Mean Reversion Strategy

Overview

Based on the theory that prices tend to return to their average over time. Buys when price is below average and sells when above.

How It Works

Calculates distance from moving average: - Below average: Expect price to rise (buy) - Above average: Expect price to fall (sell)

Trading Signals

Buy Signal

Price significantly below SMA (undervalued).

Sell Signal

Price significantly above SMA (overvalued).

Usage Example

prices = stock.df["adj_close_price"].to_a
sma = SQAI.sma(prices, period: 20)

current_price = prices.last
current_sma = sma.last
deviation = ((current_price - current_sma) / current_sma) * 100

# Buy if more than 5% below SMA
signal = if deviation < -5.0
  :buy
elsif deviation > 5.0
  :sell
else
  :hold
end

Characteristics

• Complexity: Low
• Best Market: Range-bound, low volatility
• Win Rate: 55-65%

Strengths

✅ High win rate in ranging markets
✅ Clear mathematical basis
✅ Works well with statistical analysis

Weaknesses

❌ Fails in trending markets
❌ Can lead to "catching falling knives"
❌ Requires stable market conditions