Time Series Data Plot
=====================
Introduction
------------
In this example, we are introducing our plot engine for the time series
data and stock data. We are able to create a general plot like scatter
plot, bar chart, line chart and more graphs based on the input. To be
more specific, this package is also able to create the candle plot in
one line of code.
.. code:: ipython3
import numpy as np
import pandas as pd
from time_series_transform.stock_transform import *
from time_series_transform.plot import *
from time_series_transform.io import *
from time_series_transform import (Time_Series_Transformer,Stock_Transformer)
To extract the stock data, we can use the **from_stock_engine_period**
and **from_stock_engine_date** from the **Stock_Transformer** class.
1. symbols: **string** of the symbol or a **list** of the symbols
2. start_date/end_date: **string** of the date in the format of
%Y-%m-%d, eg. “2020-02-20” (used in from_stock_engine_date)
3. period: **string** of the period, eg. “1y” (used in
from_stock_engine_period)
4. engine: **string** of the engine, valid input: *yahoo*, *investing*
In this example, we will create 3 types of data for demonstration.
**ts_data**: Time_Series_Data, which is a general time series data
**stock_data**: Stock data which contains data of a single stock
**port_data**: Portfolio data which contains a list of Stock data
.. code:: ipython3
data = pd.DataFrame({'time':[1,2,3,4,5,6,7,8,9,10],'x1':[1,2,3,4,5,6,7,8,9,10],'x2':[6,7,8,9,10,16,27,38,49,50]})
ts_data = Time_Series_Transformer(data, 'time')
stock_data = Stock_Transformer.from_stock_engine_date("aapl", "2020-02-01", "2020-12-10", "yahoo")
port_data = Stock_Transformer.from_stock_engine_period(["aapl", "msft", "amzn"], "1y" , "yahoo")
print("ts_data: ")
print(to_pandas(ts_data.time_series_data,None,False,None).head())
print("===============================")
print("stock_data: ")
print(to_pandas(stock_data.time_series_data, None, False, None).head())
print("===============================")
print("port_data (amzn data): ")
print(to_pandas(port_data.time_series_data['amzn'], None, False, None).head())
print("===============================")
.. parsed-literal::
ts_data:
time x1 x2
0 1 1 6
1 2 2 7
2 3 3 8
3 4 4 9
4 5 5 10
===============================
stock_data:
Date Open High Low Close Volume \
0 2020-01-31 79.545540 79.979294 76.412600 76.714989 199588400
1 2020-02-03 75.423639 77.701469 74.908095 76.504311 173985600
2 2020-02-04 78.152574 79.225810 77.736172 79.029999 136616400
3 2020-02-05 80.187502 80.494853 79.054790 79.674438 118826800
4 2020-02-06 79.952039 80.608865 79.379484 80.606384 105425600
Dividends Stock Splits
0 0.0 0.0
1 0.0 0.0
2 0.0 0.0
3 0.0 0.0
4 0.0 0.0
===============================
port_data (amzn data):
Date Open High Low Close Volume \
0 2020-01-06 1860.000000 1903.689941 1860.000000 1902.880005 4061800
1 2020-01-07 1904.500000 1913.890015 1892.040039 1906.859985 4044900
2 2020-01-08 1898.040039 1911.000000 1886.439941 1891.969971 3508000
3 2020-01-09 1909.890015 1917.819946 1895.800049 1901.050049 3167300
4 2020-01-10 1905.369995 1906.939941 1880.000000 1883.160034 2853700
Dividends Stock Splits
0 0 0
1 0 0
2 0 0
3 0 0
4 0 0
===============================
Time_Series_Plot
----------------
This package provides the plot function for the time series data. We can
simply create the plot using the **create_plot** function, or simply
call the **.plot** from the transformer, or just use **.plot.line**.
Functions
~~~~~~~~~
The Plot module has several functions to support figure with more
details.
