Polymarket Candle Three Soldiers Trader

Trades crypto "Up or Down" 5-minute interval markets on Polymarket by detecting Three White Soldiers (3+ consecutive strong UP intervals all p>0.57) and Thre...

Security Scan

The skill's code, instructions, and declared dependencies are coherent with its stated purpose (a Polymarket trading strategy); it requires a single trading API key and has sensible defaults (paper trading) and tunables.

Purpose & Capability

Name/description (Polymarket 5-min candlestick trader) match the code and SKILL.md. The code uses a SimmerClient SDK and only asks for a trading API key (SIMMER_API_KEY), which is appropriate for executing paper or live trades. Note: the registry summary at the top of the report initially listed "Required env vars: none", but both SKILL.md and clawhub.json declare SIMMER_API_KEY as required — this is likely a metadata mismatch rather than a functional incoherence.

Instruction Scope

SKILL.md instructs the agent to discover markets, parse intervals, detect 'Three Soldiers'/'Three Crows' and place trades; trader.py implements those steps and limits live trading to an explicit --live flag. The instructions and implementation do not ask the agent to read unrelated files or fetch secrets beyond the declared SIMMER_API_KEY.

Install Mechanism

There is no download/extract install spec, but clawhub.json declares a pip dependency on 'simmer-sdk'. Installing a public PyPI package is normal for this use-case but has moderate risk compared to instruction-only skills; users may wish to review the simmer-sdk source or its provenance before granting credentials.

Credentials

Only one required credential (SIMMER_API_KEY) is declared and used. The code reads several tunable environment variables, but these are optional and exposed as UI tunables in clawhub.json. The requested env access is proportional to a trading skill.

Persistence & Privilege

autostart is false and the skill does not set always:true. automaton entrypoint is trader.py but default behavior is paper trading and nothing runs automatically until the user configures/autostarts it. No evidence the skill attempts to modify other skills or system-wide settings.

Assessment

This skill appears to be what it says: an automated Polymarket trading strategy that uses the Simmer SDK and requires SIMMER_API_KEY. Before installing, verify the simmer-sdk package (source, version, reviews) because pip packages can run arbitrary code. Treat SIMMER_API_KEY as high-value — use it first in paper/sim mode (the code defaults to sim) and only enable live trading with the explicit --live flag after you have tested behavior and settings. Also check the repository or author provenance if you need stronger assurance, and confirm the registry metadata correctly lists required env vars (the package files do require SIMMER_API_KEY even though the registry summary initially omitted it).

Candle Three Soldiers Trader

This is a template. The default signal detects Three White Soldiers and Three Black Crows patterns in crypto 5-min interval markets and trades continuation on lagging intervals -- remix it with volume confirmation, real-time price feeds, or volatility filters. The skill handles all the plumbing (market discovery, interval parsing, pattern detection, trade execution, safeguards). Your agent provides the alpha.

Strategy Overview

Polymarket lists hundreds of live crypto "Up or Down" 5-minute interval markets per day across Bitcoin, Ethereum, and Solana. Each asks whether a coin will go up or down in a specific 5-minute window (e.g. "Bitcoin Up or Down - March 29, 10:50AM-10:55AM ET").

"Three White Soldiers" is a classic candlestick continuation pattern: three consecutive intervals all showing strong UP bias (p > 0.57). When this pattern appears but the NEXT interval hasn't caught up yet (p < 0.55), the continuation hasn't been fully priced in. "Three Black Crows" is the bearish mirror: three consecutive strong DOWN intervals (p < 0.43), and the next interval hasn't caught down (p > 0.45).

The skill detects these patterns, identifies lagging next intervals, and trades the continuation direction with conviction scaled by the lag distance.

