mirror of
https://github.com/jaxperro/winning-wallet-finder.git
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live: train/test wallet-selection study + capital-constrained copy sims
strategy.py (train pre-May-30 / test June1+ on copy-ROI + z + consistency + diversification) and followability.py (entry-time/lead-time/cadence filter) surface wallets with real, copyable, out-of-sample edge (49/77 profitable forward, p=0.011, +23.4% pooled). pnl_basket.py / pnl_focused.py add $1000 capital-constrained copy sims with missed-trade accounting: the broad basket loses (can't follow 1,200 trades on $1k), but 1-2 wallets + a conviction (bet-size) filter clears out-of-sample. cache.py gains entry-time caching. Findings documented. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
jaxperro
+23
@@ -142,6 +142,29 @@ misleading signal on the platform; favorite-riders are uncopyable.* The
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underdog/`value` archetype (beats longshot prices) is the only one left worth
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testing.
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## Train/test wallet selection, and the capital wall (June 2026)
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Built `live/strategy.py` (train on bets resolved before May 30, validate June 1+)
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and `live/followability.py` (entry-time + lead-time + cadence filter). Selecting
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on **copy-ROI + z + monthly consistency + diversification** (not win rate) gave
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150 wallets; **59/100 stayed profitable forward** (p=0.044), and filtering to
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*followable* markets lifted it to **49/77 (p=0.011), +23.4% pooled** out-of-
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sample. So a real, persistent, copyable edge **does** exist — unlike favorites.
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Then the reality check (`live/pnl_basket.py`, `live/pnl_focused.py`): a $1,000
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copier with **missed-trade accounting** (capital tied in open positions).
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- **Broad 10-wallet basket:** the wallets fire **1,210 markets** in June; $1,000
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can follow only ~2–13% of them. At realistic stakes it **loses** (−$384 to
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−$800); the gains sit in the trades you couldn't afford ($14k–$153k "missed").
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**Capital, not edge, is the binding constraint.**
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- **Focused + conviction:** copy only 1–2 top wallets and only their larger-stake
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(≥$200) bets → trade count drops to ~30–40, $1,000 affords them all, and it
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**clears: +91% to +247% across stakes, stable, no blowup.**
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*Lesson: a small-bankroll copier cannot follow a skilled wallet's whole feed —
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the edge is only capturable by concentrating on few wallets' high-conviction
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bets. The live tracker (jaxperro.com/trading) now runs exactly that config.*
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## Repo layout
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- `insider.py` — the detector: z-score/p-value, timing/freshness/sizing signals,
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@@ -62,6 +62,18 @@ turn into losers out-of-sample. **Don't copy favorite-riders.** The `value`
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archetype (beats underdog prices) is where real alpha may live — test it with
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`backtest_june.py value`.
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## Strategy backtests
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- `strategy.py` — train (pre-May-30) / test (June1+) wallet selection on copy-ROI
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+ z + monthly consistency + diversification. → `selection.json`.
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- `followability.py` — pull entry timestamps (cached), drop wallets whose edge is
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in un-followable fast/live markets, re-rank on followable forward bets. →
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`watch_final.json` (the execution-realistic list).
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- `pnl_basket.py` / `pnl_focused.py` — $1,000 capital-constrained copy sims with
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**missed-trade accounting**. Key result: the broad basket loses on $1k (can't
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follow 1,200 trades), but **1–2 wallets + a conviction (bet-size) filter clears**
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out-of-sample. See `../FINDINGS.md`.
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## Daily (`daily.sh`)
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1. discover (enumerate last 14d) → 2. freshen cache (force-refresh watchlist +
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@@ -33,6 +33,31 @@ _con.execute("""CREATE TABLE IF NOT EXISTS bets(
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wallet TEXT, cond TEXT, won BOOLEAN, p DOUBLE, res_t BIGINT, size DOUBLE)""")
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_con.execute("CREATE INDEX IF NOT EXISTS bets_w ON bets(wallet)")
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_con.execute("CREATE TABLE IF NOT EXISTS pulled(wallet TEXT PRIMARY KEY, pulled_at BIGINT)")
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_con.execute("CREATE TABLE IF NOT EXISTS entries(wallet TEXT, cond TEXT, first_buy BIGINT)")
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_con.execute("CREATE INDEX IF NOT EXISTS entries_w ON entries(wallet)")
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_con.execute("CREATE TABLE IF NOT EXISTS pulled_entries(wallet TEXT PRIMARY KEY, pulled_at BIGINT)")
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def get_entries(wallet):
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"""{conditionId: earliest BUY timestamp} for a wallet — cached. Lets us
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compute entry->resolution lead time and trade cadence (followability)."""
