signal-platform/app/services/breadth_service.py

"""Market-breadth state and early-warning indicators.

Breadth is a genuinely *leading* construct: a few mega-caps can keep an index
rising while participation narrows underneath — the classic pre-top divergence.
V2 measures an explicit, frozen basket rather than every ticker currently stored
in the database.  That keeps the live series reproducible when the wider product
universe changes.

Two layers:
  - breadth = % of the universe trading above its own 200-DMA (0-100).
  - divergence = an early-warning score (0-100, high = fragile): the benchmark
    price holding/rising *while* breadth falls. Absolute low breadth stays in the
    State index so it is not counted twice.

The live monitor uses the breadth level in State and the pure divergence in
Warning. The event study evaluates the latter chronologically.
"""

from __future__ import annotations

import logging
from datetime import date

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

from app.models.ticker import Ticker
from app.services.price_service import query_ohlcv

logger = logging.getLogger(__name__)

Series = list[tuple[date, float]]


def _breadth_with_counts(
    closes_by_symbol: dict[str, Series], window: int = 200, min_tickers: int = 20
) -> tuple[dict[date, float], dict[date, int]]:
    """Pure core: % of symbols above their own rolling SMA(window), per date.

    Each symbol's SMA is computed once with a sliding sum (O(bars)); dates with
    fewer than ``min_tickers`` qualifying names are dropped (too thin to trust).
    """
    counts: dict[date, list[int]] = {}  # date -> [above, total]
    for series in closes_by_symbol.values():
        ordered = sorted(series, key=lambda x: x[0])
        dates = [d for d, _ in ordered]
        closes = [c for _, c in ordered]
        if len(closes) < window:
            continue
        running = sum(closes[:window])
        for i in range(window - 1, len(closes)):
            if i >= window:
                running += closes[i] - closes[i - window]
            sma = running / window
            entry = counts.setdefault(dates[i], [0, 0])
            entry[1] += 1
            if closes[i] > sma:
                entry[0] += 1
    values = {
        d: round(above / total * 100.0, 2)
        for d, (above, total) in counts.items()
        if total >= min_tickers
    }
    eligible = {d: total for d, (_, total) in counts.items() if total >= min_tickers}
    return values, eligible


def _breadth_from_closes(
    closes_by_symbol: dict[str, Series], window: int = 200, min_tickers: int = 20
) -> dict[date, float]:
    """Compatibility wrapper returning only the breadth percentage series."""
    return _breadth_with_counts(closes_by_symbol, window, min_tickers)[0]


# Breadth deterioration counts fully when price masks it (true divergence, the
# dangerous pre-top case) and at CONFIRMED_FLOOR when price falls with it.
# v2 used a hard ``price_ret >= 0`` cliff, which zeroed the sensor during every
# decline -- so on 2026-07-24, with the basket shedding 10 percentage points
# above their 200-DMA in 20 sessions, Warning read exactly 0.  Breadth *level*
# lives in State but breadth *velocity* appears nowhere else, so partial credit
# here is not double counting.
DIVERGENCE_CONFIRMED_FLOOR = 0.35
DIVERGENCE_TAPER_PCT = 3.0


def compute_divergence_series(
    breadth: dict[date, float], benchmark_closes: Series, lookback: int = 20
) -> dict[date, float]:
    """Early-warning score (0-100, high = fragile) per date.

    A 20 percentage-point breadth deterioration maps to 100 when the benchmark
    is flat or rising, tapering to ``DIVERGENCE_CONFIRMED_FLOOR`` of that once
    the benchmark is down ``DIVERGENCE_TAPER_PCT`` or more over the window.
    """
    bench = {d: c for d, c in benchmark_closes}
    common = sorted(d for d in bench if d in breadth)
    out: dict[date, float] = {}
    for i in range(lookback, len(common)):
        d, d0 = common[i], common[i - lookback]
        price_past = bench[d0]
        if price_past <= 0:
            continue
        price_ret = (bench[d] / price_past - 1.0) * 100.0   # %
        breadth_chg = breadth[d] - breadth[d0]              # percentage points
        deterioration = max(0.0, -breadth_chg)
        taper = max(0.0, min(1.0, (price_ret + DIVERGENCE_TAPER_PCT) / DIVERGENCE_TAPER_PCT))
        gate = DIVERGENCE_CONFIRMED_FLOOR + (1.0 - DIVERGENCE_CONFIRMED_FLOOR) * taper
        out[d] = max(0.0, min(100.0, round(deterioration * 5.0 * gate, 2)))
    return out


async def _load_universe_closes(
    db: AsyncSession, symbols: list[str] | None = None
) -> dict[str, Series]:
    stmt = select(Ticker).order_by(Ticker.symbol)
    if symbols is not None:
        stmt = stmt.where(Ticker.symbol.in_(symbols))
    result = await db.execute(stmt)
    closes_by_symbol: dict[str, Series] = {}
    for ticker in result.scalars().all():
        try:
            records = await query_ohlcv(db, ticker.symbol)
        except Exception:
            logger.exception("Breadth: OHLCV load failed for %s", ticker.symbol)
            continue
        if records:
            closes_by_symbol[ticker.symbol] = [(r.date, float(r.close)) for r in records]
    return closes_by_symbol


async def compute_breadth_series(
    db: AsyncSession,
    window: int = 200,
    min_tickers: int = 20,
    symbols: list[str] | None = None,
) -> dict[date, float]:
    """Historical breadth series across an explicit basket (or all stored names)."""
    closes_by_symbol = await _load_universe_closes(db, symbols)
    return _breadth_from_closes(closes_by_symbol, window, min_tickers)


async def compute_breadth_details(
    db: AsyncSession,
    symbols: list[str],
    window: int = 200,
    min_tickers: int = 20,
) -> tuple[dict[date, float], dict[date, int]]:
    """Breadth values plus the qualifying-member count for snapshot metadata."""
    closes_by_symbol = await _load_universe_closes(db, symbols)
    return _breadth_with_counts(closes_by_symbol, window, min_tickers)