Edge

Three consecutive strong-bias intervals establish a micro-trend that tends to persist one more interval:

1. Momentum persistence -- When three intervals all show >57% bias in one direction, the underlying move is real, not noise. The 4th interval often follows but market makers are slow to adjust its price
2. Lag exploitation -- The next interval's price lags because Polymarket participants price each interval somewhat independently rather than incorporating the trend from adjacent intervals
3. Multi-coin coverage -- Scanning Bitcoin, Ethereum, and Solana multiplies pattern opportunities by 3x
4. Short resolution -- 5-minute markets resolve quickly; capital is recycled fast and the edge compounds

Signal Logic

1. Discover active crypto "Up or Down" 5-min interval markets via keyword search + get_markets(limit=200) fallback
2. Parse each question to extract coin, date, and time window (e.g. "Bitcoin", "March 29", "10:50AM", "10:55AM")
3. Group by (coin, date) and sort by time window
4. Scan sorted intervals for three consecutive where ALL > SOLDIER_THRESHOLD (default 0.57) or ALL < (1 - 0.57 = 0.43)
5. After detecting pattern, check the NEXT interval:
   • Three soldiers (UP) + next < 0.55: buy YES (hasn't caught up)
   • Three crows (DOWN) + next > 0.45: buy NO (hasn't caught down)
6. Conviction scales with lag distance from trend
7. Size = max(MIN_TRADE, conviction * MAX_POSITION)

Remix Signal Ideas

• Volume confirmation: Only trade soldiers/crows patterns where the three intervals also show above-average volume -- filters out low-liquidity noise
• Binance real-time feed: Confirm the underlying coin is actually trending in the soldiers/crows direction on spot markets before entering
• Acceleration filter: Require the three intervals to show increasing bias (p1 < p2 < p3 for soldiers) -- an accelerating trend is more likely to continue
• Time-of-day weighting: Crypto trends are stronger during US market hours (14:00-21:00 UTC); weight continuation signals higher during these periods
• Multi-timeframe confirmation: If both 5-min and 15-min intervals show soldiers pattern, increase conviction

Safety & Execution Mode

The skill defaults to paper trading (venue="sim"). Real trades only with --live flag.

Scenario	Mode	Financial risk
python trader.py	Paper (sim)	None
Cron / automaton	Paper (sim)	None
python trader.py --live	Live (polymarket)	Real USDC

autostart: false and cron: null mean nothing runs automatically until configured in Simmer UI.

Required Credentials

Variable	Required	Notes
SIMMER_API_KEY	Yes	Trading authority. Treat as a high-value credential.

Tunables (Risk Parameters)

All declared as tunables in clawhub.json and adjustable from the Simmer UI.

Variable	Default	Purpose
SIMMER_MAX_POSITION	40	Max USDC per trade at full conviction
SIMMER_MIN_TRADE	5	Floor for any trade
SIMMER_MIN_VOLUME	3000	Min market volume filter (USD)
SIMMER_MAX_SPREAD	0.08	Max bid-ask spread
SIMMER_MIN_DAYS	0	Min days until resolution (0 = allow same-session)
SIMMER_MAX_POSITIONS	8	Max concurrent open positions
SIMMER_YES_THRESHOLD	0.38	Buy YES only if market probability <= this
SIMMER_NO_THRESHOLD	0.62	Sell NO only if market probability >= this
SIMMER_SOLDIER_THRESHOLD	0.57	Min probability for a strong UP interval (crows = 1 - this)

Edge Thesis

Candlestick continuation patterns work because they identify intervals where a micro-trend has established but hasn't fully propagated to adjacent intervals. Three consecutive strong-bias intervals are statistically significant -- they represent genuine directional momentum rather than noise. Polymarket's interval pricing is somewhat independent across adjacent windows, meaning a strong trend in intervals N, N+1, N+2 doesn't automatically get priced into interval N+3. This lag creates a window where the continuation trade has positive expected value. The skill exploits this structural inefficiency in how Polymarket participants price sequential interval markets.

Dependency

simmer-sdk by Simmer Markets (SpartanLabsXyz)

• PyPI: https://pypi.org/project/simmer-sdk/
• GitHub: https://github.com/SpartanLabsXyz/simmer-sdk