jaxperro
2f4f2ccbbe
research/ is a hard silo (README rules): read-only tape, no bot imports, own launchd (com.jaxperro.research-nightly 09:15, after daily ingest). - tape.py: proxy-resolution (the 742/742-validated method), niche + crypto strike/expiry/sprint parsers, tick loaders - sim.py: FAK execution replayer; hold_s=3 fitted on 29 real labeled live attempts (79% fill/miss classification), price noise 2-4c, measured OPTIMISM BIAS -2c/fill carried into every verdict threshold - requote.py: crater refill timing per niche (crypto 94% <4s, esports 83% <10s, sports needs ~25s, geo/politics minutes) -> params/requote_timing.json - study_flow.py + robustness: in-play surge momentum. Identity NULL result: 10 pooled controls +23.85/fill == informed +23.68 -> hypothesis revised at freeze, surge-EV primary, identity secondary (#16) - study_oracle.py: oracle digital fair value. 86% craters, winner's-curse inversion at big edges, nothing frozen (no cell at 30 fills) (#17) - forward.py + nightly.sh: re-scores frozen studies on last 3 tape days, appends forward_ledger.jsonl; verdicts ONLY from post-freeze rows Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
#!/usr/bin/env python3
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"""Crater -> requote timing: after an aggressive up-move print (>= 3c above
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the previous print, the shape our FAK misses die in), how long until the
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SAME token prints again — i.e. how long does the crater stay empty?
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Directly tunes copybot's fak_retry_s (currently a flat 10s): the retry
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should arrive when liquidity is back, per niche. Pure measurement, no bot
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changes here.
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"""
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import json
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import os
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import time
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import tape
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JUMP = 0.03
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def run():
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db = tape.connect()
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rows = db.execute(f"""
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WITH p AS (
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SELECT asset, ts, price, title,
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lag(price) OVER (PARTITION BY asset ORDER BY ts, tx) prev_p,
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lead(ts) OVER (PARTITION BY asset ORDER BY ts, tx) next_ts
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FROM trades
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)
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SELECT title, ts, next_ts - ts AS gap
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FROM p
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WHERE prev_p IS NOT NULL AND price - prev_p >= {JUMP}
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AND next_ts IS NOT NULL
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""").fetchall()
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by = {}
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for title, ts, gap in rows:
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by.setdefault(tape.niche(title), []).append(gap)
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out = {}
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print(f"{len(rows):,} crater prints (>= {JUMP:.02f} up-moves)\n")
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print(f"{'niche':<10} {'n':>8} {'p50':>7} {'p75':>7} {'p90':>7} "
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f"{'<=4s':>6} {'<=10s':>6} {'<=25s':>6}")
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for niche, gaps in sorted(by.items(), key=lambda kv: -len(kv[1])):
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gaps.sort()
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n = len(gaps)
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q = lambda f: gaps[min(int(n * f), n - 1)]
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frac = lambda s: sum(g <= s for g in gaps) / n
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out[niche] = {"n": n, "p50": q(.5), "p75": q(.75), "p90": q(.9),
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"within_4s": round(frac(4), 3),
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"within_10s": round(frac(10), 3),
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"within_25s": round(frac(25), 3)}
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print(f"{niche:<10} {n:>8,} {q(.5):>7.1f} {q(.75):>7.1f} {q(.9):>7.1f} "
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f"{frac(4):>6.0%} {frac(10):>6.0%} {frac(25):>6.0%}")
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out["_meta"] = {"jump": JUMP, "generated": time.strftime("%Y-%m-%d %H:%M")}
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json.dump(out, open(os.path.join(tape.HERE, "params", "requote_timing.json"),
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"w"), indent=1)
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if __name__ == "__main__":
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run()
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