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ACN-Mean-Reversion SuperTrend Filter Strategy

Stochastic Oversold Meets SuperTrend: A 24-Year Test on Accenture (ACN)

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Disclaimer: The backtest results in this article are based solely on historical data and do not guarantee future performance. Anyone wishing to use this trading system should conduct their own research and testing before applying it.

What happens when you combine one of the oldest momentum oscillators with a modern volatility-adaptive trend filter — and run it on one of the most consistently compounding stocks of the past two decades?

The answer, it turns out, is surprisingly compelling.

The Strategy in Plain English

This system is built on two ideas that sound almost contradictory at first glance.

STOCHASTIC_D_PERIOD = 3
STOCHASTIC_K_PERIOD = 14
STOCHASTIC_OVERSOLD = 20

def calculate_stochastic(df, k_period=STOCHASTIC_K_PERIOD, d_period=STOCHASTIC_D_PERIOD):
    """Calculate Fast %K and Slow %D"""
    low_min = df['Low'].rolling(window=k_period).min()
    high_max = df['High'].rolling(window=k_period).max()

    df['Fast_%K'] = 100 * (df['Close'] - low_min) / (high_max - low_min)
    df['Slow_%D'] = df['Fast_%K'].rolling(window=d_period).mean()
    return df