scan.py: real bid-depth liquidity flag, real-variance flow bound, profit/area

- max_units_at_impact(): walks the LIVE bid book for a genuine, ticker-
  specific instant-exit liquidity check, replacing a flat 25%-of-flow
  guess (a price-vs-volume elasticity regression was tried first and
  abandoned - confounded by demand-driven volume on real data). Surfaced
  as an [exit-thin] WARNING flag, not a hard cap - a single order-book
  snapshot is less reliable than the 30-day flow aggregate that still
  drives n_lim/filtering (using it as a hard cap once silently zeroed
  out a real recipe from one noisy snapshot).
- tref_precise(): real variance of daily traded volume instead of a
  Poisson unit-independence bound, wired into stage 2 only (bounded
  candidate set, cached). Also fixed: was averaging only over trading
  days, overstating flow ~3x for sparse tickers.
- n_out(): dropped the demand-coverage term, which penalized thin
  resting-bid books even though production sells via patient asks taken
  by walk-up buyers with no resting bid at all.
- --sort area: profit/day per tile of TOTAL footprint, now including the
  single-tier hab area a building's workforce drags along (was building
  area only).
- --deprec/--reclaim-frac: amortizes unreclaimed capex, not full capex -
  real FIO reclaim data showed 50-86% returned even at near-100%
  condition, contradicting a 100%->0 decay-curve assumption.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

puga/saturation.py

@@ -1,34 +1,96 @@
 """Saturation model v1 (docs/saturation-design.md, after Opus review 2026-09-18). Pure functions.
-Idea: N buildings is limited by (a) share of the market's steady traded flow, (b) the queue of existing
+Idea: N buildings is limited by (a) share of the market's steady traded flow and (b) the queue of
-sell orders (supply days) and (c) whether buy-side stock covers it. Price is a haircut model, not a book walk."""
+existing sell orders (supply days). Price is a haircut model, not a book walk.
-import math
+
+is_thin() checks demand depth as an independent red flag (a genuinely dead bid book is worth
+knowing about); n_out() does not multiply by it (see n_out's own docstring for why)."""
+import math, statistics
+from .history import trading_days
 
 S_SHARE = 0.25     # max share of traded flow one producer takes
 T_Q = 7            # days of flow the standing sell queue is compared against
-T_D = 7            # days of flow standing demand should cover
+T_D = 7            # days of flow standing demand should cover (used by is_thin only)
 T_W = 3            # patient-selling window (days)
 
 
 def tref(traded7: float, traded30: float) -> float:
-    """Conservative daily flow: min(7d, 30d); with a Poisson lower bound when 30d volume is small."""
+    """Conservative daily flow: min(7d, 30d); with a Poisson lower bound when 30d volume is small.
+    CAVEAT: the Poisson term treats each traded UNIT as an independent event, which overstates
+    confidence for lumpy real trades (one 500-unit trade is one event, not 500) - see
+    `tref_precise()` for the real-variance version, used once a candidate reaches stage 2 (this
+    cheap version stays for stage-1 screening across every recipe, where fetching daily history
+    for every ticker would be too expensive)."""
     t = min(traded7, traded30)
     if 0 < traded30 < 20:
         t = min(t, max(0.0, traded30 * (1 - 1.96 / math.sqrt(30 * traded30))))
     return max(0.0, t)
 
