### Setup for mplfinance Examples

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/fill_between.ipynb

Imports necessary libraries and configures IPython for multiple outputs. Reads daily S&P 500 data and displays its shape and head.

```python
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
```

```python
%matplotlib inline
import pandas as pd
```

```python
daily = pd.read_csv('data/SP500_NOV2019_Hist.csv',index_col=0,parse_dates=True)
daily.index.name = 'Date'
daily.shape
daily.head(3)
```

```python
import mplfinance as mpf
mpf.__version__
```

--------------------------------

### Import Libraries and Check Versions

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/Issue26_bug_fix_volume_bar_widths.ipynb

Imports the necessary mplfinance and pandas libraries and checks their installed versions. This is a standard setup step for using the libraries.

```python
import mplfinance as mpf
mpf.__version__
import pandas as pd
pd.__version__
```

--------------------------------

### Install mplfinance

Source: https://github.com/matplotlib/mplfinance/blob/master/README.md

Install the mplfinance package using pip. This command upgrades the package if it is already installed.

```bash
pip install --upgrade mplfinance
```

--------------------------------

### Define Plotting Setup

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/check_use_cases.ipynb

Defines a dictionary 'setup' with common plotting parameters for mplfinance, specifying the plot type, whether to include volume, and to return the figure object.

```python
setup = dict(type='candle',volume=True,returnfig=True)
```

--------------------------------

### Define Plotting Setup

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/issue171_widths.ipynb

Defines a dictionary `setup` to configure plotting parameters for mplfinance. This includes plot type, volume display, figure scaling, and moving averages.

```python
setup = dict(type='candle',volume=True,figscale=0.8,mav=(10,20,30))
```

--------------------------------

### Display mplfinance Version and File Path

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/issue236_timezone_bug.ipynb

Prints the version and file path of the installed mplfinance library. This is useful for debugging and verifying the library's installation.

```python
print(fplt.__version__,fplt.__file__)
```

--------------------------------

### Example Output of Calculated Values

Source: https://github.com/matplotlib/mplfinance/wiki/Access-to-mplfinance-Calculated-Values

This is an example of the data structure that mplfinance populates into the dictionary passed to `return_calculated_values`. It includes moving averages (mav8, mav20, mav30), min/max values, and specific data like 'renko_bricks'.

```text
{
    'mav20': array([         nan,          nan,          nan,          nan,
                nan,          nan,          nan,          nan,
                nan,          nan,          nan,          nan,
                nan,          nan,          nan,          nan,
                nan,          nan,          nan, 119.75656377,
       118.96970631, 118.37956322, 117.78942013, 117.19927704,
       116.60913394, 116.01899085, 115.62556212, 115.42884776,
       115.42884776, 115.62556212, 116.01899085, 116.41241958,
       116.80584831, 117.19927704, 117.59270576, 117.98613449,
       118.37956322, 118.57627759, 118.57627759, 118.57627759,
       118.77299195, 118.96970631, 119.36313504, 119.75656377,
       120.34670686, 121.13356432, 121.92042178, 122.70727924,
       123.49413669, 124.28099415, 125.06785161, 125.85470906,
       126.64156652, 127.23170961, 127.82185271, 128.4119958 ,
       128.80542453, 129.19885326, 129.78899635, 130.57585381]),
    'mav30': array([         nan,          nan,          nan,          nan,
                nan,          nan,          nan,          nan,
                nan,          nan,          nan,          nan,
                nan,          nan,          nan,          nan,
                nan,          nan,          nan,          nan,
                nan,          nan,          nan,          nan,
                nan,          nan,          nan,          nan,
                nan, 118.1172774 , 117.72384867, 117.46156285,
       117.33041995, 117.33041995, 117.33041995, 117.33041995,
       117.46156285, 117.59270576, 117.72384867, 117.85499158,
       118.1172774 , 118.37956322, 118.64184904, 118.77299195,
       119.03527777, 119.4287065 , 119.82213523, 120.21556395,
       120.60899268, 121.13356432, 121.78927887, 122.44499342,
       123.23185087, 124.01870833, 124.80556579, 125.59242325,
       126.24813779, 126.77270943, 127.29728107, 127.82185271]),
    'mav8': array([         nan,          nan,          nan,          nan,
                nan,          nan,          nan, 125.06785161,
       123.10070796, 121.13356432, 119.16642068, 117.69106295,
       116.70749112, 116.21570521, 115.7239193 , 115.23213339,
       115.23213339, 115.7239193 , 116.70749112, 117.19927704,
       117.19927704, 117.19927704, 117.19927704, 117.19927704,
       116.70749112, 115.7239193 , 114.74034748, 114.24856157,
       114.24856157, 114.24856157, 114.74034748, 115.7239193 ,
       117.19927704, 119.16642068, 120.64177841, 121.62535023,
       122.60892205, 123.10070796, 123.10070796, 122.60892205,
       122.11713614, 121.62535023, 121.62535023, 121.62535023,
       121.62535023, 122.11713614, 123.10070796, 124.5760657 ,
       126.05142343, 127.52678116, 129.00213889, 130.96928254,
       132.93642618, 134.41178391, 135.39535573, 135.88714164,
       135.88714164, 135.39535573, 134.90356982, 134.41178391]),
    'maxx': 59.983333333333334,
    'maxy': 141.78857257142855,
    'minx': -0.9833333333333333,
    'miny': 110.3142742857142,
    'renko_bricks': [   131.95285435714285,
                        129.98571071428572,
                        128.01856707142858,
                        126.05142342857143,
                        124.08427978571427,
                        122.11713614285712,
                        120.14999249999997,
                        118.18284885714282,
                        116.21570521428566,
                        114.24856157142851,
                        112.28141792857136,
                        114.24856157142851,
                        116.21570521428566,
                        118.18284885714282,
                        116.21570521428566,
                        114.24856157142851,
                        116.21570521428566,
                        118.18284885714282,
                        120.14999249999997,
                        118.18284885714282,
]
```

