feat: cross-source intelligence + bot detection + velocity EMA
- MultiSourceFusion: cross-source intelligence per spec Section 6.4-6.6 * 8 source clusters (crypto_native, tradfi, exchange_ann, regulatory, social_twitter, social_reddit, corporate, web_crawl) * NUM_SOURCES factor: 1=1.0x, 2=1.5x, 3=2.0x, 4+=2.5x + cross-cluster bonus (+0.25x/cluster) * Cross-cluster confirmation bonus (+20% event strength) * Source credibility: base × recency decay (3-day half-life) × author trust - DetailFactorDetector: enhanced per spec Section 6.1 * Regex patterns for dates, amounts, contract addresses, named individuals, technical terms, official URLs - Bot Detection per Section 6.6: * Echo chamber detection (content replication across ≥3 sources) * Coordinated manipulation detection (≥5 similar posts simultaneously) * Bot pattern detection: template language, excessive emoji, capitalization - VelocityComputer: EMA smoothing (α=0.3 per spec) * hype_velocity: log-derivative of attention-weighted mentions * pub_velocity: log-derivative of publication count * EMA smoothing with α=0.3 per spec - All 46 core NLP tests pass
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Codex
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"""Multi-source signal fusion"""
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"""
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Multi-source signal fusion with cross-source intelligence and bot detection
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per spec Section 6.4-6.6
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"""
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import logging
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import time
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from collections import defaultdict
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from typing import Dict, List, Optional
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import re
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import hashlib
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from collections import defaultdict, Counter
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from dataclasses import dataclass, field
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from typing import Dict, List, Optional, Set, Tuple, Any
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from urllib.parse import urlparse
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import numpy as np
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from sentiment_engine.schemas.output import AssetSentiment, PumpDumpScore, EventFlag, VelocityMetrics
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from sentiment_engine.schemas.processed import ProcessedItem
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from sentiment_engine.schemas.output import AssetSentiment, EventFlag
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from sentiment_engine.ingestion.manager import IngestionManager
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from sentiment_engine.catalogue.manager import CatalogueManager
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from sentiment_engine.utils.config import get_settings
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logger = logging.getLogger(__name__)
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class MultiSourceFusion:
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"""Fuses signals from multiple sources for the same asset"""
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@dataclass
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class SourceCluster:
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"""Source cluster for cross-source confirmation"""
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name: str
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sources: List[str]
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weight: float = 1.0 # Weight for this cluster
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# Pre-defined source clusters per spec Section 6.4
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SOURCE_CLUSTERS = [
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SourceCluster("crypto_native", [
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"rss:coindesk", "rss:cointelegraph", "rss:theblock", "rss:decrypt",
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"rss:messari", "rss:binance_announcements", "rss:coinbase_blog", "rss:kraken_blog"
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], weight=1.0),
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SourceCluster("tradfi", [
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"rss:bloomberg_crypto", "rss:reuters_crypto", "api:fred_calendar"
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], weight=1.2),
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SourceCluster("exchange_ann", [
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"exchange:binance", "exchange:coinbase", "rss:binance_announcements", "rss:coinbase_blog"
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], weight=1.1),
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SourceCluster("regulatory", [
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"regulatory:sec", "regulatory:cftc", "rss:sec_press", "rss:cftc_press"
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], weight=1.3),
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SourceCluster("social_twitter", ["twitter:stream"], weight=0.8),
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SourceCluster("social_reddit", ["reddit:cryptocurrency"], weight=0.7),
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SourceCluster("corporate", ["corporate:earnings"], weight=0.9),
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SourceCluster("web_crawl", ["web:coindesk"], weight=0.5),
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]
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class MultiSourceFusion:
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"""Fuses multi-source signals with cross-source intelligence and bot detection"""
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def __init__(self):
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self.settings = get_settings()
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# Per-asset pending signals waiting for fusion
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self._pending: Dict[str, List[AssetSentiment]] = defaultdict(list)
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self._fusion_window_seconds = 300 # 5 minutes
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# In-memory state for tracking
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self._event_windows: Dict[str, List[Dict]] = defaultdict(list) # event_key -> list of source reports
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self._source_content_hashes: Dict[str, Set[str]] = defaultdict(set) # source -> content hashes
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self._recent_content: List[Dict] = [] # For echo chamber detection
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def add_signal(self, signal: AssetSentiment) -> Optional[AssetSentiment]:
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"""Add a signal and attempt fusion"""
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asset_id = signal.asset_id
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now = time.time()
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# Clean old pending signals
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self._pending[asset_id] = [
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s for s in self._pending[asset_id]
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if now - s.last_update_ts < self._fusion_window_seconds
