AWR Pearsons R & LR Oscillator MTF

1. Overview
This indicator is designed to analyze the correlation between a price series (or any custom indicator) and the bar index using Pearson’s correlation coefficient. It performs multiple linear regressions over shifted periods and then aggregates these results to create an oscillator. In addition, it integrates a multi-timeframe (MTF) analysis by retrieving the same calculations on 3 different time intervals, providing a more comprehensive view of the trend evolution.

2. User Parameters
The indicator offers several configurable parameters that allow the user to adjust both the calculations and the display:

Source (Linear Regression): The data source on which the regressions are applied (by default, the closing price).

Number of Linear Regressions (numOfLinReg): Allows choosing the number of correlation calculations (up to 10) to be carried out on different shifted periods.

Start Period (startPeriod) and Period Increment (periodIncrement): These parameters define the reference window for each regression. The calculation starts with a base period and then increases with each regression by a fixed increment, creating several time windows to assess the relationship between price evolution and time progression.

Deviation (def_deviation): Although defined, this parameter is intended to control the sensitivity of the calculations. It can be used in further developments of the indicator.

For Multi Time Frames analysis, three additional timeframes are provided through inputs in addition of the current period:

Sum up :
Timeframe 1 = current
Timeframe 2 = 30-minute (default settings)
Timeframe 3 = 1-hour (default settings)
Timeframe 4 = 4-hour (default settings)

These different timeframes allow you to obtain consistent or divergent signals over multiple resolutions, thereby enhancing the confidence of trading decisions.

3. Calculation Logic
At the core of the indicator is the f_calcConditions() function, which performs several essential tasks:

Calculating Pearson's Coefficients For each linear regression, the script uses ta.correlation() to measure the correlation between the chosen source (for example, the closing price) and the chronological index (bar_index). Up to 10 coefficients are computed over shifted windows, providing an evolving view of the linear relationship over different intervals.

Averaging the Results Once the coefficients are calculated, they are stored in an array and averaged to produce a global correlation value called avgPR_local.

Applying Moving Averages
The resulting average is then smoothed using several moving averages (SMA):

A short-term SMA (period of 14),

An intermediate SMA (period of 100),

A long-term SMA (period of 400).
These moving averages help to highlight the underlying trend of the oscillator by indicating the direction in which the correlation is moving.

Defining Trading Conditions Based on avgPR_local and its associated SMAs, multiple conditions are set to generate buy or sell signals:

Simple SMA Conditions :
Small signal :

Light blue below bar signal :
When the averaged coefficients lie between -1 and -0.63, are above the short-term SMA (14 periods), and are increasing, it may indicate a bullish dynamic (buy signal).

Orange above bar signal :
Conversely, when the value is higher (between 0.63 and 1) and below its SMA (14 periods), and are decreasing the trend is considered bearish (sell signal).

Medium signal :

Dark green signal
When the averaged coefficients lie between -1 and -0.45, are above the short-term SMA (14 periods), and are increasing, and also the average 100 is increasing. It may indicate a bullish dynamic (buy signal).

Light red signal :
Conversely, when the value is higher (between 0.45 and 1) and below its SMA (14 periods), the trend and are decreasing, and also the average 100 is decreasing. It may indicate a bearish dynamic(sell signal).


Light green signal :
When the averaged coefficients lie between -1 and -0.15, are above the short-term SMA (14 periods), and are increasing, and also the average 100 & 400 is increasing . It may indicate a bullish dynamic (buy signal).

Dark red signal :
Conversely, when the value is higher (between 0.45 and 1) and below its SMA (14 periods), the trend and are decreasing, and also the average 100 & 400 is decreasing. It may indicate a bearish dynamic(sell signal).


These additional conditions further refine the signals by verifying the consistency of the movement over longer periods. They check that the trends from the respective averages (intermediate and long-term) are in line with the direction indicated by the initial moving average.

These conditions are designed to capture moments when the oscillator's dynamics change, which can be interpreted as opportunities to enter or exit a trade.

4. Multi-Timeframes and Display
One of the main strengths of this indicator is its multi-timeframe approach.
This offers several advantages:

Comparative Analysis: Compare short-term dynamics with broader trends.

Enhanced Signal Reliability: A signal confirmed across multiple timeframes has a higher probability of success.

To visually highlight these signals on the chart, the indicator uses the plotchar() function with distinct symbols for each timeframe:

Current Timeframe: Signals are represented by the character "1"

30-Minute Timeframe: Displayed with the character "2".

1-Hour Timeframe: Displayed with the character "3".

4-Hour Timeframe: Displayed with the character "4".

The colors used are various shades of green for buy signals and shades of red/orange for sell signals, making it easy to distinguish between the different alerts.

5. Integrated Alerts
To avoid missing any trading opportunities, the indicator includes an alert condition via the alertcondition() function. This alert is triggered if any buy or sell signal is generated on any of the analyzed timeframes. The message "MTF valide" indicates that multiple timeframes are confirming the signal, enabling more informed decision-making.

6. How to Use This Indicator
Installation and Configuration: Copy the script into the TradingView Pine Script editor and add it to your chart. The default parameters can be tuned according to market behavior or personal preferences regarding sensitivity and responsiveness.

Interpreting the Signals:

Watch for the symbols on the chart corresponding to each timeframe.

A buy signal appears as a specific symbol below the bar (indicating a bullish condition based on a rising or less negative correlation), while a sell signal appears above the bar.

Multi-Timeframe Analysis: By comparing signals across timeframes, you can filter out false signals. For example, if the short-term timeframe shows a buy signal but the 4-hour timeframe indicates a bearish trend, you may need to reassess your position.

Adjusting the Settings: Depending on the asset type or market volatility, you might need to tweak the periods (startPeriod, periodIncrement) or the number of linear regressions to generate signals that better align with the price dynamics.

Using Alerts: Activate the built-in alert feature so that TradingView notifies you as soon as a multi-timeframe signal is detected. This ensures you stay informed even if you are not continuously monitoring the chart.

In Conclusion
The AWR Pearsons R & LR Oscillator MTF is a powerful tool for traders seeking a detailed understanding of market trends by combining statistical rigor (via Pearson's correlation coefficient) with a multi-timeframe approach. It is capable of generating clear entry and exit signals, visualized with specific symbols and colors depending on the timeframe. By adjusting the parameters to match your trading strategy and leveraging the alert system, you now have a robust instrument for making well-informed market decisions.

Feel free to dive deeper into each component and experiment with different configurations to see how the oscillator integrates with your overall technical analysis strategy. Enjoy exploring its potential and refining your trading approach!