dodoxandClaude Sonnet 5
3f67cc7ed5
- 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>
79 lines
3.5 KiB
Python
79 lines
3.5 KiB
Python
import datetime
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import math
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from puga import saturation as s, market, fio
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def test_tref_uses_min_and_poisson_floor():
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assert s.tref(60, 40) == 40
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assert s.tref(15, 13) < 13 # low volume gets a lower bound
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assert s.tref(0, 0) == 0
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def test_tref_precise_pads_missing_calendar_days_not_just_trading_days(monkeypatch):
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# 10 trading days out of 30, each with volume 30 -> real per-calendar-day mean is 10, not 30.
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# The original bug averaged only over the 10 trading-day rows (mean 30), overstating the
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# bound 3x for exactly the sparse tickers it's meant to protect.
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base = datetime.date(2026, 1, 1)
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fake_td = [(base + datetime.timedelta(days=i * 3), 30.0) for i in range(10)]
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monkeypatch.setattr(s, "trading_days", lambda tk, cx, days: fake_td)
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bound = s.tref_precise("FAKE", "AI1", traded7=30, traded30=30, days=30)
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assert bound < 15.0 # well below the naive trading-day mean of 30, reflecting real sparsity
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def test_n_out_penalises_queue():
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free = s.n_out(100, 0, 5) # no queue
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assert math.isclose(free, 0.25 * 100 / 5)
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assert s.n_out(100, 7000, 5) < free # 70 days of queue cuts it
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assert s.n_out(0, 0, 5) == 0
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def test_thin_flag():
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assert s.is_thin(1.3, 100, 5) and not s.is_thin(60, 500, 5)
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def test_p_patient_clamps_and_falls_to_bid_when_oversupplied():
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asks = [(100, 50), (110, 50), (120, 50)]
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assert s.p_patient(asks, 10, 20, bid=90, vwap7=105, vwap30=105, ask=100) == 90 # we out-produce flow: sell at bid
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p = s.p_patient(asks, 30, 1, 90, 200, 200, 100) # ample flow: capped by ask level 100
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assert p == 100
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def test_walk_treats_mm_null_count_as_unlimited(monkeypatch):
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monkeypatch.setattr(fio, "order_book", lambda m, c, r=False: {
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"SellingOrders": [], "BuyingOrders": [{"ItemCount": 4, "ItemCost": 90}, {"ItemCount": None, "ItemCost": 80}]})
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r = market.walk("X", "AI1", 1000, "sell")
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assert r["filled"] == 1000 and not r["short"] and r["worst"] == 80
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def test_effective_supply_ignores_stale_far_asks():
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asks = [(100, 10), (110, 20), (150, 1500)]
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assert s.effective_supply(asks, 100) == 30 # 1500 units at 1.5x are not competition
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assert s.effective_supply(asks, None) == 1530
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def test_effective_supply_hard_filters_out_of_band_asks():
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asks = [(100, 10), (110, 20), (150, 1500)]
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# band excludes the 150 tier outright, even though ref_price=None would otherwise count it
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assert s.effective_supply(asks, None, band=(50, 120)) == 30
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def test_in_band():
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assert s.in_band(700, (663, 16575)) is True
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assert s.in_band(334, (663, 16575)) is False
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assert s.in_band(20000, (663, 16575)) is False
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assert s.in_band(1, None) is True # no band given: nothing excluded
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def test_effective_demand_excludes_stale_bids_below_the_band():
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bids = [(4550, 8), (4200, 250), (2810, 8), (334, 19)]
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assert s.effective_demand(bids, band=(663, 16575)) == 8 + 250 + 8 # the 334 bid is stale, excluded
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assert s.effective_demand(bids, band=None) == 8 + 250 + 8 + 19
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def test_max_units_at_impact_stops_at_the_price_threshold():
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bids = [(100, 10), (95, 20), (85, 1000)] # ref 100, 10% floor = 90
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assert s.max_units_at_impact(bids, 100, max_impact_pct=10.0) == 30 # 100 and 95 tiers only
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assert s.max_units_at_impact(bids, 100, max_impact_pct=20.0) == 1030 # all three tiers now qualify
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assert s.max_units_at_impact([], 100) == 0.0
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assert s.max_units_at_impact(bids, 0) == 0.0
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