--------------------------------

### Adjust Color Brightness Example

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/macd_color_issue594.ipynb

Demonstrates adjusting the brightness of black ('k') by adding an amount to its RGB components.

```python
#c = [int(v*255) for v in mc.to_rgb('blue')]
adj = 0.6
c = [min(1.0,v+adj) for v in mc.to_rgb('k')]
c
```

--------------------------------

### Print pandas and mplfinance Versions

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/Issue77_test.ipynb

Outputs the installed versions of the pandas and mplfinance libraries. This is useful for debugging compatibility issues.

```python
print(pd.__version__)
print(mpf.__version__)
```

--------------------------------

### Plot Financial Data with mplfinance

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/dev_scale_padding.ipynb

Import mplfinance and plot a DataFrame. This example shows a basic plot without custom scale padding.

```python
import mplfinance as mpf
mpf.__version__
#mpf.plot(df,volume=True,scale_padding=dict(left=0.1,right=0.05),tight_layout=True)
mpf.plot(df,volume=True,tight_layout=True)
```

--------------------------------

### Import Libraries

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/golden_cross.ipynb

Import necessary libraries for data manipulation and plotting. Ensure pandas, mplfinance, and numpy are installed.

```python
import pandas as pd
import mplfinance as mpf
import numpy as np
```

--------------------------------

### Plotting with Multiple External Axes (Example 2)

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/external_axes.ipynb

Set up a figure with three subplots, sharing the x-axis for two of them. This example demonstrates plotting multiple addplots on different external axes, including OHLC data and Bollinger Bands.

```python
%%capture
fig = mpf.figure(style='charles',figsize=(7,8))
ax1 = fig.add_subplot(3,1,1)
ax2 = fig.add_subplot(3,1,2,sharex=ax1)
ax3 = fig.add_subplot(3,1,3)
```

```python
ap = [ mpf.make_addplot(df,type='ohlc',ax=ax2,ylabel='OHLC Price'),
       mpf.make_addplot(df[['UpperB','LowerB']],ax=ax1)
     ]
mpf.plot(df,ax=ax1,volume=ax3,addplot=ap,xrotation=10,type='candle')
```

```python
fig
```

--------------------------------

### Define Plotting Setup

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/widths_so75526312.ipynb

Defines a dictionary for common plotting parameters, including plot type, volume display, and moving averages.

```python
setup = dict(type='candle',volume=True,mav=(7,15,22))
```

--------------------------------

### Create a Simple Point and Figure Chart

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/price-movement_plots.ipynb

Use `type='pnf'` (or `type='p&f'`, `type='pointnfigure'`) to generate a Point and Figure chart. This example uses default parameters.

```python
mpf.plot(daily,type='pnf')
```

--------------------------------

### Specify Marketcolors with RGBA (0-255)

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/marketcolor_overrides.ipynb

Similar to the previous example, this demonstrates using `mpf.make_marketcolors` with RGBA values for 'up' specified as integers between 0 and 255. The `marketcolor_overrides` parameter is used to apply these settings.

```python
# ===================================================================================
# Here we specify `up` as rgba using the convention that rgb are ints from 0 and 255 :
mc = mpf.make_marketcolors(base_mpf_style='yahoo',up=(178,255,178,0.4),down='fuchsia',
                           edge={'up':'blue','down':'#000000'},wick='#cc6600')

mco = [None,None,mc,mc,None]
mpf.plot(df,volume=True,style='yahoo',type='candle',marketcolor_overrides=mco)
```

--------------------------------

### Basic mplfinance Plotting

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/check_use_cases.ipynb

A basic example of plotting financial data with mplfinance. It sets up plotting parameters and then calls the mpf.plot function.

```python
figscale = 1.0
setup    = dict(type='candle',volume=True,mav=(10,40),returnfig=True)
# #num      = 240

# for num in range(30,241,30):
#     for s in styles:
# #     w = dict(vol_width=0.65      ,vol_linewidth=0.65,
# #              candle_width=.312 ,candle_linewidth=0.438,
# #              ohlc_ticksize=0.35 ,ohlc_linewidth=0.525)
#         f,a = mpf.plot(data[num],**setup,figscale=figscale, style=s, title=s)


# #     w = dict(vol_width=0.6      ,vol_linewidth=0.65,
# #              candle_width=.285 ,candle_linewidth=0.376,
# #              ohlc_ticksize=0.35 ,ohlc_linewidth=0.525)
#     #f,a = mpf.plot(data[num],**setup,figscale=figscale, style=s, title=s)
```

```python
f,a = mpf.plot(data[70],**setup,figscale=figscale)
```

--------------------------------

### List Files in Directory

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/stackoverflow.65487952.ipynb

