
if impact_match:
|
|
impact = impact_match.group(1)
|
|
|
|
cells = re.findall(r"<td[^>]*>(.*?)</td>", desc_text, re.DOTALL)
|
|
cells = [re.sub(r"<[^>]+>", "", c).strip() for c in cells]
|
|
previous = cells[2] if len(cells) > 2 and cells[2] else None
|
|
forecast = cells[3] if len(cells) > 3 and cells[3] else None
|
|
actual = cells[4] if len(cells) > 4 and cells[4] else None
|
|
|
|
events.append(EconomicEvent(
|
|
time=event_time,
|
|
currency=currency,
|
|
impact=impact,
|
|
event=title,
|
|
forecast=forecast,
|
|
previous=previous,
|
|
actual=actual,
|
|
))
|
|
except Exception:
|
|
continue
|
|
|
|
logger.debug(f"MyFXBook calendar: parsed {len(events)} events")
|
|
return events
|
|
|
|
def _fetch_news_sentiment(self) -> List[NewsSentiment]:
|
|
"""Cached headline fetch + sentiment analysis."""
|
|
return self._cached_fetch("_headlines_cache", self._fetch_news_sentiment_raw)
|
|
|
|
def _fetch_news_sentiment_raw(self) -> List[NewsSentiment]:
|
|
"""Fetch and analyze news headlines for sentiment."""
|
|
headlines = []
|
|
seen_titles = set()
|
|
|
|
for feed_url in self.config.rss_feeds:
|
|
try:
|
|
resp = self._session.get(feed_url, timeout=10)
|
|
if resp.status_code != 200:
|
|
continue
|
|
|
|
root = ET.fromstring(resp.content)
|
|
|
|
for item in root.iter("item"):
|
|
title_el = item.find("title")
|
|
pub_date_el = item.find("pubDate")
|
|
|
|
if title_el is None:
|
|
continue
|
|
|
|
title = title_el.text or ""
|
|
pub_date = self._parse_rss_date(
|
|
pub_date_el.text if pub_date_el is not None else None
|
|
)
|
|
|
|
# Only recent headlines (last 24h)
|
|
if pub_date and (datetime.now(timezone.utc) - pub_date).total_seconds() > 86400:
|
|
continue
|
|
|
|
# Dedup within and across feeds so a syndicated story
|
|
# doesn't count twice in the sentiment average. Google
|
|
# News appends " - Publisher", so compare without it.
|
|
title_key = title.lower().rsplit(" - ", 1)[0].strip()
|
|
if title_key in seen_titles:
|
|
continue
|
|
seen_titles.add(title_key)
|
|
|
|
sentiment = self._analyze_headline_sentiment(title)
|
|
currencies = self._extract_currencies(title)
|
|
|
|
if currencies:
|
|
headlines.append(NewsSentiment(
|
|
headline=title,
|
|
source=feed_url,
|
|
timestamp=pub_date or datetime.now(timezone.utc),
|
|
currencies_mentioned=currencies,
|
|
sentiment_score=sentiment,
|
|
keywords=self._extract_keywords(title),
|
|
sentiment_by_currency={
|
|
cur: self._analyze_headline_sentiment(title, cur)
|
|
for cur in currencies
|
|
},
|
|
))
|
|
|
|
except Exception as e:
|
|
logger.debug(f"Failed to fetch RSS from {feed_url}: {e}")
|
|
|
|
try:
|
|
self._apply_llm_sentiment(headlines)
|
|
except Exception as e:
|
|
logger.debug(f"LLM sentiment failed, keyword scores stand: {e}")
|
|
|
|
return headlines
|
|
|
|
def _analyze_headline_sentiment(
|
|
self, headline: str, for_currency: Optional[str] = None
|
|
) -> float:
|
|
"""
|
|
Simple keyword-based sentiment analysis.
|
|
|
|
Returns -1.0 (very bearish) to +1.0 (very bullish).
|
|
|
|
When for_currency is "XAU", monetary-policy keywords contribute
|
|
with inverted sign (see GOLD_INVERSE_KEYWORDS).
|
|
|
|
Note: This is intentionally simple. NLP models like FinBERT
|
|
would be more accurate but require large dependencies. This catches
|
|
the most impactful sentiment signals from headlines.
