Support Quantitative Qualitative Estimation (QQE) #173
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The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index (RSI) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR). These ATR lines are smoothed making this indicator less susceptible to short term volatility. Implementation reference: https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/momentum/qqe.py Example: * `df['qqe']` retrieves the QQE with RSI window 14, MA window 5. * `df['qqel']` retrieves the QQE long * `df['qqes']` retrieves the QQE short * `df['qqe_10,4']` retrieves the QQE with RSI window 10, MA window 4 * `df['qqel_10,4']` retrieves the QQE long with customized windows. Initialized by retrieving `df['qqe_10,4']` * `df['qqes_10,4']` retrieves the QQE short with customized windows Initialized by retrieving `df['qqe_10,4']` The period of short, long EMA and signal line can be tuned with `set_dft_window('qqe', (rsi, rsi_ma))`. The default windows are 14 and 5.
jealous
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Jul 10, 2023
The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index (RSI) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR). These ATR lines are smoothed making this indicator less susceptible to short term volatility. Implementation reference: https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/momentum/qqe.py Example: * `df['qqe']` retrieves the QQE with RSI window 14, MA window 5. * `df['qqel']` retrieves the QQE long * `df['qqes']` retrieves the QQE short * `df['qqe_10,4']` retrieves the QQE with RSI window 10, MA window 4 * `df['qqel_10,4']` retrieves the QQE long with customized windows. Initialized by retrieving `df['qqe_10,4']` * `df['qqes_10,4']` retrieves the QQE short with customized windows Initialized by retrieving `df['qqe_10,4']` The period of short, long EMA and signal line can be tuned with `set_dft_window('qqe', (rsi, rsi_ma))`. The default windows are 14 and 5.
jealous
added a commit
that referenced
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Jul 10, 2023
The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index (RSI) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR). These ATR lines are smoothed making this indicator less susceptible to short term volatility. https://www.tradingview.com/script/0vn4HZ7O-Quantitative-Qualitative-Estimation-QQE/ Implementation reference: https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/momentum/qqe.py Example: * `df['qqe']` retrieves the QQE with RSI window 14, MA window 5. * `df['qqel']` retrieves the QQE long * `df['qqes']` retrieves the QQE short * `df['qqe_10,4']` retrieves the QQE with RSI window 10, MA window 4 * `df['qqel_10,4']` retrieves the QQE long with customized windows. Initialized by retrieving `df['qqe_10,4']` * `df['qqes_10,4']` retrieves the QQE short with customized windows Initialized by retrieving `df['qqe_10,4']` The period of short, long EMA and signal line can be tuned with `set_dft_window('qqe', (rsi, rsi_ma))`. The default windows are 14 and 5.
jealous
added a commit
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Jul 10, 2023
The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index (RSI) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR). These ATR lines are smoothed making this indicator less susceptible to short term volatility. https://www.tradingview.com/script/0vn4HZ7O-Quantitative-Qualitative-Estimation-QQE/ Implementation reference: https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/momentum/qqe.py Example: * `df['qqe']` retrieves the QQE with RSI window 14, MA window 5. * `df['qqel']` retrieves the QQE long * `df['qqes']` retrieves the QQE short * `df['qqe_10,4']` retrieves the QQE with RSI window 10, MA window 4 * `df['qqel_10,4']` retrieves the QQE long with customized windows. Initialized by retrieving `df['qqe_10,4']` * `df['qqes_10,4']` retrieves the QQE short with customized windows Initialized by retrieving `df['qqe_10,4']` The period of short, long EMA and signal line can be tuned with `set_dft_window('qqe', (rsi, rsi_ma))`. The default windows are 14 and 5.
jealous
added a commit
that referenced
this issue
Jul 10, 2023
The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index (RSI) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR). These ATR lines are smoothed making this indicator less susceptible to short term volatility. https://www.tradingview.com/script/0vn4HZ7O-Quantitative-Qualitative-Estimation-QQE/ Implementation reference: https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/momentum/qqe.py Example: * `df['qqe']` retrieves the QQE with RSI window 14, MA window 5. * `df['qqel']` retrieves the QQE long * `df['qqes']` retrieves the QQE short * `df['qqe_10,4']` retrieves the QQE with RSI window 10, MA window 4 * `df['qqel_10,4']` retrieves the QQE long with customized windows. Initialized by retrieving `df['qqe_10,4']` * `df['qqes_10,4']` retrieves the QQE short with customized windows Initialized by retrieving `df['qqe_10,4']` The period of short, long EMA and signal line can be tuned with `set_dft_window('qqe', (rsi, rsi_ma))`. The default windows are 14 and 5.
jealous
added a commit
that referenced
this issue
Jul 10, 2023
The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index (RSI) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR). These ATR lines are smoothed making this indicator less susceptible to short term volatility. https://www.tradingview.com/script/0vn4HZ7O-Quantitative-Qualitative-Estimation-QQE/ Implementation reference: https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/momentum/qqe.py Example: * `df['qqe']` retrieves the QQE with RSI window 14, MA window 5. * `df['qqel']` retrieves the QQE long * `df['qqes']` retrieves the QQE short * `df['qqe_10,4']` retrieves the QQE with RSI window 10, MA window 4 * `df['qqel_10,4']` retrieves the QQE long with customized windows. Initialized by retrieving `df['qqe_10,4']` * `df['qqes_10,4']` retrieves the QQE short with customized windows Initialized by retrieving `df['qqe_10,4']` The period of short, long EMA and signal line can be tuned with `set_dft_window('qqe', (rsi, rsi_ma))`. The default windows are 14 and 5.
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The Qualitative Quantitative Estimation (QQE) indicator works like a smoother
version of the popular Relative Strength Index (RSI) indicator. QQE expands
on RSI by adding two volatility based trailing stop lines. These trailing
stop lines are composed of a fast and a slow moving Average True Range (ATR).
These ATR lines are smoothed making this indicator less susceptible to short
term volatility.
Implementation reference:
https://github.com/twopirllc/pandas-ta/blob/main/pandas_ta/momentum/qqe.py
Example:
df['qqe']retrieves the QQE with RSI window 14, MA window 5.df['qqel']retrieves the QQE longdf['qqes']retrieves the QQE shortdf['qqe_10,4']retrieves the QQE with RSI window 10, MA window 4df['qqel_10,4']retrieves the QQE long with customized windows.Initialized by retrieving
df['qqe_10,4']df['qqes_10,4']retrieves the QQE short with customized windowsInitialized by retrieving
df['qqe_10,4']The period of short, long EMA and signal line can be tuned with
set_dft_window('qqe', (rsi, rsi_ma)). The default windows are 14 and 5.The text was updated successfully, but these errors were encountered: