dodoxandClaude Sonnet 5
11f54276b3
Where to park idle cash in a buy-low/sell-high position while it has no better use - scans every ticker for real bid/ask spread and liquidity, scoring round-trip (buy-dip, sell-recovery) via the same empirical first-passage model as sell.py's optimal-ask fix, instead of an assumed random walk. Dropped the universe's top pick from ~94%/day to ~1%/day with real waits of 19-194+ days under the corrected model. Default --rate 0.0 matches the tool's own idle-cash premise (sell.py's rate of 0.05 assumes cash is earmarked, which isn't this tool's case). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
83 lines
5.1 KiB
Python
83 lines
5.1 KiB
Python
"""Where to park idle cash as a buy-low/sell-high position (not a build) while ships are busy or
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cash has no better immediate use - the market-making tactic worked out by hand in-session before
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this existed. A big bid/ask spread alone is a trap (see OPEN GAPS below); this scores candidates
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on spread, real trading liquidity, and an EMPIRICAL round-trip estimate from the ticker's own
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price history - no random-walk/mean-reversion assumption (an earlier driftless-random-walk
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version of this kind of model was built for tools/sell.py and backtested WRONG against 3 years of
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real data - see docs/decisions.md's 2026-10-10 entries - so this one measures directly instead).
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Method per ticker:
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- spread_pct: (ask - bid) / bid at the live quote.
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- liquidity: trading_days()/trade_gaps_days() over the last `days` calendar days - trades on most
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days with a short gap is real liquidity; a huge spread with few trading days is the trap this
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project already got burned by once (AST: 130% spread, 19/30 trading days, but the resting ask
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was stale - real last-traded price had already crashed 60% below it).
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- round trip = two sequential empirical first-passage legs (puga.history.empirical_touch), using
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the ticker's own full multi-year history, not an assumed distribution:
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buy leg: y = ln(bid/latest) - how often/fast has a move of this size (usually downward)
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actually happened, ever, in this ticker's real history?
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sell leg: y = ln(ask/bid) - same question for the recovery back up to ask.
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combined_days = buy.median_days + sell.median_days (both must happen, in sequence)
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combined_hit = buy.hit_factor * sell.hit_factor (treats the two legs as independent - an
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approximation, not exact, but each factor is itself a real empirically-measured expectation,
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not a parametric guess).
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- daily_pct: (ask-bid)/bid pessimistic margin over combined_days, as a SIMPLE (non-compounding)
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rate - see docs/decisions.md's "Drop ROI/day %" entry. For ranking by speed, not a yield.
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- discounted_pct: margin_pct x combined_hit - the SAME margin, discounted by both legs' real odds
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of ever happening at all (`--rate`'s only effect on the output; a low/near-0 default matches
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this tool's "idle cash, no better use" premise - pass a higher rate if this money actually IS
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earmarked for something and waiting has a real opportunity cost). A big gap between daily_pct
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and discounted_pct means the raw speed number is flattering a trade that may just never clear.
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OPEN GAPS:
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- No CX transaction/fee field in FIO's exchange data (checked) - can't confirm or rule out a fee
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eating into the spread; not modelled.
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- Treating buy-leg and sell-leg as independent (multiplying hit factors, adding median days) is an
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approximation - the real joint distribution of "time to dip to bid, THEN time to recover to ask
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starting from wherever it actually dipped to" isn't computed exactly.
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"""
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import math, statistics
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from . import market
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from .history import daily_prices, trading_days, trade_gaps_days, full_series, empirical_touch
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def scan(cx: str = "AI1", min_spread_pct: float = 20.0, days: int = 30, rate: float = 0.0):
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"""Every (ticker, cx) with a live quote and spread_pct >= min_spread_pct, scored by pessimistic
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simple daily-equivalent return from the empirical round-trip model. Returns a list of dicts,
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unsorted (sort by 'daily_pct' - or let the caller filter on 'trade_days'/'gap_days' first,
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since those are what separate a real opportunity from a stale-quote trap)."""
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snap = market.snapshot()
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rows = []
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for (tk, q_cx), q in snap.items():
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if q_cx != cx or not q.ask or not q.bid or q.bid <= 0:
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continue
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spread_pct = 100 * (q.ask - q.bid) / q.bid
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if spread_pct < min_spread_pct:
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continue
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dp = daily_prices(tk, cx)
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if len(dp) < 2:
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continue
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td = trading_days(tk, cx, days)
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gaps = trade_gaps_days([d for d, _ in td])
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gap_days = statistics.median(gaps) if gaps else None
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if not gap_days or gap_days <= 0:
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continue
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series = full_series(tk, cx)
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latest = series[-1][1]
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if latest <= 0:
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continue
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buy = empirical_touch(series, math.log(q.bid / latest), rate)
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sell = empirical_touch(series, math.log(q.ask / q.bid), rate)
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if buy["median_days"] is None or sell["median_days"] is None:
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continue # one leg has never reliably happened in this ticker's real history
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combined_days = buy["median_days"] + sell["median_days"]
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combined_hit = buy["hit_factor"] * sell["hit_factor"]
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margin_pct = 100 * (q.ask - q.bid) / q.bid
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rows.append(dict(tk=tk, cx=cx, bid=q.bid, ask=q.ask, spread_pct=spread_pct, latest=latest,
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margin_pct=margin_pct, trade_days=len(td), window_days=days, gap_days=gap_days,
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buy_touch=buy["touched_frac"], sell_touch=sell["touched_frac"],
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wait_days=combined_days, hit_factor=combined_hit,
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daily_pct=margin_pct / combined_days if combined_days else 0.0,
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discounted_pct=margin_pct * combined_hit))
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return rows
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