 
+def tref_precise(tk: str, cx: str, traded7: float, traded30: float, days: int = 30) -> float:
+    """Like tref(), but the lower bound comes from the REAL variance of daily traded volume
+    (`puga.history.trading_days`) instead of a Poisson unit-independence assumption - one 500-unit
+    trade is one data point here, not 500 independent ones. Falls back to tref() if there's too
+    little real history to estimate variance from (<=2 trading days).
+
+    `trading_days()` only returns days with a trade - zero-volume days are absent, not zero - so
+    the volume list is padded with explicit zeros for the missing calendar days before computing
+    mean/stdev, and `days` (not the trading-day count) is used as n. Averaging only over the rows
+    that have a trade would compute the mean volume ON TRADING DAYS, overstating real
+    per-CALENDAR-day flow by 1/(fraction of days traded) - worst precisely for the sparse tickers
+    this function exists to protect."""
+    td = trading_days(tk, cx, days)
+    if len(td) <= 2:
+        return tref(traded7, traded30)
+    vols = [v for _, v in td] + [0.0] * (days - len(td))
+    mean = statistics.mean(vols)
+    sd = statistics.stdev(vols)
+    bound = max(0.0, mean - 1.96 * sd / math.sqrt(days))
+    return max(0.0, min(tref(traded7, traded30), bound))
+
+
+def max_units_at_impact(bids: list[tuple[float, float]], ref_price: float, max_impact_pct: float = 10.0) -> float:
+    """Real, ticker-specific price-impact cap, replacing the flat S_SHARE guess for THIS check:
+    how many units could be sold into the LIVE bid book before the price paid degrades more than
+    `max_impact_pct` below `ref_price`. Walks bids highest-first (puga.market.walk's own
+    mechanics) and stops at the first tier priced below the threshold.
+
+    A day-over-day price-vs-volume regression does not give a usable general elasticity estimate
+    here: volume and price co-move mostly from DEMAND bursts (more buyers -> more volume AND
+    steady/higher price), which swamps and often reverses the sign of the SUPPLY-side effect
+    actually wanted (does MY added output push price down). Walking the real book sidesteps that
+    confounding, but only captures the static liquidity snapshot, not how the market reprices
+    over weeks of sustained extra output (nothing in available data answers that cleanly). See
+    docs/decisions.md."""
+    if not ref_price or ref_price <= 0:
+        return 0.0
+    threshold = ref_price * (1 - max_impact_pct / 100)
+    cum = 0.0
+    for price, qty in sorted(bids, key=lambda x: -x[0]):
+        if price < threshold:
+            break
+        cum += qty
+    return cum
+
+
 def is_thin(tr: float, demand: float, q_out: float) -> bool:
     return tr < 3 * q_out or demand < 7 * q_out
 
 
-def n_out(tr: float, supply: float, demand: float, q_out: float) -> float:
+def n_out(tr: float, supply: float, q_out: float) -> float:
-    """Buildings the output market absorbs: flow share x queue penalty x demand coverage. 0 if no flow."""
+    """Buildings the output market absorbs: flow share x queue penalty. 0 if no flow.
+
+    Deliberately has NO resting-bid-depth term: production sells via patient ASKS (`p_patient()`)
+    that get taken by buyers crossing the spread, including walk-up buyers with no resting bid at
+    all - a thin resting bid book doesn't mean they won't show up, since `tr` (real traded flow,
+    already in `base`) is the actual evidence they do. A market with healthy ask-side flow and
+    thin standing bids is normal (most buyers don't leave a bid sitting around), not evidence of
+    absent demand. Real resting-bid demand is still checked independently by `is_thin()` as a red
+    flag, not folded into this score."""
     if tr <= 0 or q_out <= 0:
         return 0.0
     base = S_SHARE * tr / q_out
     queue = min(1.0, T_Q * tr / supply) if supply > 0 else 1.0
-    cover = min(1.0, demand / (T_D * tr))
+    return base * queue
-    return base * queue * cover
 
 
 def p_patient(asks: list[tuple[float, float]], tr: float, produced_per_day: float, bid: float, vwap7: float | None,

tests/test_saturation.py

@@ -1,3 +1,4 @@
+import datetime
 import math
 from puga import saturation as s, market, fio
 
@@ -8,12 +9,22 @@ def test_tref_uses_min_and_poisson_floor():
     assert s.tref(0, 0) == 0
 
