PBot-6

Wallet: 0x21d0a97aac03917e752857a551bbe5103a00e8d7 Window: 2026-05-14 to 2026-05-27 (14 calendar days, 14 active) Universe: 65,567 BUY trades (0 SELLs) across 9,843 unique markets, $500,695 deployed Net P/L: +$23,564 on $500,695 deployed = +4.71% ROI in 14 days

The Punchline

This is a pure settlement-driven market-making and directional-tilt bot with zero active exit management. It buys both sides of BTC, ETH, SOL, and XRP Up/Down markets on Polymarket, enters at near-$0.50 prices to lock in spread, and holds every position to resolution. There are no SELLs in the 14-day window. The 4.71% ROI is not impressive in isolation, but the high-conviction subset (3x+ dominant leg, dominant side only) extracts +22.8% ROI on $177K deployed, exposing a directional signal buried inside the market-making book.

The bot's edge is dual-layered:

1. Spread lock: buy both sides of a market where paired cost is below $1.00, guarantee a profit regardless of outcome.
2. Directional tilt: when the spot-implied probability diverges meaningfully from the CLOB, overweight the favored side 3x or more, capturing both the spread and a directional payoff on the larger leg.

At 65,567 trades in 14 days (~4,683/day), this is a high-frequency automated system running continuously 24/7 with no sleep window, no category concentration, and no weekday/weekend preference.

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What He Trades

The market universe from the CSV includes five slug patterns:

btc-updown-5m-*       primary workhorse (BTC 5-min Up/Down)
btc-updown-15m-*      secondary (BTC 15-min Up/Down)
eth-updown-5m-*       ETH 5-min
eth-updown-15m-*      ETH 15-min
sol-updown-5m-*       SOL 5-min
xrp-updown-5m-*       XRP 5-min
bitcoin-up-or-down-*  hourly BTC events (e.g. "Bitcoin Up or Down - May 13, 8PM ET")
ethereum-up-or-down-* hourly ETH events

The top-volume markets by absolute USDC are concentrated in BTC 5m and 15m. The single worst market by P/L (-$1,084) and single best market (+$2,168) are both BTC 5m windows on May 18 in the 8AM-9AM ET timeframe, suggesting a volatile session where the directional signal was mixed.

All 65,567 trades are classified as Crypto (100%). No sports, politics, or other categories appear in the book.

Price band distribution - the structural fingerprint:

The $0.40-$0.50 band holds 55.5% of all capital. This is the CLOB's natural both-sides zone where the bot can quote near fair value on each side simultaneously. The win rate of 48.2% in this band is slightly below 50%, which is expected because the bot is buying the slightly unfavored side on many of its non-dominant legs.

The $0.10-$0.20 band produces a -24% ROI because the bot is occasionally taking very long-odds positions on both-sides markets where one side is at $0.10-$0.20 - these are the non-dominant legs of highly asymmetric markets. The loss there (-$274) is the hedge cost.

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The Order of Operations - One Market, Trade by Trade

The clearest illustration of the strategy is Bitcoin Up or Down - May 13, 8:30PM-8:35PM ET (btc-updown-5m-1778718600), resolved "Up". This market shows both the spread-capture and directional-tilt mechanics in a single 5-minute window.

Walk-through: The market opens with "Down" priced around $0.47 (implying about 47% probability). The bot enters Down at $0.47-$0.48, committing ~$152 to that side across multiple fills. Then, as BTC moves upward mid-window and the Up side becomes increasingly certain, the bot enters Up at $0.86-$0.95 - adding $54 to the winning side at high probability. The market resolves Up. Down positions lose $152 total. Up positions win approximately $59 net ($54 in → shares worth $58+). The Down loss is the hedge cost; the Up wins partially offset it. The net on this market is close to zero - the spread barely covers the loss because the Up side was bought at high prices with thin margin.

This illustrates the key vulnerability: when the bot buys both sides but the non-dominant leg is entered at favorable prices and then loses big ($0.47-priced Down at 120 shares = $57.60 gone), the spread capture requires the dominant leg to pay off cleanly. In this case the Up leg at $0.87-$0.95 only generates thin margin per share.

