#!/usr/bin/env python3
"""Where to post a BUY order (a resting bid) when there's a hard DEADLINE - not "is patience
worth it forever" (that's tools/sell.py's problem, where foregone margin is the only cost of
waiting). Here, a bid that never fills means a production-line STOCKOUT (check tools/runway.py
for your real deadline), so the right question is: what's the cheapest price that's still likely
to fill BY that deadline, with paying the ask as the fallback if it doesn't?

  puga buyat PG 150 --deadline 3        # 3 days of runway left on PG

For each candidate price between the current ask and best bid, uses puga.history.empirical_touch
with a fixed `horizon` (the deadline) to get the REAL empirical P(price actually falls to this
level within that many days) - not "ever", which is the wrong question under a deadline. Expected
cost = price * P(fill by deadline) + current_ask * (1 - P(fill by deadline)) - the fallback term
assumes paying the ask at the deadline if the bid never filled; the ask itself could move by then,
so this is an approximation, not a guarantee. Recommends the price minimizing expected cost, but
shows the whole curve - pick a more conservative (higher-P) price than the cost-minimizer if a
stockout would be expensive in ways this doesn't price in (e.g. the whole base's workforce idling).
"""
import argparse, sys
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from puga import config, fio, market
from puga.history import daily_prices, full_series, empirical_touch
import math


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("ticker")
    ap.add_argument("qty", type=float)
    ap.add_argument("--deadline", type=float, required=True, help="days until this input runs out (tools/runway.py) - the hard constraint, not a soft discount rate")
    ap.add_argument("--cx", default=config.DEFAULT_CX)
    a = ap.parse_args()
    t = a.ticker.upper()
    ob = fio.order_book(t, a.cx)
    bids = sorted(((o["ItemCost"], o["ItemCount"] or 0) for o in ob["BuyingOrders"] if o.get("ItemCost")), reverse=True)
    best_bid = bids[0][0] if bids else None
    best_ask = ob.get("Ask")

    hit = market.walk(t, a.cx, a.qty, "buy")
    ask_cost = hit["total"] if not hit["short"] else None
    print(f"{t}.{a.cx}  best bid {best_bid}  ask {best_ask}  vwap7 {ob.get('PriceAverage')}  deadline {a.deadline:g}d")
    if ask_cost is not None:
        print(f"instant (pay the asks now): {ask_cost:,.0f}  (avg {hit['avg']:.0f}) - the fallback if nothing fills in time")
    else:
        print("WARNING: book too thin to fill this quantity at any price right now - the 'pay the ask' fallback isn't actually available at this size")

    dp = daily_prices(t, a.cx)
    s0 = sorted(dp.items())[-1][1][0] if dp else (best_bid or ob.get("Bid") or 0)
    series = full_series(t, a.cx)
    if len(series) < 10:
        print("\nnot enough price history to estimate fill probability - fall back to paying the ask")
        return

    candidates = sorted({p for p, _ in bids if p <= s0} | {round(s0 * m, 0) for m in (0.99, 0.97, 0.95, 0.9, 0.85, 0.8, 0.7, 0.6)})
    candidates = [p for p in candidates if p > 0 and (best_bid is None or p >= best_bid * 0.5)]

    print(f"\nlast traded {s0:.0f}, {len(series)}d history - P(fill) is specifically BY the {a.deadline:g}d deadline, not ever:")
    print(f"  {'price':>10} {'P(fill by deadline)':>20} {'cost if filled':>15} {'expected cost':>14}")
    rows = []
    for p in candidates:
        y = math.log(p / s0) if s0 else 0.0
        if y >= 0:
            p_fill = 1.0
        else:
            p_fill = empirical_touch(series, y, 0.0, horizon=a.deadline)["touched_frac"]
        cost_if_filled = p * a.qty
        fallback = ask_cost if ask_cost is not None else p * a.qty * 1.5   # crude: no real fallback price known
        expected = p_fill * cost_if_filled + (1 - p_fill) * fallback
        rows.append((p, p_fill, cost_if_filled, expected))

    best = min(rows, key=lambda r: r[3]) if rows else None
    for p, p_fill, cost_if_filled, expected in rows:
        flag = "  <- min expected cost" if (p, p_fill, cost_if_filled, expected) == best else ""
        print(f"  {p:>10.0f} {100*p_fill:>19.0f}% {cost_if_filled:>15,.0f} {expected:>14,.0f}{flag}")

    print("\nNOTE: 'expected cost' assumes paying today's ask as the fallback if the bid never fills by the "
          "deadline - the real ask could move by then, this is an estimate. Pick a price with HIGHER P(fill) "
          "than the cost-minimizer if a stockout costs more than this model sees (idle workforce, missed orders).")


if __name__ == "__main__":
    main()