#!/usr/bin/env python
# coding: utf-8

# # Cufflinks v0.6

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import cufflinks as cf
import pandas as pd


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get_ipython().run_line_magic('reload_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')


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cf.set_config_file(world_readable=True,offline=False)


# ## Pie Charts

# **datagen** can now generate a `DataFrame` with the structured required for a pie charts

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pie=cf.datagen.pie()
pie.head()


# `iplot` now accepts the paramter `kind=pie` to generate a pie chart  
# `labels` indicates the column that contains the category labels, and `values` indicates the column that contain the values to be charted

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pie.iplot(kind='pie',labels='labels',values='values')


# Extra parameters can also be passed  
#     `sort` : If True it sorts the labels by value  
# 	`pull` : Pulls the slices from the centre  
#     `hole` : Sets the size of the inner hole  
# 	`textposition` Sets the position of the legends for each slice ('outside'|'inside')  
#     `textinfo` : Sets the information to be displayed on the legends 

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pie.iplot(kind='pie',labels='labels',values='values',pull=.2,hole=.2,
         colorscale='blues',textposition='outside',textinfo='value+percent')


# ## Open, High, Low, Close Data Generation

# **datagen** now includes a method `ohlc` to generate Open, High, Low, Close data

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ohlc=cf.datagen.ohlc()
ohlc.head()


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ohlc.iplot()


# ## Candle and Bar Charts

# Of course *OHLC* can always be better appreciated with candle and bar (OHLC) charts.  
# 
# You can now achieve this with `kind=candle` and `kind=ohlc` in `iplot`.

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ohlc.iplot(kind='ohlc')


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ohlc.head().iplot(kind='candle',theme='ggplot',up_color='blue',down_color='pink')


# ## Secondary Axis

# You can now use `secondary_y` in `iplot` to indicate if a given trace should be plotted in a secondary (right) axis

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lines=cf.datagen.lines(4,mode='abc')

# We multiply 2 of the 4 lines by 100
lines[['c','d']]=lines[['c','d']]*100

#If we plot the resulting DataFrame then we get the following chart
lines.iplot()


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# We can however move those lines to the secondary axis

lines.iplot(kind='lines',secondary_y=['c','d'])


# ## Logarithmic Charts

# `iplot` now support logarithmic charts for boths **Y** and **X** axis, by using `logy=True` and `logx=True` respectively

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df=pd.DataFrame([x**2] for x in range(100))


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df.iplot()


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df.iplot(logy=True)


# ## Support for Multi-Index DataFrames

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ix3 = pd.MultiIndex.from_arrays([['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b'], ['foo', 'foo', 'bar', 'bar', 'foo', 'foo', 'bar', 'bar']], names=['letter', 'word'])
df3 = pd.DataFrame({'data1': [3, 2, 4, 3, 2, 4, 3, 2], 'data2': [6, 5, 7, 5, 4, 5, 6, 5]}, index=ix3)
gp3 = df3.groupby(level=('letter', 'word'))
means = gp3.mean()


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means


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means.iplot(kind='bar')


# ## Error Bars

# You can now add error bars in *scatter*,*bar* and *line* charts by passing the errors to `error_y` and `error_x` to `iplot`.

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cf.datagen.lines(1,5).iplot(kind='bar',error_y=[1,2,3.5,2,2])


# There are more type of error bars which can be set with `error_type`: `data`,`constant`,`percent`,`sqrt`  
# To see more info regarding each of them check `help(cf.ErrorY)`

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# 20% error bars
cf.datagen.bars().iplot(kind='bar',error_y=20,error_type='percent',error_color='red')


# ### Continuous Error Bars

# Continous error bars (shades) can also be plotted with `error_type='continuous'` or `error_type='continuous_percent'`
# 

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# Line chart with a tolerance of 20%
cf.datagen.lines(1).iplot(kind='scatter',error_y=20,error_type='continuous_percent')


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# Line chart with an error of 10 
cf.datagen.lines(1).iplot(kind='scatter',error_y=10,error_type='continuous',color='blue')


# ## Technical Analysis (Beta) 

# Cufflinks v0.6.0 includes a set of Technical Analysis studies for financial data. 
# 
# These are accessed with a new method called **ta_plot**. 

# #### Simple Moving Averages

# One or more moving averages can be added to a time series. 

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cf.datagen.lines(1,500).ta_plot(study='sma',periods=[13,21,55],title='Simple Moving Averages')


# ### Bollinger Bands

# These are a volatility indicator - and tell us the number the distance of the current value (at a given point) to the N number of standard deviations. 
# 

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cf.datagen.lines(1,200).ta_plot(study='boll',periods=14,title='Bollinger Bands')


# ###  Relative Strength Index (RSI)

# The relative strength index tells the historical **strength** or **weakness** of the historial price. 

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cf.datagen.lines(1,200).ta_plot(study='rsi',periods=14,title='Relative Strength Index')


# ### Moving Average Convergence Divergence (MACD)

# A trend-following momentum indicator that shows the relationship between two moving averages of prices.

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cf.datagen.lines(1,200).ta_plot(study='macd',fast_period=12,slow_period=26,
                                signal_period=9)


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