openbb-invest-api/routes_sentiment.py

"""Routes for sentiment, insider trades, and analyst data (Finnhub + Alpha Vantage)."""

import asyncio

from fastapi import APIRouter, Path, Query

from models import ApiResponse
from route_utils import safe, validate_symbol
import alphavantage_service
import finnhub_service
import openbb_service

import logging

logger = logging.getLogger(__name__)

router = APIRouter(prefix="/api/v1")


# --- Sentiment & News ---


@router.get("/stock/{symbol}/sentiment", response_model=ApiResponse)
@safe
async def stock_sentiment(symbol: str = Path(..., min_length=1, max_length=20)):
    """Aggregated sentiment from all sources with composite score.

    Combines: Alpha Vantage news sentiment, Finnhub analyst data,
    Reddit mentions, and analyst upgrades into a single composite score.
    Score range: -1.0 (extreme bearish) to +1.0 (extreme bullish).
    """
    symbol = validate_symbol(symbol)

    # Fetch all sources in parallel
    av_data, finnhub_data, reddit_data, upgrades_data, recs_data = await asyncio.gather(
        alphavantage_service.get_news_sentiment(symbol, limit=20),
        finnhub_service.get_sentiment_summary(symbol),
        finnhub_service.get_reddit_sentiment(symbol),
        openbb_service.get_upgrades_downgrades(symbol, limit=10),
        finnhub_service.get_recommendation_trends(symbol),
        return_exceptions=True,
    )

    def _safe(result, default):
        return default if isinstance(result, BaseException) else result

    av_data = _safe(av_data, {})
    finnhub_data = _safe(finnhub_data, {})
    reddit_data = _safe(reddit_data, {})
    upgrades_data = _safe(upgrades_data, [])
    recs_data = _safe(recs_data, [])

    # --- Score each source ---
    scores: list[tuple[str, float, float]] = []  # (source, score, weight)

    # 1. News sentiment (Alpha Vantage): avg_score ranges ~-0.35 to +0.35
    if isinstance(av_data, dict) and av_data.get("overall_sentiment"):
        av_score = av_data["overall_sentiment"].get("avg_score")
        if av_score is not None:
            # Normalize to -1..+1 (AV scores are typically -0.35 to +0.35)
            normalized = max(-1.0, min(1.0, av_score * 2.5))
            scores.append(("news", round(normalized, 3), 0.25))

    # 2. Analyst recommendations (Finnhub): buy/sell ratio
    if isinstance(recs_data, list) and recs_data:
        latest = recs_data[0]
        total = sum(latest.get(k, 0) for k in ("strongBuy", "buy", "hold", "sell", "strongSell"))
        if total > 0:
            bullish = latest.get("strongBuy", 0) + latest.get("buy", 0)
            bearish = latest.get("sell", 0) + latest.get("strongSell", 0)
            ratio = (bullish - bearish) / total  # -1 to +1
            scores.append(("analysts", round(ratio, 3), 0.30))

    # 3. Analyst upgrades vs downgrades (yfinance)
    if isinstance(upgrades_data, list) and upgrades_data:
        ups = sum(1 for u in upgrades_data if u.get("action") in ("up", "init"))
        downs = sum(1 for u in upgrades_data if u.get("action") == "down")
        maintains = len(upgrades_data) - ups - downs
        if len(upgrades_data) > 0:
            upgrade_score = (ups - downs) / len(upgrades_data)
            scores.append(("upgrades", round(upgrade_score, 3), 0.20))

    # 4. Reddit buzz (ApeWisdom)
    if isinstance(reddit_data, dict) and reddit_data.get("found"):
        mentions = reddit_data.get("mentions_24h", 0)
        change = reddit_data.get("mentions_change_pct")
        if change is not None and mentions > 10:
            # Positive change = bullish buzz, capped at +/- 1
            reddit_score = max(-1.0, min(1.0, change / 100))
            scores.append(("reddit", round(reddit_score, 3), 0.25))

    # --- Compute weighted composite ---
    if scores:
        total_weight = sum(w for _, _, w in scores)
        composite = sum(s * w for _, s, w in scores) / total_weight
        composite = round(composite, 3)
    else:
        composite = None

