Trading Strategy

A skill for backtesting specific trading strategies, evaluating their performance, and visualizing entry/exit points.

Capabilities

• •Backtesting: Simulate trading strategies on historical data
• •Strategy Library:
  • •sma_crossover: Buy when Fast SMA > Slow SMA
  • •rsi_reversal: Contrarion strategy based on RSI overbought/oversold levels
• •Performance Metrics:
  • •Total Return
  • •Annualized Return
  • •Win Rate
  • •Max Drawdown
  • •Sharpe Ratio
• •Visualization: Generates charts with Buy/Sell markers

Workflow

1. Run Backtest

Users specify a ticker, strategy, and time period.

Example:

• •"Test SMA crossover on AAPL for the last 2 years" -> python3 ${CLAUDE_PLUGIN_ROOT}/skills/trading-strategy/scripts/main.py --ticker AAPL --strategy sma_crossover --period 2y

2. Parse Output

The script returns a JSON object with:

• •metrics: Performance summary
• •trades: List of executed trades
• •chart: Path to the visualization image

Command Options

# Basic SMA Crossover backtest
python3 main.py --ticker AAPL --strategy sma_crossover

# RSI Reversal with custom capital
python3 main.py --ticker TSLA --strategy rsi_reversal --initial-capital 50000

# Custom period
python3 main.py --ticker MSFT --strategy sma_crossover --period 5y

Strategies Details

SMA Crossover

• •Logic:
  • •Buy when SMA(Fast) crosses above SMA(Slow)
  • •Sell when SMA(Fast) crosses below SMA(Slow)
• •Parameters (Default):
  • •Fast Period: 50
  • •Slow Period: 200

RSI Reversal

• •Logic:
  • •Buy when RSI < 30 (Oversold) AND RSI increases (turns up)
  • •Sell when RSI > 70 (Overbought) AND RSI decreases (turns down)
• •Parameters (Default):
  • •RSI Period: 14