Add liquidity-rewards market screener (lp_screener.py)

Ranks Polymarket's ~8000 reward-eligible markets by risk-adjusted LP yield: reward pool / order-book competition near mid (gross APR for a $1000 two-sided position), penalized by 24h midpoint volatility (adverse-selection proxy) and time-to-resolution. One-shot snapshot -> lp_markets.csv. README documents the rewards mechanics, the screener, and the open caveats before the paper LP loop. Generated data files gitignored. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-01 02:07:44 +00:00 · 2026-06-13 11:01:26 -04:00
parent 34b39fa1ba
commit d1ca52a33f
3 changed files with 221 additions and 0 deletions
@@ -12,3 +12,5 @@ copytrade_state.json
 edge_metrics.jsonl
 edge_profitable.json
 copyable_77.csv
+lp_markets.csv
+follow_10.json
@@ -19,6 +19,7 @@ live), and backtest the strategy. Zero dependencies — Python 3 stdlib only
 | `table_77.py` | Aggregate a filtered wallet set into one CSV (ROI, total staked, consistency). |
 | `copytrade.py` | Copy-trade engine — mirror a watchlist (paper by default, live gated). |
 | `backtest.py` | Replay a watchlist over a recent window and mark outcomes. |
+| `lp_screener.py` | Rank reward-eligible markets by risk-adjusted LP yield (pool ÷ competition, penalized by volatility). |

 ## Run the dashboard

@@ -200,3 +201,37 @@ story. At 72 days old, we can't yet tell.
 - **Copying entries ≠ copying edge.** A working strategy likely needs to model
  sizing/pricing, or pivot to a consensus signal (bet where many vetted wallets
  agree) rather than blind mirroring.
+
+## Liquidity rewards (the market-making pivot)
+
+After copy-trading proved unreliable, the research pointed to **liquidity
+rewards** as the lowest-risk edge. Polymarket pays makers a daily USDC pool for
+resting limit orders near a market's midpoint — your share = your score ÷ total
+score, where score rewards size and closeness to mid (quadratic:
+`((max_spread − your_spread) / max_spread)²`). ~$200K/day is distributed across
+~8,000 eligible markets (queryable via the CLOB `sampling-markets` endpoint;
+each market exposes `rewards.rates[].rewards_daily_rate`, `min_size`,
+`max_spread`).
+
+`lp_screener.py` ranks those markets by **risk-adjusted** yield — reward pool ÷
+order-book competition near mid (gross APR for a $1000 two-sided position),
+penalized by 24h midpoint volatility (the adverse-selection proxy) and by
+time-to-resolution (imminent = live = toxic).
+
+```bash
+python3 lp_screener.py --min-rate 50 --capital 1000   # one-shot snapshot → lp_markets.csv
+```
+
+**It's a one-shot snapshot, not a daemon** — reward pools, books, and the
+markets themselves churn daily, so re-run before each session.
+
+**What it found:** the sweet spot is **long-dated, low-volatility prop markets**
+(World Cup player props, eliminations) — thin books, decent pools, vol ~0, days
+to resolution. Live esports markets show astronomical gross APR but get
+correctly de-ranked: that's where you get picked off.
+
+**Caveats that still gate real money:** headline APRs are a *snapshot* — thin
+pools attract competitors and yield mean-reverts down; they're *gross*, ignoring
+inventory losses when you get filled; and we have not yet confirmed near-empty
+books actually pay the full pool. The paper LP loop (post near mid, requote on
+moves, track **net** = rewards − pick-off) is the next and decisive test.
@@ -0,0 +1,184 @@
+#!/usr/bin/env python3
+"""Liquidity-rewards market screener.
+
+Ranks Polymarket's reward-eligible markets by *risk-adjusted* yield, so we can
+find where providing liquidity actually pays — high reward pool, thin enough
+book to capture share, but stable enough not to get picked off.
+
+For each market it pulls the order book and a 24h price series and computes:
+  - gross APR  : reward pool / competition, for a $1000 two-sided position
+  - vol_24h    : stdev of 15-min midpoint moves, in cents = adverse-selection proxy
+  - hrs_to_end : time to resolution (imminent = toxic/live)
+  - score      : gross APR penalized by volatility and imminence
+
+GROSS APR ignores pick-off losses — that's exactly what vol_24h flags. A high
+APR with high vol is a trap; the sweet spot is decent APR with low vol and
+days (not hours) to resolution.
+
+    python3 lp_screener.py --min-rate 50 --capital 1000
+"""
+
+import argparse
+import csv
+import json
+import ssl
+import statistics
+import time
+import urllib.request
+from concurrent.futures import ThreadPoolExecutor, as_completed
+
+CLOB = "https://clob.polymarket.com"
+SSL_CTX = ssl._create_unverified_context()
+
+
+def get(url):
+    req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
+    with urllib.request.urlopen(req, timeout=20, context=SSL_CTX) as r:
+        return json.loads(r.read().decode())
+
+
+def reward_markets():
+    out, cursor = [], ""
+    for _ in range(20):
+        try:
+            url = CLOB + "/sampling-markets" + (f"?next_cursor={cursor}" if cursor else "")
+            d = get(url)
+        except Exception:
+            break
+        out += d.get("data", [])
+        cursor = d.get("next_cursor")
+        if not cursor or not d.get("data"):
+            break
+    return out
+
+
+def daily_rate(m):
+    rts = (m.get("rewards") or {}).get("rates") or []
+    return sum(x.get("rewards_daily_rate", 0) for x in rts)
+
+
+def hours_to_end(m):
+    iso = m.get("end_date_iso")
+    if not iso:
+        return None
+    try:
+        end = time.mktime(time.strptime(iso.replace("Z", ""), "%Y-%m-%dT%H:%M:%S"))
+        return (end - time.time()) / 3600
+    except ValueError:
+        return None
+
+
+def realized_vol_cents(token_id):
+    """Stdev of 15-min midpoint moves over the last 24h, in cents."""
+    now = int(time.time())
+    try:
+        h = get(f"{CLOB}/prices-history?market={token_id}"
+                f"&startTs={now - 86400}&endTs={now}&fidelity=15").get("history", [])
+    except Exception:
+        return None
+    prices = [p["p"] for p in h if "p" in p]
+    if len(prices) < 4:
+        return None
+    diffs = [abs(prices[i] - prices[i - 1]) * 100 for i in range(1, len(prices))]
+    return round(statistics.pstdev(diffs), 2)
+
+
+def analyze(m, capital):
+    pool = daily_rate(m)
+    r = m.get("rewards") or {}
+    ms = r.get("max_spread", 0) / 100.0
+    toks = m.get("tokens") or []
+    if not toks or ms <= 0:
+        return None
+    tok = toks[0].get("token_id")
+    try:
+        bk = get(f"{CLOB}/book?token_id={tok}")
+    except Exception:
+        return None
+    bids = [(float(o["price"]), float(o["size"])) for o in bk.get("bids", [])]
+    asks = [(float(o["price"]), float(o["size"])) for o in bk.get("asks", [])]
+    if not bids or not asks:
+        return None
+    bb = max(p for p, _ in bids)
+    ba = min(p for p, _ in asks)
+    mid = (bb + ba) / 2
+    comp_bid = sum(p * s for p, s in bids if p >= mid - ms)
+    comp_ask = sum((1 - p) * s for p, s in asks if p <= mid + ms)
+    myside = capital / 2
+    share = min(myside / (myside + comp_bid), myside / (myside + comp_ask))
+    apr = pool * share / capital * 365 * 100
+    vol = realized_vol_cents(tok)
+    hrs = hours_to_end(m)
+    # risk-adjusted score: reward yield, penalized by adverse selection (vol)
+    # and by imminence (markets resolving within a day are live/toxic).
+    vol_pen = 1 + (vol if vol is not None else 5)        # unknown vol treated as risky
+    time_pen = 1.0 if (hrs is None or hrs >= 48) else max(0.15, hrs / 48)
+    score = round(apr * time_pen / vol_pen, 1)
+    return {
+        "question": m.get("question", "?")[:50],
+        "daily_usd": round(pool),
+        "max_spread_c": r.get("max_spread", 0),
+        "min_size": r.get("min_size", 0),
+        "mid": round(mid, 3),
+        "comp_usd": round(min(comp_bid, comp_ask)),
+        "gross_apr": round(apr),
+        "vol_24h_c": vol if vol is not None else -1,
+        "hrs_to_end": round(hrs) if hrs is not None else -1,
+        "score": score,
+        "token_id": tok,
+    }
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__,
+                                 formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--min-rate", type=float, default=50, help="min $/day pool")
+    ap.add_argument("--capital", type=float, default=1000)
+    ap.add_argument("--workers", type=int, default=16)
+    ap.add_argument("--top", type=int, default=30)
+    args = ap.parse_args()
+
+    print(f"[{time.strftime('%H:%M:%S')}] pulling reward markets...", flush=True)
+    mkts = [m for m in reward_markets()
+            if m.get("active") and not m.get("closed") and daily_rate(m) >= args.min_rate]
+    print(f"[{time.strftime('%H:%M:%S')}] {len(mkts)} markets with >=${args.min_rate}/day "
+          f"· analyzing books + volatility...", flush=True)
+
+    rows = []
+    with ThreadPoolExecutor(max_workers=args.workers) as ex:
+        futs = {ex.submit(analyze, m, args.capital): m for m in mkts}
+        done = 0
+        for f in as_completed(futs):
+            done += 1
+            try:
+                r = f.result()
+            except Exception:
+                r = None
+            if r:
+                rows.append(r)
+            if done % 100 == 0:
+                print(f"  {done}/{len(mkts)}", flush=True)
+
+    rows.sort(key=lambda x: x["score"], reverse=True)
+    cols = ["question", "score", "gross_apr", "vol_24h_c", "hrs_to_end", "daily_usd",
+            "comp_usd", "max_spread_c", "min_size", "mid", "token_id"]
+    with open("lp_markets.csv", "w", newline="") as fp:
+        w = csv.DictWriter(fp, fieldnames=cols)
+        w.writeheader()
+        w.writerows(rows)
+
+    print(f"\n{'score':>6}{'grossAPR':>9}{'vol_c':>7}{'hrs':>6}{'$/day':>7}"
+          f"{'comp$':>9}{'spr':>5}  market")
+    print("-" * 92)
+    for r in rows[:args.top]:
+        vol = "n/a" if r["vol_24h_c"] < 0 else f"{r['vol_24h_c']:.1f}"
+        hrs = "?" if r["hrs_to_end"] < 0 else r["hrs_to_end"]
+        print(f"{r['score']:>6.0f}{r['gross_apr']:>8}%{vol:>7}{hrs:>6}{r['daily_usd']:>7}"
+              f"{r['comp_usd']:>9}{r['max_spread_c']:>5}  {r['question'][:42]}")
+    print("-" * 92)
+    print(f"{len(rows)} markets ranked → lp_markets.csv")
+    print("score = gross APR × time-factor ÷ (1+vol).  High APR + low vol + days-to-end = real.")
+
+
+if __name__ == "__main__":
+    main()