    # Map to label
    if composite is None:
        label = "Unknown"
    elif composite >= 0.5:
        label = "Strong Bullish"
    elif composite >= 0.15:
        label = "Bullish"
    elif composite >= -0.15:
        label = "Neutral"
    elif composite >= -0.5:
        label = "Bearish"
    else:
        label = "Strong Bearish"

    return ApiResponse(data={
        "symbol": symbol,
        "composite_score": composite,
        "composite_label": label,
        "source_scores": {name: score for name, score, _ in scores},
        "source_weights": {name: weight for name, _, weight in scores},
        "details": {
            "news_sentiment": av_data if isinstance(av_data, dict) else {},
            "analyst_recommendations": recs_data[0] if isinstance(recs_data, list) and recs_data else {},
            "recent_upgrades": upgrades_data[:5] if isinstance(upgrades_data, list) else [],
            "reddit": reddit_data if isinstance(reddit_data, dict) else {},
            "finnhub_news": (
                finnhub_data.get("recent_news", [])[:5]
                if isinstance(finnhub_data, dict)
                else []
            ),
        },
    })


@router.get("/stock/{symbol}/news-sentiment", response_model=ApiResponse)
@safe
async def stock_news_sentiment(
    symbol: str = Path(..., min_length=1, max_length=20),
    limit: int = Query(default=30, ge=1, le=200),
):
    """Get news articles with per-ticker sentiment scores (Alpha Vantage)."""
    symbol = validate_symbol(symbol)
    data = await alphavantage_service.get_news_sentiment(symbol, limit=limit)
    return ApiResponse(data=data)


@router.get("/stock/{symbol}/insider-trades", response_model=ApiResponse)
@safe
async def stock_insider_trades(symbol: str = Path(..., min_length=1, max_length=20)):
    """Get insider transactions (CEO/CFO buys and sells)."""
    symbol = validate_symbol(symbol)
    raw = await finnhub_service.get_insider_transactions(symbol)
    trades = [
        {
            "name": t.get("name"),
            "shares": t.get("share"),
            "change": t.get("change"),
            "transaction_date": t.get("transactionDate"),
            "transaction_code": t.get("transactionCode"),
            "transaction_price": t.get("transactionPrice"),
            "filing_date": t.get("filingDate"),
        }
        for t in raw[:20]
    ]
    return ApiResponse(data=trades)


@router.get("/stock/{symbol}/recommendations", response_model=ApiResponse)
@safe
async def stock_recommendations(symbol: str = Path(..., min_length=1, max_length=20)):
    """Get analyst recommendation trends (monthly buy/hold/sell counts)."""
    symbol = validate_symbol(symbol)
    raw = await finnhub_service.get_recommendation_trends(symbol)
    recs = [
        {
            "period": r.get("period"),
            "strong_buy": r.get("strongBuy"),
            "buy": r.get("buy"),
            "hold": r.get("hold"),
            "sell": r.get("sell"),
            "strong_sell": r.get("strongSell"),
        }
        for r in raw[:12]
    ]
    return ApiResponse(data=recs)


@router.get("/stock/{symbol}/upgrades", response_model=ApiResponse)
@safe
async def stock_upgrades(symbol: str = Path(..., min_length=1, max_length=20)):
    """Get recent analyst upgrades and downgrades (via yfinance)."""
    symbol = validate_symbol(symbol)
    data = await openbb_service.get_upgrades_downgrades(symbol)
    return ApiResponse(data=data)


@router.get("/stock/{symbol}/social-sentiment", response_model=ApiResponse)
@safe
async def stock_social_sentiment(
    symbol: str = Path(..., min_length=1, max_length=20),
):
    """Social media sentiment from Reddit and Twitter (Finnhub)."""
    symbol = validate_symbol(symbol)
    data = await finnhub_service.get_social_sentiment(symbol)
    return ApiResponse(data=data)


@router.get("/stock/{symbol}/reddit-sentiment", response_model=ApiResponse)
@safe
async def stock_reddit_sentiment(
    symbol: str = Path(..., min_length=1, max_length=20),
):
    """Reddit sentiment: mentions, upvotes, rank on WSB/stocks/investing (free, no key)."""
    symbol = validate_symbol(symbol)
    data = await finnhub_service.get_reddit_sentiment(symbol)
    return ApiResponse(data=data)


@router.get("/discover/reddit-trending", response_model=ApiResponse)
@safe
async def reddit_trending():
    """Top 25 trending stocks on Reddit (WSB, r/stocks, r/investing). Free, no key."""
    data = await finnhub_service.get_reddit_trending()
    return ApiResponse(data=data)