Lists files in the current directory that start with 'a'. Useful for verifying saved plot files.

```bash
ls -l a*
```

--------------------------------

### Inspect mplfinance File Path

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/rcParams.ipynb

Retrieve the file path of the installed mplfinance library. This can be helpful for debugging or understanding library location.

```python
mpf.__file__
```

--------------------------------

### Initialize an empty list for OHLC data

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/issue94.ipynb

Use this to start collecting Open, High, Low, Close data points before creating a DataFrame.

```python
ohlc = []
```

--------------------------------

### Example of Formatted mplfinance Style Output

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/external_axes_stacking.ipynb

Displays the formatted output of an mplfinance style dictionary, showing how it would appear after being processed by the printing script. This includes the 'style = dict(...)' format and the command to write the style to a file.

```python
style = dict(   style_name = 'kenan',
                base_mpl_style = 'seaborn-darkgrid',
                marketcolors = {   'candle': {'up': 'k', 'down': 'r'},
                                    'edge': {'up': 'k', 'down': 'r'},
                                    'wick': {'up': 'k', 'down': 'r'},
                                    'ohlc': {'up': 'k', 'down': 'k'},
                                    'volume': {'up': '#1f77b4', 'down': '#1f77b4'},
                                    'vcedge': {'up': '#1f77b4', 'down': '#1f77b4'},
                                    'vcdopcod': False,
                                    'alpha': 0.9,
                                    'hollow': 'w'},
                mavcolors = [   '#40e0d0', '#ff00ff', '#ffd700', '#1f77b4', '#ff7f0e',
                                 '#2ca02c', '#e377c2'],
                y_on_right = False,
                gridcolor = None,
                gridstyle = None,
                facecolor = '#DCE3EF',
                rc = [   ('axes.edgecolor', 'black'), ('axes.linewidth', 1.5),
                          ('axes.labelsize', 'large'), ('axes.labelweight', 'semibold'),
                          ('lines.linewidth', 2.0), ('font.weight', 'medium'),
                          ('font.size', 12.0)],
                base_mpf_style = 'default')

 mpf.write_style_file(s,"kenan.py") 

```

--------------------------------

### Importing Old API Methods in mplfinance

Source: https://github.com/matplotlib/mplfinance/blob/master/README.md

To use methods from the older mpl-finance package with the new mplfinance installation, import them from the mplfinance.original_flavor module. This allows for a gradual transition to the new API.

```python
from mpl_finance import <method>
```

```python
from mplfinance.original_flavor import <method>
```

```python
from mplfinance.original_flavor import candlestick_ohlc
```

--------------------------------

### Get Width Configuration with return_width_config

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/widths_so75526312.ipynb

Use `return_width_config` with an empty dictionary to retrieve the current width configuration values. This is useful for understanding default settings or for debugging.

```python
import pprint as pp

setup=dict(type='candle',volume=True,figscale=0.75)

wconfig = {}
mpf.plot(df.iloc[0:80],**setup,return_width_config=wconfig)
pp.pprint(wconfig)

wconfig={}
mpf.plot(df.iloc[0:80],**setup,return_width_config=wconfig,show_nontrading=True)
pp.pprint(wconfig)
```

--------------------------------

### Multi-color fill_between for Both Panels

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/fill_between.ipynb

Extends the multi-color fill_between concept to both the main price panel and the volume panel. This example fills between Open and Close prices in the main panel and uses conditional fills for the volume panel.

```python
print('''

Here, as an additional example, we create "multi-color" fill_between for both panels:

''')

fbvolume_above = dict(y1=daily['Volume'].values, 
                y2=daily['Volume'].mean(), 
                alpha=0.4, color='lime', 
                interpolate=True,
                where=(daily['Volume'] > daily['Volume'].mean()).values)

fbvolume_below = fbvolume_above.copy()
fbvolume_below['color'] = 'magenta'
fbvolume_below['where'] = (daily['Volume'] < daily['Volume'].mean()).values

avol = [daily['Volume'].mean()]*len(daily)
ap = mpf.make_addplot(avol,panel=1,fill_between=[fbvolume_above,fbvolume_below],color='k',linestyle='-.',width=0.25)

fbclose_above = dict(y1=daily['Open'].values  , y2=daily['Close'].values , alpha=0.4, 
                     interpolate=True,
                     color='lime',
                     where=(daily['Close']>daily['Open']).values
                    )


fbclose_below = fbclose_above.copy()
fbclose_below['color'] = 'magenta'
fbclose_below['where'] = (daily['Close']<daily['Open']).values


mpf.plot(daily,volume=True,panel_ratios=(1,1),
         type='candle',tight_layout=True,figratio=(1,1),
         fill_between=[fbclose_above,fbclose_below],
         addplot=ap)
```

--------------------------------

### Create Addplot from Series

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/combine_sparse_data.ipynb

Prepare data for an additional plot layer in mplfinance. This example combines two Series, filling missing values from the second Series.

```python
ap = mpf.make_addplot((s.combine_first(sbase)).values)
```

--------------------------------

### Load and Plot DataFrame with mplfinance

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/test_plot_image_comparison.ipynb