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now = time.time()
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with _lock:
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r = _con.execute("SELECT pulled_at FROM pulled_entries WHERE wallet=?", [wallet]).fetchone()
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if r and now - r[0] < MAX_AGE_DAYS * 86400:
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rows = _con.execute("SELECT cond,first_buy FROM entries WHERE wallet=?", [wallet]).fetchall()
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return {c: t for c, t in rows}
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try:
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first_buy, _ = insider.entry_times(wallet)
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except Exception:
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first_buy = {}
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with _lock:
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_con.execute("DELETE FROM entries WHERE wallet=?", [wallet])
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if first_buy:
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_con.executemany("INSERT INTO entries(wallet,cond,first_buy) VALUES (?,?,?)",
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[(wallet, c, t) for c, t in first_buy.items()])
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_con.execute("INSERT OR REPLACE INTO pulled_entries VALUES (?,?)", [wallet, int(now)])
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return first_buy
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def get_bets(wallet):
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@@ -0,0 +1,126 @@
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#!/usr/bin/env python3
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"""Execution-realistic re-ranking of the selected wallets.
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Pulls entry timestamps (cached) for the strategy.py shortlist, then judges each
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wallet on whether we could actually FOLLOW it with $1,000:
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* lead time = resolution - entry. Bets that resolve within MIN_LEAD_H of the
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wallet's entry (live/in-game/instant markets) are NOT copyable — you can't
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see and mirror the trade in time. We drop them.
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* cadence = distinct markets entered per active day. Extreme cadence = a bot
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we can't hand-follow.
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Then it recomputes forward (June1+, resolved) copy-ROI on ONLY the followable
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bets — the realistic number — and re-ranks. Output: watch_final.json.
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python3 followability.py
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"""
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import json
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import os
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import statistics as st
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import time
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from concurrent.futures import ThreadPoolExecutor, as_completed
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import cache
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HERE = os.path.dirname(__file__)
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TEST_START = time.mktime(time.strptime(os.environ.get("TEST_START", "2026-06-01"), "%Y-%m-%d"))
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MIN_LEAD_H = 1.0 # a bet must resolve >= 1h after entry to be followable
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MED_LEAD_MIN = 2.0 # wallet's median lead must clear this to qualify
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MAX_CADENCE = 50.0 # entries/day above this = bot-like, hard to follow
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MIN_FOLL_FRAC = 0.5 # >= half the wallet's bets must be followable
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MIN_FWD_FOLL = 5 # need this many followable forward bets to rank
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def ret(p, won):
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return (1 - p) / p if won else -1.0
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def assess(wallet):
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ent = cache.get_entries(wallet) # {cond: first_buy_ts}
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bets = cache.get_bets(wallet) # cached
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leads = []
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for b in bets:
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e = ent.get(b["cond"])
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if e and b.get("res_t"):
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lh = (b["res_t"] - e) / 3600.0
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if lh >= 0:
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leads.append((b, lh))
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if not leads:
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return None
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med_lead = st.median([lh for _, lh in leads])
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foll_frac = sum(1 for _, lh in leads if lh >= MIN_LEAD_H) / len(leads)
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ts = sorted(ent.values())
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span = max(1.0, (ts[-1] - ts[0]) / 86400) if len(ts) > 1 else 1.0
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cadence = len(ent) / span
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# forward, followable only
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fwd_foll = [b for b, lh in leads if b["res_t"] >= TEST_START and lh >= MIN_LEAD_H]
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fwd_all = [b for b, lh in leads if b["res_t"] >= TEST_START]
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foll_roi = (sum(ret(b["p"], b["won"]) for b in fwd_foll) / len(fwd_foll)
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if fwd_foll else None)
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raw_roi = (sum(ret(b["p"], b["won"]) for b in fwd_all) / len(fwd_all)
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if fwd_all else None)
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wins = sum(1 for b in fwd_foll if b["won"])
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return dict(wallet=wallet, med_lead=med_lead, foll_frac=foll_frac, cadence=cadence,
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fwd_foll_n=len(fwd_foll), foll_roi=foll_roi, raw_roi=raw_roi,
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fwd_win=100 * wins / len(fwd_foll) if fwd_foll else 0)
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def main():
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sel = json.load(open(os.path.join(HERE, "selection.json")))
