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Zhongwei Li
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references/examples/stock-analyzer-cskill/scripts/main.py
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397
references/examples/stock-analyzer-cskill/scripts/main.py
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"""
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Stock Analyzer Skill - Main Orchestrator
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This is a simplified reference implementation demonstrating the structure
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of a skill with robust 3-layer activation. For a production version,
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integrate with actual data sources and indicator libraries.
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Example Usage:
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analyzer = StockAnalyzer()
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result = analyzer.analyze("AAPL", ["RSI", "MACD"])
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print(result)
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"""
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from typing import List, Dict, Optional, Any
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from datetime import datetime
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class StockAnalyzer:
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"""
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Main orchestrator for technical stock analysis
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Capabilities:
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- Technical indicator calculation (RSI, MACD, Bollinger)
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- Buy/sell signal generation
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- Multi-stock comparison
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- Price monitoring and alerts
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"""
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def __init__(self, config: Optional[Dict] = None):
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"""
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Initialize stock analyzer with optional configuration
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Args:
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config: Optional configuration dict with indicator parameters
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"""
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self.config = config or self._default_config()
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print(f"[StockAnalyzer] Initialized with config: {self.config['data_source']}")
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def analyze(
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self,
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ticker: str,
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indicators: Optional[List[str]] = None,
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period: str = "1y"
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) -> Dict[str, Any]:
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"""
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Perform technical analysis on a stock
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Args:
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ticker: Stock symbol (e.g., "AAPL", "MSFT")
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indicators: List of indicators to calculate (default: ["RSI", "MACD"])
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period: Time period for analysis (default: "1y")
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Returns:
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Dict containing:
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- ticker: Stock symbol
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- current_price: Latest price
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- indicators: Dict of indicator results
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- signal: Buy/sell/hold recommendation
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- timestamp: Analysis timestamp
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Example:
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>>> analyzer = StockAnalyzer()
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>>> result = analyzer.analyze("AAPL", ["RSI", "MACD"])
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>>> print(result['signal']['action'])
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BUY
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"""
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indicators = indicators or ["RSI", "MACD"]
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print(f"\n[StockAnalyzer] Analyzing {ticker}...")
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print(f" - Indicators: {indicators}")
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print(f" - Period: {period}")
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# Step 1: Fetch price data (simplified - production would use yfinance)
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price_data = self._fetch_data(ticker, period)
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# Step 2: Calculate indicators
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indicator_results = {}
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for indicator_name in indicators:
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indicator_results[indicator_name] = self._calculate_indicator(
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indicator_name,
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price_data
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)
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# Step 3: Generate trading signal
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signal = self._generate_signal(ticker, price_data, indicator_results)
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# Step 4: Compile results
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result = {
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'ticker': ticker.upper(),
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'current_price': price_data['close'],
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'indicators': indicator_results,
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'signal': signal,
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'timestamp': datetime.now().isoformat(),
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'period': period
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}
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print(f"[StockAnalyzer] Analysis complete for {ticker}")
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print(f" → Signal: {signal['action']} (confidence: {signal['confidence']})")
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return result
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def compare(
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self,
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tickers: List[str],
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rank_by: str = "momentum",
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indicators: Optional[List[str]] = None
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) -> Dict[str, Any]:
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"""
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Compare multiple stocks and rank by technical strength
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Args:
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tickers: List of stock symbols
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rank_by: Ranking method ("momentum", "rsi", "composite")
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indicators: Indicators to use for comparison
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Returns:
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Dict containing ranked stocks with scores and analysis
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Example:
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>>> analyzer = StockAnalyzer()
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>>> result = analyzer.compare(["AAPL", "MSFT", "GOOGL"])
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>>> for stock in result['ranked_stocks']:
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>>> print(f"{stock['ticker']}: {stock['score']}")
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"""
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indicators = indicators or ["RSI", "MACD"]
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print(f"\n[StockAnalyzer] Comparing {len(tickers)} stocks...")
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print(f" - Tickers: {', '.join(tickers)}")
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print(f" - Rank by: {rank_by}")
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comparisons = []
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for ticker in tickers:
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# Analyze each stock
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analysis = self.analyze(ticker, indicators, period="6mo")
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# Calculate ranking score
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score = self._calculate_ranking_score(analysis, rank_by)
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comparisons.append({
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'ticker': ticker.upper(),
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'analysis': analysis,
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'score': score,
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'rank': 0 # Will be set after sorting
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})
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# Sort by score (highest first)
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comparisons.sort(key=lambda x: x['score'], reverse=True)
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# Assign ranks
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for idx, comparison in enumerate(comparisons, 1):
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comparison['rank'] = idx
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result = {
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'ranked_stocks': comparisons,
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'ranking_method': rank_by,
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'total_analyzed': len(tickers),
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'timestamp': datetime.now().isoformat()
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}
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print(f"[StockAnalyzer] Comparison complete")
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print(" Rankings:")
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for comp in comparisons:
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print(f" #{comp['rank']}: {comp['ticker']} (score: {comp['score']:.2f})")
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return result
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def monitor(
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self,
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ticker: str,
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condition: str,
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action: str = "notify"
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) -> Dict[str, Any]:
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"""
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Set up monitoring and alerts for a stock
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Args:
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ticker: Stock symbol to monitor
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condition: Alert condition (e.g., "RSI < 30", "MACD crossover")
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action: Action to take when condition met (default: "notify")
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Returns:
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Dict with monitoring configuration
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Example:
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>>> analyzer = StockAnalyzer()
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>>> alert = analyzer.monitor("AAPL", "RSI < 30", "notify")
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>>> print(alert['status'])
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active
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"""
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print(f"\n[StockAnalyzer] Setting up monitoring...")
