Stochastic Oscillator Strategy

Overview

Compares closing price to the price range over a period to identify overbought/oversold conditions and momentum changes.

How It Works

Calculates two lines: - %K Line: (Current Close - Lowest Low) / (Highest High - Lowest Low) × 100 - %D Line: 3-period SMA of %K

Range: 0-100

Trading Signals

Buy Signal

• %K crosses above %D
• Both below 20 (oversold zone)

Sell Signal

• %K crosses below %D
• Both above 80 (overbought zone)

Usage Example

high = stock.df["high_price"].to_a
low = stock.df["low_price"].to_a
close = stock.df["adj_close_price"].to_a

stoch_k, stoch_d = SQAI.stoch(
  high, low, close,
  fastk_period: 14,
  slowk_period: 3,
  slowd_period: 3
)

vector = OpenStruct.new(
  stoch_k: stoch_k,
  stoch_d: stoch_d
)

signal = SQA::Strategy::Stochastic.trade(vector)

Characteristics

• Complexity: Medium
• Best Market: Range-bound
• Win Rate: 50-60%

Strengths

✅ Good for reversals
✅ Works in ranging markets
✅ Early signals

Weaknesses

❌ Many false signals in trends
❌ Can stay overbought/oversold