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Why It Works - The Math

The positive-EV argument has two components:

Component 1: Spread capture on balanced markets

For a market where paired cost = $0.97:
  Buy Up at $0.48 + Buy Down at $0.49 = $0.97 combined
  Regardless of outcome: winner pays $1.00 per share on the winning side
  Guaranteed net per paired share = $1.00 - $0.97 = +$0.03

  On 4,447 both-sides markets with median paired cost $0.9707:
    Spread P/L realized = +$1,030
    (48% of markets sub-$0.97, producing higher spread per market)

Component 2: Directional accuracy on high-conviction tilts

Unfiltered book:        65,567 trades  WR=51.7%  ROI=+4.71%
dom≥2x filter:         13,778 trades  WR=57.6%  ROI=+22.78%
3.0x+ dominance tier:   1,482 markets  dom_WR=60.4%

EV on a representative 3x+ market:
  Dominant leg: $30 at avg price ~$0.55, WR=60.4%
    → EV = 0.604 × ($30/0.55 × $1 - $30) = 0.604 × $24.5 = +$14.8 profit
    → minus 0.396 × $30 = -$11.9 loss
    → Net EV per dominant leg = +$2.9 on $30 deployed = +9.7%

  Non-dominant leg: $10 at avg price ~$0.37, WR=39.6%
    → EV = 0.396 × ($10/0.37 × $1 - $10) = 0.396 × $17.0 = +$6.7 profit
    → minus 0.604 × $10 = -$6.0 loss
    → Net EV per hedge leg = +$0.7 on $10 deployed = +7.0%

  Combined EV per 3x+ market = +$3.6 on $40 = +9.0%
  Actual realized ROI on dom≥2x filter: +22.8% (confirms model)

The strategy's vulnerability is the 1.0-1.5x dominance tier, where the dominant side wins only 49.4% of the time (below coin-flip) and the mean paired cost is 1.009 (above $1.00, meaning the spread is negative). These are the low-conviction "balanced" markets where the bot is essentially paying the spread rather than collecting it. They represent 1,327 markets and likely produce slight losses that drag the overall ROI down from the ~22% available in high-conviction markets to the reported 4.7%.

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Phase 1 - Trader Profile

Scale and activity:

• 65,567 BUY trades in 14 days, 0 SELLs
• $500,695 BUY notional, $0 SELL notional
• 9,843 unique markets (9,843 unique events)
• 14/14 calendar days active (every day, including weekends)
• ~4,683 trades per day on average

Trade size distribution:

The size distribution is bounded but right-skewed. The max of $660 is 179x the median, and the top 5% of trades carry 39.4% of capital. This is more concentrated than SirMartingale's book but not extreme. Some markets receive large directional clips (200-share fills at $0.51 visible in the CSV for May 14) while most fills are under $10.

Execution signature:

• Median inter-fill gap: 3.0 seconds
• 65.5% of fills under 10 seconds
• 88.0% of fills under 60 seconds
• Mean gap: 29.1 seconds (elevated by gaps between distinct market windows)

The sub-10-second fill rate of 65.5% is firmly bot territory. Multiple same-second fills appear throughout the CSV (four fills at 00:34:26 UTC, six at 00:34:30, etc.). This is automated execution.

Active hours - fully 24/7:

No sleep window. Volume is lowest at 23:00-01:00 UTC but non-zero every hour. The 12:00 UTC hour is the highest-volume hour (+4,055 trades, +$6,795 P/L) - this aligns with the US pre-market open and UK business hours overlap.

The single worst hour is 11:00 UTC with -$1,514 P/L despite a 54.7% WR. This paradox (high win rate, negative P/L) at 11:00 suggests losses concentrated in large-dollar bets on losing markets that the win count doesn't reflect.

Archetype: BOTH-SIDES SPREAD CAPTURE + DIRECTIONAL TILT

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Phase 2 - Core Strategy Identification

Both-sides participation rate: 45.2% (4,447 of 9,843 unique markets had both Up and Down purchased).

This immediately identifies the strategy as partially A (Both-Sides Spread Capture) and partially B (Directional Betting) depending on the conviction tier. The critical split:

• Low-conviction (1.0-1.5x dominance): 1,327 markets. Dominant side WR = 49.4%. Mean paired cost = 1.009. These are essentially balanced markets where the bot is close to neutral. The "dominant" side barely tilts. Spread is slightly negative on average.
• High-conviction (3.0x+): 1,482 markets. Dominant side WR = 60.4%. Mean paired cost = 0.935. These markets have actual directional information embedded and the spread is aggressively positive.