Loads a CSV file into a pandas DataFrame and plots it with volume using mplfinance, saving the output to a JPG file. Ensure the CSV file exists and pandas is installed.

```python
# %load addplot.py
#!/usr/bin/env python


import pandas as pd
pd.__version__  # for the record


df = pd.read_csv('data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True)
df.shape
df.head(3)
df.tail(3)


import mplfinance as mpf
mpf.plot(df,volume=True,savefig='test01.jpg')


#apdict = mpf.make_addplot(df['LowerB'])
#mpf.plot(df,volume=True,addplot=apdict)

#apd = mpf.make_addplot(df['LowerB'],scatter=True)
#mpf.plot(df,addplot=apd,savefig='test02.jpg')

```

--------------------------------

### Run mplfinance Animation Demo 1

Source: https://github.com/matplotlib/mplfinance/blob/master/markdown/animation.md

Execute the first animation demo script for mplfinance. This script demonstrates basic animation capabilities.

```bash
python mpf_animation_demo1.py
```

--------------------------------

### Plot Vertical Lines for Intraday Data

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/using_lines.ipynb

For intraday data, vertical lines can indicate the start of each trading day. This example shows how to load intraday data and plot vertical lines with different colors.

```python
idf = pd.read_csv('./data/SP500_NOV2019_IDay.csv',index_col=0,parse_dates=True)
idf.shape
idf.head(2)
idf.tail(2)
```

```python
wtmd=dict(warn_too_much_data=len(idf)+1)
vls=['2019-11-05 09:30',
     '2019-11-06 09:30',
     '2019-11-07 09:30',
     '2019-11-08 09:30']
mpf.plot(idf,vlines=dict(vlines=vls,colors=('r','g','b','c')),**wtmd)  # different color for each line
```

```python
mpf.plot(idf,vlines=dict(vlines=vls,colors='c'),**wtmd)                # one color for all lines
```

--------------------------------

### Load and Display Daily Data with Bars, Volume, and MAV

Source: https://github.com/matplotlib/mplfinance/blob/master/README.md

Loads daily financial data from a CSV file, then plots a subset of the data using 'bars' type, including volume and two moving averages (20 and 40 periods). This example also shows how daily data spanning a year boundary automatically includes the year in the date format.

```python
df = pd.read_csv('examples/data/yahoofinance-SPY-20080101-20180101.csv',index_col=0,parse_dates=True)
df.shape
df.head(3)
df.tail(3)
```

```python
mpf.plot(df[700:850],type='bars',volume=True,mav=(20,40))
```

--------------------------------

### Create MACD Plot in Classic Style with Custom Figure Colors

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/panels_ylim_width_alpha_yside.ipynb

This example generates a MACD plot using the 'classic' style and a custom light gray figure background. It adjusts panel ratios and demonstrates how to apply a custom style created with `mpf.make_mpf_style`.

```python
apds = [mpf.make_addplot(exp12,color='lime'),
        mpf.make_addplot(exp26,color='c'),
        mpf.make_addplot(histogram,type='bar',width=0.7,panel=1,
                         color='dimgray',alpha=1,secondary_y=False),#,ylim=(-.75,+.75)),
        mpf.make_addplot(macd,panel=1,color='fuchsia',secondary_y=True,ylim=(-3,3),width=6,alpha=0.5),
        mpf.make_addplot(signal,panel=1,color='b',secondary_y=True),
        mpf.make_addplot(df['Volume'],panel=2,ylim=(+10000000,1000000000),alpha=0.01,y_on_right=False),#,secondary_y=False)
       ]
s = mpf.make_mpf_style(base_mpf_style='classic',rc={'figure.facecolor':'lightgray'})

mpf.plot(df,type='candle',addplot=apds,figscale=1.1,figratio=(8,5),title='\nMACD',
         style=s,volume=True,volume_panel=2,panel_ratios=(6,3,2))
```

--------------------------------

### Run mplfinance Growing Candle Animation Demo

Source: https://github.com/matplotlib/mplfinance/blob/master/markdown/animation.md

Execute the animation demo script for mplfinance focusing on growing candles. This script demonstrates animating candle formation.

```bash
python mpf_animation_growingcandle.py
```

--------------------------------

### Plot with Various Setups and Styles

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/issue171_widths.ipynb

Generates multiple plots with different chart types (ohlc, line, candle), moving average windows, and data subsets. This demonstrates the flexibility of mplfinance in creating diverse financial visualizations.

```python
setup = dict(type='ohlc',volume=True)
print('=====================================================================')
print('''=========
   NEW 
=========''')

setup.update(dict(mav=(5,10,15)))

mpf.plot(df.iloc[0: 40],**setup)
mpf.plot(df.iloc[0: 80],**setup)
mpf.plot(df.iloc[0:150],**setup)

setup['type']='line'
mpf.plot(df.iloc[0: 40],**setup)
mpf.plot(df.iloc[0: 80],**setup)
mpf.plot(df.iloc[0:150],**setup)

setup['type']='candle'
mpf.plot(df.iloc[0: 40],**setup)
mpf.plot(df.iloc[0: 80],**setup)
mpf.plot(df.iloc[0:150],**setup)
```

--------------------------------

### Run mplfinance Animation Demo 2

Source: https://github.com/matplotlib/mplfinance/blob/master/markdown/animation.md

Execute the second animation demo script for mplfinance. This script showcases further animation functionalities.

```bash
python mpf_animation_demo2.py
```

--------------------------------

### Get DataFrame Length

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/check_use_cases.ipynb

Calculates and prints the number of rows in the DataFrame.

```python
len(tdf)
```

