quad

quad_resolutions.csv

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+#!/usr/bin/env python3
+"""quad_resolve.py - resolve paper BUY_YES signals against real market outcomes.
+
+Reads the append-only ledger (mode=paper, action=BUY_YES_15M_UP), fetches each
+market from gamma-api, and writes quad_resolutions.csv with win/loss + ROI per
+signal. This is what the OOS evaluation needs (ledger alone never resolves).
+
+Math (standard Polymarket YES): entry price p, shares = notional/p.
+  win  -> payout shares*$1, ROI = (1-p)/p
+  loss -> payout 0,        ROI = -1.0
+outcomes = ["Up","Down"]; outcomePrices[0] is Up (the side we buy).
+
+Idempotent: skips markets already in the resolutions file.
+"""
+import csv
+import json
+import os
+import sys
+import urllib.request
+from datetime import datetime, timezone
+
+LEDGER = os.environ.get("LEDGER_PATH", "/home/neo/quad_ledger.csv")
+RESOLUTIONS = os.environ.get("RESOLUTIONS_PATH", "/home/neo/quad_resolutions.csv")
+FIELDS = ["ts", "coin", "market_id", "question", "yes_price", "resolve_ts",
+          "outcome", "win", "roi"]
+
+
+def now_iso():
+    return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+
+def parse_yes_price(raw):
+    """Ledger stores yes_price as either a JSON array of both outcome prices
+    (autopilot) or a single YES price float (live-order paper mode)."""
+    if not raw:
+        return None
+    raw = raw.strip()
+    try:
+        arr = json.loads(raw)
+        if isinstance(arr, list) and arr:
+            return float(arr[0])  # index 0 == Up == our side
+    except Exception:
+        pass
+    try:
+        return float(raw)
+    except Exception:
+        return None
+
+
+def fetch_market(mid):
+    url = f"https://gamma-api.polymarket.com/markets/{mid}"
+    req = urllib.request.Request(url, headers={"User-Agent": "quad-lab/0.1"})
+    with urllib.request.urlopen(req, timeout=30) as r:
+        return json.load(r)
+
+
+def outcome_prices(m):
+    """gamma-api returns outcomePrices as a JSON *string* like '["1", "0"]'."""
+    raw = m.get("outcomePrices")
+    if isinstance(raw, list):
+        return [str(x) for x in raw]
+    if isinstance(raw, str):
+        try:
+            arr = json.loads(raw)
+            return [str(x) for x in arr]
+        except Exception:
+            return []
+    return []
+
+
+def load_resolved():
+    ids = set()
+    if os.path.exists(RESOLUTIONS):
+        with open(RESOLUTIONS) as f:
+            for r in csv.DictReader(f):
+                ids.add(r["market_id"])
+    return ids
+
+
+def append_resolution(row):
+    exists = os.path.exists(RESOLUTIONS)
+    with open(RESOLUTIONS, "a", newline="") as f:
+        w = csv.DictWriter(f, fieldnames=FIELDS)
+        if not exists:
+            w.writeheader()
+        w.writerow({k: row.get(k, "") for k in FIELDS})
+
+
+def main():
+    if not os.path.exists(LEDGER):
+        sys.exit(f"ledger not found: {LEDGER}")
+    rows = []
+    with open(LEDGER) as f:
+        rows = [r for r in csv.DictReader(f)]
+
+    signals = [r for r in rows if r.get("action") == "BUY_YES_15M_UP"
+               and r.get("mode") == "paper" and r.get("market_id")]
+    done = load_resolved()
+    pending = [r for r in signals if r.get("market_id") not in done]
+    print(f"signals={len(signals)} already_resolved={len(signals) - len(pending)} pending={len(pending)}")
+
+    wins = losses = unresolved = 0
+    total_roi = 0.0
+    for r in pending:
+        mid = r["market_id"]
+        try:
+            m = fetch_market(mid)
+        except Exception as e:
+            print(f"  {mid} {r['coin']}: fetch err {e}")
+            unresolved += 1
+            continue
+        status = m.get("umaResolutionStatus")
+        prices = outcome_prices(m)
+        if status != "resolved" or len(prices) < 2 or m.get("closed") is not True:
+            print(f"  {mid} {r['coin']}: not resolved yet (status={status})")
+            unresolved += 1
+            continue
+        p = parse_yes_price(r.get("yes_price"))
+        if p is None:
+            print(f"  {mid} {r['coin']}: unparsable yes_price={r.get('yes_price')!r}")
+            unresolved += 1
+            continue
+        up_won = prices[0] == "1"
+        roi = (1 - p) / p if up_won else -1.0
+        row = {"ts": now_iso(), "coin": r["coin"], "market_id": mid,
+               "question": r.get("question", ""), "yes_price": str(p),
+               "resolve_ts": r.get("resolve_ts", ""),
+               "outcome": "UP" if up_won else "DOWN",
+               "win": str(up_won), "roi": f"{roi:.4f}"}
+        append_resolution(row)
+        wins += 1 if up_won else 0
+        losses += 0 if up_won else 1
+        total_roi += roi
+        print(f"  {mid} {r['coin']}: price={p} -> {'WIN' if up_won else 'LOSS'} roi={roi:+.3f}")
+
+    if wins + losses:
+        n = wins + losses
+        print(f"\nresolved now: {wins}W/{losses}L  hit={wins/n:.3f}  mean_roi={total_roi/n:+.3f}")
+    if unresolved:
+        print(f"unresolved still: {unresolved}")
+    print("done", now_iso())
+
+
+if __name__ == "__main__":
+    main()