|
|
"""
|
|
headline_lower = headline.lower()
|
|
invert_policy = for_currency == "XAU"
|
|
|
|
bullish_score = 0
|
|
bearish_score = 0
|
|
for kw in self.BULLISH_KEYWORDS:
|
|
if kw in headline_lower:
|
|
if invert_policy and kw in self.GOLD_INVERSE_KEYWORDS:
|
|
bearish_score += 1
|
|
else:
|
|
bullish_score += 1
|
|
for kw in self.BEARISH_KEYWORDS:
|
|
if kw in headline_lower:
|
|
if invert_policy and kw in self.GOLD_INVERSE_KEYWORDS:
|
|
bullish_score += 1
|
|
else:
|
|
bearish_score += 1
|
|
|
|
total = bullish_score + bearish_score
|
|
if total == 0:
|
|
return 0.0
|
|
|
|
return (bullish_score - bearish_score) / total
|
|
|
|
# ── DeepSeek enrichment (display/warning-only, keyword fallback) ──
|
|
|
|
def _llm_headline_scores(
|
|
self, texts: List[str]
|
|
) -> Optional[List[Tuple[float, float]]]:
|
|
"""One batched DeepSeek call → [(overall, xau), ...] aligned with
|
|
`texts`, or None on any failure/mismatch (caller keeps keyword
|
|
scores). Tests stub this method directly."""
|
|
result = get_llm_client().chat_json(
|
|
_LLM_SENTIMENT_SYSTEM, {"headlines": texts})
|
|
if not result:
|
|
return None
|
|
scores = result.get("scores")
|
|
if not isinstance(scores, list) or len(scores) != len(texts):
|
|
return None
|
|
out = []
|
|
for item in scores:
|
|
try:
|
|
s = max(-1.0, min(1.0, float(item[0])))
|
|
x = max(-1.0, min(1.0, float(item[1])))
|
|
except (TypeError, KeyError, ValueError, IndexError):
|
|
return None
|
|
out.append((s, x))
|
|
return out
|
|
|
|
def _apply_llm_sentiment(self, headlines: List[NewsSentiment]) -> None:
|
|
"""Overwrite keyword sentiment on `headlines` with LLM scores.
|
|
|
|
Runs inside _cached_fetch, i.e. UNDER _cache_lock — cache access
|
|
is direct and the (rare) HTTP call blocks sibling scans at most
|
|
once per cache window, matching the existing shared-fetch design.
|
|
Only headlines missing from the cache are sent; a failed batch
|
|
changes nothing.
|
|
"""
|
|
if not headlines or not get_llm_client().available:
|
|
return
|
|
cache = NewsEngine._llm_sent_cache
|
|
keyed = [(hashlib.sha1(h.headline.encode()).hexdigest(), h)
|
|
for h in headlines]
|
|
# Cap the batch so the reply fits the client's max_tokens — the
|
|
# overflow stays keyword-scored this cycle and, still uncached,
|
|
# goes out with the next window's batch.
|
|
new = [(k, h) for k, h in keyed if k not in cache][:25]
|
|
if new:
|
|
scores = self._llm_headline_scores([h.headline for _, h in new])
|
|
if scores is not None:
|
|
for (k, _), sc in zip(new, scores):
|
|
cache[k] = sc
|
|
while len(cache) > self._LLM_SENT_CACHE_MAX:
|
|
cache.pop(next(iter(cache)))
|
|
for k, h in keyed:
|
|
sc = cache.get(k)
|
|
if sc is None:
|
|
continue
|
|
overall, xau = sc
|
|
h.sentiment_score = overall
|
|
h.sentiment_by_currency = {
|
|
cur: (xau if cur == "XAU" else overall)
|
|
for cur in h.currencies_mentioned
|
|
}
|
|
|
|
def _llm_event_direction(self, event: EconomicEvent) -> Optional[str]:
|
|
"""Gold direction for a released surprise, e.g. "bearish: strong
|
|
jobs lift yields". None → caller keeps the numeric description.
|
|
Cached per release; failures are NOT cached (retry next scan)."""
|
|
key = (event.event, event.currency, str(event.actual or ""))
|
|
with NewsEngine._cache_lock:
|
|
if key in NewsEngine._llm_event_cache:
|
|
return NewsEngine._llm_event_cache[key] or None
|
|
client = get_llm_client()
|
|
if not client.available:
|
|
return None
|
|
result = client.chat_json(_LLM_EVENT_SYSTEM, {
|
|
"event": event.event,
|
|
"currency": event.currency,
|
|
"actual": event.actual,
|
|
"forecast": event.forecast,
|
|
"previous": event.previous,
|
|
})
|
|
if not result:
|
|
return None
|
|
direction = result.get("gold_direction")
|
|
if direction not in ("bullish", "bearish", "neutral"):
|
|
return None
|
|
reason = str(result.get("reason", "")).strip()[:80]
|
|
text = "" if direction == "neutral" else (
|
|
f"{direction}: {reason}" if reason else direction)
|
|
with NewsEngine._cache_lock:
|
|
NewsEngine._llm_event_cache[key] = text
|
|
while len(NewsEngine._llm_event_cache) > self._LLM_EVENT_CACHE_MAX:
|
|
NewsEngine._llm_event_cache.pop(
|
|
next(iter(NewsEngine._llm_event_cache)))
|
|
return text or None
|
|
|
|
def _llm_market_summary(self, context: FundamentalContext) -> str:
|
|
"""1-2 sentence macro blurb for alerts; "" when unavailable.