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wallets = [c["wallet"] for c in sel]
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meta = {c["wallet"]: c for c in sel}
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print(f"assessing followability of {len(wallets)} selected wallets "
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f"(entry-time pull, cached)…\n", flush=True)
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rows, done = [], 0
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with ThreadPoolExecutor(max_workers=10) as ex:
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for r in ex.map(assess, wallets):
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done += 1
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if r:
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rows.append(r)
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if done % 30 == 0:
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print(f" {done}/{len(wallets)}", flush=True)
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# followability gates
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foll = [r for r in rows if
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r["med_lead"] >= MED_LEAD_MIN and
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r["foll_frac"] >= MIN_FOLL_FRAC and
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r["cadence"] <= MAX_CADENCE]
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ranked = [r for r in foll if r["fwd_foll_n"] >= MIN_FWD_FOLL]
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ranked.sort(key=lambda r: r["foll_roi"], reverse=True)
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dropped = len(rows) - len(foll)
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print(f"\n{len(rows)} assessed · {dropped} dropped as un-followable "
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f"(fast/live markets or bot cadence) · {len(foll)} copyable\n")
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if ranked:
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pooled_num = sum(r["foll_roi"] * r["fwd_foll_n"] for r in ranked)
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pooled_den = sum(r["fwd_foll_n"] for r in ranked)
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pos = sum(1 for r in ranked if r["foll_roi"] > 0)
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print(f"FOLLOWABLE forward verdict ({len(ranked)} wallets w/ >= {MIN_FWD_FOLL} "
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f"followable June+ bets):")
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print(f" {pos}/{len(ranked)} profitable · pooled followable copy-ROI "
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f"{pooled_num/pooled_den:+.1%} · median {st.median([r['foll_roi'] for r in ranked]):+.1%}\n")
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h = (f"{'foll_roi':>9}{'raw_roi':>8}{'medLeadH':>9}{'foll%':>6}{'cad/d':>7}"
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f"{'fwd_n':>6}{'tr_z':>6} wallet")
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print(h); print("-" * len(h))
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for r in ranked[:40]:
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m = meta[r["wallet"]]
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raw = f"{r['raw_roi']:+.0%}" if r["raw_roi"] is not None else "—"
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print(f"{r['foll_roi']:>+8.0%}{raw:>8}{r['med_lead']:>8.1f}h{r['foll_frac']*100:>5.0f}%"
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f"{r['cadence']:>7.1f}{r['fwd_foll_n']:>6}{m['train_z']:>6.1f} {r['wallet']}")
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out = [{"wallet": r["wallet"], "name": r["wallet"][:10],
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"foll_fwd_copy_roi": round(r["foll_roi"], 4),
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"med_lead_h": round(r["med_lead"], 1), "cadence_per_day": round(r["cadence"], 1),
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"followable_frac": round(r["foll_frac"], 2), "fwd_followable_n": r["fwd_foll_n"],
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"train_z": meta[r["wallet"]]["train_z"],
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"train_copy_roi": meta[r["wallet"]]["train_copy_roi"]} for r in ranked]
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json.dump(out, open(os.path.join(HERE, "watch_final.json"), "w"), indent=2)
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print(f"\n-> watch_final.json ({len(out)} execution-realistic wallets)")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,101 @@
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#!/usr/bin/env python3
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"""Capital-constrained copy backtest of the 10-wallet basket, June 1 -> now.
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$1,000 bankroll. Replay the wallets' June-1+ entries in time order (using cached
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entry timestamps). At each entry: first settle any held bets that have resolved
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(free the cash + realize P&L), then enter IF we can afford the stake — otherwise
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it's a MISSED trade (counted, with its hypothetical outcome). Capital stays tied
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in still-open positions, which is what forces the misses. Realized P&L only.
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One position per market (earliest of the 10 wallets to enter it). Shown across a
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few flat stake sizes since that's the knob that trades off coverage vs misses.
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"""
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import time
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import cache
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JUN1 = time.mktime(time.strptime("2026-06-01", "%Y-%m-%d"))
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NOW = time.time()
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BANK = 1000.0
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WALLETS = [
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"0xe8ca3f758c93f44f3ec210542ab78afb7c0bcccb", "0x0a7aaf83341b52df34e8ffef52aa295538d6df1b",
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"0xfd4263b3ad08226034fe1b1ea678a46d80b58895", "0x13464aabec792c36b062316f474713e681330448",
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"0x36bfcd8ab96dce2ddea30145ab749b59c6362864", "0x2d4bf8f846bf68f43b9157bf30810d334ac6ca7a",
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"0x1cff72c8dddc30a64486fda6eab71ab5f9243984", "0xfc81760d44a21acc9fd4b749a5bf9a9b2eeae072",
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"0x86c878cde72660ec52f5e6f0f0438b76de8fc867", "0x6fdddf25b92251ed1515703cda43bf8ff5f5d385",
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]
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def gather():
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"""One copy signal per market: (entry_ts, p, won, res_t|None). res_t None =
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still open (ties up capital, no realized P&L)."""