 
-def test_n_out_penalises_queue_and_thin_demand():
+def test_tref_precise_pads_missing_calendar_days_not_just_trading_days(monkeypatch):
-    free = s.n_out(100, 0, 10_000, 5)             # no queue, ample demand
+    # 10 trading days out of 30, each with volume 30 -> real per-calendar-day mean is 10, not 30.
+    # The original bug averaged only over the 10 trading-day rows (mean 30), overstating the
+    # bound 3x for exactly the sparse tickers it's meant to protect.
+    base = datetime.date(2026, 1, 1)
+    fake_td = [(base + datetime.timedelta(days=i * 3), 30.0) for i in range(10)]
+    monkeypatch.setattr(s, "trading_days", lambda tk, cx, days: fake_td)
+    bound = s.tref_precise("FAKE", "AI1", traded7=30, traded30=30, days=30)
+    assert bound < 15.0   # well below the naive trading-day mean of 30, reflecting real sparsity
+
+
+def test_n_out_penalises_queue():
+    free = s.n_out(100, 0, 5)                     # no queue
     assert math.isclose(free, 0.25 * 100 / 5)
-    assert s.n_out(100, 7000, 10_000, 5) < free   # 70 days of queue cuts it
+    assert s.n_out(100, 7000, 5) < free           # 70 days of queue cuts it
-    assert s.n_out(100, 0, 100, 5) < free         # demand only 1 day of flow cuts it
+    assert s.n_out(0, 0, 5) == 0
-    assert s.n_out(0, 0, 100, 5) == 0
 
 
 def test_thin_flag():
@@ -57,3 +68,11 @@ def test_effective_demand_excludes_stale_bids_below_the_band():
     bids = [(4550, 8), (4200, 250), (2810, 8), (334, 19)]
     assert s.effective_demand(bids, band=(663, 16575)) == 8 + 250 + 8   # the 334 bid is stale, excluded
     assert s.effective_demand(bids, band=None) == 8 + 250 + 8 + 19
+
+
+def test_max_units_at_impact_stops_at_the_price_threshold():
+    bids = [(100, 10), (95, 20), (85, 1000)]            # ref 100, 10% floor = 90
+    assert s.max_units_at_impact(bids, 100, max_impact_pct=10.0) == 30   # 100 and 95 tiers only
+    assert s.max_units_at_impact(bids, 100, max_impact_pct=20.0) == 1030  # all three tiers now qualify
+    assert s.max_units_at_impact([], 100) == 0.0
+    assert s.max_units_at_impact(bids, 0) == 0.0

tools/scan.py

@@ -33,7 +33,9 @@ def main():
     ap.add_argument("--cargo", type=float, default=500.0, help="t and m3 per trip (starter ship)")
     ap.add_argument("--no-hq", action="store_true", help="ignore HQ from state (new base without the HQ)")
     ap.add_argument("--permits-used", type=int, help="override permits used (affects faction bonus multiplier), e.g. 2 for a second base")
-    ap.add_argument("--deprec", type=float, default=0, help="demolish-later mode: building value decays linearly to 0 over this many days (game: ~60); subtracts capex/deprec per day")
+    ap.add_argument("--deprec", type=float, default=0, help="demolish-later mode: assumed holding days before demolition; amortizes the UNRECLAIMED portion of capex (capex x (1 - --reclaim-frac)) over that period, NOT full capex - real FIO reclaimables data (this account's own buildings, 2026-10-10) showed ~50-86%% returned even at near-100%% condition, contradicting a 100%%->0 time-decay story. See docs/decisions.md.")
+    ap.add_argument("--reclaim-frac", type=float, default=0.65, help="fraction of capex recovered on demolition - real observed range 50-86%% across 7 owned building types (CM a low outlier at ~7%%, excluded); single base/age, small sample, treat as a rough placeholder not a calibrated curve")
+    ap.add_argument("--max-impact", type=float, default=10.0, help="max %% the realized price may degrade below vwap7/bid when walking the LIVE bid book for your planned daily output (puga.saturation.max_units_at_impact) - feeds the [exit-thin] warning flag: can you instantly DUMP a day of output into resting bids if you needed cash now, not whether your patient asks clear (that's the market/lim columns). See docs/decisions.md")
     ap.add_argument("--input", help="only recipes consuming this ticker as an input, e.g. --input AL for recipes built on top of your own AL chain")
     ap.add_argument("--planet", help="planet natural id: adds extraction (EXT/COL/RIG) from its resources, uses its fertility and active COGC; new base (not in state) => no HQ, permits+1")
     ap.add_argument("--own", type=int, default=1, help="how many buildings WE would run; yield/payback is measured at this size (default 1). --min-n is only a market-size filter")
@@ -45,7 +47,7 @@ def main():
     ap.add_argument("--max-n", type=int, default=50, help="cap N* (capital/attention limit)")
     ap.add_argument("--budget", type=float, help="capital limit in AIC: caps N and drops recipes whose single-building capex exceeds it")
     ap.add_argument("--mcg", type=float, default=4.0, help="MCG per area (4 on normal planets)")
-    ap.add_argument("--sort", default="roi", choices=["roi", "total", "roi1"])
+    ap.add_argument("--sort", default="roi", choices=["roi", "total", "roi1", "area"])
     ap.add_argument("--top", type=int, default=30)
     ap.add_argument("--k", type=int, default=80, help="candidates refined with order books")
     ap.add_argument("--show-thin", action="store_true")
@@ -83,10 +85,11 @@ def main():
             return None
         return sum(n * p for n, p in parts) + a.mcg * b["AreaCost"] * mcg
 
