Detect valleys and peaks in stockmarket data
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Facebook
##########

In the following example we load the 2016 elections data of the USA for various candidates.
We will check whether the votes are fraudulent based on benfords distribution.

.. code:: python
	
	# Import library
	from caerus import caerus

	# Initialize
	cs = caerus()

	# Import example dataset
	X = cs.download_example(name='facebook')

	# Fit
	cs.fit(X)

	# Plot
	cs.plot()

	# Results are stored in the object
	print(cs.results.keys())
	# ['X', 'simmat', 'loc_start', 'loc_stop', 'loc_start_best', 'loc_stop_best', 'agg', 'df']

	# Results are stored in DataFrame
	print(cs.results['df'])
	#	 X	  labx  peak    valley
	# 0      38.2318     0  False   False
	# 1      34.0300     0  False   False
	# 2      31.0000     0  False   False
	# 3      32.0000     0  False   False
	# 4      33.0300     0  False   False
	#        ...   ...    ...     ...
	# 1467  169.3900     0  False   False
	# 1468  164.8900     0  False   False
	# 1469  159.3900     0  False   False
	# 1470  160.0600     0  False   False
	# 1471  152.1900     0  False   False

	[1472 rows x 4 columns]


.. |fig1| image:: ../figs/figure_fb.png

.. table:: Facebook data
   :align: center

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Bitcoin
##########

For demontration purposes, we will detect the peaks and valley of the *Bitcoin*.
It can be seen that the method easily pickups the peaks and valleys at the early years, and the last years.

.. code:: python
	
	# Import library
	from caerus import caerus

	# Initialize
	cs = caerus()

	# Import example dataset
	X = cs.download_example(name='bitcoin')

	# Fit
	cs.fit(X)
	# cs.fit(X[-300:])

	# Plot
	cs.plot()

.. |fig2| image:: ../figs/figure_btc.png

.. table:: Bitcoin trend
   :align: center

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Threshold effect
*********************

The threshold will change the strength of peaks and valleys that detected (middle plot). 
The higher the threshold cut-off, the better the peaks and valleys.


.. code:: python
	
	# Set the threshold higher

	# Top figure
	cs = caerus(threshold=0.025)

	# Bottom figure
	cs = caerus(threshold=0.9)

	# Search last 300 datapoints
	cs.fit(X[-300:])

	# Plot
	cs.plot()



.. |fig3| image:: ../figs/figure_btc_last_300days_new.png
.. |fig4| image:: ../figs/figure_btc_last_300days_new_th.png

.. table:: Top figure with threshold 0.25 and bottom with 0.9.
   :align: center

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   | |fig4|   |
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Window effect
*********************

The ``window`` size is used to determine the change in percentages. It is computed by the distance of start location + window.
A smaller window size is able to pickup better **local** minima, and larger window sizes will stress more on the **global** minma.
See below a demontration where the window size is increased. The figures clearly shows (top figures) that the windows are larger as the detected regions become more horizontal.

.. code:: python
	
	# Change the window size
	cs = caerus(window=50)
	cs = caerus(window=100)
	cs = caerus(window=200)

	# Search last 300 datapoints
	cs.fit(X[-300:])

	# Plot
	cs.plot()


.. |fig5| image:: ../figs/figure_btc_last_300days_window_50.png
.. |fig6| image:: ../figs/figure_btc_last_300days_window_100.png
.. |fig7| image:: ../figs/figure_btc_last_300days_window_200.png

.. table:: Effect of changing hte window size.
   :align: center

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   | |fig5|   |
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   | |fig6|   |
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   | |fig7|   |
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Gridsearch
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With the gridsearch it is possible to automatically search across various windows (``window``) and percentages (``minperc``).

.. code:: python
	
	# Initialize
	cs = caerus()

	# Gridsearch parameters
	cs.gridsearch(X)

	# Change search window and minperc
	# cs.gridsearch(X, window=np.arange(50,550,100), minperc=np.arange(1,20,5))

	# Plot
	cs.plot()


.. |fig8| image:: ../figs/figure_gridsearch_btc.png

.. table:: Gridsearch
   :align: center

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.. include:: add_bottom.add