Technical Analysis

#!/usr/bin/env python3
# ABOUTME: CLI wrapper for price correlation computation.
# ABOUTME: Use for portfolio diversification analysis and pair trading.

import argparse
import json

from trading_skills.correlation import compute_correlation
from trading_skills.utils import generated_at_str


def main():
    parser = argparse.ArgumentParser(description="Compute price correlation matrix")
    parser.add_argument("symbols", help="Comma-separated ticker symbols (min 2)")
    parser.add_argument("--period", default="3mo", help="Historical period (default: 3mo)")

    args = parser.parse_args()
    symbols = [s.strip().upper() for s in args.symbols.split(",")]

    result = compute_correlation(symbols, args.period)
    result["generated_at"] = generated_at_str()
    result["data_delay"] = "15min"
    print(json.dumps(result, indent=2))


if __name__ == "__main__":
    main()


#!/usr/bin/env python3
# ABOUTME: CLI wrapper for technical indicator computation.
# ABOUTME: Supports multi-symbol analysis and earnings data.

import argparse
import json

from trading_skills.technicals import compute_indicators, compute_multi_symbol
from trading_skills.utils import generated_at_str


def main():
    parser = argparse.ArgumentParser(description="Compute technical indicators")
    parser.add_argument("symbol", help="Ticker symbol (comma-separated for multiple)")
    parser.add_argument("--period", default="3mo", help="Historical period")
    parser.add_argument("--indicators", default=None, help="Comma-separated indicators")
    parser.add_argument("--earnings", action="store_true", help="Include earnings data")

    args = parser.parse_args()
    indicators = args.indicators.split(",") if args.indicators else None

    # Parse symbols
    symbols = [s.strip().upper() for s in args.symbol.split(",")]

    if len(symbols) == 1:
        result = compute_indicators(symbols[0], args.period, indicators, args.earnings)
    else:
        result = compute_multi_symbol(symbols, args.period, indicators, args.earnings)

    result["generated_at"] = generated_at_str()
    result["data_delay"] = "15min"
    print(json.dumps(result, indent=2))


if __name__ == "__main__":
    main()


---
name: technical-analysis
description: Compute technical indicators like RSI, MACD, Bollinger Bands, SMA, EMA for a stock. Use when user asks about technical analysis, indicators, RSI, MACD, moving averages, overbought/oversold, or chart analysis.
dependencies: ["trading-skills"]
---

# Technical Analysis

Compute technical indicators using pandas-ta. Supports multi-symbol analysis and earnings data.

## Instructions

> **Note:** If `uv` is not installed or `pyproject.toml` is not found, replace `uv run python` with `python` in all commands below.

```bash
uv run python scripts/technicals.py SYMBOL [--period PERIOD] [--indicators INDICATORS] [--earnings]
```

## Arguments

- `SYMBOL` - Ticker symbol or comma-separated list (e.g., `AAPL` or `AAPL,MSFT,GOOGL`)
- `--period` - Historical period: 1mo, 3mo, 6mo, 1y (default: 3mo)
- `--indicators` - Comma-separated list: rsi,macd,bb,sma,ema,atr,adx (default: all)
- `--earnings` - Include earnings data (upcoming date + history)

## Output

Single symbol returns:
- `price` - Current price and recent change
- `indicators` - Computed values for each indicator
- `risk_metrics` - Volatility (annualized %) and Sharpe ratio
- `signals` - Buy/sell signals based on indicator levels
- `earnings` - Upcoming date and EPS history (if `--earnings`)

Multiple symbols returns:
- `results` - Array of individual symbol results

## Interpretation

- RSI > 70 = overbought, RSI < 30 = oversold
- MACD crossover = momentum shift
- Price near Bollinger Band = potential reversal
- Golden cross (SMA20 > SMA50) = bullish
- ADX > 25 = strong trend
- Sharpe ratio > 1 = good risk-adjusted returns, > 2 = excellent
- Volatility (annualized) = standard deviation of returns scaled to annual basis

## Examples

```bash
# Single symbol with all indicators
uv run py