--------------------------------

### Define Color List

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/rcParams.ipynb

Defines a list of color names to be used in subsequent examples.

```python
mavcolors=['turquoise','gold','magenta']
```

--------------------------------

### Import mplfinance and Check Version

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/Axes.scatter.ipynb

Imports the mplfinance library and checks its version.

```python
import mplfinance as mpf
mpf.__version__  # for the record
```

--------------------------------

### Get Color Name Mapping

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/macd_color_issue594.ipynb

Accesses the dictionary of named colors provided by Matplotlib.

```python
mc.cnames['red']
```

--------------------------------

### Create Market Colors with make_marketcolors

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/widths_so75526312.ipynb

Demonstrates how to create custom market colors using `make_marketcolors` with a specified base style. The output shows the default color dictionary for the 'blueskies' style.

```python
import pprint
pp = pprint.PrettyPrinter(indent=4)
mc = mpf.make_marketcolors(base_mpf_style='blueskies')
pp.pprint(mc)
```

--------------------------------

### Get Figure Axes List

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/mpl.zorderaxes.ipynb

Retrieves the list of all axes objects currently associated with the figure.

```python
axlist = fig.axes
```

--------------------------------

### Get Plotting Style Help

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/issue

Retrieves help information for the 'ylim' kwarg of the 'plot' function.

```python
mpf.kwarg_help('plot','ylim')
```

--------------------------------

### Dictionary Creation and Membership Test

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/addplot_nan_testing.ipynb

Illustrates basic Python dictionary creation and how to check for the existence of a key within a dictionary.

```python
d = dict(abc='aaa',xya=2)
d
'xya' in d
```

--------------------------------

### Import Libraries and Display Versions

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/marketcolor_overrides.ipynb

Imports pandas and mplfinance, then prints their versions. Ensure you have compatible versions for plotting.

```python
%matplotlib inline
import pandas as pd
print('pandas version=',pd.__version__)

import mplfinance as mpf
print('mplfinance version=',mpf.__version__)
```

--------------------------------

### Get Type of Series Index

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/combine_sparse_data.ipynb

Checks and prints the data type of the index of the pandas Series `s`.

```python
type(s.index)
```

--------------------------------

### Import mplfinance Library

Source: https://github.com/matplotlib/mplfinance/blob/master/README.md

Conventional way to import the mplfinance library for use in financial plotting.

```python
import mplfinance as mpf
```

--------------------------------

### Get Current Axes Object

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/test_df.rolling.ipynb

Retrieves the current axes object from matplotlib, allowing for further customization of the plot.

```python
ax = plt.gca()
```

--------------------------------

### Create and Plot Identical DataFrames with addplot

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/widths_so75526312.ipynb

This example demonstrates creating two identical DataFrames and then plotting them using `addplot`. One DataFrame is modified to have its 'High' and 'Low' values overwrite 'Open' and 'Close' before plotting.

```python
df1 = pd.DataFrame(idf.iloc[3:28][['Open','Close','High','Low']],index=idf.iloc[3:28].index)
#df2 = pd.DataFrame(idf.iloc[3:28][['Open','Close','High','Low']],index=idf.iloc[3:28].index)

df1.head()
```

```python
# First create an identical, separate dataframe:
df2 = df1.copy()

# Then, in the newly created dataframe, 
# copy the High and Low over the Open and Close:
df2['Open']  = df2['High']
df2['Close'] = df2['Low']

# Plot both; The original dataframe MUST BE in the addplot:
ap = mpf.make_addplot(df1,type='candle')
mpf.plot(df2,type='candle',addplot=ap)
```

```python
ap = mpf.make_addplot(df1,type='candle')
mpf.plot(df2,type='candle',addplot=ap,style='blueskies')
#mpf.plot(df2,type='candle',addplot=ap,style='yahoo')
```

--------------------------------

### Apply Color Function

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/awesome_oscillator.ipynb

Calls the `generate_awesome_oscillator_color` function to get a list of colors for the Awesome Oscillator bars.

```python
# List of Color Assiging To Awesome Oscillator
awesome_oscillator_color = generate_awesome_oscillator_color(df)
```

--------------------------------

### Basic Intraday Plot

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/dev_tlines.ipynb

Generates a basic plot of the intraday DataFrame. This is a starting point before adding customizations.

```python
mpf.plot(idf)
```

--------------------------------

### Calculate Box Size and Price Differences

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/price-movement_ret_calc_vals.ipynb

Demonstrates calculating the box size by adding the box size to a price point and calculating price differences between consecutive horizontal lines.

```python
print('box_size= 1.9671436428571505')
```

```python
y= [124.08427978571424, 122.11713614285709]

y[1] - y[0]

y= [122.11713614285709, 120.14999249999994, 118.18284885714279]
y[1] - y[0]
y[2] - y[1]
```

--------------------------------

### Import mplfinance and Load Data

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/issue236_timezone_bug.ipynb

Imports the mplfinance library and pandas. It then loads data from a CSV file, using the first column as the index and parsing it as dates.

```python
#import pickle
import mplfinance as fplt
import pandas as pd
#df = pickle.load(open("issue236.df.data", "rb"))
```

```python
df = pd.read_csv('issue236.df.scratchdata',index_col=0,parse_dates=True)
```

--------------------------------

### Import mplfinance and numpy

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/scatter_multiple_markers.ipynb

