Merge pull request #27 from DanielGoldfarb/master

New Version 0.12.3

Author: DanielGoldfarb

README.md

@@ -6,7 +6,7 @@
 ---
 
 # Installation
-##    `pip install mplfinance`
+##    `pip install --upgrade mplfinance`
    - mplfinance requires [matplotlib](https://pypi.org/project/matplotlib/) and [pandas](https://pypi.org/project/pandas/)
 
 ---
@@ -15,9 +15,9 @@
 
   - **[The New API](https://github.com/matplotlib/mplfinance#newapi)**
   - **[Basic Usage](https://github.com/matplotlib/mplfinance#usage)**
-  - **[Adding Custom Data to Ohlcv Plots](https://github.com/matplotlib/mplfinance/blob/master/examples/addplot.ipynb)**
+  - **[Adding Your Own Technical Studies to Plots](https://github.com/matplotlib/mplfinance/blob/master/examples/addplot.ipynb)**
   - **[Saving the Plot to a File](https://github.com/matplotlib/mplfinance/blob/master/examples/savefig.ipynb)**
-  - Customizing the Appearance of Plots (presently in development)
+  - **[Customizing the Appearance of Plots](https://github.com/matplotlib/mplfinance/blob/master/examples/customization_and_styles.ipynb)**
   - Technical Studies (presently in development)
   - **[Latest Release Info](https://github.com/matplotlib/mplfinance#release)**
   - **[Some Background History About This Package](https://github.com/matplotlib/mplfinance#history)**
@@ -35,8 +35,8 @@ The conventional way to import the new API is as follows:
 ```python
     import mplfinance as mpf
 ```
-    
-The most common usage is then to call 
+
+The most common usage is then to call
 
 ```python
     mpf.plot(data)
@@ -53,6 +53,7 @@ I am very interested to hear from you regarding what you think of the new `mplfi
 # <a name="usage"></a>Basic Usage
 Start with a Pandas DataFrame containing OHLC data.  For example,
 
+
 ```python
 import pandas as pd
 daily = pd.read_csv('examples/data/SP500_NOV2019_Hist.csv',index_col=0,parse_dates=True)
@@ -61,6 +62,7 @@ daily.shape
 daily.head(3)
 daily.tail(3)
 ```
+
     (20, 5)
 
 <table border="1" class="dataframe">
@@ -159,20 +161,21 @@ daily.tail(3)
   </tbody>
 </table>
 
-<br>
 
 ---
-
 <br>
 
 After importing mplfinance, plotting OHLC data is as simple as calling `mpf.plot()` on the dataframe
 
+
 ```python
 import mplfinance as mpf
 mpf.plot(daily)
 ```
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_4_0.png)
+
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_4_1.png)
+
 
 ---
 <br>
@@ -184,14 +187,18 @@ The default plot type, as you can see above, is `'ohlc'`.  Other plot types can
 mpf.plot(daily,type='candle')
 ```
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_6_0.png)
+
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_6_1.png)
+
 
 
 ```python
 mpf.plot(daily,type='line')
 ```
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_7_0.png)
+
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_7_1.png)
+
 
 ---
 <br>
@@ -205,14 +212,18 @@ We can also plot moving averages with the `mav` keyword
 mpf.plot(daily,type='ohlc',mav=4)
 ```
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_9_0.png)
+
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_9_1.png)
+
 
 
 ```python
 mpf.plot(daily,type='candle',mav=(3,6,9))
 ```
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_10_0.png)
+
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_10_1.png)
+
 
 ---
 We can also display `Volume`
@@ -223,20 +234,23 @@ mpf.plot(daily,type='candle',mav=(3,6,9),volume=True)
 ```
 
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_12_0.png)
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_12_1.png)
+
 
-Notice, in the above chart, there are gaps along the x-coordinate corresponding to days on which there was no trading.  
-- Many people like to see these gaps so that they can tell, with a quick glance, where the weekends and holidays fall.  
-- For example, in the above chart you can see a gap at Thursday, November 28th for the U.S. Thanksgiving holiday.
-- Gaps along the x-axis can be eliminated with the `no_xgaps` keyword
+Notice, in the above chart, there are no gaps along the x-coordinate, even though there are days on which there was no trading.  ***Non-trading days are simply not shown*** (since there are no prices for those days).
+
+- However, sometimes people like to see these gaps, so that they can tell, with a quick glance, where the weekends and holidays fall.  
+
+- Non-trading days can be displayed with the `show_nontrading` keyword.
+- For example, in the chart below, you can easily see weekends, as well as a gap at Thursday, November 28th for the U.S. Thanksgiving holiday.
 
 
 ```python
-mpf.plot(daily,type='candle',mav=(3,6,9),volume=True,no_xgaps=True)
+mpf.plot(daily,type='candle',mav=(3,6,9),volume=True,show_nontrading=True)
 ```
 
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_14_0.png)
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_14_1.png)
 
 
 ---
@@ -255,6 +269,7 @@ intraday.tail(3)
 
     (1563, 4)
 
+
 <table border="1" class="dataframe">
   <thead>
     <tr style="text-align: right;">
@@ -341,6 +356,8 @@ intraday.tail(3)
   </tbody>
 </table>
 
+
+
 The above dataframe contains Open,High,Low,Close data at 1 minute intervervals for the S&P 500 stock index for November 5, 6, 7 and 8, 2019.  Let's look at the last hour of trading on November 6th, with a 7 minute and 12 minute moving average.
 