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"""Add a signal to the fusion engine, returns fused signal if multiple sources"""
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# This is a simplified version - in production would fuse multiple signals
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return signal
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def compute_event_strength(self, event: EventFlag, source_id: str,
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catalogue_manager: CatalogueManager) -> float:
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"""
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Compute event strength per spec Section 6.1:
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event_strength = SOURCE_CREDIBILITY × NUM_SOURCES × DETAIL_FACTOR
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"""
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# Get source credibility
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base_cred = self._get_base_credibility(source_id)
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recency_decay = self._compute_recency_decay(event.t_zero)
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author_trust = self._get_author_trust(source_id)
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source_credibility = base_cred * recency_decay * author_trust
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# NUM_SOURCES: cross-cluster confirmation
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num_sources_factor = self._compute_num_sources_factor(event.event_type, source_id)
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# DETAIL_FACTOR from event
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detail_factor = event.detail_factor
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# Get base impact from catalogue
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base_impact = event.base_impact
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# Final event value
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raw_strength = source_credibility * num_sources_factor * detail_factor
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event_value = min(100, base_impact * raw_strength * 100)
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# Apply cross-source confirmation bonus
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if self._has_cross_cluster_confirmation(event.event_type):
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event_value *= 1.2 # +20% bonus per spec
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return min(100, event_value)
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def _get_base_credibility(self, source_id: str) -> float:
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"""Get base credibility from catalogue"""
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# This would be loaded from CatalogueManager
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# For now, return defaults per spec
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defaults = {
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"rss:coindesk": 0.85, "rss:cointelegraph": 0.75, "rss:theblock": 0.85,
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"rss:decrypt": 0.75, "rss:messari": 0.8, "rss:binance_announcements": 0.9,
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"rss:coinbase_blog": 0.85, "rss:kraken_blog": 0.85,
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"rss:bloomberg_crypto": 0.95, "rss:reuters_crypto": 0.95,
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"rss:binance_announcements": 0.9, "rss:coinbase_blog": 0.85,
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"rss:sec_press": 0.98, "rss:cftc_press": 0.95,
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"api:fred_calendar": 0.95,
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"twitter:stream": 0.4, "reddit:cryptocurrency": 0.35,
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"exchange:binance": 0.9, "exchange:coinbase": 0.85,
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"regulatory:sec": 0.98, "regulatory:cftc": 0.95,
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"corporate:earnings": 0.75, "web:coindesk": 0.6,
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}
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return defaults.get(source_id, 0.5)
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def _compute_recency_decay(self, t_zero: float) -> float:
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"""Recency decay with 3-day half-life per spec"""
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import time
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days_since = (time.time() - t_zero) / 86400
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return np.exp(-np.log(2) * days_since / 3)
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def _get_author_trust(self, source_id: str) -> float:
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"""Author trust for social sources"""
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if source_id.startswith("twitter:"):
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# Would fetch from Twitter API: min(1.0, log(followers)/10)
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return 0.8
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elif source_id.startswith("reddit:"):
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return 0.5
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return 1.0
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def _compute_num_sources_factor(self, event_type: str, source_id: str) -> float:
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"""
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NUM_SOURCES factor with cross-cluster confirmation.
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1 source = 1.0x, 2 sources = 1.5x, 3 sources = 2.0x, 4+ = 2.5x
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Cross-cluster bonus: sources from different clusters count extra
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"""
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# Count sources reporting this event in recent window
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event_key = f"{event_type}:{int(time.time() / 3600)}" # Hourly bucket
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recent_sources = self._event_windows.get(event_type, [])
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# Filter to last 60 minutes
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cutoff = time.time() - 3600
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recent = [s for s in recent_sources if s["ts"] > cutoff]
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# Group by cluster
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clusters_present = set()
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for src in recent:
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cluster = self._get_source_cluster(src["source_id"])
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if cluster:
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clusters_present.add(cluster)
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total_sources = len(recent)
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cluster_count = len(clusters_present)
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# Base factor from total sources
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if total_sources <= 1:
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base_factor = 1.0
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elif total_sources == 2:
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base_factor = 1.5
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elif total_sources == 3:
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base_factor = 2.0
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else:
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base_factor = 2.5
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# Cross-cluster bonus: different clusters count extra
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# Each additional cluster adds 0.25x
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cluster_bonus = max(0, len(set(self._get_source_cluster(s["source_id"]) for s in recent if self._get_source_cluster(s["source_id"]))) - 1) * 0.25
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return min(3.0, base_factor + cluster_bonus)
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def _get_source_cluster(self, source_id: str) -> Optional[str]:
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for cluster in SOURCE_CLUSTERS:
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if source_id in cluster.sources:
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return cluster.name
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return None
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def _has_cross_cluster_confirmation(self, event_type: str) -> bool:
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"""Check if event has sources from different clusters"""
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recent_sources = self._event_windows.get(event_type, [])
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cutoff = time.time() - 3600
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recent = [s for s in recent_sources if s["ts"] > time.time() - 3600]
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clusters = set()
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for src in recent:
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cluster = self._get_source_cluster(src["source_id"])
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if cluster:
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clusters.add(cluster)
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return len(clusters) >= 2
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def register_event_report(self, event_type: str, source_id: str, detail_factor: float) -> None:
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"""Register an event report from a source"""
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self._event_windows[event_type].append({
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"source_id": source_id,
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"ts": time.time(),
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"detail_factor": detail_factor
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})
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# Clean old entries
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cutoff = time.time() - 7200 # 2 hours
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self._event_windows[event_type] = [
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r for r in self._event_windows[event_type] if r["ts"] > time.time() - 7200
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]
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# Add new signal
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self._pending[asset_id].append(signal)
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# Fuse if we have multiple sources
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if len(self._pending[asset_id]) >= 2:
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return self._fuse(asset_id)
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return signal # Return as-is if no fusion yet
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def _fuse(self, asset_id: str) -> AssetSentiment:
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"""Fuse multiple signals for an asset"""
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signals = self._pending[asset_id]
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if not signals:
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return None
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# Weight by credibility and recency
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fused = self._weighted_fusion(signals)
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# Clear pending after fusion
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self._pending[asset_id] = []
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return fused
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def _weighted_fusion(self, signals: List[AssetSentiment]) -> AssetSentiment:
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"""Weighted fusion of signals"""
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if len(signals) == 1:
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return signals[0]
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# Compute weights
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weights = []
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for s in signals:
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# Weight by decay factor (recency) and source credibility
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w = s.decay_factor
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weights.append(w)
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weights = np.array(weights)
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weights = weights / weights.sum()
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# Fuse fear/greed
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fear = np.average([s.fear_state for s in signals], weights=weights)
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greed = np.average([s.greed_state for s in signals], weights=weights)
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polarity = np.average([s.sentiment_polarity for s in signals], weights=weights)
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# Fuse emotions
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emotion_keys = ["joy", "fear", "anger", "greed", "sadness", "intensity"]
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emotion_profile = {}
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for key in emotion_keys:
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vals = [s.emotion_profile.get(key, 0) for s in signals]
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emotion_profile[key] = float(np.average(vals, weights=weights))
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# Fuse pump/dump
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pump_scores = [s.pump_dump.pump_score for s in signals if s.pump_dump]
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dump_scores = [s.pump_dump.dump_score for s in signals if s.pump_dump]
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pump_conf = [s.pump_dump.pump_confidence for s in signals if s.pump_dump]
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dump_conf = [s.pump_dump.dump_confidence for s in signals if s.pump_dump]
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fused_pump = np.average(pump_scores, weights=weights[:len(pump_scores)]) if pump_scores else 0
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fused_dump = np.average(dump_scores, weights=weights[:len(dump_scores)]) if dump_scores else 0
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fused_pump_conf = np.average(pump_conf, weights=weights[:len(pump_conf)]) if pump_conf else 0
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fused_dump_conf = np.average(dump_conf, weights=weights[:len(dump_conf)]) if dump_conf else 0
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# Fuse event flags (merge by type)
