Chapter 1: Introduction to the Red Indicator

This article delves into a distinctive technical tool known as the Red Indicator, part of a broader set called the Rainbow Indicators. These indicators comprise innovative combinations of price-based techniques aimed at assisting traders in identifying market reversals or confirming existing trends. The Red Indicator employs a contrarian approach centered around the idea of volatility re-integration.

For an extensive compilation of trend-following trading strategies, consider checking my book. It presents a wide array of both classical and contemporary methods, exploring the field of technical analysis alongside diverse trading strategies. The book also includes its own GitHub repository.

Section 1.1: Understanding Volatility Bands

Volatility bands are dynamic support and resistance levels derived from moving averages and standard deviations. The most recognized of these bands are Bollinger Bands, which utilize simple moving averages and standard deviations.

The Red Indicator calculates a 20-period exponential moving average, which is then adjusted by adding or subtracting a value derived from the 20-period standard deviation multiplied by 2.

Thus, the application of the Red Indicator is defined as follows: A bullish signal occurs when the current closing price crosses above the lower volatility band after remaining below it for a minimum of five periods. Conversely, a bearish signal is identified when the current closing price breaks below the upper volatility band after sustaining above it for at least five periods.

For ease of interpretation, the Rainbow Indicators are displayed using an overlay arrow-based method, where only confirmed signals are presented instead of the indicator standing alone.

Subsection 1.1.1: Coding the Red Indicator

Pine Script, the primary programming language for TradingView, is known for its user-friendliness. We will code the Red Indicator and examine its generated signals. The following code snippet showcases the signals produced by the indicator.

// © Sofien-Kaabar

//@version=5

indicator("Rainbow Collection - Red", overlay = true)

lower_band = ta.ema(close, 20) - (ta.stdev(close, 20) * 2)

upper_band = ta.ema(close, 20) + (ta.stdev(close, 20) * 2)

buy = close > lower_band and close[1] < lower_band[1] and close[2] < lower_band[2] and close[3] < lower_band[3] and close[4] < lower_band[4] and close[5] < lower_band[5]

sell = close < upper_band and close[1] > upper_band[1] and close[2] > upper_band[2] and close[3] > upper_band[3] and close[4] > upper_band[4] and close[5] > upper_band[5]

plotshape(buy and sell[1] == 0, style = shape.triangleup, color = color.red, location = location.belowbar, size = size.small)

plotshape(sell and buy[1] == 0, style = shape.triangledown, color = color.red, location = location.abovebar, size = size.small)

Chapter 2: Summary and Recommendations

In this comprehensive overview, the aim is to contribute to the domain of objective technical analysis by advocating for clearer techniques and strategies that warrant thorough back-testing before implementation. Such an approach aims to mitigate the negative perception surrounding technical analysis as being overly subjective and lacking scientific grounding.

When encountering any trading technique or strategy, I recommend adhering to the following steps:

1. Maintain a critical mindset and set aside emotional responses.
2. Conduct back-testing under real-world simulations and conditions.
3. If promising results emerge, consider optimizing and performing a forward test.
4. Always account for transaction costs and potential slippage in your simulations.
5. Incorporate risk management and position sizing into your assessments.

Lastly, even after ensuring adherence to the above guidelines, remain vigilant and continuously monitor the strategy, as market dynamics may evolve, potentially affecting the strategy's profitability.