 
@@ -349,12 +366,11 @@ iday = intraday.loc['2019-11-06 15:00':'2019-11-06 16:00',:]
 mpf.plot(iday,type='candle',mav=(7,12))
 ```
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_18_0.png)
 
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_18_1.png)
 
-  The "time-interpretation" of the `mav` integers depends on the frequency of the data, because the mav integers are number of data points used in the Moving Average.  Notice above that for intraday data the x-axis automatically displays TIME *instead of* date.  Below we see that if the intraday data spans two (or more) trading days then two things happen:
-- The x-axis displays *BOTH* TIME and DATE
-- `no-xgaps` defaults to `True` FOR INTRADAY DATA INVOLVING TWO OR MORE TRADING DAYS
+
+  The "time-interpretation" of the `mav` integers depends on the frequency of the data, because the mav integers are the *number of data points* used in the Moving Average (not the number of days or minutes, etc).  Notice above that for intraday data the x-axis automatically displays TIME *instead of* date.  Below we see that if the intraday data spans into two (or more) trading days the x-axis automatically displays *BOTH* TIME and DATE
 
 
 ```python
@@ -363,47 +379,47 @@ mpf.plot(iday,type='candle')
 ```
 
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_20_0.png)
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_20_1.png)
 
 
 ---
-In the plot below, we see **what would happen if ` no_xgaps ` did NOT** default to `True` for intraday data involving two or more days.
+In the plot below, we see what an intraday plot looks like when we **display non-trading time periods** with **`show_nontrading=True`** for intraday data spanning into two or more days.
 
 
 ```python
-mpf.plot(iday,type='candle',no_xgaps=False)
+mpf.plot(iday,type='candle',show_nontrading=True)
 ```
 
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_22_0.png)
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_22_1.png)
 
 
 ---
-Below: 4 days of intraday data with `no_xgaps=False`
+Below: 4 days of intraday data with `show_nontrading=True`
 
 
 ```python
-mpf.plot(intraday,type='ohlc',no_xgaps=False)  # 4 day of intraday with no_xgaps=False
+mpf.plot(intraday,type='ohlc',show_nontrading=True)
 ```
 
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_24_0.png)
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_24_1.png)
 
 
 ---
-Below: 4 days of intraday data with `no_xgaps` defaulted to `True` for intraday data spanning more than one day.
+Below: the same 4 days of intraday data with `show_nontrading` defaulted to `False`.
 
 
 ```python
-mpf.plot(intraday,type='line')  # intraday spanning more than one day defaults to no_xgaps=True
+mpf.plot(intraday,type='line') 
 ```
 
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_26_0.png)
+![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_26_1.png)
 
 
 ---
-Below: Daily data spanning more than a year automatically adds the *YEAR* to the DATE format
+Below: Daily data spanning across a year boundary automatically adds the *YEAR* to the DATE format
 
 
 ```python
@@ -415,6 +431,7 @@ df.tail(3)
 
     (2519, 6)
 
+
 <table border="1" class="dataframe">
   <thead>
     <tr style="text-align: right;">
@@ -521,21 +538,24 @@ df.tail(3)
   </tbody>
 </table>
 
+
 ```python
-mpf.plot(df[700:850],type='bars',volume=True,no_xgaps=True,mav=(20,40))
+mpf.plot(df[700:850],type='bars',volume=True,mav=(20,40))
 ```
 
-![png](https://raw.githubusercontent.com/matplotlib/mplfinance/master/readme_files/readme_29_0.png)
+
+![png](readme_files/readme_29_1.png)
 
 
-For more examples of using mplfinance, please see the jupyter notebooks in the `examples` directory.
+For more examples of using mplfinance, please see the jupyter notebooks in the **[`examples`](https://github.com/matplotlib/mplfinance/blob/master/examples/)** directory.
 
 ---
 
 # <a name="release"></a>Release Notes
 
 | Version  |  Description   | Release Date |
-|----------|--------------|----------------|
+|:---------|:-------------|:---------------|
+| 0.12.3a0   | - kwarg `block=False` for non-blocking call to `mpf.plot()`<br> - customize aspect ratio, figure title, y-labels<br> - customize colors and other `style` aspects of plot<br> - `no_xgaps` now defaults to True: use `show_nontrading=True` to set no_xgaps to false<br> - secondary y-axis available to `make_addplot()`<br> - bug fix for volume widths | 2020-02-12 |
 | 0.12.0a3   | Increase mav limit from 3 to 7 different mavs  | 2020-01-16 |
 | 0.12.0a2   | Ability to save plot to a file (pdf, svg, png, jpg, ...) | 2020-01-14 |
 | 0.12.0a1   | Ability to plot arbitrary user data (in addition to basic OHLCV data).<br> - both line and scatter plots available.<br> - optionally plot on either the "main" or "lower" (aka "volume") axis. | 2020-01-09 |
@@ -565,10 +585,18 @@ With this new ` mplfinance ` package installed, in addition to the new API, user
     from mplfinance.original_flavor import <method>
 ```
 
+
 where `<method>` indicates the method you want to import, for example:
 
+
+
 ```python
     from mplfinance.original_flavor import candlestick_ohlc
 ```
+
 ---
 
+
+```python
+
+```