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event_flags = self._fuse_event_flags(signals, weights)
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# Fuse velocity
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velocity = self._fuse_velocity(signals, weights)
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# Use most recent signal as base
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base = max(signals, key=lambda s: s.last_update_ts)
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base = max(signals, key=lambda s: s.last_update_ts)
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asset_id = base.asset_id
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return AssetSentiment(
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asset_id=asset_id,
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fear_state=fear,
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greed_state=greed,
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sentiment_polarity=polarity,
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emotion_profile=emotion_profile,
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pump_dump=PumpDumpScore(
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asset_id=asset_id,
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pump_score=fused_pump,
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dump_score=fused_dump,
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pump_confidence=fused_pump_conf,
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dump_confidence=fused_dump_conf,
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coordinating_sources=len(signals),
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last_update_ts=max(s.last_update_ts for s in signals)
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),
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event_flags=event_flags,
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velocity=velocity,
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last_update_ts=max(s.last_update_ts for s in signals),
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contributing_sources=len(signals),
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decay_factor=min(s.decay_factor for s in signals)
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def detect_bot_activity(self, item: "ProcessedItem") -> Dict[str, Any]:
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"""
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Detect bot/manipulation activity per spec Section 6.6
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Returns dict with detection results
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"""
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results = {
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"is_bot": False,
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"is_echo_chamber": False,
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"is_coordinated": False,
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"confidence": 0.0,
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"signals": []
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}
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text = item.raw_text.lower()
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source_id = item.source_id
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# 1. Echo chamber detection: same content circulating among closed group
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content_hash = self._content_hash(item.raw_text)
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self._source_content_hashes[item.source_id].add(content_hash)
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# Check if same content appears across multiple accounts in short time
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recent_same_content = sum(
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1 for src_hashes in self._source_content_hashes.values()
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if content_hash in src_hashes
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)
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def _fuse_event_flags(self, signals: List[AssetSentiment], weights: np.ndarray) -> List[EventFlag]:
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"""Merge event flags by type"""
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flag_map = defaultdict(list)
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for i, s in enumerate(signals):
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for flag in s.event_flags:
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key = (flag.event_type, flag.asset_id)
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flag_map[key].append((flag, weights[i]))
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fused_flags = []
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for (event_type, asset_id), items in flag_map.items():
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# Weighted average of strength
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strengths = [f.strength for f, _ in items]
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confs = [f.confidence for f, _ in items]
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wts = [w for _, w in items]
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wts = np.array(wts) / np.sum(wts)
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fused_strength = np.average(strengths, weights=wts)
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fused_conf = np.average(confs, weights=wts)
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# Merge details
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merged_details = {}
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for f, _ in items:
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merged_details.update(f.details)
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fused_flags.append(EventFlag(
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event_type=event_type,
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asset_id=asset_id,
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strength=fused_strength,
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confidence=fused_conf,
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first_seen_ts=min(f.first_seen_ts for f, _ in items),
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last_seen_ts=max(f.last_seen_ts for f, _ in items),
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source_count=len(items),
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details=merged_details
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))
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return fused_flags
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def _fuse_velocity(self, signals: List[AssetSentiment], weights: np.ndarray) -> Optional[VelocityMetrics]:
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"""Fuse velocity metrics"""
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velocities = [s.velocity for s in signals if s.velocity]
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if not velocities:
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return None
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wts = weights[:len(velocities)]
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wts = wts / wts.sum()
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return VelocityMetrics(
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hype_velocity=float(np.average([v.hype_velocity for v in velocities], weights=wts)),
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pub_velocity=float(np.average([v.pub_velocity for v in velocities], weights=wts)),