-    hab_head = {}
+    hab_head, hab_area = {}, {}
     for tier, hab in (("pioneer", "HB1"), ("settler", "HB2"), ("technician", "HB3"), ("engineer", "HB4"), ("scientist", "HB5")):
         c = bcost(hab)
         hab_head[tier] = None if c is None else c / econ.HAB_CAP[hab][tier]
+        hab_area[tier] = econ.HAB_AREA[hab] / econ.HAB_CAP[hab][tier]
 
     def wage(tier):  # per worker per day, both luxuries supplied, at ask
         tot = 0.0
@@ -156,7 +159,7 @@ def main():
                     mm[t] = x.mm_buy          # market maker floor: unlimited depth at mm_buy
                     continue
                 thin |= sat.is_thin(tr, x.demand, qo)
-                n = sat.n_out(tr, 0, x.demand, qo)      # stage 1: flow + demand only; queue penalty applied in stage 2 with the book
+                n = sat.n_out(tr, 0, qo)      # stage 1: flow only; queue penalty applied in stage 2 with the book
                 if n < n_lim:
                     n_lim, lim = n, t
             if n_lim == float("inf"):
@@ -170,7 +173,8 @@ def main():
             rough = sum(io["out"][t] * price0[t] for t in io["out"]) - sum(io["in"][t] * ask(t) for t in io["in"]) - wcost
             staff = "".join(TIER_CODE[t] for t in keep) + ("" if len(keep) == len(need) else "-")
             cands.append(dict(rec=rec, b=b, io=io, eff=eff, capex=capex, wcost=wcost, n_lim=n_lim, lim=lim, thin=thin, mm=mm,
-                              rough_roi=100 * rough / capex, top=top, staff=staff, ins=ins, outs=outs))
+                              rough_roi=100 * rough / capex, top=top, staff=staff, ins=ins, outs=outs,
+                              hab_area_per_bld=sum(heads[t] * hab_area[t] for t in keep)))
 
     cands.sort(key=lambda c: c["rough_roi"], reverse=True)
     rows = []
@@ -184,6 +188,9 @@ def main():
     def asks_of(t):
         return sorted((o["ItemCost"], (o["ItemCount"] or 0)) for o in raw_book(t)["SellingOrders"])
 
+    def bids_of(t):
+        return [(o["ItemCost"], (o["ItemCount"] or 0)) for o in raw_book(t)["BuyingOrders"] if o.get("ItemCost")]
+
     def band_of(t):
         if t not in bandcache:
             ob = raw_book(t)
@@ -192,21 +199,29 @@ def main():
             bandcache[t] = (lo, hi)
         return bandcache[t]
 