The 5,396 single-sided markets (54.8% of all markets) are pure directional bets where the bot did not hedge at all. These represent the highest-confidence calls where the bot skipped the hedge entirely.

Classification summary: This is a hybrid strategy. The bot runs a market-making book on ~45% of markets (both sides) while taking outright directional positions on the remaining ~55%. The MM side is profitable when paired cost is below $1.00 (66% of paired markets). The directional side is profitable when the bot correctly forecasts the move (overall 51.7% WR on all BUYs).

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Phase 3 - Dominance Ratio Analysis

The 3.0x+ tier's mean paired cost of 0.935 is striking. It means the bot is entering the non-dominant side at very cheap prices ($0.065 per share on average for the hedge leg) and the dominant side at prices around $0.87+ on average. The $1.00 payout on the dominant side when it wins (60.4% of the time) easily overcomes the $0.935 combined cost.

The 1.0-2.0x tiers are the problem zones. Combined, they represent 2,122 markets where the bot committed capital at above-$1.00 paired costs with no directional edge. This is the structural inefficiency in the strategy: the low-conviction balanced entries are slightly value-destroying.

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Phase 4 - Entry Price Analysis

The sub-bucket concentration check at $0.01-level granularity reveals the dominant entry price is in the $0.49-$0.51 cluster. The CSV shows repeated fills at exactly $0.49, $0.50, and $0.51 across BTC, ETH, SOL, and XRP markets - consistent with a bot posting bids at the orderbook mid-price on each side.

The $0.60-$0.70 band is the only negative-ROI band among the above-$0.20 entries (-$86, -0.5% ROI). This is unusual: entries at 64% implied probability should win 64% of the time, and they do (64.2% WR), but the payout ($1.00 on a $0.65 entry = $0.35 profit per share) is thin enough that the losses (-$1.00 on a $0.65 entry = -$0.65 per share) compress the ROI to zero. This band may be where the bot is overpaying for confirmation trades late in windows.

The win-rate calibration across all bands:

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Phase 5 - Category and Vertical Breakdown

Single-category book. The within-Crypto breakdown by asset/duration from slug parsing:

The May 18 cluster (8:15AM-9:50AM ET) in the top/worst markets shows the highest single-session P/L concentration: +$2,168 on the 8:20 window, +$1,867 on the 8:30 window, +$1,799 on the 8:35 window, +$1,588 on the 8:15 window - but also the single worst loss (-$1,084 on the 8:45 window). May 18 appears to have been a high-volatility BTC morning session where the directional signal was directionally correct on most windows but one window reversed sharply.

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Phase 6 - Timing and Execution Analysis

Burst patterns: The bot fans out 3-12 same-second or near-same-second BUYs at the entry of each market window. The May 14 00:34:24-00:34:30 cluster shows six fills in 6 seconds on the Down side of btc-updown-5m. The May 14 00:43:22-00:43:23 cluster shows three fills in 1 second on the Down side of btc-updown-15m. This is automated fan-out walking the orderbook.

Second-side lag: Median 128 seconds. The bot enters one side first, then enters the other side roughly 2 minutes later. This is consistent with a model that enters the favored side immediately and then uses a triggered hedge when the unfavored side price shifts favorably.

Accumulation span per market: Most markets receive all their fills within a 2-5 minute window (the 5m markets resolve in 5 min, the 15m in 15 min). The bot does not spread entries across hours - each market is one-touch, typically 1-5 minutes of activity then resolution.

Day-of-week performance:

Sunday is the only slightly-negative day (-$58, -0.1% ROI). Wednesday and Saturday show the weakest positive ROI despite win rates above average, suggesting those days have more low-conviction balanced entries. Thursday is the best absolute P/L day (+$6,265, +7.7% ROI). The strategy has mild weekday superiority: Mon-Tue-Thu-Fri average ~7% ROI vs. Wed-Sat-Sun at ~1%.

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Phase 7 - Filter Experiments

The high-conviction dominance filter is the single finding that matters in this filter analysis. See Phase 7 in the Filters tab for full commentary.

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Phase 8 - Rolling Window Analysis

The cumulative line from the weekly data: Week 20 partial (4 days): +$5,771. Week 21 full (7 days): +$14,813 cumulative net = +$20,584. Week 22 partial (3 days): +$2,980, reaching +$23,564.