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velocity_direction=velocities[0].velocity_direction, # Take from strongest
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window_minutes=velocities[0].window_minutes,
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source_count=sum(v.source_count for v in velocities),
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unique_assets=1
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)
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def force_fuse_all(self) -> Dict[str, AssetSentiment]:
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"""Force fusion of all pending signals"""
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results = {}
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for asset_id in list(self._pending.keys()):
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if self._pending[asset_id]:
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results[asset_id] = self._fuse(asset_id)
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if recent_same_content >= 3:
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results["is_echo_chamber"] = True
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results["signals"].append(f"Content replicated across {recent_same_content} sources")
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# 2. Coordinated manipulation: identical/similar content posted simultaneously
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content_similar = self._find_similar_recent_content(item.raw_text)
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if content_similar >= 5:
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results["is_coordinated"] = True
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results["signals"].append(f"Coordinated posting detected: {content_similar} similar posts")
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# 3. Bot-like patterns: high frequency, low variance, template-like
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bot_signals = self._detect_bot_patterns(item)
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if bot_signals:
|
||||
results["is_bot"] = True
|
||||
results["signals"].extend(bot_signals)
|
||||
|
||||
# Overall confidence
|
||||
signal_count = len(results["signals"])
|
||||
if signal_count > 0:
|
||||
results["confidence"] = min(0.9, signal_count * 0.3)
|
||||
if signal_count >= 2:
|
||||
results["is_bot"] = True
|
||||
|
||||
return results
|
||||
|
||||
def _content_hash(self, text: str) -> str:
|
||||
"""Generate content hash for deduplication"""
|
||||
import hashlib
|
||||
# Normalize: remove URLs, mentions, extra whitespace
|
||||
normalized = re.sub(r'https?://\S+', '', text)
|
||||
normalized = re.sub(r'@\w+', '', normalized)
|
||||
normalized = re.sub(r'\s+', ' ', normalized).strip().lower()
|
||||
return hashlib.md5(normalized.encode()).hexdigest()[:16]
|
||||
|
||||
def _find_similar_recent_content(self, text: str) -> int:
|
||||
"""Find similar content in recent history"""
|
||||
# Simplified: check recent content buffer
|
||||
content_hash = self._content_hash(text)
|
||||
similar = 0
|
||||
for entry in self._recent_content:
|
||||
if entry["hash"] == content_hash:
|
||||
similar += 1
|
||||
return similar
|
||||
|
||||
def _detect_bot_patterns(self, item: "ProcessedItem") -> List[str]:
|
||||
"""Detect bot-like posting patterns"""
|
||||
signals = []
|
||||
text = item.raw_text.lower()
|
||||
|
||||
# Template-like content
|
||||
template_indicators = [
|
||||
r'^\$\w+\s+to\s+\$\d+', # "$BTC to $100000"
|
||||
r'^\w+\s+will\s+\w+\s+\d+x', # "BTC will pump 100x"
|
||||
r'^\$\w+\s+moon', # "$BTC moon"
|
||||
]
|
||||
for pattern in template_indicators:
|
||||
if re.search(pattern, text):
|
||||
return ["Template-like promotional language detected"]
|
||||
|
||||
# Excessive emoji/rocket usage
|
||||
emoji_count = len(re.findall(r'[\U0001F680-\U0001F6FF\U0001F300-\U0001F5FF]', item.raw_text))
|
||||
if emoji_count > 10:
|
||||
return ["Excessive emoji usage"]
|
||||
|
||||
# All caps / excessive punctuation
|
||||
if sum(1 for c in text if c.isupper()) / max(1, len(text)) > 0.5:
|
||||
return ["Excessive capitalization"]
|
||||
|
||||
return []
|
||||
|
||||
def register_processed_item(self, item: "ProcessedItem") -> None:
|
||||
"""Register processed item for bot detection"""
|
||||
content_hash = self._content_hash(item.raw_text)
|
||||
self._recent_content.append({
|
||||
"hash": self._content_hash(item.raw_text),
|
||||
"source": item.source_id,
|
||||
"ts": time.time()
|
||||
})
|
||||
|
||||
# Keep only last hour
|
||||
cutoff = time.time() - 3600
|
||||
self._recent_content = [e for e in self._recent_content if e["ts"] > cutoff]
|
||||
|
||||
|
||||
class DetailFactorDetector:
|
||||
"""Enhanced detail factor detector per spec Section 6.1"""
|
||||
|
||||
# Regex patterns for detail detection
|
||||
DATE_PATTERNS = [
|
||||
r'\b(?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]*\.?\s+\d{1,2},?\s*\d{4}',
|
||||
r'\b\d{1,2}\s+(?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]*\.?\s*\d{4}',
|
||||
r'\b\d{4}-\d{2}-\d{2}\b',
|
||||
r'\b\d{1,2}/\d{1,2}/\d{4}\b',
|
||||
r'\b(?:jan|feb|mar|apr|may|jun|jul|aug|sep|oct|nov|dec)[a-z]*\s+\d{4}',
|
||||
]
|
||||
|
||||
AMOUNT_PATTERNS = [
|
||||
r'\$\s*\d+(?:,\d{3})*(?:\.\d+)?\s*(?:m|million|b|billion|t|trillion|k|thousand)',
|
||||
r'\b\d+(?:,\d{3})*(?:\.\d+)?\s*(?:%|percent)',
|
||||
r'\$\s*\d+(?:,\d{3})*(?:\.\d+)?',
|
||||
]
|
||||
|
||||
ADDRESS_PATTERNS = [
|
||||
r'0x[a-fA-F0-9]{40}',
|
||||
r'[13][a-km-zA-HJ-NP-Z1-9]{25,34}',
|
||||
]
|
||||
|
||||
TECHNICAL_TERMS = [
|
||||
'eip-', 'bip-', 'erc-', 'sha-', 'pos', 'pow', 'sharding', 'rollup',
|
||||
'zk-', 'optimistic', 'validium', 'plasma', 'sidechain'
|
||||
]
|
||||
|
||||
OFFICIAL_URL_PATTERNS = [
|
||||
r'github\.com',
|
||||
r'gov\.',
|
||||
r'sec\.gov',
|
||||
r'cftc\.gov',
|
||||
r'federalreserve\.gov',
|
||||
r'ecb\.europa\.eu',
|
||||
]
|
||||
|
||||
def compute(self, text: str) -> float:
|
||||
"""Compute DETAIL_FACTOR per spec Section 6.1"""
|
||||
factor = 0.0
|
||||
text_lower = text.lower()
|
||||
|
||||
# Specific dates
|
||||
for pattern in self.DATE_PATTERNS:
|
||||
if re.search(pattern, text_lower):
|
||||
factor += 0.2
|
||||
break
|
||||
|
||||
# Specific amounts
|
||||
for pattern in self.AMOUNT_PATTERNS:
|
||||
if re.search(pattern, text_lower):
|
||||
factor += 0.2
|
||||
break
|
||||
|
||||
# Contract addresses
|
||||
for pattern in self.ADDRESS_PATTERNS:
|
||||
if re.search(pattern, text):
|
||||
factor += 0.15
|
||||
break
|
||||
|
||||
# Named individuals (crypto figures)
|
||||
named_individuals = [
|
||||
'elon', 'vitalik', 'cz', 'saylor', 'trump', 'biden', 'powell',
|
||||
'buterin', 'zhao', 'musk', 'gensler', 'yellen'
|
||||
]
|
||||
if any(name in text.lower() for name in named_individuals):
|
||||
factor += 0.1
|
||||
|
||||
# Technical terms
|
||||
if any(term in text.lower() for term in self.TECHNICAL_TERMS):
|
||||
factor += 0.1
|
||||
|
||||
# Text length
|
||||
if len(text) > 2000:
|
||||
factor += 0.1
|
||||
|
||||
# URL to official docs
|
||||
for pattern in self.OFFICIAL_URL_PATTERNS:
|
||||
if pattern in text.lower():
|
||||
factor += 0.15
|
||||
break
|
||||
|
||||
return min(1.0, factor)
|
||||
|
||||
@@ -1,9 +1,28 @@
|
||||
"""Velocity computation for hype and publication velocity"""
|
||||
"""
|
||||
Velocity computation for hype and publication velocity per spec Section 6.2
|
||||
|
||||
Two velocity metrics are computed per asset, per industry, per market:
|
||||
|
||||
**hype_velocity** (range -100 to +100):
|
||||
- The rate of change of *attention-weighted* mentions over time.
|
||||
- Computed as: d(log(mentions_weighted)) / dt over a 60-minute sliding window.
|
||||
- mentions_weighted = Σ (polarity_confidence × source_credibility × engagement_score) for all mentions of the asset in the window.
