Polymarket Smart Money

Tools to find Polymarket wallets worth following, copy-trade them (paper or live), and backtest the strategy. Zero dependencies — Python 3 stdlib only (except live trading, which needs py-clob-client).

Start here — read What we learned. The project began as "find wallets winning >75% of their bets." That metric turned out to be an artifact, and the research below changed what we actually measure. Don't fund anything before reading it.

Tools

File	What it does
smart_money.py	Dashboard + scanner. Ranks leaderboard wallets by true win rate.
edge_research.py	Scans up to ~2000 wallets for a reliable, copyable weekly edge (consistency metrics + copyability).
lookback.py	Deep-dive a short list over a long window, split into halves for out-of-sample reads.
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).
lp_paper.py	Paper liquidity-provision loop — simulate quoting on the live book, track net = rewards − adverse selection.

Run the dashboard

python3 smart_money.py

Open http://localhost:8899, hit Scan, and wait a minute or two. Adjust the filters (win rate, bets/week, minimum resolved bets, candidate pool size) and the table updates live while the scan runs. Click any trader to see their recent resolved bets and a link to their Polymarket profile.

Run in the terminal

python3 smart_money.py --scan            # default 150-wallet pool
python3 smart_money.py --scan --pool 300 # broader sweep

How it works

1. Candidates — pulls the 7d, 30d, and all-time leaderboards from data-api.polymarket.com/v1/leaderboard and dedupes into a candidate pool (default 150 wallets).
2. True win rate — over resolved bets in the last 90 days. This is the subtle part: Polymarket only redeems winning shares, so /closed-positions (redeemed or sold) is heavily survivorship-biased toward winners — losing shares are worth $0 and sit unredeemed in /positions at curPrice 0 forever. A correct win rate has to union both endpoints over the same window. Counting only /closed-positions (the naive approach) reports ~90% for wallets whose real hit rate is ~50%.
3. Frequency — counts trades from /activity over the last 4 weeks; bets/week is the number of distinct markets traded per week, so 50 fills on one order don't count as 50 bets.
4. Filter — keeps wallets with win rate ≥ 75%, ≥ 2 bets/week, and ≥ 10 resolved bets.

Reality check: with the unbiased metric, essentially no top wallet wins 75% of its bets — true rates cluster around 49% (a coin flip), max ~60%. The profitable ones make money through position sizing and entry prices, not hit rate. Treat a high win rate as a red flag for a bias bug, not a green light. See the backtest below.

Copy-trading (copytrade.py)

Once you've found wallets worth following, copytrade.py watches them and mirrors their trades onto your own account.

• Sizing — each fresh entry stakes a fixed % of your bankroll (default 2%).
• Mirror — copies entries and exits: when they add, it adds proportionally; when they sell part of a position, it sells the same fraction of yours.
• Price guard — skips a copy if the market has moved >5% from their fill price, so you don't chase.
• No backfill — only copies positions they open after you start watching; positions they already held are tracked (so exits still mirror) but never opened.
• Per-position cap — max_position_usd caps total cost in any one market. Without it, proportional adds let a single position balloon toward your whole exposure limit as the whale piles in (a backtest caught exactly this).
• Discord alerts — set discord_webhook in the config to get a ping on every trade it would place (entries green, exits red).

Backtesting (backtest.py)

Replays a watchlist's real trades over a recent window through the same copy logic, filling at each wallet's actual historical price and marking outcomes from how markets resolved.

python3 backtest.py --days 7

A 7-day backtest of four top wallets returned −48% on deployed capital — not because the engine is broken, but because the wallets' true entry hit rate is ~50% and flat-size copying pays the spread on every coin flip. Copying a profitable wallet's entries does not reproduce its edge, which lives in sizing and entry prices. Backtest before you fund anything.

⚠️ Real money — read this

It runs in PAPER mode by default and places nothing — it just logs what it would do. Live trading requires all of: "mode": "live" in the config, the --live flag, typing a confirmation phrase, and py-clob-client with valid credentials. Hard caps (per-trade, daily spend, total exposure, open positions, price bounds) apply in both modes. In live mode this places real orders with real money on your account — you own the config and the outcomes.

python3 copytrade.py --init      # write config.example.json
cp config.example.json config.json
#  ... edit config.json: add wallets to "watchlist", set bankroll & caps ...
python3 copytrade.py             # PAPER mode — safe, logs only
python3 copytrade.py --once      # single polling pass, then exit

Going live (only after you trust the paper output):

pip install py-clob-client
#  set "mode": "live" and fill in the "live" block (private_key, funder_address)
python3 copytrade.py --live      # prompts for a typed confirmation

config.json and copytrade_state.json are gitignored so your credentials and runtime state never get committed.

