Social Sentiment Scraper

Social Sentiment Scraper

Overview

Aggregates sentiment from social platforms and retail positioning data. The primary value is as a contrarian indicator — when retail is overwhelmingly long, consider selling, and vice versa.

1. Sentiment Sources & Priority

SENTIMENT_SOURCES = {
    # Quantitative (most reliable)
    "retail_positioning": {
        "ig_client_sentiment": {"url": "https://www.ig.com/en/trading-sentiment", "reliability": 0.8},
        "myfxbook_outlook": {"url": "https://www.myfxbook.com/community/outlook", "reliability": 0.7},
        "oanda_positions": {"url": "https://www.oanda.com/forex-trading/analysis/open-position-ratios", "reliability": 0.7},
    },
    # Index-based
    "fear_greed": {
        "cnn_fear_greed": {"url": "https://edition.cnn.com/markets/fear-and-greed", "reliability": 0.6},
        "crypto_fear_greed": {"url": "https://alternative.me/crypto/fear-and-greed-index/", "reliability": 0.5},
    },
    # Social (noisy but informative at extremes)
    "social_media": {
        "twitter_fintwit": {"query": "$EURUSD OR #forex OR #trading", "reliability": 0.4},
        "reddit_forex": {"url": "https://www.reddit.com/r/Forex/", "reliability": 0.3},
        "tradingview_ideas": {"url": "https://www.tradingview.com/ideas/", "reliability": 0.5},
    },
}

2. Retail Positioning Analysis

from dataclasses import dataclass
import numpy as np

@dataclass
class RetailPositioning:
    symbol: str
    long_pct: float
    short_pct: float
    source: str

    @property
    def ratio(self) -> float:
        return self.long_pct / max(self.short_pct, 1)

    @property
    def contrarian_signal(self) -> dict:
        """
        Retail is wrong at extremes. >70% one way = contrarian signal.
        This is one of the most reliable sentiment indicators.
        """
        if self.long_pct > 75:
            return {"signal": "CONTRARIAN SELL", "strength": "strong",
                    "reason": f"{self.long_pct:.0f}% retail is long — crowd usually wrong at extremes"}
        if self.short_pct > 75:
            return {"signal": "CONTRARIAN BUY", "strength": "strong",
                    "reason": f"{self.short_pct:.0f}% retail is short — crowd usually wrong at extremes"}
        if self.long_pct > 65:
            return {"signal": "MILD CONTRARIAN SELL", "strength": "moderate",
                    "reason": f"{self.long_pct:.0f}% retail long — leaning contrarian"}
        if self.short_pct > 65:
            return {"signal": "MILD CONTRARIAN BUY", "strength": "moderate",
                    "reason": f"{self.short_pct:.0f}% retail short — leaning contrarian"}
        return {"signal": "NEUTRAL", "strength": "none",
                "reason": "No extreme positioning — no contrarian signal"}

class SentimentAggregator:
    """Combine multiple sentiment sources into actionable score."""

    def __init__(self):
        self.data_points = []

    def add_retail_positioning(self, pos: RetailPositioning):
        signal = pos.contrarian_signal
        score = -1 if "SELL" in signal["signal"] else 1 if "BUY" in signal["signal"] else 0
        self.data_points.append({
            "source": f"retail_{pos.source}",
            "symbol": pos.symbol,
            "score": score * (0.8 if signal["strength"] == "strong" else 0.4),
            "raw": {"long": pos.long_pct, "short": pos.short_pct},
            "signal": signal["signal"],
        })

    def add_fear_greed(self, index_value: int, index_name: str = "CNN"):
        """Fear & Greed: 0 = extreme fear, 100 = extreme greed."""
        if index_value < 20:
            score, label = 0.7, "EXTREME FEAR — contrarian buy"
        elif index_value < 35:
            score, label = 0.3, "FEAR — mild buy signal"
        elif index_value > 80:
            score, label = -0.7, "EXTREME GREED — contrarian sell"
        elif index_value > 65:
            score, label = -0.3, "GREED — mild sell signal"
        else:
            score, label = 0, "NEUTRAL"
        self.data_points.append({"source": f"fear_greed_{index_name}", "score": score, "label": label, "value": index_value})

    def add_social_sentiment(self, bullish_pct: float, bearish_pct: float, platform: str, sample_size: int):
        """Process social media sentiment. Weight by sample size."""
        if sample_size < 50:
            return  # Too small to be meaningful
        net = (bullish_pct - bearish_pct) / 100
        # Social sentiment as contrarian (inverted)
        contrarian = -net * min(sample_size / 500, 1.0) * 0.3
        self.data_points.append({
            "source": f"social_{platform}", "score": round(contrarian, 3),
            "raw_bullish": bullish_pct, "raw_bearish": bearish_pct, "sample_size": sample_size,
        })

    def aggregate(self) -> dict:
        if not self.data_points:
            return {"overall_score": 0, "signal": "NO DATA", "data_points": []}
        scores = [dp["score"] for dp in self.data_points]
        overall = np.mean(scores)
        return {
            "overall_score": round(overall, 3),
            "signal": "CONTRARIAN BUY" if overall > 0.3 else "CONTRARIAN SELL" if overall < -0.3 else "NEUTRAL",
            "confidence": round(min(abs(overall) * 1.5, 0.9), 2),
            "data_points": self.data_points,
            "n_sources": len(self.data_points),
            "WARNING": "Sentiment is a SECONDARY indicator — never trade on sentiment alone",
        }

3. Web Search Queries

# Retail positioning
web_search("IG client sentiment EURUSD")
web_search("Myfxbook forex outlook community")
web_search("OANDA open position ratios forex")

# Fear & Greed
web_search("CNN Fear Greed Index today")
web_search("crypto fear greed index")

# Social
web_search("Twitter forex sentiment EURUSD today")
web_search("Reddit r/forex daily discussion")
web_search("TradingView EURUSD ideas bullish bearish")

Key Principle: Retail crowd is reliably wrong at extremes (>70% one direction). Use as contrarian confirmation, never as primary signal.