Pro Divergence Adaptive [regular + hidden] by TradingClue

🔴 Overview 🔴
ProDivergence Adaptive is a versatile tool designed to identify regular and hidden divergences, including regular divergences from higher timeframes, on any given chart.
This indicator combines two major approaches of identifying divergences:
- Using price-action, identification of higher-highs, higher-lows etc.
- Using a statistical model (linear regression) to determine the direction of movements
To be more sensitive to the current market conditions, both were implemented in an adaptive way.

🔴 How the mechanics and calculations work 🔴

✅ short recap on what divergences are
Regular divergences occur when the price moves opposite to a selected oscillator, signaling potential trend reversals. They can be spotted on higher timeframes, providing stronger confirmation for potential reversals when aligned with signals on the current timeframe.
Hidden divergences indicate trend continuation. Combining these on various timeframes provides a more comprehensive analysis for traders to identify entry or exit points in the market

✅ Price Action
A regular bullish divergence happens when the price of an asset is forming lower lows, while the oscillator is forming higher lows.
A regular bearish divergence occurs when the price of an asset is forming higher highs, while the oscillator is forming lower highs
A hidden bullish divergence happens when the price of an asset is making higher lows during an uptrend, but the oscillator is making lower lows.
A hidden bearish divergence occurs when the price of an asset is making lower highs during a downtrend, but the oscillator is making higher highs.
"Lows" are signaled by a minimum value that is surrounded by higher low values.
"Highs" are signaled by a maximum value that is surrounded by lower high values.
Since there is no smoothing involved, there is only minimal lag.

✅ Statistical Model
When using linear regression to identify divergences between the price of an asset and an oscillator, we apply a statistical approach to find trends in the data. This method allows for a more nuanced detection of divergences, as it considers the overall direction of price points and oscillator values rather than individual highs and lows.
Regular Bullish Divergence: Occurs when the linear regression of the asset's price shows a downward trend while the linear regression of the oscillator indicates an upward trend.
Regular Bearish Divergence: Happens when the linear regression of the asset's price is upward but the linear regression of the oscillator is downward.
Hidden Bullish Divergence: Identified when, during an uptrend, the linear regression of the price is upward, but the linear regression of the oscillator trends downward.
Hidden Bearish Divergence: Occurs when, during a downtrend, the linear regression of the price trends downward, but the linear regression of the oscillator is upward.
Using linear regression smooths out fluctuations and focuses on the overall trend direction of both price and oscillator, reducing the impact of short-term volatility and providing a clearer view of the market's momentum.

✅ Adaptive Approach
Traditional oscillator settings do not account for changes in market volatility, leading to potential misalignment with current market conditions. This can result in either too many false signals during high volatility periods or missed opportunities in lower volatility settings due to the oscillator's static nature.
The adaptive approach adjusts the oscillator's settings based on the Average True Range (ATR), a measure of market volatility. By dynamically altering the oscillator length in relation to the current ATR value compared to its historical range, the settings become responsive to the market's volatility.
This adaptive method enhances the detection of meaningful divergences between price and oscillator movements by ensuring the oscillator is finely tuned to the current market environment. It results in a more accurate identification of potential reversal points, crucial for the success of divergence-based trading strategies.

🔴 Key Features 🔴

• Identifies regular and hidden divergences
  
• Multi-Timeframe Analysis
  
• Adaptive Oscillator Configuration, level of adaptiveness can be adjusted
  
• Supported Oscillators: CCI, Momentum, RSI
  
• Boundaries for the dynamic length of the oscillator can be applied
  
• Works for all kinds of assets (Forex, Stocks, Crypto, Commodities, Futures, ...)
  
• Works on all timeframes
  

🔴 Examples 🔴
✅ Canadian Dollar / Japanese Yen, 4H Chart, CADJPY
Divergences of Price vs CCI. The adaptive/ dynamic length of the CCI can range between 5 and 12.

✅ Bitcoin / U.S. Dollar, 2H Chart BTCUSD
Divergences of Price vs Momentum. The adaptive/ dynamic length of the Momentum can range between 4 and 13.

Caution: Trading carries a significant risk of financial loss, and past performance does not guarantee future results. Signals may be conflicting or ambiguous. Employ risk reduction techniques, such as setting stop losses, to mitigate potential losses.