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print(f" - Ticker: {ticker}")
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print(f" - Condition: {condition}")
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print(f" - Action: {action}")
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return {
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'ticker': ticker.upper(),
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'condition': condition,
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'action': action,
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'status': 'active',
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'created': datetime.now().isoformat()
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}
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# Private helper methods
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def _default_config(self) -> Dict:
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"""Default configuration for indicators and data sources"""
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return {
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'data_source': 'yahoo_finance',
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'indicators': {
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'RSI': {
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'period': 14,
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'overbought': 70,
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'oversold': 30
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},
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'MACD': {
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'fast_period': 12,
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'slow_period': 26,
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'signal_period': 9
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},
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'Bollinger': {
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'period': 20,
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'std_dev': 2
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}
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},
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'signals': {
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'confidence_threshold': 0.7
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}
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}
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def _fetch_data(self, ticker: str, period: str) -> Dict[str, float]:
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"""
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Fetch price data for ticker (simplified mock)
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Production version would use yfinance or similar
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"""
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# Mock data - production would fetch real data
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return {
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'open': 175.20,
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'high': 178.90,
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'low': 174.50,
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'close': 178.45,
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'volume': 52_000_000
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}
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def _calculate_indicator(
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self,
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indicator_name: str,
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price_data: Dict
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) -> Dict[str, Any]:
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"""
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Calculate technical indicator (simplified mock)
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Production version would use ta-lib or pandas-ta
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"""
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if indicator_name == "RSI":
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return {
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'value': 62.3,
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'signal': 'neutral',
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'interpretation': 'RSI above 50 indicates bullish momentum, but not overbought'
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}
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elif indicator_name == "MACD":
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return {
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'macd_line': 2.15,
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'signal_line': 1.89,
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'histogram': 0.26,
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'signal': 'buy',
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'interpretation': 'MACD line crossed above signal line - bullish crossover'
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}
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elif indicator_name == "Bollinger":
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return {
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'upper_band': 185.20,
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'middle_band': 178.45,
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'lower_band': 171.70,
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'position': 'middle',
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'interpretation': 'Price near middle band - neutral volatility'
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}
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else:
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return {'error': f'Unknown indicator: {indicator_name}'}
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def _generate_signal(
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self,
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ticker: str,
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price_data: Dict,
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indicators: Dict
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) -> Dict[str, Any]:
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"""
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Generate trading signal from indicator combination
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Strategy: Combined RSI + MACD approach
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- BUY: RSI healthy and MACD bullish crossover
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- SELL: RSI overbought and MACD bearish
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- HOLD: Otherwise
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"""
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rsi = indicators.get('RSI', {}).get('value', 50)
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macd_signal = indicators.get('MACD', {}).get('signal', 'neutral')
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reasoning = []
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# RSI analysis
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if rsi < 30:
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reasoning.append("RSI oversold (< 30) - potential buy opportunity")
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base_signal = "BUY"
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confidence = "moderate"
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elif rsi > 70:
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reasoning.append("RSI overbought (> 70) - potential sell signal")
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base_signal = "SELL"
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confidence = "moderate"
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else:
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reasoning.append(f"RSI at {rsi:.1f} - neutral zone")
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base_signal = "HOLD"
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confidence = "low"
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# MACD analysis
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if macd_signal == "buy":
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reasoning.append("MACD bullish crossover detected")
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if base_signal == "BUY":
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confidence = "high"
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else:
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base_signal = "BUY"
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confidence = "moderate"
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return {
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'action': base_signal,
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'confidence': confidence,
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'reasoning': reasoning,
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'price': price_data['close']
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}
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def _calculate_ranking_score(
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self,
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analysis: Dict,
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method: str
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) -> float:
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"""
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Calculate ranking score based on method
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Args:
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analysis: Stock analysis results
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method: Ranking method (momentum, rsi, composite)
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Returns:
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Numeric score (higher is better)
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"""
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if method == "rsi":
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# Higher RSI = higher score (up to 70)
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rsi = analysis['indicators'].get('RSI', {}).get('value', 50)
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return min(rsi, 70)
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elif method == "momentum":
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# Composite momentum score
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rsi = analysis['indicators'].get('RSI', {}).get('value', 50)
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macd_signal = analysis['indicators'].get('MACD', {}).get('signal', 'neutral')
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score = rsi
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if macd_signal == "buy":
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score += 10
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elif macd_signal == "sell":
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score -= 10
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return score
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else: # composite
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# Weighted combination of indicators
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rsi = analysis['indicators'].get('RSI', {}).get('value', 50)
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macd_hist = analysis['indicators'].get('MACD', {}).get('histogram', 0)
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return (rsi * 0.6) + (macd_hist * 20 * 0.4)
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def main():
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"""Demo usage of StockAnalyzer skill"""
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print("=" * 60)
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print("Stock Analyzer Skill - Demo")
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print("=" * 60)
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analyzer = StockAnalyzer()
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# Example 1: Single stock analysis
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print("\n--- Example 1: Analyze AAPL ---")
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result = analyzer.analyze("AAPL", ["RSI", "MACD"])
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print(f"\nResult: {result['signal']['action']}")
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print(f"Reasoning: {', '.join(result['signal']['reasoning'])}")
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# Example 2: Multi-stock comparison
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print("\n\n--- Example 2: Compare Tech Stocks ---")
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comparison = analyzer.compare(["AAPL", "MSFT", "GOOGL"], rank_by="momentum")
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# Example 3: Set up monitoring
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print("\n\n--- Example 3: Monitor Stock ---")
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alert = analyzer.monitor("TSLA", "RSI < 30", "notify")
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print(f"\nMonitoring status: {alert['status']}")
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print("\n" + "=" * 60)
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print("Demo complete!")
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print("=" * 60)
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if __name__ == "__main__":
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main()
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