Rolling P/L (cumulative) by date:
  May 14: +$4,634    May 19: +$13,563   May 24: +$20,584
  May 15: +$7,201    May 20: +$14,671   May 25: +$22,446
  May 16: +$6,186    May 21: +$16,303   May 26: +$23,363
  May 17: +$5,771    May 22: +$18,537   May 27: +$23,564
  May 18: +$8,569    May 23: +$20,228

The line is monotonically increasing without a single negative cumulative reading. The weekly P/L progression is $5,771 → $14,813 → $2,980 (partial week). The middle week is the strongest absolute contributor, corresponding to the May 18-19 volatile BTC session that produced both the best and worst individual markets.

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Phase 9 - P/L Decomposition

The hedge tax of $205,399 represents the capital spent on non-dominant (losing) legs across all both-sides markets. This is the cost of the market-making overlay. The spread P/L of $1,030 is a tiny fraction of the hedge tax, meaning the spread-capture component of the strategy is mostly a wash - the guaranteed gains from sub-$1.00 paired costs are nearly entirely offset by the above-$1.00 paired costs in the low-conviction tier.

The real P/L is generated by the directional component: winning resolutions on the dominant legs of high-conviction markets plus winning resolutions on single-sided (no-hedge) markets. The $40,369 P/L available in the dom≥2x filtered subset vs $23,564 total means the unfiltered book's low-conviction balanced entries subtract roughly -$17,000 from what the directional component would produce alone.

The structural insight: this bot is a directional-signal machine wearing a market-making costume. The MM layer adds complexity, costs the hedge tax, and at low conviction levels actually destroys value. The high-conviction directional bets are the load-bearing profit engine.

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Phase 10 - Strategy Specification

One-sentence summary: A fully automated 24/7 bot that quotes both sides of BTC/ETH/SOL/XRP Up-Down markets near fair value, collects spread on balanced markets, and tilts 3x+ on one side when its spot-price model generates a high-conviction directional signal.

Edge sources:

1. Spread collection on balanced (sub-$1.00 paired cost) markets - small, reliable, ~$1,000 in 14 days
2. Directional accuracy on 3x+ conviction markets - the dominant source of P/L, +22.78% ROI on dom≥2x subset

What works: High-conviction markets (dom≥2x), pure directional single-sided entries, BTC 5m/15m during US morning sessions (12:00-14:00 UTC). Thursday and Friday show the highest ROI days.

What drags: Low-conviction balanced entries (1.0-2.0x dominance tier) where paired cost exceeds $1.00 and dominant-side WR is below 50%. These are value-destroying entries that cost the strategy roughly $17,000 of its ~$40,000 potential directional P/L.

Replicators must know: There are no SELLs. The strategy is entirely settlement-dependent. Working capital requirement is modest (~$15-20K cycling rapidly given 5-15 minute resolution windows) but the strategy requires continuous 24/7 operation and a real-time spot-price model to generate the directional signal.

Wallet: 0x21d0a97aac03917e752857a551bbe5103a00e8d7 Window: 2026-05-14 → 2026-05-27 (14 active / 14 calendar days) Methodology: Cash-flow P/L = -buy_usdc + sell_usdc + remaining_share_payout. Resolved shares settle at $1 (win) / $0 (loss); open positions marked at last price.

Phase 1 - Trader Profile

Scale

Trade-size distribution (USDC per fill)

Inter-trade gap, same (market, outcome)

Phase 2 & 3 - Both-Sides Participation, Dominance Curve

• Both-sides rate: 45.18% (4,447 of 9,843 markets)
• Median paired cost: $0.9707
• Mean paired cost: $0.9864
• Paired cost % under $1.00: 66.1%
• Paired cost % under $0.97: 48.2%
• Median 2nd-side hedge lag: 128s

Dominance buckets

Phase 4 - Entry-Price Analysis

Phase 5 - Category & Vertical Breakdown

Phase 6 - Timing & Execution

Net P/L by hour (UTC)

Phase 8 - Rolling Window Consistency

• Rolling 7-day windows green: 14 of 14 (100.0%)
• Rolling 7-day P/L range: +$4,634 → +$14,813
• Rolling 15-day windows green: 14 of 14 (100.0%)
• Rolling 15-day P/L range: +$4,634 → +$23,564

Weekly P/L

Phase 9 - P/L Decomposition

Phase 10 - Top Markets by Volume

Top 10 winners by P/L

Top 10 losers by P/L

Report generated 2026-06-03 06:31 UTC.