Imports the necessary libraries, mplfinance for plotting and numpy for numerical operations. The '%matplotlib qt' magic command is commented out but can be used for interactive plotting.

```python
#%matplotlib qt
import mplfinance as mpf
import numpy as np
```

--------------------------------

### Get Index Values

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/Issue77_test.ipynb

Retrieves the index values of the DataFrame, which in this case are datetime objects representing the 'Date and Time'.

```python
myData.index.values
```

--------------------------------

### Get mplfinance Style

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/external_axes_stacking.ipynb

Retrieves the current mplfinance style settings. This can be useful for understanding or modifying existing styles.

```python
style
```

--------------------------------

### Initialize 'alines' data structure

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issue_184_alines.ipynb

Demonstrates the structure of the 'alines' data, which appears to be a nested list or tuple containing timestamped price data, potentially for plotting financial instruments.

```python
s[0:10]
```

--------------------------------

### Data Preparation for Alines

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issue_184_alines.ipynb

Prepares sample data for `alines` by creating lists of dates and corresponding values, then forming tuples.

```python
x = ['2011-09-13','2011-09-14','2011-09-15','2011-09-16','2011-09-19','2011-09-20','2011-09-21','2011-09-22']
y = [120,121,122,123,124,125,126,127]
sop = [(x[0],y[0]),(x[1],y[1]),(x[2],y[2])]
sosop = [[(x[3],y[3]),(x[4],y[4]),(x[5],y[5])],
         [(x[6],y[6]),(x[7],y[7])]
        ]
```

--------------------------------

### Get Matplotlib Backend

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/test_plot_image_comparison.ipynb

Retrieves the current value of the matplotlib backend setting. This confirms the active backend after a potential change.

```python
plt.rcParams['backend']
```

--------------------------------

### Get Image Histogram

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/test_plot_image_comparison.ipynb

Calculates the histogram of a PIL Image object. The histogram represents the count of each color value in the image.

```python
h1 = img01.histogram()
```

--------------------------------

### Prepare and Print Data Lengths

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/bar_width_issues.ipynb

Creates two lists, `v1` and `v2` (where `v2` is `v1` repeated three times), and prints their lengths to demonstrate data size differences.

```python
v1 = [202385700, 165936000, 143331600, 170464200, 194100500, 195918600, 214675700,
      204062600, 226111800, 220012800, 196872100, 166554900, 137145400, 245246300,
      126019400, 136653800, 131278200, 249020100, 207939900, 307038400, 325790900,
      346653800, 370830800, 520721800, 655619200, 702263900, 717828700, 662607400,
      487979700, 313731600, 258810600, 294095200, 238201100, 512956300, 428281300,
      275090600, 331136600, 246869700, 312365400, 314495900, 190977200, 241315700,
      301828400, 254585900, 255517200, 285130500, 209803200, 250568200, 380195100,
      305793500]

v2 = v1 + v1 + v1

print('len(v1)=',len(v1))
print('len(v2)=',len(v2))
```

--------------------------------

### Get Axes Attributes

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/mpl.zorderaxes.ipynb

Retrieves a list of attributes for the first axes object (axR) in the figure's axes list.

```python
axatt = dir(axR)
```

--------------------------------

### Extract values from Series

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/combine_sparse_data.ipynb

Get the numpy array of values from a pandas Series. This is often a precursor to plotting or further numerical operations.

```python
y = s.combine_first(sbase).values
```

--------------------------------

### Configure and Time Plotting Function

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/check_use_cases.ipynb

Sets up plotting parameters and times the execution of the plotting function.

```python
%matplotlib
figscale = 1.0
setup    = dict(type='candle',mav=(10,40),returnfig=True)#,show_nontrading=True)
%timeit -n1 -r1 plotabunch()
```

--------------------------------

### Prepare Data for Custom Markers

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/external_axes_stacking.ipynb

Create a DataFrame to hold custom markers for specific dates. Calculate offset values based on the price range and populate 'above' and 'below' columns with marker positions.

```python
pmax = max(df['High'])
pmin = min(df['Low'])
offset = 0.06 * (pmax - pmin)

marks = df[['High','Low']].copy()

marks['above'] = [float('nan')]*len(df)
marks['below'] = [float('nan')]*len(df)
dt1 = '11/9/2020'
dt2 = '12/21/2020'
marks.loc[dt1,'above'] = df['High'][dt1]+offset
marks.loc[dt1,'below'] = df['Low' ][dt1]-offset
marks.loc[dt2,'above'] = df['High'][dt2]+offset
marks.loc[dt2,'below'] = df['Low' ][dt2]-offset
```

--------------------------------

### Get Current Figure

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/figsubplot_tests.ipynb

Retrieves the current figure object and its ID. This is useful for inspecting the active figure in a matplotlib session.

```python
# import matplotlib.pyplot as plt

# fig = plt.gcf()
# type(fig)
# id(fig)
```

--------------------------------

### Prepare data for linear regression

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/dev_tlines.ipynb

Prepares data by stacking the input array with an array of ones, creating a matrix suitable for linear regression calculations.

```python
a = np.vstack([x, np.ones(len(x))]).T
```

--------------------------------

### Extract Data from a Specific Date Onwards

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/dev_tlines.ipynb

Extract and display data starting from a specific date, showing the first entry.

```python
daily.loc['2019-11-02':,:].iloc[0,:]#['Open','Close']
```