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pos = {}
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for w in WALLETS:
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ent = cache.get_entries(w) # {cond: first_buy_ts}
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resolved = {b["cond"]: b for b in cache.get_bets(w)}
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for cond, ets in ent.items():
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if ets < JUN1:
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continue
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b = resolved.get(cond)
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p = max(0.001, min(0.999, b["p"])) if b else None
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rec = dict(ets=ets, p=p, won=b["won"] if b else None,
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res_t=(b["res_t"] if b else None))
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if cond not in pos or ets < pos[cond]["ets"]:
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pos[cond] = rec
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return sorted(pos.values(), key=lambda r: r["ets"])
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def sim(events, stake):
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cash, realized = BANK, 0.0
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held = [] # (res_t, p, won, stake)
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entered = missed = openn = 0
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missed_pnl = 0.0
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def settle2(upto):
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nonlocal cash, realized
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keep = []
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for res_t, p, won, s in held:
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if res_t is not None and res_t <= upto:
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payout = (s / p) if won else 0.0
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cash += payout
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realized += payout - s
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else:
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keep.append((res_t, p, won, s))
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held[:] = keep
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for e in events:
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settle2(e["ets"])
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if cash >= stake:
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cash -= stake
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held.append((e["res_t"], e["p"], e["won"], stake))
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entered += 1
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if e["res_t"] is None:
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openn += 1
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else:
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missed += 1
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if e["res_t"] is not None: # hypothetical realized miss
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missed_pnl += (stake / e["p"] - stake) if e["won"] else -stake
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settle2(NOW)
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open_left = sum(1 for h in held if h[0] is None or h[0] > NOW)
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equity = BANK + realized # open held at cost
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return dict(stake=stake, entered=entered, missed=missed, open_left=open_left,
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realized=realized, equity=equity, missed_pnl=missed_pnl)
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def main():
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ev = gather()
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res = sum(1 for e in ev if e["res_t"] is not None)
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print(f"10-wallet basket · {len(ev)} unique June1+ markets entered "
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f"({res} resolved, {len(ev)-res} still open) · $1000 bankroll, miss when broke\n")
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h = f"{'stake':>6}{'entered':>8}{'missed':>7}{'open':>5}{'realized P&L':>14}{'equity':>10}{'missed P&L':>12}"
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print(h); print("-" * len(h))
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for s in (20, 50, 100, 200):
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r = sim(ev, s)
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print(f"${r['stake']:>4}{r['entered']:>8}{r['missed']:>7}{r['open_left']:>5}"
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f"{r['realized']:>+13,.0f}{r['equity']:>10,.0f}{r['missed_pnl']:>+12,.0f}")
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print("\nrealized P&L = settled bets only · equity = $1000 + realized (open held at cost)")
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print("missed P&L = hypothetical resolved P&L of trades skipped for lack of cash")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,66 @@
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#!/usr/bin/env python3
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"""Does a FOCUSED copy strategy clear where the broad 10-wallet basket didn't?
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Same $1000 capital-constrained engine + missed-trade accounting (pnl_basket.sim),
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but on narrower signal sets: fewer wallets, and/or only the wallet's higher-
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conviction (larger-stake) bets — so $1000 isn't spread across 1,210 markets.
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"""
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import time
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import cache
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import pnl_basket as pb
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JUN1 = time.mktime(time.strptime("2026-06-01", "%Y-%m-%d"))
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E8 = "0xe8ca3f758c93f44f3ec210542ab78afb7c0bcccb"
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A0 = "0x0a7aaf83341b52df34e8ffef52aa295538d6df1b"
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def gather(wallets, size_min=None):
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"""One signal per market. size_min filters to the wallet's larger-stake
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(higher-conviction) bets; in that mode we only use resolved bets (open ones
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have no known stake to filter on)."""
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pos = {}
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for w in wallets:
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ent = cache.get_entries(w)
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resolved = {b["cond"]: b for b in cache.get_bets(w)}
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for cond, ets in ent.items():
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if ets < JUN1:
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continue
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b = resolved.get(cond)
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if b:
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if size_min and (b["size"] or 0) < size_min:
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continue
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rec = dict(ets=ets, p=max(0.001, min(0.999, b["p"])),
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won=b["won"], res_t=b["res_t"])
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else:
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if size_min:
|
||||
continue
|
||||
rec = dict(ets=ets, p=None, won=None, res_t=None)
|
||||
if cond not in pos or ets < pos[cond]["ets"]:
|
||||
pos[cond] = rec
|
||||
return sorted(pos.values(), key=lambda r: r["ets"])
|
||||
|
||||
|
||||
def run(label, wallets, size_min=None):
|
||||
ev = gather(wallets, size_min)
|
||||
res = sum(1 for e in ev if e["res_t"] is not None)
|
||||
print(f"\n### {label} — {len(ev)} markets ({res} resolved)")
|
||||
h = f"{'stake':>6}{'entered':>8}{'missed':>7}{'open':>5}{'realized':>11}{'equity':>9}"
|
||||
print(h)
|
||||
for s in (50, 100, 200):
|
||||
r = pb.sim(ev, s)
|
||||
print(f"${s:>4}{r['entered']:>8}{r['missed']:>7}{r['open_left']:>5}"
|
||||
f"{r['realized']:>+10,.0f}{r['equity']:>9,.0f}")
|
||||
|
||||
|
||||
def main():
|
||||
run("0xe8 only — all June+ entries", [E8])
|
||||
run("0xe8 only — conviction (their bets >= $200)", [E8], size_min=200)
|
||||
run("0xe8 only — conviction (their bets >= $1000)", [E8], size_min=1000)
|
||||
run("0xe8 + 0x0a — all June+ entries", [E8, A0])
|
||||
run("0xe8 + 0x0a — conviction (>= $200)", [E8, A0], size_min=200)
|
||||
print("\nrealized = settled-bet P&L · equity = $1000 + realized (open at cost)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
+1502
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,162 @@
|
||||
#!/usr/bin/env python3
|
||||
"""Train/test wallet-selection study, entirely from the local cache.
