Add paper liquidity-provision loop (lp_paper.py)
Simulates two-sided quoting on the screener's top low-vol markets against the live order book, tracking net = rewards accrued - adverse-selection bleed. Clean cash + mark-to-market accounting; fills modeled when midpoint crosses a resting quote (slightly pessimistic on fill rate); rewards accrue by score-share of each pool. Discord summaries + state persistence so it can run for days. This is the decisive, no-money test before any funded/hosted bot. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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jaxperro
@@ -14,3 +14,4 @@ edge_profitable.json
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copyable_77.csv
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lp_markets.csv
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follow_10.json
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lp_paper_state.json
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@@ -20,6 +20,7 @@ live), and backtest the strategy. Zero dependencies — Python 3 stdlib only
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| `copytrade.py` | Copy-trade engine — mirror a watchlist (paper by default, live gated). |
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| `backtest.py` | Replay a watchlist over a recent window and mark outcomes. |
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| `lp_screener.py` | Rank reward-eligible markets by risk-adjusted LP yield (pool ÷ competition, penalized by volatility). |
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| `lp_paper.py` | Paper liquidity-provision loop — simulate quoting on the live book, track **net = rewards − adverse selection**. |
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## Run the dashboard
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@@ -233,5 +234,18 @@ correctly de-ranked: that's where you get picked off.
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**Caveats that still gate real money:** headline APRs are a *snapshot* — thin
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pools attract competitors and yield mean-reverts down; they're *gross*, ignoring
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inventory losses when you get filled; and we have not yet confirmed near-empty
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books actually pay the full pool. The paper LP loop (post near mid, requote on
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moves, track **net** = rewards − pick-off) is the next and decisive test.
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books actually pay the full pool.
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`lp_paper.py` is the decisive test — no money, no host, no key. It picks the
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screener's top low-vol markets, simulates two-sided quotes against the **live**
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order book, and tracks **net = rewards accrued − adverse-selection bleed**:
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```bash
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python3 lp_paper.py --capital 1000 --markets 6 --poll 20 # runs until stopped
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```
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Fills are modeled when the midpoint crosses a resting quote (deliberately a bit
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pessimistic on fill rate); rewards accrue by score-share of each pool. Net P&L,
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per-market breakdown, and Discord summaries let it run for days to see whether
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the edge survives mean-reversion. **Only if net stays clearly positive does a
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real, funded, hosted bot make sense.**
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+234
@@ -0,0 +1,234 @@
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#!/usr/bin/env python3
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"""Paper liquidity-provision loop — measures NET reward yield without real money.
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It simulates posting two-sided limit orders near the midpoint on the screener's
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top markets, against the LIVE order book, and tracks:
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net P&L = rewards accrued - adverse-selection / inventory P&L
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Reward model (per poll, per market):
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your_share = your_notional / (your_notional + competing_notional_near_mid)
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rewards += daily_pool * your_share * (seconds_elapsed / 86400)
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(matches Polymarket's score-share mechanic; assumes ~equal price-quality, which
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is conservative since we quote tight to mid.)
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Fill / adverse-selection model:
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We rest a bid at mid-tick and an ask at mid+tick. Between polls, if the
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midpoint crosses a quote, that quote is assumed FILLED at its price, and we
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take on inventory marked at the NEW mid — so a price that runs through us
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books an immediate loss. This is the bleed that must stay below rewards.
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Each poll we "cancel and re-quote" around the new mid (a bot requoting every
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poll interval). Shorter --poll = less bleed, fewer missed requotes.
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This is an APPROXIMATION (ignores queue position, partial fills, requote
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latency), deliberately a bit pessimistic on fills. Good enough to answer the
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one question that gates real money: is net positive, and how big?
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python3 lp_paper.py --capital 1000 --markets 6 --poll 20
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"""
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import argparse
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import json
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import os
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import time
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from concurrent.futures import ThreadPoolExecutor
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from lp_screener import get, reward_markets, daily_rate, hours_to_end, realized_vol_cents, CLOB
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from copytrade import post_discord, load_json
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STATE_PATH = "lp_paper_state.json"
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def screen_targets(n, min_rate, capital, max_vol):
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"""Pick the top-N low-volatility reward markets to make on."""