Trace data
''''''''''
1. add_line
2. remove_line
3. add_marker
Layout
''''''
1. update_layout
2. remove_subplot
*User is able to add new subplot by changing the value of subplot in the
add_line function*
.. code:: ipython3
tsp = ts_data.plot(cols=['x1','x2'], title = 'sample title', type = 'scatter')
tsp
.. raw:: html
.. raw:: html
.. parsed-literal::
.. code:: ipython3
tsp.add_line(col = "x2", lineType="bar")
tsp
.. raw:: html
.. parsed-literal::
.. code:: ipython3
markerDf = ts_data.to_pandas()
markerDf['x3'] = 50-ts_data.time_series_data.data['x2']
tsp.add_marker(
x = markerDf['time'],
y = markerDf['x3'],
color = '#8815EB',
legendName = 'inverse_x2'
)
tsp
.. raw:: html
.. parsed-literal::
Stock Plot
----------
Next, we are going to demonstrate how the candle plot works.
First, we can take a look at the general candle plot with one stock,
**stock_data**.
Simply call the **.plot** from the transformer we created previously.
.. code:: ipython3
fig = stock_data.plot
fig
.. raw:: html
.. parsed-literal::
Now we can add a horizontal line of the mean of *Close* data throughout
the period with the *customized data*
.. code:: ipython3
time_series_data = stock_data.time_series_data
horiLine = np.ones(len(time_series_data.time_index['Date'])) * np.mean(time_series_data.data['Close'])
fig.add_line(data = horiLine, lineType = 'scatter', legendName = 'mean', color = '#4358F1')
fig
.. raw:: html
.. parsed-literal::
Combine with the **get_technial_indicator** from the Stock_Transformer,
we can create more advanced graph.
With the **pandas_ta** package, we are able to create various technical
indicator. In this example, we are going to add the *SMA_10*, *Bollinger
Bands*, and *MACD* in a different subplot.
.. code:: ipython3
import pandas_ta as ta
MyStrategy = ta.Strategy(
name="DCSMA10",
ta=[
{"kind": "macd"},
{"kind": "ema", "length": 10},
{"kind": "bbands", "length": 20, "col_names": ("BBL", "BBM", "BBU")},
]
)
stock_data = stock_data.get_technial_indicator(MyStrategy)
print(stock_data.to_pandas().head())
.. parsed-literal::
Date Open High Low Close Volume \
0 2020-01-31 79.545540 79.979294 76.412600 76.714989 199588400
1 2020-02-03 75.423639 77.701469 74.908095 76.504311 173985600
2 2020-02-04 78.152574 79.225810 77.736172 79.029999 136616400
3 2020-02-05 80.187502 80.494853 79.054790 79.674438 118826800
4 2020-02-06 79.952039 80.608865 79.379484 80.606384 105425600
Dividends Stock Splits MACD_12_26_9 MACDh_12_26_9 MACDs_12_26_9 \
0 0.0 0.0 NaN NaN NaN
1 0.0 0.0 NaN NaN NaN
2 0.0 0.0 NaN NaN NaN
3 0.0 0.0 NaN NaN NaN
4 0.0 0.0 NaN NaN NaN
EMA_10 BBL BBM BBU
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
.. code:: ipython3
# Bollinger Band
fig.add_line(col = 'BBL', lineType = 'scatter', color = '#F2E64C')
fig.add_line(col = 'BBM', lineType = 'scatter', color = '#F2E64C')
fig.add_line(col = 'BBU', lineType = 'scatter', color = '#F2E64C')
# EMA_10
fig.add_line(col = 'EMA_10', lineType = 'scatter')
# MACD in a different subplot
fig.add_line(col = 'MACD_12_26_9', lineType = 'scatter', subplot = 'y3')
fig.add_line(col = 'MACDh_12_26_9', lineType = 'scatter', subplot = 'y3')
fig.add_line(col = 'MACDs_12_26_9', lineType = 'scatter', subplot = 'y3')
fig.show()
.. raw:: html
Next, we can move on to the **Portfolio** plot, which contains multiple
layers of plots.
.. code:: ipython3
port_fig = port_data.plot
port_fig
.. raw:: html
.. parsed-literal::