|
||||
|
||||
TRAIN = bets resolved before --train-end (default 2026-05-30): pick wallets.
|
||||
TEST = bets resolved on/after --test-start (default 2026-06-01), resolved only:
|
||||
validate which picks were actually profitable to follow forward.
|
||||
|
||||
Lens: we have $1,000 and copy flat-size, so the key metric is COPY-ROI — the
|
||||
mean per-bet return if we mirror each entry with the same stake:
|
||||
win -> (1-p)/p per $1 (entry at price p pays out at 1)
|
||||
loss -> -1
|
||||
A wallet with positive copy-ROI would have made us money (before lag/fees).
|
||||
We also compute z (beats entry prices) and the wallet's own $ ROI.
|
||||
|
||||
Selection gates (all on TRAIN):
|
||||
* n >= MIN_N resolved bets
|
||||
* z significant (Benjamini-Hochberg FDR)
|
||||
* copy-ROI > 0 (copying them actually paid)
|
||||
* consistent: positive copy-ROI in >= CONSISTENCY of monthly buckets
|
||||
* copyable proxies: median bet size in [$5,$5000] (not dust, not whale),
|
||||
not at the ~2000-bet HFT cap, not one-market-dependent
|
||||
|
||||
python3 strategy.py
|
||||
"""
|
||||
|
||||
import math
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
from collections import defaultdict
|
||||
|
||||
import duckdb
|
||||
|
||||
HERE = os.path.dirname(__file__)
|
||||
DB = os.path.join(HERE, "cache.duckdb")
|
||||
TRAIN_END = time.mktime(time.strptime(os.environ.get("TRAIN_END", "2026-05-30"), "%Y-%m-%d"))
|
||||
TEST_START = time.mktime(time.strptime(os.environ.get("TEST_START", "2026-06-01"), "%Y-%m-%d"))
|
||||
MIN_N = 30 # train bets needed to judge a wallet
|
||||
MIN_N_TEST = 5 # forward bets needed to report a forward number
|
||||
CAP = 1990 # >= this looks API-capped (HFT/truncated) -> deprioritize
|
||||
SIZE_LO, SIZE_HI = 5.0, 5000.0 # copyable median bet size ($)
|
||||
CONSISTENCY = 0.6 # fraction of monthly buckets that must be profitable
|
||||
FDR_Q = 0.05
|
||||
|
||||
|
||||
def ret(p, won):
|
||||
return (1 - p) / p if won else -1.0 # copy return per $1 staked
|
||||
|
||||
|
||||
def metrics(bets):
|
||||
n = len(bets)
|
||||
wins = sum(1 for b in bets if b["won"])
|
||||
exp = sum(b["p"] for b in bets)
|
||||
var = sum(b["p"] * (1 - b["p"]) for b in bets) or 1e-9
|
||||
z = (wins - exp) / math.sqrt(var)
|
||||
copy_roi = sum(ret(b["p"], b["won"]) for b in bets) / n
|
||||
staked = sum(b["size"] for b in bets) or 1e-9
|
||||
dollars = sum((b["size"] * (1 - b["p"]) / b["p"] if b["won"] else -b["size"]) for b in bets)
|
||||
return dict(n=n, wins=wins, win_rate=100 * wins / n, z=z, copy_roi=copy_roi,
|
||||
own_roi=dollars / staked, exp=exp)
|
||||
|
||||
|
||||
def consistency(bets):
|
||||
"""fraction of calendar-month buckets (by res_t) with positive copy-ROI."""
|
||||
buck = defaultdict(list)
|
||||
for b in bets:
|
||||
buck[time.strftime("%Y-%m", time.localtime(b["res_t"] or 0))].append(b)
|
||||
months = [m for m in buck.values() if len(m) >= 5]
|
||||
if not months:
|
||||
return 0.0, 0
|
||||
good = sum(1 for m in months if sum(ret(b["p"], b["won"]) for b in m) > 0)
|
||||
return good / len(months), len(months)
|
||||
|
||||
|
||||
def concentration(bets):
|
||||
"""share of total copy-return coming from the single best market."""
