Harwinder Sekhon
ea306df484
Improvements from a live-verified audit of the news/calendar pipeline: - Currency tagging: keyword matching now uses letter-boundary regexes and drops ambiguous "us "/"eu "/"uk " tokens — "versus" no longer tags headlines as USD (confirmed live false positive polluting the XAU/USD sentiment average). - Fetch caching: calendar events and headlines are cached class-level (10 min TTL, 60s retry after empty/failed fetch, lock-guarded for concurrent scans). Stops per-scan refetching that was tripping the Forex Factory rate limit; TTL stays under the 30-min pre-event buffer so the danger window can't go stale. - Surprise detection: EconomicEvent.surprise is computed from parsed actual vs forecast (handles %, K/M/B/T suffixes, negatives). A surprise print widens the post-release caution window to 45 min; inline releases keep the original 15 min. - Gold-aware sentiment: headlines are scored per currency, with monetary-policy keywords (hawkish/rate hike/tightening vs dovish/ rate cut/easing/stimulus) inverted for XAU — "hawkish Fed" headlines scored +1.0 for gold before, -1.0 now (verified on live feed). - New sentiment sources: Google News gold-focused query and CNBC Economy RSS (verified live; 40 → 74 unique headlines). Added cross-feed title dedup so syndicated stories count once. - events_today now buckets by UTC date (feed timestamps carry US- Eastern offsets); --json output includes news_description. - Removed dead NewsConfig.min_impact and unused HIGH_IMPACT_EVENTS. Tests: 142 passed (7 new covering boundary matching, surprise classification, post-event windows, cache reuse, gold inversion).
680 lines
26 KiB
Python
Executable File
680 lines
26 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 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, timedelta, timezone
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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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logger = logging.getLogger(__name__)
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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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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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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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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
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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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mins = event.minutes_until
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if mins <= self.config.pre_event_buffer_minutes:
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return "danger", f"{event.currency} {event.event} in {mins:.0f}min"
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else:
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return "caution", f"{event.currency} {event.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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return "caution", (
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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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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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# ── Private Methods ────────────────────────────────────────────
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# Maps country names in MyFXBook titles to currency codes
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_COUNTRY_TO_CURRENCY = {
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"united states": "USD", "euro area": "EUR", "united kingdom": "GBP",
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"japan": "JPY", "switzerland": "CHF", "australia": "AUD",
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"canada": "CAD", "new zealand": "NZD",
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}
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def _cached_fetch(self, cache_attr: str, fetch_fn) -> list:
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"""Return cached data for `cache_attr` or refetch when the TTL
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expired. Successful results live for context_cache_ttl_seconds;
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empty results only _EMPTY_RESULT_TTL so outages retry quickly.
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The lock is held across the fetch so concurrent scans share one
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request instead of stampeding the source."""
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ts, data = getattr(NewsEngine, cache_attr)
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ttl = self.config.context_cache_ttl_seconds if data else self._EMPTY_RESULT_TTL
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if time.time() - ts < ttl:
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return data
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with NewsEngine._cache_lock:
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ts, data = getattr(NewsEngine, cache_attr) # re-check under lock
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ttl = self.config.context_cache_ttl_seconds if data else self._EMPTY_RESULT_TTL
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if time.time() - ts < ttl:
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return data
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data = fetch_fn()
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setattr(NewsEngine, cache_attr, (time.time(), data))
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return data
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def _fetch_economic_calendar(self, currencies: List[str]) -> List[EconomicEvent]:
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"""Cached calendar fetch, filtered to the tracked currencies."""
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events = self._cached_fetch("_calendar_cache", self._fetch_calendar_raw)
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return [e for e in events if e.currency in currencies]
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def _fetch_calendar_raw(self) -> List[EconomicEvent]:
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"""
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Fetch economic calendar events from a free API.
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Tries Forex Factory first, falls back to MyFXBook RSS.
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Returns an empty list (with a log warning) if unavailable —
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we never generate fake events since that causes constant false alarms.
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"""
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for fetcher in (self._try_fetch_calendar_api, self._try_fetch_myfxbook_rss):
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try:
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events = fetcher()
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if events:
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return events
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except Exception as e:
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logger.debug(f"Calendar fetch exception ({fetcher.__name__}): {e}")
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logger.info(
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"Economic calendar data unavailable. "
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"Event risk checks will be skipped for this cycle."
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)
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return []
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def _try_fetch_calendar_api(self) -> List[EconomicEvent]:
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"""Attempt to fetch from the Forex Factory free JSON calendar."""
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now = time.time()
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if now < NewsEngine._calendar_backoff_until:
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wait = NewsEngine._calendar_backoff_until - now
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logger.debug(f"Calendar API in backoff for {wait:.0f}s more")
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return []
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url = "https://nfs.faireconomy.media/ff_calendar_thisweek.json"
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for attempt in range(3):
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try:
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resp = self._session.get(url, timeout=10)
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except requests.RequestException as e:
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logger.debug(f"Calendar request failed (attempt {attempt + 1}): {e}")
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if attempt < 2:
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time.sleep(2 ** attempt)
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continue
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if resp.status_code == 429:
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retry_after = int(resp.headers.get("Retry-After", 300))
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retry_after = min(retry_after, 600)
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NewsEngine._calendar_backoff_until = time.time() + retry_after
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logger.warning(
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f"Calendar API rate-limited (429), backing off {retry_after}s"
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)
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return []
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if resp.status_code != 200:
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logger.debug(f"Calendar API returned {resp.status_code}")
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return []
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data = resp.json()
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events = []
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impact_map = {"High": "high", "Medium": "medium", "Low": "low"}
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for item in data:
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try:
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impact = impact_map.get(item.get("impact", ""), "low")
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date_str = item.get("date", "")
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if not date_str:
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continue
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event_time = datetime.fromisoformat(date_str.replace("Z", "+00:00"))
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events.append(EconomicEvent(
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time=event_time,
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currency=item.get("country", ""),
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impact=impact,
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event=item.get("title", ""),
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forecast=item.get("forecast") or None,
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previous=item.get("previous") or None,
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actual=item.get("actual") or None,
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))
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except Exception:
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continue
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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}")
|
|
|
|
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
|
|
|
|
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
|
|
|
|
formats = [
|
|
"%a, %d %b %Y %H:%M:%S %z", # RFC 822 with numeric tz offset
|
|
"%a, %d %b %Y %H:%M:%S %Z", # RFC 822 with tz abbreviation (GMT/UTC)
|
|
"%a, %d %b %Y %H:%M %Z", # RFC 822 without seconds (MyFXBook)
|
|
"%a, %d %b %Y %H:%M %z", # RFC 822 without seconds, numeric offset
|
|
"%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, [])
|