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mkts = [m for m in reward_markets()
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if m.get("active") and not m.get("closed") and daily_rate(m) >= min_rate]
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scored = []
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def assess(m):
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r = m.get("rewards") or {}
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ms = r.get("max_spread", 0) / 100.0
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toks = m.get("tokens") or []
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if not toks or ms <= 0:
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return None
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tok = toks[0]["token_id"]
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try:
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bk = get(f"{CLOB}/book?token_id={tok}")
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except Exception:
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return None
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bids = [(float(o["price"]), float(o["size"])) for o in bk.get("bids", [])]
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asks = [(float(o["price"]), float(o["size"])) for o in bk.get("asks", [])]
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if not bids or not asks:
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return None
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mid = (max(p for p, _ in bids) + min(p for p, _ in asks)) / 2
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comp = min(sum(p * s for p, s in bids if p >= mid - ms),
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sum((1 - p) * s for p, s in asks if p <= mid + ms))
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vol = realized_vol_cents(tok)
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hrs = hours_to_end(m)
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if vol is None or vol > max_vol: # skip toxic / unknown-vol
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return None
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if hrs is not None and hrs < 24: # skip imminent/live
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return None
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return {
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"token": tok, "question": m.get("question", "?")[:50],
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"pool": daily_rate(m), "max_spread": ms,
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"min_size": r.get("min_size", 0),
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"tick": float(bk.get("tick_size", 0.01)),
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"comp": comp, "mid": mid, "vol": vol,
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}
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with ThreadPoolExecutor(max_workers=16) as ex:
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for res in ex.map(assess, mkts):
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if res:
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scored.append(res)
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# rank by reward-yield / competition, low vol
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scored.sort(key=lambda x: x["pool"] / (x["comp"] + x["pool"]) / (1 + x["vol"]),
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reverse=True)
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return scored[:n]
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def fresh_market_state(t, per_market):
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# cash = cumulative cash flow from simulated trades (buys negative, sells
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# positive); inv = signed share position. Net trading P&L = cash + inv*mid.
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return {**t, "notional": per_market / 2, "bid": None, "ask": None,
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"inv": 0.0, "cash": 0.0, "rewards": 0.0, "fills": 0,
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"last_t": time.time()}
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def poll_market(s, max_inv_shares):
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"""One observe-fill-accrue-requote step against the live book."""
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try:
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bk = get(f"{CLOB}/book?token_id={s['token']}")
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except Exception:
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return
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bids = [(float(o["price"]), float(o["size"])) for o in bk.get("bids", [])]
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asks = [(float(o["price"]), float(o["size"])) for o in bk.get("asks", [])]
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if not bids or not asks:
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return
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mid = (max(p for p, _ in bids) + min(p for p, _ in asks)) / 2
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now = time.time()
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dt = now - s["last_t"]
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s["last_t"] = now
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size = s["notional"] / mid if mid > 0 else 0
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# 1) fills: did the mid cross our resting quotes since last poll?
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# cash + mark-to-market captures the adverse-selection loss directly.
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if s["bid"] is not None and mid <= s["bid"]: # bought at our bid
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s["inv"] += size
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s["cash"] -= size * s["bid"]
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s["fills"] += 1
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if s["ask"] is not None and mid >= s["ask"]: # sold at our ask
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s["inv"] -= size
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s["cash"] += size * s["ask"]
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s["fills"] += 1
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# 2) accrue rewards for the elapsed time
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comp = s["comp"]
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share = s["notional"] / (s["notional"] + comp) if (s["notional"] + comp) > 0 else 0
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s["rewards"] += s["pool"] * share * (dt / 86400.0)
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# 3) re-quote around the new mid (within max_spread), respecting inventory cap
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s["mid"] = mid
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s["bid"] = mid - s["tick"] if s["inv"] < max_inv_shares else None # stop adding if long
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s["ask"] = mid + s["tick"] if s["inv"] > -max_inv_shares else None
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ms = s["max_spread"]
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s["comp"] = min(sum(p * sz for p, sz in bids if p >= mid - ms),
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sum((1 - p) * sz for p, sz in asks if p <= mid + ms))
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def net_pnl(s):
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return s["rewards"] + s["cash"] + s["inv"] * s["mid"]
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def summary(states, started, capital):
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rew = sum(s["rewards"] for s in states)
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trading = sum(s["cash"] + s["inv"] * s["mid"] for s in states)
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net = rew + trading
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hrs = (time.time() - started) / 3600 or 1e-9
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apr = net / capital / (hrs / 24) * 365 * 100 if capital else 0
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lines = [
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f"⏱ {hrs:.1f}h · capital ${capital:,.0f}",
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f" rewards accrued : +${rew:,.2f}",
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f" trading/inventory: {trading:+,.2f} (adverse-selection bleed)",
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f" ── NET : {net:+,.2f} (~{apr:,.0f}% APR if it holds)",
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]
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return net, "\n".join(lines)
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def run(args):
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cfg = load_json("config.json", {})
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webhook = cfg.get("discord_webhook", "")
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per_market = args.capital / args.markets
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print(f"[{time.strftime('%H:%M:%S')}] screening for {args.markets} low-vol markets...",
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flush=True)
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targets = screen_targets(args.markets, args.min_rate, args.capital, args.max_vol)
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if not targets:
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print("No suitable low-vol markets found right now.")
