generating-trading-signals

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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".

2installs
Sourceaaaaqwq/claude-code-skills
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NPX Install

npx skill4agent add aaaaqwq/claude-code-skills generating-trading-signals

SKILL.md Content

Generating Trading Signals

Overview

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:
  • RSI (Relative Strength Index) - Overbought/oversold
  • MACD (Moving Average Convergence Divergence) - Trend and momentum
  • Bollinger Bands - Mean reversion and volatility
  • Trend (SMA 20/50/200 crossovers) - Trend direction
  • Volume - Confirmation of moves
  • Stochastic Oscillator - Short-term momentum
  • ADX (Average Directional Index) - Trend strength

Prerequisites

Install required dependencies:
bash
pip install yfinance pandas numpy
Optional for visualization:
bash
pip install matplotlib

Instructions

Step 1: Quick Signal Scan

Scan multiple assets for trading opportunities:
bash
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.

Step 2: Detailed Signal Analysis

Get full indicator breakdown for a specific symbol:
bash
python {baseDir}/scripts/scanner.py --symbols BTC-USD --detail
Shows each indicator's contribution:
  • Individual signal (BUY/SELL/NEUTRAL)
  • Indicator value
  • Reasoning (e.g., "RSI oversold at 28.5")

Step 3: Filter and Rank Signals

Find the best opportunities:
bash
# 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

Step 4: Use Custom Watchlists

Available predefined watchlists:
bash
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

Output

Signal Summary Table

================================================================================
  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]
================================================================================

Detailed Signal Output

======================================================================
  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 Types

SignalScoreMeaning
STRONG_BUY+2Multiple strong buy signals aligned
BUY+1Moderate buy signals
NEUTRAL0No clear direction
SELL-1Moderate sell signals
STRONG_SELL-2Multiple strong sell signals aligned

Confidence Interpretation

ConfidenceInterpretation
70-100%High conviction, strong signal
50-70%Moderate conviction
30-50%Weak signal, mixed indicators
0-30%No clear direction, avoid trading

Configuration

Edit 
{baseDir}/config/settings.yaml
:
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

Error Handling

See 
{baseDir}/references/errors.md
for common issues:
  • API rate limits
  • Insufficient data handling
  • Network errors

Examples

See 
{baseDir}/references/examples.md
for detailed examples:
  • Multi-timeframe analysis
  • Custom indicator parameters
  • Combining with backtester
  • Automated scanning schedules

Integration with Backtester

Test signals historically:
bash
# 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

Files

FilePurpose
scripts/scanner.py
Main signal scanner
scripts/signals.py
Signal generation logic
scripts/indicators.py
Technical indicator calculations
config/settings.yaml
Configuration

Resources

  • yfinance for price data
  • pandas/numpy for calculations
  • Compatible with trading-strategy-backtester plugin