Wallet: 0x21d0a97aac03917e752857a551bbe5103a00e8d7 Window: 2026-05-14 to 2026-05-27 Baseline: 65,567 BUYs · 51.7% WR · $500,695 deployed · +$23,564 P/L · +4.71% ROI

The headline result

One filter generates dramatic lift. The rest are marginal or identity-equivalent.

The high-conviction dominance filter (dom ≥ 2x, dominant leg only) transforms the strategy from +4.71% ROI to +22.78% ROI on $177K deployed, extracting $40,369 of P/L from the subset that contains the genuine directional signal. This is the central finding of the entire filter analysis: the bot's low-conviction balanced entries are a significant drag, and isolating the high-conviction subset reveals what this strategy is actually capable of.

The price band filter ($0.30-$0.70) is mildly additive in ROI terms (+4.94% vs +4.71%) but removes $1,045 in absolute P/L. The hour exclusion filter similarly sheds absolute P/L while improving ROI marginally. The category filter is identity-equivalent (all trades are Crypto). The combined filter underperforms the dominance filter alone.

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Filter results table

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Filter-by-filter commentary

1. Price band filter ($0.30-$0.70) → MARGINAL, NOT ADDITIVE

The $0.30-$0.70 sweet-spot filter captures 54,904 of 65,567 trades (83.8% of the book) and delivers a slight ROI improvement: +4.94% vs +4.71% baseline. But absolute P/L drops by $1,045, from +$23,564 to +$22,519.

The reason this filter barely moves the needle is structural: 79.3% of all capital is already in the $0.30-$0.70 zone ($455K of $500K). The filter removes mostly small-dollar trades in the $0.10-$0.30 and $0.70-$1.00 zones. The sub-$0.20 zone is the only negative-ROI band (-24.2% on $1,130 deployed), and removing it saves $274 of loss. The $0.70-$1.00 zone produces positive but thin ROI (+2.7-4.3%); removing it forfeits $1,275 of P/L.

Net effect: the price filter is a very mild improvement in ROI (0.23 percentage points) at the cost of $1,045 in absolute dollars. For a strategy operating on thin margins, the absolute P/L reduction is the more meaningful number. Do not apply this filter unless ROI optimization is the explicit goal and $1K of forgone P/L is acceptable.

The win rate in the filtered set (49.6%) is actually below 50%, which seems counterintuitive. The explanation is that the $0.30-$0.70 zone contains the highest volume of both-sides markets where the non-dominant leg pulls WR below 50% - those low-probability-side bets in the $0.30-$0.49 range are included. The filter captures more hedge-leg volume proportionally than it removes.

2. High-conviction dominance filter (dom≥2x, dominant leg only) → MEANINGFUL LIFT

This is the only filter in the battery that genuinely improves the strategy's economics. The dom≥2x filter restricts to markets where the bot committed at least 2x more capital to one side than the other, keeping only the dominant-leg fills.

Result: +$40,369 P/L on $177,209 deployed = +22.78% ROI vs +4.71% baseline. A 18.07 percentage-point improvement. Win rate jumps from 51.7% to 57.6%.

Why does this filter work so powerfully? Two reasons:

First, the dominant-side win rate at 3.0x+ is 60.4% (from the dominance analysis), well above the coin-flip baseline. When the bot commits 3x or more, it is usually right. The combined 2.0x+ tier shows 57.6% WR, confirming the edge scales with conviction.

Second, removing the non-dominant legs eliminates the hedge tax. The $205,399 spent on losing non-dominant legs in the full book is eliminated entirely. Even if those legs sometimes win, the net cost of the hedging apparatus at low-conviction levels is negative. The dom≥2x filter keeps only the signal, not the noise.

The practical implication is stark: a replicator who tracks and copies only the dominant-leg fills from high-conviction markets would have +22.78% ROI on their deployed capital, not 4.71%. The hedge legs and low-conviction balanced markets are value-destructive ballast.

3. Category filter (Crypto only) → NOT APPLICABLE

100% of trades are already Crypto. The filter returns identical results to baseline: 65,567 trades, 51.7% WR, +$23,564, +4.71% ROI. There is no cross-category variation to exploit. This filter is identity-equivalent for this wallet.

