"""Finds manufactured goods currently selling below their implied production cost (output price
minus input cost, from live daily VWAP candles) - the mirror image of saturation.py's "is this
worth building" question: here we're asking "is this worth buying and holding for a price-
reversion bounce."

Backtested 2026-10-09 across 326 output tickers / 112 distinct tickers that ever showed the
signal, 287 independent dip-onset episodes (deduplicated so a persisting dip only counts once,
at onset, not once per month it continues): at >=10 units/day traded volume, +3 months forward
price return was mean +21.3%, median +8.9%, 73.2% of episodes positive (see empire/docs or the
session that built this for the raw numbers). That is ~0.10-0.24%/day - MUCH lower than this
project's production-building marginal ROI (14-29%/day seen this session). Only worth deploying
*excess* capital into once better-yielding builds are exhausted, not as a primary strategy, and
it's a gross-return backtest with no spread/freight/storage-fee/capital-lockup cost subtracted.
"""
from . import fio
from .history import daily_prices


def manufacturing_candidates():
    """One representative recipe per output ticker, excluding pure-extraction buildings (no
    material inputs to compute an implied cost from)."""
    seen = {}
    for r in fio.recipes():
        if r["BuildingTicker"] in ("EXT", "COL", "RIG") or not r["Inputs"]:
            continue
        for o in r["Outputs"]:
            seen.setdefault(o["Ticker"], r)
    return seen


def current_margin(ticker: str, recipe: dict, cx: str, window_days: int = 35, recent_days: int = 14):
    """Mean margin%/price/volume over the most recent `recent_days` of overlapping price history
    for this recipe's inputs+output, within a `window_days` lookback. None if not enough history."""
    ins = {i["Ticker"]: i["Amount"] for i in recipe["Inputs"]}
    outs = {o["Ticker"]: o["Amount"] for o in recipe["Outputs"]}
    series = {m: daily_prices(m, cx) for m in list(ins) + list(outs)}
    if any(not s for s in series.values()):
        return None
    days = sorted(set().union(*[set(s) for s in series.values()]))[-window_days:]
    last = {}
    rows = []
    for d in days:
        for m, s in series.items():
            if d in s:
                last[m] = s[d][0]
        if len(last) == len(series):
            rev = sum(last[m] * n for m, n in outs.items())
            cost = sum(last[m] * n for m, n in ins.items())
            rows.append((rev, cost, last[ticker], series[ticker].get(d, (0, 0))[1]))
    recent = rows[-recent_days:]
    if len(recent) < recent_days // 2:
        return None
    rev_m = sum(x[0] for x in recent) / len(recent)
    cost_m = sum(x[1] for x in recent) / len(recent)
    px_m = sum(x[2] for x in recent) / len(recent)
    vol_m = sum(x[3] for x in recent) / len(recent)
    margin_pct = 100 * (rev_m - cost_m) / cost_m if cost_m else 0.0
    return {"margin_pct": margin_pct, "price": px_m, "volume": vol_m, "building": recipe["BuildingTicker"]}


def scan_current(cx: str, min_vol: float = 5.0):
    """Every manufactured good currently priced below its implied production cost, with enough
    volume to actually trade, sorted most-undervalued first."""
    out = []
    for t, r in manufacturing_candidates().items():
        m = current_margin(t, r, cx)
        if m and m["margin_pct"] < 0 and m["volume"] >= min_vol:
            out.append({"ticker": t, **m})
    out.sort(key=lambda x: x["margin_pct"])
    return out