signal-platform/app/services/watchlist_service.py

"""Watchlist service.

A purely user-curated watchlist: the user adds and removes tickers, capped at
WATCHLIST_MAX entries. Each entry is enriched on read with its composite score,
best trade setup, active S/R levels, and latest price + day-over-day move.
"""

from __future__ import annotations

import logging
from datetime import datetime, timezone

from sqlalchemy import func, select
from sqlalchemy.ext.asyncio import AsyncSession

from app.exceptions import DuplicateError, NotFoundError, ValidationError
from app.models.ohlcv import OHLCVRecord
from app.models.score import CompositeScore, DimensionScore
from app.models.sr_level import SRLevel
from app.models.ticker import Ticker
from app.models.trade_setup import TradeSetup
from app.models.watchlist import WatchlistEntry

logger = logging.getLogger(__name__)

WATCHLIST_MAX = 20


async def _get_ticker(db: AsyncSession, symbol: str) -> Ticker:
    normalised = symbol.strip().upper()
    result = await db.execute(select(Ticker).where(Ticker.symbol == normalised))
    ticker = result.scalar_one_or_none()
    if ticker is None:
        raise NotFoundError(f"Ticker not found: {normalised}")
    return ticker


async def add_manual_entry(
    db: AsyncSession,
    user_id: int,
    symbol: str,
) -> WatchlistEntry:
    """Add a ticker to the user's watchlist.

    Raises DuplicateError if already on the watchlist.
    Raises ValidationError if the watchlist cap is reached.
    """
    ticker = await _get_ticker(db, symbol)

    existing = await db.execute(
        select(WatchlistEntry).where(
            WatchlistEntry.user_id == user_id,
            WatchlistEntry.ticker_id == ticker.id,
        )
    )
    if existing.scalar_one_or_none() is not None:
        raise DuplicateError(f"Ticker already on watchlist: {ticker.symbol}")

    count_result = await db.execute(
        select(func.count()).select_from(WatchlistEntry).where(
            WatchlistEntry.user_id == user_id,
        )
    )
    total = count_result.scalar() or 0
    if total >= WATCHLIST_MAX:
        raise ValidationError(
            f"Watchlist cap reached ({WATCHLIST_MAX}). "
            "Remove an entry before adding a new one."
        )

    entry = WatchlistEntry(
        user_id=user_id,
        ticker_id=ticker.id,
        entry_type="manual",
        added_at=datetime.now(timezone.utc),
    )
    db.add(entry)
    await db.commit()
    await db.refresh(entry)
    return entry


async def remove_entry(
    db: AsyncSession,
    user_id: int,
    symbol: str,
) -> None:
    """Remove a ticker from the user's watchlist."""
    ticker = await _get_ticker(db, symbol)

    result = await db.execute(
        select(WatchlistEntry).where(
            WatchlistEntry.user_id == user_id,
            WatchlistEntry.ticker_id == ticker.id,
        )
    )
    entry = result.scalar_one_or_none()
    if entry is None:
        raise NotFoundError(f"Ticker not on watchlist: {ticker.symbol}")

    await db.delete(entry)
    await db.commit()


async def _enrich_entry(
    db: AsyncSession,
    entry: WatchlistEntry,
    symbol: str,
) -> dict:
    """Build enriched watchlist entry dict with scores, R:R, SR levels, price."""
    ticker_id = entry.ticker_id

    # Composite score
    comp_result = await db.execute(
        select(CompositeScore).where(CompositeScore.ticker_id == ticker_id)
    )
    comp = comp_result.scalar_one_or_none()

    # Dimension scores
    dim_result = await db.execute(
        select(DimensionScore).where(DimensionScore.ticker_id == ticker_id)
    )
    dims = [
        {"dimension": ds.dimension, "score": ds.score}
        for ds in dim_result.scalars().all()
    ]

    # Best trade setup (highest R:R) for this ticker
    setup_result = await db.execute(
        select(TradeSetup)
        .where(TradeSetup.ticker_id == ticker_id)
        .order_by(TradeSetup.rr_ratio.desc())
        .limit(1)
    )
    setup = setup_result.scalar_one_or_none()

    # Active SR levels
    sr_result = await db.execute(
        select(SRLevel)
        .where(SRLevel.ticker_id == ticker_id)
        .order_by(SRLevel.strength.desc())
    )
    sr_levels = [
        {
            "price_level": lv.price_level,
            "type": lv.type,
            "strength": lv.strength,
        }
        for lv in sr_result.scalars().all()
    ]

    # Latest two daily closes → current price + day-over-day move
    price_result = await db.execute(
        select(OHLCVRecord.close, OHLCVRecord.date)
        .where(OHLCVRecord.ticker_id == ticker_id)
        .order_by(OHLCVRecord.date.desc())
        .limit(2)
    )
    bars = price_result.all()
    last_close = bars[0].close if bars else None
    prev_close = bars[1].close if len(bars) > 1 else None
    change_pct = (
        (last_close - prev_close) / prev_close * 100
        if last_close is not None and prev_close
        else None
    )
    price_date = bars[0].date if bars else None

    return {
        "symbol": symbol,
        "entry_type": entry.entry_type,
        "composite_score": comp.score if comp else None,
        "dimensions": dims,
        "rr_ratio": setup.rr_ratio if setup else None,
        "rr_direction": setup.direction if setup else None,
        "sr_levels": sr_levels,
        "last_close": last_close,
        "change_pct": change_pct,
        "price_date": price_date,
        "added_at": entry.added_at,
    }


async def get_watchlist(
    db: AsyncSession,
    user_id: int,
    sort_by: str = "composite",
) -> list[dict]:
    """Get the user's watchlist with enriched data.

    sort_by: "composite", "rr", "change", or a dimension name
    (e.g. "technical", "sr_quality", "sentiment", "fundamental", "momentum").
    """
    stmt = (
        select(WatchlistEntry, Ticker.symbol)
        .join(Ticker, WatchlistEntry.ticker_id == Ticker.id)
        .where(WatchlistEntry.user_id == user_id)
    )
    result = await db.execute(stmt)
    rows = result.all()

    entries: list[dict] = []
    for entry, symbol in rows:
        enriched = await _enrich_entry(db, entry, symbol)
        entries.append(enriched)

    # Sort
    if sort_by == "composite":
        entries.sort(
            key=lambda e: e["composite_score"] if e["composite_score"] is not None else -1,
            reverse=True,
        )
    elif sort_by == "rr":
        entries.sort(
            key=lambda e: e["rr_ratio"] if e["rr_ratio"] is not None else -1,
            reverse=True,
        )
    elif sort_by == "change":
        entries.sort(
            key=lambda e: e["change_pct"] if e["change_pct"] is not None else float("-inf"),
            reverse=True,
        )
    else:
        # Sort by a specific dimension score
        def _dim_sort_key(e: dict) -> float:
            for d in e["dimensions"]:
                if d["dimension"] == sort_by:
                    return d["score"]
            return -1.0

        entries.sort(key=_dim_sort_key, reverse=True)

    return entries