|
|
Cached (success OR failure) for the context-cache TTL so at most
|
|
one call is made per window."""
|
|
with NewsEngine._cache_lock:
|
|
ts, cached = NewsEngine._llm_summary_cache
|
|
if time.time() - ts < self.config.context_cache_ttl_seconds:
|
|
return cached
|
|
high_impact = [e for e in context.events_today if e.is_high_impact]
|
|
if not context.news_headlines and not high_impact:
|
|
return ""
|
|
client = get_llm_client()
|
|
if not client.available:
|
|
return ""
|
|
result = client.chat_json(_LLM_SUMMARY_SYSTEM, {
|
|
"headlines": [h.headline for h in context.news_headlines[:15]],
|
|
"events": [
|
|
{"event": e.event, "currency": e.currency,
|
|
"actual": e.actual, "forecast": e.forecast,
|
|
"released": not e.is_upcoming}
|
|
for e in high_impact[:10]
|
|
],
|
|
})
|
|
summary = ""
|
|
if result:
|
|
summary = str(result.get("summary", "")).strip()[:300]
|
|
with NewsEngine._cache_lock:
|
|
NewsEngine._llm_summary_cache = (time.time(), summary)
|
|
return summary
|
|
|
|
def _extract_currencies(self, headline: str) -> List[str]:
|
|
"""Extract mentioned currencies from a headline."""
|
|
headline_lower = headline.lower()
|
|
return [
|
|
currency
|
|
for currency, pattern in self._CURRENCY_PATTERNS.items()
|
|
if pattern.search(headline_lower)
|
|
]
|
|
|
|
def _extract_keywords(self, headline: str) -> List[str]:
|
|
"""Extract relevant trading keywords from headline."""
|
|
headline_lower = headline.lower()
|
|
return [kw for kw in self.BULLISH_KEYWORDS | self.BEARISH_KEYWORDS if kw in headline_lower]
|
|
|
|
@staticmethod
|
|
def _parse_rss_date(date_str: Optional[str]) -> Optional[datetime]:
|
|
"""Parse various RSS date formats into a timezone-aware datetime."""
|
|
if not date_str:
|
|
return None
|
|
|
|
# RFC 822/2822 forms, with or without seconds, numeric offsets AND
|
|
# named zones (GMT, EST/EDT, PST…). The old strptime "%Z" path
|
|
# accepted ANY abbreviation but attached no offset, so e.g. an EST
|
|
# pubDate was silently stamped UTC — a 5-hour error.
|
|
try:
|
|
dt = parsedate_to_datetime(date_str.strip())
|
|
if dt is not None:
|
|
if dt.tzinfo is None: # unknown zone name — assume UTC
|
|
return dt.replace(tzinfo=timezone.utc)
|
|
return dt.astimezone(timezone.utc)
|
|
except (TypeError, ValueError):
|
|
pass
|
|
|
|
formats = [
|
|
"%Y-%m-%dT%H:%M:%S%z", # ISO 8601 with offset
|
|
"%Y-%m-%d %H:%M:%S", # ISO 8601 naive (assume UTC)
|
|
]
|
|
for fmt in formats:
|
|
try:
|
|
dt = datetime.strptime(date_str.strip(), fmt)
|
|
# If strptime already attached tzinfo (from %z), keep it.
|
|
# Otherwise the string had no timezone info — assume UTC.
|
|
if dt.tzinfo is not None:
|
|
return dt
|
|
return dt.replace(tzinfo=timezone.utc)
|
|
except ValueError:
|
|
continue
|
|
return None
|
|
|
|
@staticmethod
|
|
def _get_affected_pairs(currency: str) -> List[str]:
|
|
_MAP = {
|
|
"USD": ["XAU/USD"],
|
|
"XAU": ["XAU/USD"],
|
|
}
|
|
return _MAP.get(currency, [])
|