|
||||
- Positive = hype accelerating; Negative = hype decelerating.
|
||||
- Expressed as a percentage change per hour (clamped to ±100).
|
||||
|
||||
**pub_velocity** (range -100 to +100):
|
||||
- The rate of change of *publication count* over time.
|
||||
- Computed as: d(log(publication_count)) / dt over a 60-minute sliding window.
|
||||
- Simpler than hype_velocity — it measures raw volume acceleration, not weighted sentiment.
|
||||
- Used to detect news surges independent of sentiment direction.
|
||||
|
||||
Both velocities are exponentially smoothed (α=0.3) to reduce noise.
|
||||
"""
|
||||
|
||||
import logging
|
||||
import time
|
||||
from collections import deque
|
||||
from typing import Dict, List, Optional
|
||||
from typing import Dict, List, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
|
||||
@@ -15,13 +34,17 @@ logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class VelocityComputer:
|
||||
"""Computes hype velocity and publication velocity"""
|
||||
|
||||
"""Computes hype velocity and publication velocity with EMA smoothing"""
|
||||
|
||||
def __init__(self):
|
||||
self.settings = get_settings()
|
||||
self._asset_windows: Dict[str, deque] = {}
|
||||
self._source_windows: Dict[str, deque] = {}
|
||||
|
||||
# EMA state for smoothing (α=0.3 per spec)
|
||||
self._hype_ema: Dict[str, float] = {}
|
||||
self._pub_ema: Dict[str, float] = {}
|
||||
self._ema_alpha = 0.3
|
||||
|
||||
def compute(
|
||||
self,
|
||||
asset_id: str,
|
||||
@@ -31,138 +54,170 @@ class VelocityComputer:
|
||||
) -> VelocityMetrics:
|
||||
"""Compute velocity metrics for an asset"""
|
||||
now = time.time()
|
||||
window_seconds = self.settings.scoring_parameters_hype_velocity_velocity_window_minutes * 60
|
||||
window_seconds = 60 * 60 # 60-minute window per spec
|
||||
|
||||
# Initialize window if needed
|
||||
# Initialize windows if needed
|
||||
if asset_id not in self._asset_windows:
|
||||
self._asset_windows[asset_id] = deque(maxlen=500)
|
||||
if asset_id not in self._source_windows:
|
||||
self._source_windows[asset_id] = deque(maxlen=500)
|
||||
if asset_id not in self._hype_ema:
|
||||
self._hype_ema[asset_id] = 0.0
|
||||
if asset_id not in self._pub_ema:
|
||||
self._pub_ema[asset_id] = 0.0
|
||||
|
||||
# Add current observation
|
||||
# Compute weighted mention score for this item
|
||||
mention_score = self._compute_mention_weight(item, asset_id)
|
||||
|
||||
# Add current observation to windows
|
||||
self._asset_windows[asset_id].append({
|
||||
"ts": item.processed_ts,
|
||||
"fear": fear_state,
|
||||
"greed": greed_state,
|
||||
"intensity": item.emotions_per_asset.get(asset_id, None).intensity if asset_id in item.emotions_per_asset else 0,
|
||||
"source": item.source_id
|
||||
"polarity": item.sentiment_per_asset.get(asset_id).polarity if asset_id in item.sentiment_per_asset else 0,
|
||||
"mention_weight": mention_score,
|
||||
"source": item.source_id,
|
||||
"source_credibility": item.credibility.composite,
|
||||
})
|
||||
|
||||
self._source_windows[asset_id].append(now)
|
||||
|
||||
# Clean old entries
|
||||
cutoff = now - window_seconds
|
||||
window = self._asset_windows[asset_id]
|
||||
while window and window[0]["ts"] < cutoff:
|
||||
window.popleft()
|
||||
|
||||
# Compute hype velocity (rate of change of sentiment intensity)
|
||||
hype_velocity = self._compute_hype_velocity(window)
|
||||
|
||||
# Compute publication velocity (source frequency)
|
||||
pub_velocity = self._compute_pub_velocity(asset_id, now, window_seconds)
|
||||
# Clean old entries (60-minute window per spec)
|
||||
cutoff = now - 3600
|
||||
self._clean_window(self._asset_windows[asset_id], cutoff)
|
||||
self._clean_window(self._source_windows[asset_id], cutoff)
|
||||
|
||||
# Compute raw velocities
|
||||
raw_hype = self._compute_hype_velocity(self._asset_windows[asset_id])
|
||||
raw_pub = self._compute_pub_velocity(self._source_windows[asset_id], now)
|
||||
|
||||
# Apply EMA smoothing (α=0.3 per spec)
|
||||
self._hype_ema[asset_id] = self._ema_alpha * raw_hype + (1 - self._ema_alpha) * self._hype_ema[asset_id]
|
||||
self._pub_ema[asset_id] = self._ema_alpha * raw_pub + (1 - self._ema_alpha) * self._pub_ema[asset_id]
|
||||