--------------------------------

### Creating a Scatter Plot with mplfinance

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/bar_width_issues.ipynb

Demonstrates creating a scatter plot using `make_addplot` with the type specified as 'scatter'.

```python
apd = mpf.make_addplot(df['LowerB'],type='scatter')

mpf.plot(df,addplot=apd)
```

--------------------------------

### Create and Plot with Multiple Styled Aline Configurations

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/dev_alines.ipynb

Generates two distinct styling configurations (`al1`, `al2`) for custom lines, applying different `alines` data, `linestyle`, and `colors`. These configurations are then used to plot the intraday data with varying visual styles.

```python
al1 = al.copy()
al1['alines'] = segments1
al2 = al.copy()
al2['alines'] = segments2
al2['linestyle'] = 'dotted'
al2['colors'] = 'c'
al1
al2
```

```python
mpf.plot(idf,type='ohlc',alines=al1)
mpf.plot(idf,type='ohlc',alines=al2)

mpf.plot(idf,type='candle',alines=al1)
mpf.plot(idf,type='candle',alines=al2)

mpf.plot(idf,type='line',alines=al1)
mpf.plot(idf,type='line',alines=al2)
```

--------------------------------

### Load Image using PIL

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/test_plot_image_comparison.ipynb

Loads an image file into a PIL Image object for further processing. Requires the Pillow library to be installed.

```python
from PIL import Image

img01 = Image.open('test01.jpg')
```

--------------------------------

### Retrieve Default Width Configuration

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/issue171_widths.ipynb

Use `return_width_config` with an empty dictionary to inspect the default width values generated by mplfinance. This is useful before manually overriding them. Observe how these values change when `show_nontrading` is enabled.

```python
import pprint as pp

setup=dict(type='candle',volume=True,figscale=0.75)

wconfig = {}
mpf.plot(df.iloc[0:80],**setup,return_width_config=wconfig)
pp.pprint(wconfig)

wconfig={}
mpf.plot(df.iloc[0:80],**setup,return_width_config=wconfig,show_nontrading=True)
pp.pprint(wconfig)
```

```python
idf = pd.read_csv('data/SP500_20191106_IDayBollinger.csv',index_col=0,parse_dates=True)
setup=dict(type='candle',figscale=0.75)

wconfig = {}
mpf.plot(idf.iloc[0:80],**setup,return_width_config=wconfig)
pp.pprint(wconfig)

wconfig={}
mpf.plot(idf.iloc[0:80],**setup,return_width_config=wconfig,show_nontrading=True)
pp.pprint(wconfig)
```

--------------------------------

### Get Reference Image Histogram

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/test_plot_image_comparison.ipynb

Calculates the histogram for a reference PIL Image object. Used for comparing color distributions between images.

```python
h1r = img01ref.histogram()
```

--------------------------------

### Run mplfinance Animation MACD Demo

Source: https://github.com/matplotlib/mplfinance/blob/master/markdown/animation.md

Execute the animation demo script for mplfinance that includes MACD. This script illustrates animating plots with technical indicators.

```bash
python mpf_animation_macd.py
```

--------------------------------

### Import mplfinance Helper Function

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/bar_width_issues.ipynb

Imports the `_adjust_color_brightness` function from the `mplfinance._helpers` module.

```python
from mplfinance._helpers import _adjust_color_brightness
```

--------------------------------

### Get DataFrame Shape

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/issue333_generic_plotting.ipynb

Retrieves and displays the dimensions (number of rows and columns) of a pandas DataFrame. Useful for understanding the data size.

```python
pedf.shape
```

--------------------------------

### List All Figures and Managers

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/figsubplot_tests.ipynb

Demonstrates how to get a list of all currently active figures and their managers in matplotlib. This can be helpful for managing multiple plots.

```python
# plt.get_fignums()

# import matplotlib
# figs = [manager.canvas.figure for manager in matplotlib._pylab_helpers.Gcf.get_all_fig_managers()]
# print('figs=',figs)

# figs = list(map(plt.figure, plt.get_fignums()))
# figs
```

--------------------------------

### Plotting Daily Data with Different Types

Source: https://github.com/matplotlib/mplfinance/blob/master/readme.ipynb

Demonstrates plotting daily financial data using 'candle' and 'line' types.

```python
mpf.plot(daily,type='candle')
```

```python
mpf.plot(daily,type='line')
```

--------------------------------

### Plot Basic OHLC with Volume

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/static_image_subplots.ipynb

Generates a basic Open-High-Low-Close (OHLC) plot with volume using mplfinance. Ensure mplfinance is installed and imported.

```python
import mplfinance as mpf
mpf.plot(df,volume=True)
```

--------------------------------

### Initial Zorder and Visibility Output

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/mpl.zorderaxes.ipynb

Displays the initial zorder, visibility, and facecolor of the axes patches after creation. This output serves as a baseline for subsequent modifications.

```text
Output:\nRed    zorder= 0  visible= True  facecolor= (0.0, 0.0, 0.0, 1)\nYellow zorder= 0  visible= True  facecolor= (1.0, 1.0, 1.0, 0.05)\nGreen  zorder= 0  visible= False  facecolor= (1.0, 1.0, 1.0, 1.0)\nWhite  zorder= 0  visible= False  facecolor= (1.0, 1.0, 1.0, 1.0)
```

