Update Indicator.md

DrChandrakant · Jun 14, 2023 · Jun 14, 2023 · Jun 14, 2023 · Jun 14, 2023 · Jun 14, 2023
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-# Indicator's in mplfinance
-#### "Indicator" In the world of investing, indicators typically refer to technical chart patterns deriving from the price, volume, or open interest of a given security.
+# Indicators in mplfinance
+#### "Indicators" In the world of investing is a vital tool, indicators typically refer to technical chart patterns deriving from the price, volume, or open interest of a given security.
 
 ---
 
-## There are two ways to build indicator in mplfinance:
-- Building Indicator With addplot Functiona
+## There are two ways to build indicators in mplfinance:
+- Building a indicator with **`mpf.addplot()`** method
 - Inbuilt Indicator Method (Upcoming Feature)
 #### Below is a brief description of each method, with links to tutorials on how to use each method:
 
 ---
-### [Addplot](https://github.com/matplotlib/mplfinance/blob/master/examples/addplot.ipynb)
-* The Addplot Method ***is easy to use and requires little or no knowledge of matplotlib***, and no need to import matplotlib.
-* The Addplot With make_addplot handles 95% of the most common types of indicator and market studies.
-* The make_addplot Method attains its simplicity, in part, by having certain limitations.<br>These limitiations are:
-   - Lenghtly Code Required For Complex Indicators.
-   - Added Plot Not Provide Ticker On Y-Axis.
-   - Legend Support Limited.
-* The Inbuilt Method is adequate to any kind complex indicator:
-  - Complex Indicator.
-  - Legend Fully Supported
-  - with one or more studies/indicators, such as:
-    - MACD, DMI, RSI, Bollinger, Accumulation/Distribution Oscillator, Commodity Channel Index, Etc.
-* [**See here for a tutorial and details on implementing the mplfinance for plot indicator.**](https://github.com/matplotlib/mplfinance/tree/master/examples/indicators)
+### [Addplot Method](https://github.com/matplotlib/mplfinance/blob/master/examples/addplot.ipynb)
+* The `mpf.addplot()` Method ***is easy to use and requires little or no knowledge of matplotlib***, and no need to import matplotlib.
+* The `mpf.addplot()` with `mpf.make_addplot` handles 95% of the most common types of indicators and market studies.
+* The `mpf.make_addplot` method attains its simplicity, in part, by having certain limitations.<br>These limitations are:
+   - Large code required for complex indicators.
+   - Larger calculation required.
+   - Legend box support Limited.
+* The Inbuilt method is adequate for any kind of complex indicator:
+  - Complex indicators can be handled.
+  - Legend box fully supported
+  - With one or more studies/indicators, such as:
+    - Ichimoku, MACD, DMI, RSI, Bollinger, Accumulation/Distribution Oscillator, Commodity Channel Index, Etc.
+* [**See here for a tutorial and details on implementing the mplfinance for addplot method.**](https://github.com/matplotlib/mplfinance/blob/master/examples/addplot.ipynb)
 