|
||||
by = defaultdict(float)
|
||||
for b in bets:
|
||||
by[b["cond"]] += ret(b["p"], b["won"])
|
||||
tot = sum(v for v in by.values() if v > 0) or 1e-9
|
||||
return max(by.values()) / tot if by else 1.0
|
||||
|
||||
|
||||
def norm_sf(z):
|
||||
return 0.5 * math.erfc(z / math.sqrt(2))
|
||||
|
||||
|
||||
def main():
|
||||
con = duckdb.connect(DB, read_only=True)
|
||||
wallets = [r[0] for r in con.execute("SELECT DISTINCT wallet FROM bets").fetchall()]
|
||||
print(f"loaded {len(wallets):,} wallets; train<{time.strftime('%F', time.localtime(TRAIN_END))}"
|
||||
f" test>={time.strftime('%F', time.localtime(TEST_START))}\n", flush=True)
|
||||
|
||||
cand = []
|
||||
for w in wallets:
|
||||
rows = con.execute(
|
||||
"SELECT won,p,res_t,size,cond FROM bets WHERE wallet=?", [w]).fetchall()
|
||||
bets = [dict(won=x[0], p=max(0.001, min(0.999, x[1] or 0)),
|
||||
res_t=x[2], size=x[3] or 0, cond=x[4]) for x in rows]
|
||||
train = [b for b in bets if (b["res_t"] or 0) < TRAIN_END]
|
||||
if len(train) < MIN_N:
|
||||
continue
|
||||
m = metrics(train)
|
||||
med = sorted(b["size"] for b in train)[len(train) // 2]
|
||||
cons, nmonths = consistency(train)
|
||||
conc = concentration(train)
|
||||
test = [b for b in bets if (b["res_t"] or 0) >= TEST_START]
|
||||
tm = metrics(test) if len(test) >= MIN_N_TEST else None
|
||||
cand.append(dict(w=w, m=m, med=med, cons=cons, nmonths=nmonths, conc=conc,
|
||||
capped=len(train) >= CAP, tm=tm, ntest=len(test)))
|
||||
|
||||
# FDR over the candidate edge p-values
|
||||
pvals = sorted(norm_sf(c["m"]["z"]) for c in cand)
|
||||
k = 0
|
||||
for i, p in enumerate(pvals, 1):
|
||||
if p <= FDR_Q * i / len(pvals):
|
||||
k = i
|
||||
thr = pvals[k - 1] if k else 0.0
|
||||
|
||||
# selection gates (all on TRAIN)
|
||||
sel = [c for c in cand if
|
||||
norm_sf(c["m"]["z"]) <= thr and thr > 0 and
|
||||
c["m"]["copy_roi"] > 0 and
|
||||
c["cons"] >= CONSISTENCY and
|
||||
SIZE_LO <= c["med"] <= SIZE_HI and
|
||||
not c["capped"] and
|
||||
c["conc"] < 0.5]
|
||||
sel.sort(key=lambda c: c["m"]["copy_roi"], reverse=True)
|
||||
|
||||
print(f"{len(cand):,} wallets with >= {MIN_N} train bets · BH thr p<= {thr:.1e}")
|
||||
print(f"SELECTED (skilled + consistent + copyable on TRAIN): {len(sel)}\n")
|
||||
|
||||
# how did the SELECTED set do FORWARD?
|
||||
fwd = [c for c in sel if c["tm"]]
|
||||
if fwd:
|
||||
avg_fwd = sum(c["tm"]["copy_roi"] for c in fwd) / len(fwd)
|
||||
win_fwd = sum(1 for c in fwd if c["tm"]["copy_roi"] > 0)
|
||||
print(f"FORWARD (June1+, resolved): {len(fwd)} selected wallets had test bets · "
|
||||
f"{win_fwd}/{len(fwd)} stayed profitable · mean fwd copy-ROI {avg_fwd:+.1%}\n")
|
||||
|
||||
h = (f"{'train_roi':>10}{'fwd_roi':>9}{'tr_z':>6}{'tr_wr':>6}{'cons':>6}"
|
||||
f"{'medSz':>7}{'tr_n':>6}{'fwd_n':>6} wallet")
|
||||
print(h); print("-" * len(h))
|
||||
for c in sel[:40]:
|
||||
t = c["tm"]
|
||||
fr = f"{t['copy_roi']:+.0%}" if t else " —"
|
||||
fn = c["ntest"] if t else 0
|
||||
print(f"{c['m']['copy_roi']:>+9.0%}{fr:>9}{c['m']['z']:>6.1f}{c['m']['win_rate']:>5.0f}%"
|
||||
f"{c['cons']:>6.0%}{c['med']:>7.0f}{c['m']['n']:>6}{fn:>6} {c['w']}")
|
||||
