The Stochastic Smoothing Oscillator

The Stochastic Oscillator is familiar in technical analysis, but its variant, the Stochastic Smoothing Oscillator, offers additional smoothing. Let’s first review the Stochastic Oscillator with EURGBP displayed in one panel, and the Stochastic Oscillator in another.

The normalization technique confines values between 0 and 1 (or 0 and 100 if you prefer). This involves subtracting the minimum value from the current value over a specific lookback period and dividing it by the difference between the maximum and minimum values in that same period.

The Stochastic Oscillator identifies overbought and oversold conditions by using highs and lows through the normalization formula. An overbought zone indicates extreme bullishness, suggesting a likely consolidation, whereas an oversold zone signals extreme bearishness, hinting at a potential bounce. Thus, the Stochastic Oscillator serves as a contrarian tool, highlighting reactions to significant market movements.

Now, let's transition to TradingView to code the Stochastic Smoothing Oscillator. Why should we code this ourselves when it’s available in the platform’s indicators? Learning to create our own technical indicators on a reputable charting platform is a foundational step. By grasping the basics of Pine Script, we’ll eventually gain the expertise to develop personalized indicators and strategies.

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Creating the Stochastic Smoothing Oscillator in TradingView

To plot the Stochastic Smoothing Oscillator on EURGBP values in TradingView, you need to create an account, which is free. Begin by selecting the Chart option on the homepage and choose any asset for indicator calculation. Then, locate the Pine Editor at the bottom of your screen and prepare to code.

The first step is to set the Pine Script version. In our case, we’ll use version 4.

//@version=4

Next, declare the name of the indicator (script):

study("Stochastic Smoothing Oscillator")

Now, we can define the parameters for our oscillator. The Stochastic Smoothing Oscillator applies exponential moving averages to the highs, lows, and closing prices, utilizing the stochastic normalization function. We need to establish two lookback periods: 2 and 13. The 2-period lookback corresponds to the exponential moving average on HLC data, while the 13-period lookback refers to the normalization window.

lookback = 13

ema_lookback = 2

Next, we’ll smooth the HLC data using the built-in ema() function, which computes the exponential moving average based on our closing price and defined lookback periods.

SSO_low = ema(low, ema_lookback)

SSO_high = ema(high, ema_lookback)

SSO_close = ema(close, ema_lookback)

Now, we will apply the SSO formula, which mirrors the Stochastic Oscillator’s formula. We can utilize the highest() and lowest() functions to find the maximum and minimum values over the specified lookback period.

SSO = (SSO_close - lowest(SSO_low, lookback)) / (highest(SSO_high, lookback) - lowest(SSO_low, lookback)) * 100

Finally, we will plot the indicator alongside the chart using the plot() function, which specifies a source and color.

plot(SSO, color=color.red)

hline(10, color=color.gray, linestyle=hline.style_dashed)

hline(90, color=color.gray, linestyle=hline.style_dashed)

The full code is as follows:

//@version=4

study("Stochastic Smoothing Oscillator")

lookback = 13

ema_lookback = 2

SSO_low = ema(low, ema_lookback)

SSO_high = ema(high, ema_lookback)

SSO_close = ema(close, ema_lookback)

SSO = (SSO_close - lowest(SSO_low, lookback)) / (highest(SSO_high, lookback) - lowest(SSO_low, lookback)) * 100

plot(SSO, color=color.red)

hline(10, color=color.gray, linestyle=hline.style_dashed)

hline(90, color=color.gray, linestyle=hline.style_dashed)

The SSO operates similarly to the Stochastic Oscillator: a bullish bias emerges when the SSO is low, while a bearish bias occurs when it is high.

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This video offers a comprehensive tutorial on creating indicators from scratch using TradingView.

In this video, learn how to customize indicators in TradingView for enhanced trading strategies.