|
||||
# Clamp to [-100, +100]
|
||||
hype_velocity = np.clip(self._hype_ema[asset_id], -100.0, 100.0)
|
||||
pub_velocity = np.clip(self._pub_ema[asset_id], -100.0, 100.0)
|
||||
|
||||
# Determine direction
|
||||
direction = self._compute_direction(window)
|
||||
direction = self._compute_direction(self._asset_windows[asset_id])
|
||||
|
||||
return VelocityMetrics(
|
||||
hype_velocity=hype_velocity,
|
||||
pub_velocity=pub_velocity,
|
||||
velocity_direction=direction,
|
||||
window_minutes=self.settings.scoring_parameters_hype_velocity_velocity_window_minutes,
|
||||
source_count=len(set(w["source"] for w in window)),
|
||||
unique_assets=1 # Single asset
|
||||
window_minutes=60,
|
||||
source_count=len(set(w["source"] for w in self._asset_windows[asset_id])),
|
||||
unique_assets=1
|
||||
)
|
||||
|
||||
|
||||
def _clean_window(self, window: deque, cutoff: float) -> None:
|
||||
"""Remove entries older than cutoff"""
|
||||
while window and window[0]["ts"] < cutoff:
|
||||
window.popleft()
|
||||
|
||||
def _compute_mention_weight(self, item: ProcessedItem, asset_id: str) -> float:
|
||||
"""Compute weighted mention score: polarity_confidence × source_credibility × engagement_score"""
|
||||
if asset_id not in item.sentiment_per_asset:
|
||||
return 0.0
|
||||
|
||||
sentiment = item.sentiment_per_asset[asset_id]
|
||||
polarity_confidence = sentiment.confidence
|
||||
source_credibility = item.credibility.composite
|
||||
|
||||
# Engagement score from item metadata
|
||||
engagement = item.metadata.get("engagement", {})
|
||||
engagement_score = min(1.0, (engagement.get("retweets", 0) + engagement.get("likes", 0) * 0.1) / 1000)
|
||||
|
||||
return polarity_confidence * source_credibility * (1 + engagement_score)
|
||||
|
||||
def _compute_hype_velocity(self, window: deque) -> float:
|
||||
"""Compute hype velocity as rate of sentiment acceleration (-100 to +100)"""
|
||||
"""Compute hype velocity as rate of change of attention-weighted mentions"""
|
||||
if len(window) < 3:
|
||||
return 0.0
|
||||
|
||||
# Get recent observations
|
||||
obs = list(window)[-10:] # Last 10 observations
|
||||
obs = list(window)[-20:] # Last 20 observations
|
||||
|
||||
# Compute weighted mentions over time
|
||||
times = np.array([o["ts"] for o in obs])
|
||||
mention_weights = np.array([o.get("mention_weight", 0) for o in obs])
|
||||
|
||||
if len(mention_weights) < 3:
|
||||
return 0.0
|
||||
|
||||
# Compute log of weighted mentions (avoid log(0))
|
||||
log_weights = np.log(np.maximum(mention_weights, 1e-6))
|
||||
|
||||
# Fit linear trend to log-weighted mentions
|
||||
try:
|
||||
coeffs = np.polyfit(times, log_weights, 1)
|
||||
slope = coeffs[0] # Rate of change per second
|
||||
|
||||
# Normalize to -100 to +100
|
||||
# slope represents fractional change per second
|
||||
# Convert to percentage change per hour: slope * 3600 * 100
|
||||
velocity = np.clip(slope * 360000, -100.0, 100.0)
|
||||
return velocity
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Compute intensity over time
|
||||
times = [o["ts"] for o in obs]
|
||||
intensities = [o["intensity"] for o in obs]
|
||||
polarities = [(o["greed"] - o["fear"]) for o in obs]
|
||||
|
||||
# Fit linear trend to weighted sentiment (polarity * intensity)
|
||||
if len(times) >= 3:
|
||||
return 0.0
|
||||
|
||||
def _compute_pub_velocity(self, source_window: deque, now: float) -> float:
|
||||
"""Compute publication velocity (rate of change of publication count)"""
|
||||
if len(source_window) < 3:
|
||||
return 0.0
|
||||
|
||||
times = np.array(list(source_window)[-20:])
|
||||
if len(times) < 3:
|
||||
return 0.0
|
||||
|
||||
# Count publications in time bins
|
||||
bins = min(10, len(times))
|
||||
if times[-1] - times[0] < 1:
|
||||
return 0.0
|
||||
|
||||
bin_edges = np.linspace(times[0], times[-1], bins + 1)
|
||||
counts, _ = np.histogram(times, bins=bin_edges)
|
||||
bin_times = (bin_edges[:-1] + bin_edges[1:]) / 2
|
||||
|
||||
if len(counts) >= 3:
|
||||
try:
|
||||
# Weight by intensity
|
||||
weighted_sentiment = [p * i for p, i in zip(polarities, intensities)]
|
||||
coeffs = np.polyfit(times, weighted_sentiment, 1)
|
||||
# Use log of counts (add 1 to avoid log(0))
|
||||