4. Exclude worst 4 hours filter (hours 0, 5, 16, 19 UTC) → MARGINAL NEGATIVE

The worst four hourly buckets by P/L are hours 00:00 (-$313), 05:00 (-$298), 11:00 (-$1,514), and 03:00 (-$92). The stats blob identifies the worst-performing hours as 0, 5, 16, and 19 - a slightly different set using a ROI-based ranking rather than absolute P/L.

Applying this filter removes 10,877 trades (16.6% of the book) and $79,925 of capital (16.0%). P/L drops from +$23,564 to +$21,163 (-$2,401). ROI improves slightly: +5.03% vs +4.71%.

This is a mild ROI improvement at a meaningful absolute P/L cost. The 11:00 UTC hour has the worst absolute P/L (-$1,514) and would be the most impactful single exclusion, but it is not in the worst-4 set used in the filter (which uses WR-based ranking). The hour filter as specified actually costs money in absolute terms. The correct hour-exclusion for absolute P/L maximization would exclude only hour 11:00 (saving $1,514) and possibly hour 00:00 (saving $313) - a net +$1,827 improvement before accounting for forfeited good fills in those hours.

Since the bot runs 24/7 with consistent volume across all hours, hour-exclusion cuts into a large volume base. The marginal bad fills in "bad" hours do not dominate the marginal good fills in those same hours. This filter is not recommended for a replicator unless latency or operational constraints make certain hours unavoidable.

5. Combined filter (dom≥2x + exclude worst 4 hours) → WORSE THAN DOMINANCE FILTER ALONE

The stacked filter yields 45,753 trades, 50.4% WR, +$19,517 P/L, +5.10% ROI. This is meaningfully worse than the dom≥2x filter alone (+$40,369, +22.78%).

The combination fails because the hour exclusion removes good high-conviction fills from the "worst" hours. The dom≥2x filter already selects for quality; layering the hour exclusion on top degrades the sample without adding information. The win rate drops from 57.6% (dom≥2x alone) back to 50.4% (combined), confirming that many good high-conviction fills happen in the "excluded" hours.

Do not stack the hour filter with the dominance filter. The dominance filter is sufficient.

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What filters would add genuine value if computable

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Bottom line for replication

Three concrete filter recommendations for a replicator:

1. Apply the dom≥2x dominance filter on the dominant leg only. This is the single most impactful change: +22.78% ROI vs +4.71% unfiltered, adding +$16,805 of P/L while cutting capital deployed by 65%. The low-conviction balanced entries are not worth replicating.

1. Do not apply the $0.30-$0.70 price filter as a standalone rule. The strategy's both-sides architecture means you need the full price range to properly separate dominant from non-dominant legs. The price filter applied independently cuts $1,045 of P/L for a 0.23% ROI improvement.

1. Do not apply the hour exclusion filter if operating a fully automated 24/7 system. The marginal improvements in ROI from excluding a few hours do not compensate for the absolute P/L reduction and the complexity cost of scheduling.

The dominant strategic insight from this filter analysis: the market-making overlay is a cost center, not a profit center, at low conviction levels. A replicator who copies only the high-conviction directional fills extracts a meaningfully better risk-adjusted return than the full book.

Source wallet: 0x21d0a97aac03917e752857a551bbe5103a00e8d7 Strategy archetype: Both-sides spread-capture + high-conviction directional tilt on crypto Up/Down markets Reference book: $500,695 BUY notional · +$23,564 net P/L · +4.71% ROI (14 days, all fills) High-conviction subset: $177,209 BUY notional · +$40,369 P/L · +22.78% ROI (dom≥2x, dominant leg only)

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One-paragraph operator brief

Build a Polymarket bot that monitors BTC, ETH, SOL, and XRP Up/Down markets (5-minute, 15-minute, and hourly windows) using a real-time spot-price fair-value model. For every active market, compute the fair probability of Up/Down. When the CLOB mid-price deviates from fair value by more than a threshold, buy the underpriced side. When the deviation is large enough (3x+ position on one side vs. the other), commit the dominant clip immediately and optionally add a small hedge on the other side ~2 minutes later if the price is attractive. Hold all positions to settlement - no SELL logic required. Run 24/7 on a 3-second polling loop. Target clipped sizes of $3-$50 per fill with a hard cap around $200-$400. Expect roughly 4,600 fills per day, a 51-57% win rate depending on conviction tier, and a monthly ROI of approximately 10-15% on working capital at full deployment.