----
 
-### [Indicator Example Available](https://github.com/matplotlib/mplfinance/blob/master/examples/external_axes.ipynb)
-* The External Axes method of subplots **allows the user to create and manage their own Figure and Axes (SubPlots), and pass Axes into `mplfinance`**.
-* Details on how to use this feature are described below.<br>&nbsp;&nbsp;(code examples can be found in the [**External Axes notebook**](https://github.com/matplotlib/mplfinance/blob/master/examples/external_axes.ipynb)).
-* When passing `Axes` into `mplfinance`, some `mplfinance` features may be  _not_ available, or may behave differently.  For example,
-  - The user is responsible to configure the size and geometry of the Figure, and size and location of the Axes objects within the Figure.
-  - The user is responsible to display the Figure by calling **`mplfinance.show()`** (or `pyplot.show()`).
-* Passing external Axes into `mplfinance` results in more complex code **but it also provides all the power and flexibility of `matplotlib` for those who know how to and what to use it.** This includes:
-  - plotting on as many subplots as desired, in any geometry desired.
-  - plotting multiple ohlc/candlestick plots on the same Figure or Axes.
-  - plotting multiple candlestick plots side-by-side, or in any other geometry desired.
-  - anitmating or updating plots in real time.
-  - event handling
-* Use method **`mpf.figure()`** to create Figures.<br>This method behaves exactly like [`pyplot.figure()`](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.figure.html) except that **`mpf.figure()`** also accepts kwarg `style=` to set the mplfinance style.
-* Call the usual methods for creating Subplot Axes on the figure:
-  - [fig.add_subplot()](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.add_subplot)
-  - [fig.add_axes()](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.add_axes)
-  - [fig.subplots()](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure.subplots)
-* When calling the above subplot creation methods, if `fig` was creating using **`mpf.figure()`** then the Subplot Axes will inheret the mpfinance style information from the figure.  Alternatively the user may pass in kwarg `style=` to set different style information for an Axes than for the Figure or other Axes.
-* Please note the following:
-  - Use kwarg **`ax=`** to pass **any matplotlib Axes** that you want into **`mpf.plot()`**
-  - If you also want to plot volume, **then you must pass in an Axes instance for the volume**,<br>&nbsp; so instead of `volume=True`, use **`volume=<myVolumeAxesInstance>`**.
-  - If you specify `ax=` for `mpf.plot()` **then you must also specify** `ax=` **for all calls to `make_addplot()`**
+### [Following Example of Indicators](https://github.com/matplotlib/mplfinance/blob/master/examples/external_axes.ipynb)
+*  [**The Alphatrend Indicator**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/alphatrend.ipynb)
+   - For Building Alphatrend Indicator with  `make_addplot`. This method helps plot two lines, named k1 and k2. The area between two lines is filled with `fill_between` method. For color conditional formatting we use the `where`.
+   - Details on how to implement Alphatrend Indicator Over are described below.<br>&nbsp;&nbsp;(code examples can be found in the [**examples/indicators/**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/alphatrend.ipynb))
+*  [**Awesome Oscillator**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/awesome_oscillator.ipynb)
+   - Use method `make_addplot` method Awesome Oscillator Build as Histogram Bar type of plot `bar` in a new panel with `panel` method
+   - Details on how to implement Awesome Oscillator is described below.<br>&nbsp;&nbsp;(code examples can be found in the [**examples/indicators/**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/awesome_oscillator.ipynb))
+*  [**Dochian Channel**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/donchian_channel.ipynb)
+   - For Building Dochian Channel with `make_addplot`. This method helps plot three lines in this indicator, named upper, middle, and lower bands. The area between the upper and lower band is filled with `fill_between` method.
+   - Details on how to implement Dochian Channel Over are described below.<br>&nbsp;&nbsp;(code examples can be found in the [**examples/indicators/**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/donchian_channel.ipynb))
+*  [**Golden Cross Over**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/golden_cross.ipynb)
+   - For Building Golden Cross with `make_addplot` we use two moving averages named short-term moving averages and long-term moving averages. When One Line Cross another that point is marked with `marker` with type `scatter`. while to change the color of the long-term moving average we use a custom function.
+   - Details on how to implement Golden Cross Over are described below.<br>&nbsp;&nbsp;(code examples can be found in the [**examples/indicators/**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/golden_cross.ipynb))
+*  [**Ichimoku Cloud**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/ichimoku_cloud.ipynb)
+   - For Building Ichimoku Cloud with `make_addplot`. The following method helps to plot Five lines used in this indicator, named Tenkan-sen, Kijun-sen, Senkou_Span_A, Senkou_Span_B, Chikou_Span. The area between the  Senkou_Span_A and  Senkou_Span_B is filled with `fill_between` method.
+   - Details on how to implement Ichimoku Cloud are described below.<br>&nbsp;&nbsp;(code examples can be found in the [**examples/indicators/**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/ichimoku_cloud.ipynb))
+*  [**MACD**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/macd.py)
+   - Use method `make_addplot` MACD is built as Histogram Bar type of plot `bar` in a new panel with `panel` method
+   - Details on how to implement MACD are described below.<br>&nbsp;&nbsp;(code examples can be found in the [**examples/indicators/**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/macd.py))
+*  [**MACD Histogram**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/macd_histogram_gradient.ipynb)
+   - Use method `make_addplot` MACD with Histogram is built as Histogram Bar type of plot `bar` in a new panel with `panel` method. For Generating a color list for the histogram we use the custom function.
+   - Details on how to implement MACD Histogram are described below.<br>&nbsp;&nbsp;(code examples can be found in the [**examples/indicators/**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/macd_histogram_gradient.ipynb))
+*  [**Relative Strength Index**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/mpf_rsi_demo.py)
+   - Use methods `make_addplot` and `panel` were used to plot rsi
+   - Details on how to implement Relative Strength Index are described below.<br>&nbsp;&nbsp;(code examples can be found in the [**examples/indicators/**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/parabolic_sar.ipynb))
+*  [**Parabolic SAR**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/mpf_rsi_demo.py)
+   - For Building Parabolic SAR with `make_addplot`. This method helps plot two lines in this indicator, named upper and lower bands. The custom function is used to segregate uptrend and down-trending areas. Which later plot with type `scatter`
+   - Details on how to implement Parabolic SAR are described below.<br>&nbsp;&nbsp;(code examples can be found in the [**examples/indicators/**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/parabolic_sar.ipynb))
+*  [**Supertrend**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/supertrend.ipynb)
+   - For Building Supertrend with `make_addplot`. This method helps plot three lines in this indicator, named upper, trendline, and lower bands. when the price is above the trendline area marked uptrend and downtrend with a custom function, The area between the upper band and high of the candle is filled with `fill_between` method.
+   - Details on how to implement Supertrend described below.<br>&nbsp;&nbsp;(code examples can be found in the [**examples/indicators/**](https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/supertrend.ipynb))