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return
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states = [fresh_market_state(t, per_market) for t in targets]
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started = time.time()
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print(f"[{time.strftime('%H:%M:%S')}] making markets on {len(states)} markets "
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f"(${per_market:,.0f} each, ${args.capital:,.0f} total):", flush=True)
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for s in states:
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print(f" ${s['pool']:>4.0f}/day vol {s['vol']:.1f}c comp ${s['comp']:,.0f}"
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f" {s['question']}", flush=True)
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if webhook:
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post_discord(webhook, f"📊 **Paper LP started** · {len(states)} markets · "
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f"${args.capital:,.0f} capital. Tracking net = rewards − bleed.")
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max_inv_shares = (per_market / 2) / 0.5 * args.max_inv # rough share cap per market
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last_report = started
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next_rescreen = started + args.refresh
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try:
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while True:
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for s in states:
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poll_market(s, max_inv_shares)
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save_state(states, started, args.capital)
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now = time.time()
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if now - last_report >= args.report:
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net, txt = summary(states, started, args.capital)
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print(f"\n[{time.strftime('%H:%M:%S')}]\n{txt}", flush=True)
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if webhook:
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post_discord(webhook, "📊 **Paper LP update**\n" + txt)
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last_report = now
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if args.duration and (now - started) >= args.duration * 3600:
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break
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time.sleep(args.poll)
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except KeyboardInterrupt:
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pass
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net, txt = summary(states, started, args.capital)
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print(f"\n=== FINAL ===\n{txt}")
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print("\nPer-market:")
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for s in sorted(states, key=net_pnl, reverse=True):
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print(f" net {net_pnl(s):+8.2f} | rew +{s['rewards']:6.2f} | "
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f"fills {s['fills']:3d} | inv {s['inv']:+8.1f} | {s['question']}")
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def save_state(states, started, capital):
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slim = [{"question": s["question"], "pool": s["pool"],
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"rewards": round(s["rewards"], 2), "trading": round(s["cash"] + s["inv"] * s["mid"], 2),
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"inv": round(s["inv"], 1), "fills": s["fills"],
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"net": round(net_pnl(s), 2)} for s in states]
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net, _ = summary(states, started, capital)
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tmp = STATE_PATH + ".tmp"
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with open(tmp, "w") as f:
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json.dump({"started": started, "capital": capital, "net": round(net, 2),
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"markets": slim}, f, indent=2)
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os.replace(tmp, STATE_PATH)
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def main():
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ap = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--capital", type=float, default=1000)
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ap.add_argument("--markets", type=int, default=6)
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ap.add_argument("--poll", type=int, default=20, help="seconds between requotes")
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ap.add_argument("--report", type=int, default=900, help="seconds between summaries")
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ap.add_argument("--refresh", type=int, default=3600, help="seconds between re-screens")
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ap.add_argument("--min-rate", type=float, default=50)
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ap.add_argument("--max-vol", type=float, default=1.5, help="max 24h vol (cents) to qualify")
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ap.add_argument("--max-inv", type=float, default=1.0, help="inventory cap multiple")
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ap.add_argument("--duration", type=float, default=0, help="hours to run (0 = until killed)")
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run(ap.parse_args())
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if __name__ == "__main__":
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main()
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