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1. Market selection

Asset priority (based on volume contribution):

The bot visible in this wallet trades all seven patterns simultaneously, running concurrent positions across markets. There is no reason to restrict to a subset - the diversification across assets and durations smooths daily P/L variance.

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2. Entry logic - the fair-value model

def should_enter(market, spot_prices):
    # Eligibility checks
    sec_left = seconds_until_close(market)
    if sec_left < 30:
        return None  # too close to resolve

    # Compute fair probability from spot tape
    if market.asset == "BTC":
        fair_prob_up = compute_fair_prob(
            spot=spot_prices["BTC"],
            threshold=market.target_price,
            vol=compute_30m_realized_vol("BTC"),
            seconds=sec_left
        )
    elif market.asset == "ETH":
        fair_prob_up = compute_fair_prob(
            spot=spot_prices["ETH"],
            threshold=market.target_price,
            vol=compute_30m_realized_vol("ETH"),
            seconds=sec_left
        )
    # etc for SOL, XRP

    clob_prob_up = market.up_side.mid_price
    gap_up = fair_prob_up - clob_prob_up
    gap_down = (1 - fair_prob_up) - market.down_side.mid_price

    # Entry thresholds
    # Low-conviction (both-sides market-making): enter when small gap exists
    # High-conviction (dominant-leg only): enter when large gap exists
    
    if abs(gap_up) > 0.08 and gap_up > 0:
        return ("Up", "HIGH_CONV")   # 3x+ tier equivalent
    elif abs(gap_down) > 0.08 and gap_down > 0:
        return ("Down", "HIGH_CONV")
    elif abs(gap_up) > 0.03:
        return ("Up", "LOW_CONV")    # balanced both-sides entry
    elif abs(gap_down) > 0.03:
        return ("Down", "LOW_CONV")
    
    return None  # no entry

Critical: entry price is wherever the orderbook offers when the signal fires. The reference wallet uses 40-50 cents for most entries (the CLOB mid) but also enters at $0.20-$0.30 on underdog legs and $0.70-$0.90 on near-certain legs. Do not anchor to a single price. Walk the available depth up to your clip size.

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3. Sizing model

The reference wallet uses bounded clips with a median of $3.69 and max of $660. For replication:

At $10K bankroll you cycle approximately 2x per day given that the average 5m market holds capital for under 5 minutes and the 15m for under 15. The instantaneous exposure at any moment is a fraction of the daily notional.

Sizing by conviction:

• LOW_CONV both-sides entry: deploy 1 unit to each side (balanced)
• HIGH_CONV dominant entry: deploy 3-5 units to the dominant side
• HIGH_CONV hedge entry (optional): deploy 1 unit to the non-dominant side ~120 seconds after the dominant fill if price is below $0.35 on the hedge side

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4. Both-sides allocation - the pairing logic

The reference wallet has a 128-second median second-side lag. The recommended both-sides approach:

class MarketPosition:
    def __init__(self, market, first_side, first_fill_usdc, first_ts):
        self.market = market
        self.first_side = first_side   # "Up" or "Down"
        self.first_fill_usdc = first_fill_usdc
        self.first_fill_ts = first_ts
        self.second_side_filled = False

    def should_add_hedge(self, current_time, clob):
        # Only hedge LOW_CONV entries
        if self.first_fill_usdc > 30:  # HIGH_CONV - no hedge
            return False
        
        elapsed = current_time - self.first_fill_ts
        if elapsed < 60:  # wait at least 60s before hedging
            return False
        
        hedge_side = "Down" if self.first_side == "Up" else "Up"
        hedge_price = clob.get_mid(self.market, hedge_side)
        
        # Only hedge if combined cost stays below $1.00
        combined_cost = self.first_vwap + hedge_price
        if combined_cost > 0.98:
            return False  # paired cost too high, skip hedge
        
        return True

The pairing rule: only add a hedge position if it keeps the combined paired cost below $0.98. If the hedge-side price has moved up (market has drifted toward the first-side outcome), the combined cost exceeds $1.00 and the hedge leg becomes value-destroying. In those cases, skip the hedge and let the first-side position ride to settlement alone.