# persist the selection for the followability pull + watchlist
|
||||
import json
|
||||
json.dump([{"wallet": c["w"], "train_copy_roi": round(c["m"]["copy_roi"], 4),
|
||||
"train_z": round(c["m"]["z"], 2), "train_n": c["m"]["n"],
|
||||
"fwd_copy_roi": round(c["tm"]["copy_roi"], 4) if c["tm"] else None,
|
||||
"fwd_n": c["ntest"], "med_size": round(c["med"], 1),
|
||||
"consistency": round(c["cons"], 2)} for c in sel],
|
||||
open(os.path.join(HERE, "selection.json"), "w"), indent=2)
|
||||
print(f"\n-> selection.json ({len(sel)} wallets)")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,849 @@
|
||||
[
|
||||
{
|
||||
"wallet": "0xe8ca3f758c93f44f3ec210542ab78afb7c0bcccb",
|
||||
"name": "0xe8ca3f75",
|
||||
"foll_fwd_copy_roi": 2.7036,
|
||||
"med_lead_h": 95.6,
|
||||
"cadence_per_day": 8.7,
|
||||
"followable_frac": 0.99,
|
||||
"fwd_followable_n": 93,
|
||||
"train_z": 18.86,
|
||||
"train_copy_roi": 2.8782
|
||||
},
|
||||
{
|
||||
"wallet": "0x0a7aaf83341b52df34e8ffef52aa295538d6df1b",
|
||||
"name": "0x0a7aaf83",
|
||||
"foll_fwd_copy_roi": 2.134,
|
||||
"med_lead_h": 248.8,
|
||||
"cadence_per_day": 9.8,
|
||||
"followable_frac": 0.99,
|
||||
"fwd_followable_n": 138,
|
||||
"train_z": 7.21,
|
||||
"train_copy_roi": 0.5841
|
||||
},
|
||||
{
|
||||
"wallet": "0xfd4263b3ad08226034fe1b1ea678a46d80b58895",
|
||||
"name": "0xfd4263b3",
|
||||
"foll_fwd_copy_roi": 0.9877,
|
||||
"med_lead_h": 256.3,
|
||||
"cadence_per_day": 5.8,
|
||||
"followable_frac": 0.98,
|
||||
"fwd_followable_n": 39,
|
||||
"train_z": 15.53,
|
||||
"train_copy_roi": 2.1616
|
||||
},
|
||||
{
|
||||
"wallet": "0x13464aabec792c36b062316f474713e681330448",
|
||||
"name": "0x13464aab",
|
||||
"foll_fwd_copy_roi": 0.9692,
|
||||
"med_lead_h": 158.6,
|
||||
"cadence_per_day": 5.8,
|
||||
"followable_frac": 0.96,
|
||||
"fwd_followable_n": 89,
|
||||
"train_z": 7.33,
|
||||
"train_copy_roi": 0.512
|
||||
},
|
||||
{
|
||||
"wallet": "0xad703ba9fa7af1447bfcfe22522d052df64f4ccf",
|
||||
"name": "0xad703ba9",
|
||||
"foll_fwd_copy_roi": 0.9587,
|
||||
"med_lead_h": 39.6,
|
||||
"cadence_per_day": 11.0,
|
||||
"followable_frac": 0.99,
|
||||
"fwd_followable_n": 163,
|
||||
"train_z": 3.98,
|
||||
"train_copy_roi": 0.3063
|
||||
},
|
||||
{
|
||||
"wallet": "0x36bfcd8ab96dce2ddea30145ab749b59c6362864",
|
||||
"name": "0x36bfcd8a",
|
||||
"foll_fwd_copy_roi": 0.8856,
|
||||
"med_lead_h": 157.7,
|
||||
"cadence_per_day": 16.9,
|
||||
"followable_frac": 1.0,
|
||||
"fwd_followable_n": 96,
|
||||
"train_z": 7.03,
|
||||
"train_copy_roi": 0.2044
|
||||
},
|
||||
{
|
||||
"wallet": "0x2d4bf8f846bf68f43b9157bf30810d334ac6ca7a",
|
||||
"name": "0x2d4bf8f8",
|
||||
"foll_fwd_copy_roi": 0.6857,
|
||||
"med_lead_h": 364.3,
|
||||
"cadence_per_day": 6.8,
|
||||
"followable_frac": 0.99,
|
||||
"fwd_followable_n": 110,
|
||||
"train_z": 19.72,
|
||||
"train_copy_roi": 0.8859
|
||||
},
|
||||
{
|
||||
"wallet": "0xade74a23c444d0eebee00034459fbc269fab58af",
|
||||
"name": "0xade74a23",
|
||||
"foll_fwd_copy_roi": 0.6597,
|
||||
"med_lead_h": 161.4,
|
||||
"cadence_per_day": 1.5,
|
||||
"followable_frac": 1.0,
|
||||
"fwd_followable_n": 18,
|
||||
"train_z": 6.43,
|
||||
"train_copy_roi": 0.3886
|
