skills/crypto-signal-generator/SKILL.md
Generate trading signals using technical indicators (RSI, MACD, Bollinger Bands, etc.). Combines multiple indicators into composite signals with confidence scores. Use when analyzing assets for trading opportunities or checking technical indicators. Trigger with phrases like "get trading signals", "check indicators", "analyze for entry", "scan for opportunities", "generate buy/sell signals", or "technical analysis".
npx skillsauth add aaaaqwq/claude-code-skills generating-trading-signalsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Multi-indicator signal generation system that analyzes price action using 7 technical indicators and produces composite BUY/SELL signals with confidence scores and risk management levels.
Indicators Used:
Install required dependencies:
pip install yfinance pandas numpy
Optional for visualization:
pip install matplotlib
Scan multiple assets for trading opportunities:
python {baseDir}/scripts/scanner.py --watchlist crypto_top10 --period 6m
Output shows signal type (STRONG_BUY/BUY/NEUTRAL/SELL/STRONG_SELL) and confidence for each asset.
Get full indicator breakdown for a specific symbol:
python {baseDir}/scripts/scanner.py --symbols BTC-USD --detail
Shows each indicator's contribution:
Find the best opportunities:
# Only buy signals with 70%+ confidence
python {baseDir}/scripts/scanner.py --filter buy --min-confidence 70 --rank confidence
# Rank by most bullish
python {baseDir}/scripts/scanner.py --rank bullish
# Save results to JSON
python {baseDir}/scripts/scanner.py --output signals.json
Available predefined watchlists:
python {baseDir}/scripts/scanner.py --list-watchlists
python {baseDir}/scripts/scanner.py --watchlist crypto_defi
Watchlists: crypto_top10, crypto_defi, crypto_layer2, stocks_tech, etfs_major
================================================================================
SIGNAL SCANNER RESULTS
================================================================================
Symbol Signal Confidence Price Stop Loss
--------------------------------------------------------------------------------
BTC-USD STRONG_BUY 78.5% $67,234.00 $64,890.00
ETH-USD BUY 62.3% $3,456.00 $3,312.00
SOL-USD NEUTRAL 45.0% $142.50 N/A
--------------------------------------------------------------------------------
Summary: 2 Buy | 1 Neutral | 0 Sell
Scanned: 3 assets | [timestamp]
================================================================================
======================================================================
BTC-USD - STRONG_BUY
Confidence: 78.5% | Price: $67,234.00
======================================================================
Risk Management:
Stop Loss: $64,890.00
Take Profit: $71,922.00
Risk/Reward: 1:2.0
Signal Components:
----------------------------------------------------------------------
RSI | STRONG_BUY | Oversold at 28.5 (< 30)
MACD | BUY | MACD above signal, positive momentum
Bollinger Bands | BUY | Price near lower band (%B = 0.15)
Trend | BUY | Uptrend: price above key MAs
Volume | STRONG_BUY | High volume (2.3x) on up move
Stochastic | STRONG_BUY | Oversold (%K=18.2, %D=21.5)
ADX | BUY | Strong uptrend (ADX=32.1)
----------------------------------------------------------------------
| Signal | Score | Meaning | |--------|-------|---------| | STRONG_BUY | +2 | Multiple strong buy signals aligned | | BUY | +1 | Moderate buy signals | | NEUTRAL | 0 | No clear direction | | SELL | -1 | Moderate sell signals | | STRONG_SELL | -2 | Multiple strong sell signals aligned |
| Confidence | Interpretation | |------------|----------------| | 70-100% | High conviction, strong signal | | 50-70% | Moderate conviction | | 30-50% | Weak signal, mixed indicators | | 0-30% | No clear direction, avoid trading |
Edit {baseDir}/config/settings.yaml:
indicators:
rsi:
period: 14
overbought: 70
oversold: 30
signals:
weights:
rsi: 1.0
macd: 1.0
bollinger: 1.0
trend: 1.0
volume: 0.5
See {baseDir}/references/errors.md for common issues:
See {baseDir}/references/examples.md for detailed examples:
Test signals historically:
# Generate signal
python {baseDir}/scripts/scanner.py --symbols BTC-USD --detail
# Backtest the strategy that generated the signal
python {baseDir}/../trading-strategy-backtester/skills/backtesting-trading-strategies/scripts/backtest.py \
--strategy rsi_reversal --symbol BTC-USD --period 1y
| File | Purpose |
|------|---------|
| scripts/scanner.py | Main signal scanner |
| scripts/signals.py | Signal generation logic |
| scripts/indicators.py | Technical indicator calculations |
| config/settings.yaml | Configuration |
testing
通用自媒体文章自动发布工具。支持百家号、搜狐号、知乎、微信公众号、小红书、抖音号六个平台的自动化发布流程。使用Playwright自动化实现平台导航和发布,支持通过storageState管理Cookie实现账号切换。
development
# SKILL.md - Model Configuration Status (mcstatus) ## 触发条件 - `/mcstatus` 命令 - 用户询问模型配备、模型配置、model status、模型列表等 ## 功能 实时生成 Agent + Cron 的模型配置报告,展示当前所有 agent 的主模型/fallback链和所有 cron 任务的模型分配。 ## 执行步骤 ### Step 1: 收集 Agent 模型配置 读取各 agent 的 models.json 获取主模型和 fallback 链: ```bash for agent in main ops code quant data research content market finance pm law product sales batch; do config=$(cat ~/.openclaw/agents/$agent/agent/models.json 2>/dev/null) if [ -n "$config" ]; then echo "=== $agent
tools
MCP 服务器智能管理助手。自动检测 MCP 可用性、智能开关、功能问答,提供人性化的 MCP 管理体验。
tools
从GitHub搜索并自动安装配置MCP(Model Context Protocol)服务器工具到Claude配置文件。当用户需要安装MCP工具时触发此技能。工作流程:搜索GitHub上的MCP项目 -> 提取npx配置 -> 添加到~/.claude.json -> 处理API密钥(如有)。