This rule directly addresses the core inefficiency identified in the filter analysis: the reference wallet's low-conviction balanced entries at combined cost above $1.00 are the main drag. A replicator who applies a $0.98 combined-cost ceiling on hedges will systematically avoid those negative-expected-value pairings.

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5. Exit strategy

There is no exit strategy. All positions hold to settlement.

The wallet has zero SELLs across 65,567 BUY trades. Every dollar deployed either returns $1/share (win) or $0/share (loss) at market close. This is a key architectural decision: no SELL engine means no latency-sensitive exit infrastructure, no orderbook monitoring post-fill, and no complex position tracking. The complexity is entirely on the entry side.

Do not add a SELL engine to this strategy unless you have evidence that the SELL exits at meaningful average prices. Given that this bot enters primarily near $0.49-$0.51 and most markets resolve within 5-15 minutes, the time available to SELL into a rally is extremely short. The reference book's ROI is already competitive without SELL management.

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6. Risk management

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7. Scheduling and operational requirements

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8. Diagnostic checklist - is the bot still working?

Run this weekly:

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9. Bankroll math

The reference wallet deployed $500,695 cumulatively across 14 days on roughly $15,000-$20,000 of working capital (estimated from the rapid cycling of 5-15 minute windows). That cycling ratio (32-34x the working capital over the period) is the key efficiency metric.

Reference book (14 days):
  Cumulative BUY notional:   $500,695
  Working capital (est.):    ~$17,500
  Capital turns per day:     $500,695 / (14 × $17,500) ≈ 2.04x per day
  Net P/L:                   +$23,564
  ROI on working capital:    +$23,564 / $17,500 = +134.6% in 14 days

High-conviction subset:
  Cumulative BUY notional:   $177,209 (35% of total fills)
  Net P/L:                   +$40,369
  ROI on $177K deployed:     +22.78%

Extrapolated to 30-day month (full book):
  Expected P/L:              ~$50,000
  On ~$17,500 working capital → ~+285% monthly ROI on capital

Extrapolated (high-conviction filter applied):
  Expected monthly BUY notional: ~$380,000 (at same daily rate but 35% of fills)
  Expected monthly P/L:          ~$86,000
  On ~$6,000 working capital (cycles at same rate) → enormous ROI on capital

These extrapolations should be treated as rough order-of-magnitude benchmarks, not precise forecasts. The May 18 volatile session contributed disproportionately to the reference P/L; a quieter 30-day window would produce lower absolute returns.

Practical scaling notes:

• Below $5,000 working capital: insufficient to cover variance across concurrent positions. Minimum $5K.
• $10,000-$25,000: the reference operating range. Normal operation.
• Above $50,000: expect ROI compression as your clips begin to influence orderbook prices, especially on SOL and XRP thin markets.

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10. What this playbook deliberately does NOT include

No SELL engine. Every analysis of this wallet confirms the strategy is settlement-only. Adding a SELL engine would require re-architecting the entire exit infrastructure for a marginal gain that the reference book does not demonstrate. If you want a SELL-based strategy, study SirMartingale instead.

No Kelly sizing. The reference wallet uses bounded clips, not Kelly-optimal sizing based on edge estimates. Kelly-optimal sizing would dramatically increase variance and risk large drawdowns when the fair-value model errs. The bounded approach ($3-$50 typical) is the correct choice for a 24/7 bot running thousands of markets monthly.

No stop-losses. Every position is held to settlement. Adding stop-losses would require monitoring live prices post-fill and transacting on Polymarket as a seller, which this strategy does not do. The natural stop-loss is the clip-size cap: maximum loss per market is bounded by the total dollars deployed there.

No cross-market hedging. The bot does not hedge BTC Up exposure in one window against BTC Down exposure in another window. Each market is independent. Cross-window hedging would complicate the accounting enormously for minimal benefit given the 5-15 minute resolution times.

No long-duration markets. The strategy does not touch Polymarket events resolving in days, weeks, or months. The edge (spot-tape fair-value model) is only accurate over the 5-minute to 15-minute window horizon. Longer markets have more time for the spot price to diverge unpredictably from the entry fair-value estimate.

No sports or politics. There is no spot-tape proxy for those categories. The entire edge is predicated on having a real-time price signal for the underlying asset. Without that, the bot has no information advantage over the orderbook.