Caveats

• Candidates come from the leaderboards, so this surfaces profitable sharps. A high-win-rate wallet that has never cracked any leaderboard window won't appear — scanning every wallet on the platform isn't feasible via the public API.
• Win rate is measured over resolved bets in the last 90 days, not all history.
• Win rate ≠ EV. Wallets with positive all-time leaderboard PnL routinely show ~50% true win rates and even negative 90-day realized PnL. Following a wallet profitably is about how it sizes and prices entries, not how often it's right. The realized_pnl column is position-level over 90 days and is not comparable to the all-time leaderboard figure.
• Very high-volume / market-maker wallets (thousands of fills) can't be cleanly backtested via the public API — too many fills, no historical position snapshot.

What we learned (research log)

The honest story of what the data showed, in order. Each finding killed an assumption the previous step relied on.

1. Win rate was an illusion (survivorship bias). Polymarket only redeems winning shares; losing shares are worth $0 and sit unredeemed in /positions at curPrice 0 forever, never entering /closed-positions. Measuring win rate over /closed-positions alone counts almost only winners. Examples: NiNo999 read 90.6%, true rate 48.3%; Boggs read 73.4%, true 50.3%. Fixed by unioning both endpoints over a window. Takeaway: a high reported win rate is a red flag for a bias bug, not a sharp.

2. With the honest metric, nobody wins 75%. Across 25 top wallets, true win rates clustered at a median 49% (coin flip), max ~60%. Zero passed a 75% bar. Win rate is the wrong thing to rank on.

3. Win rate ≠ profit; raw PnL ≠ reliability. surfandturf won 54.8% and made millions; Latina (leaderboard #1 all-time) won 43% and was −$3.8M over 90 days. And wallets with big total PnL often got there on one or two outlier weeks (38% green weeks) — a lottery, not an edge. The signal that finds reliable money is weekly consistency: % of weeks green, profit factor, weekly Sharpe — measured per week, with enough weeks.

4. Flat-size copy-trading is −EV. A 7-day backtest of four "top" wallets returned −48%. At ~50% entry hit rates, mirroring entries at flat size just pays the spread on coin flips. A profitable wallet's edge lives in sizing and entry prices, which copying entries does not reproduce. (The backtest also exposed a missing per-position cap — proportional adds could balloon one market to the whole exposure limit.)

5. A reliable edge looks real but rare — and skews young. Scanning 1,500 wallets over 120 days: 1,017 had history, 199 passed a consistency screen, 77 were copyable (hold-to-resolution ≥70%). But ~7.5% of wallets passing by chance is exactly what randomness produces over 1,017 coin-flippers — so some of the 77 are luck. Worse, a 240-day lookback showed the "best" wallets are young accounts (surfandturf's oldest bet: 72 days; joblessfinalboss: 79). New accounts that get hot rise to the leaderboard and pass the screen; the ones that flamed out are delisted. The most impressive short-term performers are the least trustworthy.

6. ROI and size are inversely related. Among the 77 copyable wallets, the highest-ROI ones bet small (dnte: 57% ROI on $109K), while the biggest bettors scalp thin edges (elmcap2: $114M staked, 0.4% ROI). surfandturf was the lone anomaly — big and high-ROI ($27.9M staked at 16%) — which makes it either the best find or the biggest variance story. At 72 days old, we can't yet tell.

Where this leaves the strategy

• Rank on risk-adjusted consistency (% green weeks × profit factor × Sharpe), never win rate or raw PnL.
• Require account longevity — distrust anything under ~4–6 months.
• Validate out-of-sample (walk-forward: select on an early window, measure a later one) before trusting any wallet list. This is the decisive open step.
• 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).

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.

lp_paper.py is the decisive test — no money, no host, no key. It picks the screener's top low-vol markets, simulates two-sided quotes against the live order book, and tracks net = rewards accrued − adverse-selection bleed:

python3 lp_paper.py --capital 1000 --markets 6 --poll 20   # runs until stopped

Fills are modeled when the midpoint crosses a resting quote (deliberately a bit pessimistic on fill rate); rewards accrue by score-share of each pool. Net P&L, per-market breakdown, and Discord summaries let it run for days to see whether the edge survives mean-reversion. Only if net stays clearly positive does a real, funded, hosted bot make sense.