-    def demand_of(t):
-        bids = ((o["ItemCost"], o["ItemCount"] or 0) for o in raw_book(t)["BuyingOrders"])
-        return sat.effective_demand(bids, band_of(t))
-
     for c in cands[:a.k]:
         io, mm = c["io"], c["mm"]
-        # stage 2: queue penalty from competing asks near market price only, and demand within the tradeable band
+        # stage 2: queue penalty from competing asks near market price, flow from REAL daily-volume
-        n2 = float("inf")
+        # variance (tref_precise) now that we're down to a bounded candidate set worth the extra fetch
+        n2, bid_thin = float("inf"), False
         for t, qo in io["out"].items():
             if t in mm:
                 continue
             x = Q(t)
             se = sat.effective_supply(asks_of(t), x.vwap7 or x.ask, band=band_of(t))
-            n2 = min(n2, sat.n_out(sat.tref(x.traded7, x.traded30), se, demand_of(t), qo))
+            n2 = min(n2, sat.n_out(sat.tref_precise(t, a.cx, x.traded7, x.traded30), se, qo))
+            # Bid-book depth is a single LIVE SNAPSHOT (orders get added/withdrawn constantly) -
+            # not reliable enough to use as a hard cap like the flow-based n_out above, which
+            # aggregates real traded volume over 30 days. Surfaced as a warning flag instead, and
+            # measures a DIFFERENT thing than market/lim: not "can my patient asks clear" (that's
+            # p_patient()/n_out, ask-side, the real production-selling mechanism), but "could I
+            # instantly dump a day of output into resting BIDS right now if I needed cash" - panic-
+            # exit liquidity, not production price impact. A "yes" here doesn't guarantee it holds
+            # up over time, and a "no" might just be an unlucky instant - re-check before acting.
+            depth_units = sat.max_units_at_impact(bids_of(t), x.vwap7 or x.bid, a.max_impact)
+            bid_thin |= depth_units < qo
         c["n_lim"] = float(a.max_n) if n2 == float("inf") else n2
+        c["bid_thin"] = bid_thin
         if c["n_lim"] < a.min_n and not a.show_thin:
             continue
 
@@ -217,7 +232,7 @@ def main():
                 if t in mm:
                     p = mm[t]
                 else:
-                    p = sat.p_patient(asks_of(t), sat.tref(x.traded7, x.traded30), N * qo, x.bid, x.vwap7, x.vwap30, x.ask, band_of(t)[1])
+                    p = sat.p_patient(asks_of(t), sat.tref_precise(t, a.cx, x.traded7, x.traded30), N * qo, x.bid, x.vwap7, x.vwap30, x.ask, band_of(t)[1])
                 rev += N * qo * p
             cost = 0.0
             for t, qi in io["in"].items():
@@ -232,7 +247,7 @@ def main():
                 c["tons"] = max(wi, wo)
                 frt = max(wi, wo, vi, vo) / a.cargo * a.trip_cost
             c["frt"] = frt
-            dep = N * c["capex"] / a.deprec if a.deprec else 0.0     # value lost per day if demolished after holding
+            dep = N * c["capex"] * (1 - a.reclaim_frac) / a.deprec if a.deprec else 0.0     # unreclaimed capex lost per day if demolished after holding
             return rev - cost - N * c["wcost"] - frt - dep
 
