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Hourly Momentum Trader

Momentum-based trading agent for hourly crypto candles. Uses RSI, MACD, OBV, EMA, and Bollinger Band confluence to score directional momentum from -10 to +10...

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High Signal

Momentum-based trading agent for hourly crypto candles. Uses RSI, MACD, OBV, EMA, and Bollinger Band confluence to score directional momentum from -10 to +10...

⬇ 0 downloads ★ 0 stars Unverified but indexed

Install for OpenClaw

Quick setup
  1. Download the package from Yavira.
  2. Extract the archive and review SKILL.md first.
  3. Import or place the package into your OpenClaw setup.

Requirements

Target platform
OpenClaw
Install method
Manual import
Extraction
Extract archive
Prerequisites
OpenClaw
Primary doc
SKILL.md

Package facts

Download mode
Yavira redirect
Package format
ZIP package
Source platform
Tencent SkillHub
What's included
SKILL.md

Validation

  • Use the Yavira download entry.
  • Review SKILL.md after the package is downloaded.
  • Confirm the extracted package contains the expected setup assets.

Install with your agent

Agent handoff

Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.

  1. Download the package from Yavira.
  2. Extract it into a folder your agent can access.
  3. Paste one of the prompts below and point your agent at the extracted folder.
New install

I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.

Upgrade existing

I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.

Trust & source

Release facts

Source
Tencent SkillHub
Verification
Indexed source record
Version
1.0.0

Documentation

ClawHub primary doc Primary doc: SKILL.md 6 sections Open source page

Hourly Momentum Trader

Directional momentum scoring for 1-hour crypto candles with Polymarket binary market integration.

Momentum Score System (-10 to +10)

Each signal contributes to the composite score: SignalBullish (+)Bearish (-)WeightRSI<40 oversold>70 overbought±1RSI divergenceBull divBear div±2MACD crossBullish crossBearish cross±1MACD histogramRisingFalling±1EMA crossEMA20>EMA50EMA20<EMA50±1EMA200Price abovePrice below±1BollingerNear lower (oversold)Near upper (overbought)±1OBV trendRising (accumulation)Falling (distribution)±1OBV divergenceBull divBear div±2VolumeHigh volume on moveLow volume±1

Live Bet Integration (Polymarket Argus Strategy)

Combine momentum score with Polymarket market odds: # Edge formula our_prob = 0.50 + (momentum_score * 0.05) # score 6 = 80% confidence market_prob_up = polymarket_up_price # e.g. 0.35 (35% UP consensus) edge = our_prob - market_prob_up # e.g. 0.80 - 0.35 = 45% edge! # Bet when: # - abs(momentum_score) >= 3 # - abs(edge) >= 0.10 (10%) # - market is fresh (<30 min old) # - USDC.e balance >= $5

Counter-Consensus Setups (L023)

Highest-EV Polymarket plays: Momentum score ≥+3 AND market DOWN >70% → BET UP (counter-consensus, 3-5x payout) Momentum score ≤-3 AND market UP >70% → BET DOWN (counter-consensus, 3-5x payout) Example: SOL score=+4, Polymarket DOWN 80% → buy UP at 0.20 → if wins: 5x return

Signals Output Format

{ "asset": "BTC", "score": 3, "rsi": 64.9, "rsi_status": "neutral", "macd_hist": 10.39, "macd_direction": "rising", "obv_trend": "rising", "bb_pct": 0.72, "ema_cross": "bullish", "bias": "BULLISH", "confidence_pct": 65, "polymarket_edge": { "btc_4pm_et": { "market_up": 0.515, "our_p": 0.65, "edge": 0.135, "direction": "UP" } } }

Watchlist (Default)

BTC, ETH, SOL, XRP, ATOM, ADA, SUI, LTC, NEAR, AVAX, BNB, LINK, DOT, TRX, DOGE

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package
1 Docs
  • SKILL.md Primary doc