Divergence

Divergence is a fundamental concept in technical analysis that refers to a situation where the price of an asset is moving in the opposite direction of an indicator. This phenomenon is often interpreted as a signal that the current price trend may be weakening or reversing. Divergence can be used to predict potential reversals in price direction, and thus it is a critical tool in the arsenal of algorithmic traders.

Types of Divergence

Divergence comes in two main types: Regular Divergence and Hidden Divergence.

Regular Divergence

Regular Divergence is a situation where the price of an asset and a specific technical indicator, most commonly the Relative Strength Index (RSI) or Moving Average Convergence Divergence (MACD), move in opposite directions. It can be either bullish or bearish.

Bullish Regular Divergence

Bullish Regular Divergence occurs when the price of an asset creates a lower low while the indicator forms a higher low. This suggests that the downward momentum is slowing down, even though the price continues to fall, and could be indicative of an upcoming bullish reversal.

• Example: If Bitcoin’s price makes a lower low, but the RSI indicator makes a higher low, this could suggest that selling pressure is decreasing, and a price increase might be forthcoming.

Bearish Regular Divergence

Bearish Regular Divergence occurs when the price creates a higher high, but the indicator forms a lower high. This suggests that the upward momentum is weakening, even though the price continues to rise, and could be indicative of an impending bearish reversal.

• Example: If the S&P 500 makes a higher high, but the MACD forms a lower high, this divergence could indicate that buying pressure is decreasing, possibly leading to a price drop.

Hidden Divergence

Hidden Divergence occurs when the price of an asset moves in the same direction as the trend, but the indicator moves in the opposite direction. Like Regular Divergence, it can also be either bullish or bearish.

Bullish Hidden Divergence

Bullish Hidden Divergence happens when the price creates a higher low while the indicator forms a lower low. This suggests that the upward trend is likely to continue even though the indicator is signalling otherwise.

• Example: In a scenario where Apple Inc.’s stock price forms a higher low, but the RSI forms a lower low, this could validate the continuation of the upward trend.

Bearish Hidden Divergence

Bearish Hidden Divergence occurs when the price creates a lower high, but the indicator forms a higher high. This indicates that the downward trend is likely to continue even though the indicator suggests otherwise.

• Example: If Tesla’s stock price creates a lower high, but the MACD forms a higher high, this could confirm that the downward trend will continue.

Indicators Commonly Used to Identify Divergence

Several technical indicators can be employed to detect divergence, and the choice of indicator often depends on the asset being traded and the trading strategy in use. The most commonly used indicators include:

Relative Strength Index (RSI)

The RSI measures the speed and change of price movements and oscillates between 0 and 100. It is traditionally used to identify overbought or oversold conditions but can also be highly effective for spotting divergence.

• Bullish Divergence: Identified when RSI forms higher lows while the price forms lower lows.
• Bearish Divergence: Identified when RSI forms lower highs while the price forms higher highs.

Moving Average Convergence Divergence (MACD)

The MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. It consists of the MACD line, the signal line, and the histogram.

• Bullish Divergence: Occurs when the MACD line forms higher lows while the price forms lower lows.
• Bearish Divergence: Occurs when the MACD line forms lower highs while the price forms higher highs.

Stochastic Oscillator

The Stochastic Oscillator is a momentum indicator that compares a particular closing price of a security to a range of its prices over a certain period of time. It oscillates between 0 and 100.

• Bullish Divergence: Identified when the stochastic oscillator forms higher lows while the price forms lower lows.
• Bearish Divergence: Identified when the stochastic oscillator forms lower highs while the price forms higher highs.

Commodity Channel Index (CCI)

CCI is an oscillator used to identify cyclical trends in commodities but can also be applied to stocks. It is particularly good at identifying divergence.

• Bullish Divergence: Occurs when CCI forms higher lows while the price forms lower lows.
• Bearish Divergence: Occurs when CCI forms lower highs while the price forms higher highs.

Implementation in Algorithmic Trading

Algorithmic trading systems can be programmed to automatically detect divergence using predefined criteria and execute trades based on these signals. The efficiency of such algorithms depends on the quality of the data, the sophistication of the models, and the execution speed.

Data Collection

For accurate detection of divergence, high-quality real-time and historical data are required. This data can be sourced from various providers, including:

• Bloomberg: bloomberg.com
• Reuters: reuters.com
• Quandl: quandl.com

Signal Detection

Once the data is collected, the next step involves signal detection. This can be done using several programming languages and platforms, including Python with libraries like Pandas and NumPy, or specialized trading platforms like MetaTrader.

• Python: Python can be used to write scripts that continuously monitor live market data for divergence signals.
• MetaTrader: MetaTrader’s MQL5 language allows for the creation of automated trading robots (Expert Advisors) that can detect divergence signals in real-time.

Example Code in Python

A simple example of how to detect bullish and bearish divergence using Python is as follows:

[import](../i/import.html) pandas as pd
[import](../i/import.html) numpy as np

def find_divergence(prices, [indicator](../i/indicator.html)):
    divergences = []
    for i in [range](../r/range.html)(1, len(prices)):
        if prices[i] < prices[i-1] and [indicator](../i/indicator.html)[i] > [indicator](../i/indicator.html)[i-1]:
            divergences.append(('[Bullish Divergence](../b/bullish_divergence.html)', prices.[index](../i/index_instrument.html)[i]))
        elif prices[i] > prices[i-1] and [indicator](../i/indicator.html)[i] < [indicator](../i/indicator.html)[i-1]:
            divergences.append(('[Bearish Divergence](../b/bearish_divergence.html)', prices.[index](../i/index_instrument.html)[i]))
    [return](../r/return.html) divergences

# Example usage with dummy data
prices = pd.Series([100, 102, 101, 105, 104])
[indicator](../i/indicator.html) = pd.Series([70, 72, 71, 73, 71])

divergences = find_divergence(prices, [indicator](../i/indicator.html))
for d in divergences:
    print(d)

Execution of Trades

The final step is to execute trades based on the detected divergence signals. The efficiency of this process can be significantly enhanced by employing low-latency trading infrastructure and direct market access.

• Algorithmic Execution Services: Many brokers offer algorithmic execution services, which can be utilized to ensure faster and more efficient trade execution.
• Direct Market Access (DMA): Using DMA can reduce latency and enhance the effectiveness of the trading strategy.

Conclusion

Divergence is a powerful tool in technical analysis that can provide early signals of potential trend reversals. When implemented in algorithmic trading systems, it can enhance the ability to make informed and timely trading decisions. Utilizing various technical indicators, collecting high-quality data, accurately detecting signals, and efficiently executing trades are all critical components of leveraging divergence in algorithmic trading.