||||
},
|
||||
{
|
||||
"wallet": "0xab2067f2bfe4a5bce93cda6f418cde1515d2d9e2",
|
||||
"name": "0xab2067f2",
|
||||
"foll_fwd_copy_roi": 0.5481,
|
||||
"med_lead_h": 161.4,
|
||||
"cadence_per_day": 3.8,
|
||||
"followable_frac": 1.0,
|
||||
"fwd_followable_n": 19,
|
||||
"train_z": 5.6,
|
||||
"train_copy_roi": 0.2669
|
||||
},
|
||||
{
|
||||
"wallet": "0x1cff72c8dddc30a64486fda6eab71ab5f9243984",
|
||||
"name": "0x1cff72c8",
|
||||
"foll_fwd_copy_roi": 0.4498,
|
||||
"med_lead_h": 14.8,
|
||||
"cadence_per_day": 3.5,
|
||||
"followable_frac": 1.0,
|
||||
"fwd_followable_n": 31,
|
||||
"train_z": 5.5,
|
||||
"train_copy_roi": 0.1861
|
||||
},
|
||||
{
|
||||
"wallet": "0x68c24bf4a8ad4d79a6fe4b8eec6f93a02dfd1711",
|
||||
"name": "0x68c24bf4",
|
||||
"foll_fwd_copy_roi": 0.4469,
|
||||
"med_lead_h": 401.2,
|
||||
"cadence_per_day": 4.2,
|
||||
"followable_frac": 1.0,
|
||||
"fwd_followable_n": 30,
|
||||
"train_z": 4.23,
|
||||
"train_copy_roi": 0.3288
|
||||
},
|
||||
{
|
||||
"wallet": "0xfc81760d44a21acc9fd4b749a5bf9a9b2eeae072",
|
||||
"name": "0xfc81760d",
|
||||
"foll_fwd_copy_roi": 0.409,
|
||||
"med_lead_h": 161.5,
|
||||
"cadence_per_day": 5.7,
|
||||
"followable_frac": 0.99,
|
||||
"fwd_followable_n": 142,
|
||||
"train_z": 7.92,
|
||||
"train_copy_roi": 0.2013
|
||||
},
|
||||
{
|
||||
"wallet": "0x2f385bfefacbf73173f9cb81f46fe7a53b2c8adc",
|
||||
"name": "0x2f385bfe",
|
||||
"foll_fwd_copy_roi": 0.3809,
|
||||
"med_lead_h": 173.0,
|
||||
"cadence_per_day": 0.3,
|
||||
"followable_frac": 0.99,
|
||||
"fwd_followable_n": 25,
|
||||
"train_z": 3.75,
|
||||
"train_copy_roi": 0.2828
|
||||
},
|
||||
{
|
||||
"wallet": "0x7ed0a33693a19378318f160064e4f9c1c27a9c4f",
|
||||
"name": "0x7ed0a336",
|
||||
"foll_fwd_copy_roi": 0.361,
|
||||
"med_lead_h": 154.6,
|
||||
"cadence_per_day": 10.5,
|
||||
"followable_frac": 1.0,
|
||||
"fwd_followable_n": 250,
|
||||
"train_z": 3.9,
|
||||
"train_copy_roi": 0.1949
|
||||
},
|
||||
{
|
||||
"wallet": "0x64795fe43b184571d5eb25ca44fc9ba9e1295e64",
|
||||
"name": "0x64795fe4",
|
||||
"foll_fwd_copy_roi": 0.3407,
|
||||
"med_lead_h": 17.9,
|
||||
"cadence_per_day": 2.8,
|
||||
"followable_frac": 0.96,
|
||||
"fwd_followable_n": 7,
|
||||
"train_z": 7.29,
|
||||
"train_copy_roi": 0.2879
|
||||
},
|
||||
{
|
||||
"wallet": "0x312d268ab4d1823684d6838f24a3c96da7d66814",
|
||||
"name": "0x312d268a",
|
||||
"foll_fwd_copy_roi": 0.327,
|
||||
"med_lead_h": 5.5,
|
||||
"cadence_per_day": 16.5,
|
||||
"followable_frac": 0.87,
|
||||
"fwd_followable_n": 30,
|
||||
"train_z": 3.48,
|
||||
"train_copy_roi": 0.0428
|
||||
},
|
||||
{
|
||||
"wallet": "0x86c878cde72660ec52f5e6f0f0438b76de8fc867",
|
||||
"name": "0x86c878cd",
|
||||
"foll_fwd_copy_roi": 0.3054,
|
||||
"med_lead_h": 156.9,
|
||||
"cadence_per_day": 11.0,
|
||||
"followable_frac": 1.0,
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{
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{
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"train_z": 3.4,
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||||
"train_copy_roi": 0.1823
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||||
}
|
||||
]
|
||||
Reference in New Issue
Block a user