Chapter 1 Understanding the Pull-Back Technique

In the world of trading, a pull-back often follows a significant market movement, providing traders with an opportunity to enter the market again. This article delves into the application of the pull-back technique using a technical indicator.

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The Stochastic Oscillator

The stochastic oscillator is a widely recognized technical indicator. It normalizes price movements into a range from 0 to 100, factoring in both highs and lows to reflect market volatility.

To compute the raw stochastic oscillator, utilize the following formula:

The subsequent figure illustrates the USDCHF currency pair along with its stochastic oscillator. Typically, values approaching the lower range (0-20) suggest a potential bullish reversal, indicating the market is oversold. Conversely, values nearing the upper range (80-100) imply a potential bearish reversal, suggesting the market is overbought.

If you have an OHLC array in Python, you can add a fifth column for stochastic values using this syntax:

def add_column(data, times):

for i in range(1, times + 1):

new = np.zeros((len(data), 1), dtype=float)

data = np.append(data, new, axis=1)

return data

This code snippet forms the basis for further calculations.

Chapter 2 Implementing the Pull-Back Technique

The pull-back technique is a well-established trading method based on the premise that after a breakout, whether bullish or bearish, the market typically pauses before continuing its original trajectory due to profit-taking and increased orders. This pause often presents a favorable entry point for traders.

To illustrate, consider a scenario where the market exits a triangular formation. After breaking through a descending support line (indicated by upward arrows), the market may attempt a pull-back, encountering resistance at that same line before continuing its downward trend.

The strategy employs the stochastic oscillator with specific trading signals:

• A long signal triggers when the 20-period stochastic oscillator moves above 20 and subsequently pulls back to it.
• A short signal activates when the oscillator dips below 80 and then retraces to that level.

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def signal(data, stoch_column, buy, sell):

data = add_column(data, 10)

for i in range(len(data)):

try:

if data[i, stoch_column] > lower_barrier and all(data[i - j, stoch_column] < lower_barrier for j in range(1, 6)):

data[i + 1, buy] = 1

elif data[i, stoch_column] < upper_barrier and all(data[i - j, stoch_column] > upper_barrier for j in range(1, 6)):

data[i + 1, sell] = -1

except IndexError:

pass

return data

This code generates the trading signals based on the stochastic oscillator's movement.

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