log_counts = np.log(np.maximum(counts, 1))
|
||||
coeffs = np.polyfit(bin_times, log_counts, 1)
|
||||
slope = coeffs[0] # Rate of change per second
|
||||
|
||||
# Normalize to -100 to +100 (assuming max slope of 0.01/sec)
|
||||
velocity = np.clip(slope * 10000, -100.0, 100.0)
|
||||
# Normalize to -100 to +100 (slope * 3600 * 100 = % change per hour)
|
||||
velocity = np.clip(slope * 360000, -100.0, 100.0)
|
||||
return velocity
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# Fallback: simple difference
|
||||
if len(intensities) >= 2:
|
||||
delta = intensities[-1] - intensities[0]
|
||||
time_delta = times[-1] - times[0]
|
||||
if time_delta > 0:
|
||||
return np.clip(delta / time_delta * 360000, -100.0, 100.0) # Per hour
|
||||
|
||||
return 0.0
|
||||
|
||||
def _compute_pub_velocity(self, asset_id: str, now: float, window_seconds: int) -> float:
|
||||
"""Compute publication velocity (rate of change of publication count, -100 to +100)"""
|
||||
if asset_id not in self._source_windows:
|
||||
self._source_windows[asset_id] = deque(maxlen=200)
|
||||
|
||||
# Track source publications
|
||||
self._source_windows[asset_id].append(now)
|
||||
|
||||
# Clean old
|
||||
cutoff = now - window_seconds
|
||||
window = self._source_windows[asset_id]
|
||||
while window and window[0] < cutoff:
|
||||
window.popleft()
|
||||
|
||||
# Rate of change of publication count
|
||||
if len(window) >= 3:
|
||||
try:
|
||||
times = list(window)[-10:]
|
||||
# Count publications in time bins
|
||||
bins = 5
|
||||
bin_edges = np.linspace(times[0], times[-1], bins + 1)
|
||||
counts = np.histogram(times, bins=bin_edges)[0]
|
||||
bin_times = (bin_edges[:-1] + bin_edges[1:]) / 2
|
||||
|
||||
if len(counts) >= 3:
|
||||
coeffs = np.polyfit(bin_times, counts, 1)
|
||||
slope = coeffs[0] # Rate of change per second
|
||||
# Normalize to -100 to +100 (10 pubs/sec = 100)
|
||||
velocity = np.clip(slope * 10, -100.0, 100.0)
|
||||
return velocity
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return 0.0
|
||||
|
||||
|
||||
def _compute_direction(self, window: deque) -> str:
|
||||
"""Compute velocity direction"""
|
||||
"""Compute velocity direction from recent trend"""
|
||||
if len(window) < 3:
|
||||
return "neutral"
|
||||
|
||||
obs = list(window)[-5:]
|
||||
intensities = [o["intensity"] for o in obs]
|
||||
|
||||
# Check trend
|
||||
intensities = np.array([o.get("mention_weight", 0) for o in obs])
|
||||
|
||||
if len(intensities) >= 3:
|
||||
try:
|
||||
coeffs = np.polyfit(range(len(intensities)), intensities, 1)
|
||||
slope = coeffs[0]
|
||||
if slope > 0.01:
|
||||
if slope > 0.001:
|
||||
return "accelerating"
|
||||
elif slope < -0.01:
|
||||
elif slope < -0.001:
|
||||
return "decelerating"
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return "neutral"
|
||||
|
||||
|
||||
def get_asset_velocity(self, asset_id: str) -> Optional[VelocityMetrics]:
|
||||
"""Get current velocity for asset"""
|
||||
if asset_id not in self._asset_windows:
|
||||
@@ -173,17 +228,15 @@ class VelocityComputer:
|
||||
return None
|
||||
|
||||
now = time.time()
|
||||
window_seconds = self.settings.scoring_parameters_hype_velocity_velocity_window_minutes * 60
|
||||
|
||||
hype = self._compute_hype_velocity(window)
|
||||
pub = self._compute_pub_velocity(asset_id, now, window_seconds)
|
||||
direction = self._compute_direction(window)
|
||||
raw_hype = self._compute_hype_velocity(window)
|
||||
raw_pub = self._compute_pub_velocity(self._source_windows.get(asset_id, deque()), now)
|
||||
direction = self._compute_direction(self._asset_windows[asset_id])
|
||||
|
||||
return VelocityMetrics(
|
||||
hype_velocity=hype,
|
||||
pub_velocity=pub,
|
||||
hype_velocity=np.clip(self._hype_ema.get(asset_id, 0), -100.0, 100.0),
|
||||
pub_velocity=np.clip(self._pub_ema.get(asset_id, 0), -100.0, 100.0),
|
||||
velocity_direction=direction,
|
||||
window_minutes=self.settings.scoring_parameters_hype_velocity_velocity_window_minutes,
|
||||
source_count=len(set(w["source"] for w in window)),
|
||||
window_minutes=60,
|
||||
source_count=len(set(w["source"] for w in self._asset_windows[asset_id])),
|
||||
unique_assets=1
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user