alpacalyzer-algo-trader: Skill for Claude Code

Scope Constraint

• Indicators go in src/alpacalyzer/analysis/technical_analysis.py (methods on TechnicalAnalyzer)
• Tests go in tests/test_technical_analysis.py
• Uses pandas-ta library for indicators, pandas/numpy for custom calculations

Steps

1. Study existing indicators

Read src/alpacalyzer/analysis/technical_analysis.py — all indicators are methods on TechnicalAnalyzer. Each returns dict with value, signal ("bullish"/"bearish"/"neutral"), and description.

Also read tests/test_technical_analysis.py for the test pattern.

2. Check pandas-ta availability

import pandas_ta as ta
print(ta.version)
# See available indicators: https://github.com/twopirllc/pandas-ta#indicators

Use pandas-ta if the indicator exists there. Otherwise implement with pandas/numpy.

3. Add indicator method

Add calculate_<indicator>(self, ticker, period) to TechnicalAnalyzer. Follow the pattern:

• Fetch price data with self.get_price_data(ticker, days=max(period * 2, 30))
• Handle insufficient data → return {"value": None, "signal": "neutral", "description": "Insufficient data"}
• Calculate indicator value
• Interpret signal (be conservative — when in doubt, return neutral)
• Wrap in try/except → return neutral on error

4. Integrate with analyze_ticker()

Add your indicator to analyze_ticker() method — include in signals list and score calculation if appropriate.

5. Write tests

Test: bullish signal, bearish signal, insufficient data handling, error handling, integration in analyze_ticker(). Use mock price data (pandas DataFrame with Close/High/Low/Open/Volume columns).

6. Run and verify

uv run pytest tests/test_technical_analysis.py -v

Reference files

Purpose	File
All indicators	src/alpacalyzer/analysis/technical_analysis.py
Tests	tests/test_technical_analysis.py