         if a.budget and c["capex"] > a.budget:
@@ -247,22 +262,28 @@ def main():
             netN = evaluate(n_star)
         if netN is None:
             continue
+        area = n_star * ((c["b"].get("AreaCost") or 0) + c.get("hab_area_per_bld", 0.0))
         rows.append(dict(roi1=100 * net1 / c["capex"], roiN=100 * netN / n_star / c["capex"], total=netN, n=n_star,
                          n_lim=c["n_lim"], lim=c["lim"], capex=c["capex"] * n_star, eff=c["eff"], bld=c["b"]["Ticker"],
-                         top=c["top"], staff=c["staff"], tons=c.get("tons", 0), frt=c.get("frt", 0), h=c["rec"]["TimeMs"] / 3.6e6, thin=c["thin"],
+                         top=c["top"], staff=c["staff"], tons=c.get("tons", 0), frt=c.get("frt", 0), h=c["rec"]["TimeMs"] / 3.6e6,
+                         thin=c["thin"], bid_thin=c.get("bid_thin", False),
+                         area=area, profit_area=netN / area if area else 0.0,
                          rec=" ".join(f"{v:g}{k}" for k, v in c["ins"].items()) + " -> " + " ".join(f"{v:g}{k}" for k, v in c["outs"].items())))
-    key = {"roi": "roiN", "roi1": "roi1", "total": "total"}[a.sort]
+    key = {"roi": "roiN", "roi1": "roi1", "total": "total", "area": "profit_area"}[a.sort]
     rows.sort(key=lambda r: r[key], reverse=True)
     if a.json:
         import json
         Path(a.json).write_text(json.dumps(rows))
     print(f"# {a.cx}  tier<={a.tier or 'any'}  cogc={a.cogc or '-'}  faction={faction or '-'} ({pu}/{pt} permits)  min-n>={a.min_n}  sorted by {a.sort}")
     print("# yld/d is profit/day / capex as a %, for OUR size (--own buildings, default 1) incl. our own price impact; this is NOT a compoundable rate (1/payback, see docs/decisions.md) - pback is the number to actually reason about. market = buildings the market could absorb (--min-n filters on it, nothing else). Net of freight. Patient asks near VWAP, inputs ask-walked; all estimates")
-    print(f"{'yld/d':>6} {'pback':>6} {'own':>3} {'market':>6} {'lim':4} {'profit/d':>11} {'capex':>9} {'eff':>4} {'bld':4} {'staff':5} {'t/d':>5} {'frt/d':>6} {'h':>5}  recipe")
+    print("# profit/area ranks by AIC/day per tile of TOTAL footprint - production building AreaCost PLUS the single-tier hab area its own workforce drags along (heads/100 x hab area, e.g. a 100-head pioneer building pulls a full HB1) - once a base's area cap binds (not cash), this is the real allocation criterion, not yld/d. --sort area to rank by it.")
+    print("# [exit-thin] = INSTANT-DUMP liquidity check, not production price impact: right now, the LIVE bid book can't absorb even 1 day of 1 building's output within --max-impact%% if you needed to sell immediately. A single snapshot (orders change constantly), NOT folded into market/lim (which measures patient ask-side selling via real 30d flow, the actual production mechanism). Re-check before acting on it.")
+    print(f"{'yld/d':>6} {'pback':>6} {'own':>3} {'market':>6} {'lim':4} {'profit/d':>11} {'capex':>9} {'area':>5} {'$/area':>7} {'eff':>4} {'bld':4} {'staff':5} {'t/d':>5} {'frt/d':>6} {'h':>5}  recipe")
     for r in rows[:a.top]:
         pback = f"{100/r['roiN']:6.1f}" if r['roiN'] > 0 else "   inf"
-        print(f"{r['roiN']:6.1f} {pback} {r['n']:3d} {r['n_lim']:6.1f} {r['lim']:4} {r['total']:11.0f} {r['capex']:9.0f} {r['eff']*100:4.0f} {r['bld']:4} {r['staff']:5} {r['tons']:5.0f} {r['frt']:6.0f} "
+        flags = ('  [thin]' if r['thin'] else '') + ('  [exit-thin]' if r['bid_thin'] else '')
-              f"{r['h']:5.1f}  {r['rec']}{'  [thin]' if r['thin'] else ''}")
+        print(f"{r['roiN']:6.1f} {pback} {r['n']:3d} {r['n_lim']:6.1f} {r['lim']:4} {r['total']:11.0f} {r['capex']:9.0f} {r['area']:5.0f} {r['profit_area']:7.1f} {r['eff']*100:4.0f} {r['bld']:4} {r['staff']:5} {r['tons']:5.0f} {r['frt']:6.0f} "
+              f"{r['h']:5.1f}  {r['rec']}{flags}")
 
 
 if __name__ == "__main__":