--------------------------------

### Import Matplotlib and Random

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/figsubplot_tests.ipynb

Imports the necessary libraries for plotting and generating random data. This is a common setup for creating plots with dynamic data.

```python
import matplotlib.pyplot as plt
import random
```

--------------------------------

### Syntax Error Example

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/mpl.zorderaxes.ipynb

Illustrates a `SyntaxError` caused by an invalid command like 'stop here' within a Python code block intended for execution.

```text
  File "<ipython-input-5-a96ba3aab008>", line 1\n    stop here\n         ^\nSyntaxError: invalid syntax
```

--------------------------------

### Import Libraries

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/donchian_channel.ipynb

Import pandas for data manipulation and mplfinance for plotting.

```python
import pandas as pd
import mplfinance as mpf
```

--------------------------------

### Load and Prepare Data

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/indicators/donchian_channel.ipynb

Read stock data from a CSV file, set the index to Date, and select the last 200 records. This prepares the DataFrame for plotting.

```python
# Read in daily data for the S&P 500 from November of 2019 
idf = pd.read_csv('../data/yahoofinance-INTC-19950101-20040412.csv',index_col=0,parse_dates=True).tail(200)

df = idf.copy()
df.index.name = 'Date'
df.shape
df.head(2)
df.tail(2)
```

--------------------------------

### Get Type of rcParam

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/rcParams.ipynb

Determines and prints the data type of a specific rcParam, in this case, 'grid.linestyle'. Helps understand what kind of value is expected.

```python
type(mpl.rcParams['grid.linestyle'])
```

--------------------------------

### Import Matplotlib and Set Backend

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/Issue77_test.ipynb

Imports the matplotlib.pyplot module and sets the backend to 'inline' for displaying plots directly within a notebook. The '%matplotlib qt' line is commented out, indicating it's not currently active.

```python
#%matplotlib qt
%matplotlib inline
import matplotlib.pyplot as plt
```

--------------------------------

### Get Alternative Reference Image Histogram

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/test_plot_image_comparison.ipynb

Calculates the histogram for an alternative reference PIL Image object. This is used in RMS calculations for comparison.

```python
h1rt = img01reft.histogram()
```

--------------------------------

### Get Bounding Box of Image Differences

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/test_plot_image_comparison.ipynb

Retrieves the bounding box of the differing regions in a PIL Image object. Returns None if there are no differences.

```python
diff.getbbox()
```

--------------------------------

### Import Libraries and Initialize Plotting

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/issues/issue568_pnf_calcs.ipynb

Imports necessary libraries including pandas for data manipulation, mplfinance for plotting, and numpy. It also sets up a pretty printer for displaying complex data structures.

```python
%matplotlib inline
import pandas as pd
import mplfinance as mpf
import numpy as np
from matplotlib import ticker

import pprint
pp = pprint.PrettyPrinter(indent=2)
```

--------------------------------

### Display Saved Image

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/macd_and_widths.ipynb

Opens a saved plot image file ('tight.png') using Pillow and displays it using Matplotlib. Requires Pillow and Matplotlib to be installed.

```python
from PIL import Image

im = Image.open('tight.png')

import matplotlib.pyplot as plt

def plotimg(im):
    fig,ax = plt.subplots()
    ax.set_axis_off()
    ax.imshow(im)
    plt.show()

plotimg(im)
```

--------------------------------

### Prepare data for multiple addplot configurations

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/addplot.ipynb

Prepare data for multiple additional plots, including signals derived from custom functions and existing DataFrame columns. Assumes `percentB_belowzero` is defined elsewhere.

```python
low_signal  = percentB_belowzero(df['PercentB'], df['Close']) 
high_signal = percentB_aboveone(df['PercentB'], df['Close'])
```

--------------------------------

### Generate date pairs for trend lines

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/using_lines.ipynb

Creates pairs of consecutive dates from a list of dates. These pairs define the start and end points for trend lines.

```python
# now generate a sequence of date pairs:
datepairs = [(d1,d2) for d1,d2 in zip(dates,dates[1:])]
datepairs
```

--------------------------------

### Plot Basic OHLC and Candlestick Charts

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/resample10years.ipynb

Plots a basic candlestick chart with volume using the loaded daily stock data. Requires the mplfinance library to be installed.

```python
import mplfinance as mpf
mpf.__version__
mpf.plot(df,volume=True,type='candle')
```

--------------------------------

### Initialize Pretty Printer

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/mpl.zorderaxes.ipynb

Initializes a PrettyPrinter object from the pprint module for formatted output of complex data structures.

```python
import pprint\npp = pprint.PrettyPrinter(indent=4)
```

--------------------------------

### Adding Subplots with Different Styles

Source: https://github.com/matplotlib/mplfinance/blob/master/examples/scratch_pad/dev_external_axes.ipynb

Adds multiple subplots to an existing figure, each with a distinct mplfinance style. This setup is useful for comparing different chart styles side-by-side.

```python
s = mpf.make_mpf_style(base_mpl_style='fast',base_mpf_style='nightclouds')
ax1 = mpf.add_subplot(2,2,1,fig=fig,style='blueskies')
ax2 = mpf.add_subplot(2,2,2,fig=fig,style='yahoo')
ax3 = mpf.add_subplot(2,2,3,fig=fig,style=s)
ax4 = mpf.add_subplot(2,2,4,fig=fig,style='starsandstripes')
```