Wallet Profiler How it relates: Wallet Profiler builds on score_wallet()Wallet Analytics . To execute, see Copy-Trading .Goes beyond basic scoring to build a complete behavioral profile of any Polymarket wallet. Analyzes how a wallet trades across four dimensions and generates concrete strategies to profit from its patterns.
Behavioral Profiling Temporal patterns, market selection, order flow, and edge detection across 20+ metrics.
Strategy Generation Up to 10 rule-based strategies with triggers, sizing, and execution params.
Auto-Execution One-call hunt() or pipeline hunter() to profile and trade automatically.
MCP Integration Available as an MCP tool for AI agent workflows.
Quick Start import horizon as hz # Full profile profile = hz.profile_wallet( "0x1234..." ) print (profile.best_strategy) # Auto-execute best strategy (standalone) hz.hunt( "0x1234..." ) # Pipeline mode (inside hz.run) hz.run( markets = [ "some-market" ], pipeline = [hz.hunter( "0x1234..." )], interval = 30.0 , )
Analysis Dimensions Temporal Analysis Answers when does the wallet trade?
Metric Description Hour distribution Trade count per hour (0-23 UTC) with peak hour and Shannon entropy Day distribution Day-of-week distribution with weekend ratio Session Dominant session: US (13-21 UTC), Asia (0-8), Europe (7-16), or round-the-clock Burst analysis Burstiness metric B = (sigma - mu) / (sigma + mu) of inter-trade intervals Active days Percentage of days in the analysis window with at least one trade
Market Selection Answers what does the wallet trade?
Metric Description Categories Inferred from market titles: politics, crypto, sports, economics, entertainment, science Win rate per category Performance breakdown by category (requires position data) Herfindahl index Market concentration (0 = diversified, 1 = single market) Early entrant score Fraction of buys near opening prices (0.45-0.55)
Order Flow Answers how does the wallet enter and exit?
Metric Description Building style single_entry / scaling_in / dca (based on avg buys per market)Exit style single_exit / scaling_outHold time Average hours from first buy to first sell per market Size trend increasing / decreasing / stable (linear regression on chronological sizes)Dip buyer score Fraction of buys below market average price (0-1) Rip seller score Fraction of sells above market average price (0-1)
Edge Detection Answers is the wallet good? Includes the composite score from score_wallet()
Metric Description Timing quality Average (current - entry) / entry across positions Win rate by size Win rates for small / medium / large trades (33rd/66th percentile buckets) Streak type streaky / mean_reverting / random (lag-1 autocorrelation)Edge in bps Estimated edge derived from average PnL percentage Confidence 0.1 to 0.9 based on sample size (10+ to 200+ positions)
Strategy Generation The profiler generates up to 10 rule-based strategies, each with a concrete trigger condition, implementation type, and execution params:
Strategy Trigger Type fade_losercomposite < -0.3 fade copy_winnercomposite > 0.3 + info score > 0.15 copy session_pre_positionBursty + dominant session > 60% session_trade front_run_timingTiming CV < 0.3 front_run stop_huntStop cluster > 30% counter_trade fade_directionDirectional bias + negative edge fade copy_best_categoryCategory win rate > 60% copy concentration_exploitHHI > 0.3 + negative score fade copy_dip_buyerDip score > 0.6 + positive edge copy timing_exploitHour entropy < 2.0 session_trade
Each ProfitStrategy contains:
Field Description nameStrategy identifier descriptionHuman-readable explanation mechanismHow the strategy works expected_edge"high" / "medium" / "low"risk_level"high" / "medium" / "low"confidence0-1 impl_type"copy" / "fade" / "front_run" / "counter_trade" / "session_trade"paramsExecution parameters (inverse, size_scale, poll_interval, etc.)
Auto-Execution hunt() and hunter() use Copy-Trading under the hood - they profile the wallet, pick the best strategy, then delegate to copy_trades() / copy_trader() with the appropriate params.hunt(wallet): StandaloneProfiles the wallet, picks the best strategy, and auto-executes via copy_trades() with the appropriate params.
hz.hunt( "0x1234..." , size_scale = 0.5 , max_position = 1000.0 , exchange = "paper" , dry_run = True , )
hunter(wallet): PipelineCreates a pipeline function that profiles on first call and delegates to copy_trader() each subsequent cycle.
hz.run( markets = [ "some-market" ], pipeline = [hz.hunter( "0x1234..." , size_scale = 0.5 , dry_run = True )], interval = 30.0 , )
The profile is stored in ctx.params["wallet_profile"] for inspection.
The profiler is available as an MCP tool:
profile_wallet(address="0x1234...", trade_limit=500)
Returns a flattened dict with all metrics and strategies.
Data Types Type Fields FullWalletProfilewallet, score, analysis, temporal, market_selection, order_flow, edge, strategies, best_strategy ProfitStrategyname, description, mechanism, expected_edge, risk_level, confidence, impl_type, params TemporalProfilehour/day distributions, session, burst, active_days MarketSelectionProfilecategories, concentration, diversity, early entrant OrderFlowProfilebuilding/exit styles, hold time, size trend, dip/rip scores EdgeDetectioninformation score, timing quality, win rate buckets, streaks, edge bps
Algorithms All analysis is pure Python (no ML dependencies):
Algorithm Purpose Shannon entropy Distribution uniformity Coefficient of variation Regularity detection Linear regression slope Size trend detection Lag-1 autocorrelation Streak analysis Herfindahl index Market concentration Burstiness metric B = (sigma - mu) / (sigma + mu) tanh normalization Bounding scores to [-1, +1] Keyword matching Market category inference
Wallet profiling uses Polymarket’s public Data API. Results are based on historical behavior and may not predict future performance. Use responsibly and ethically.
