Stock Extraction
================
In this package, we provide functionalities to extract the stock data.
and user is able to retrieve the data from 2 different engines. The
module fetches the stock data from *yahoo finance* or *investing.com*.
User is able to fetch multiple stocks at once, calculate various
technical indicators and create advance plots. This example is going to
demonstrate the method of fetching the data.
Resouces link:
- yahoo finance: https://pypi.org/project/yfinance/
- investing.com: https://pypi.org/project/investpy/
.. 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)
Stock Transformer
-----------------
We can simply fetch the data by using the *Stock_Transformer*, and user
can specify the engine they would like to use.
In the *investing* engine, user need to specify the *country* as a
parameter.
Extraction Method
^^^^^^^^^^^^^^^^^
1. from_stock_engine_date
2. from_stock_engine_period
3. from_stock_engine_intraday
*investing engine doesn’t provide the intraday data*
In the following, we demonstrate 3 different methods to extract the
data, user is able to change the engine to either **yahoo** or
**investing**.
.. code:: ipython3
stock_data = Stock_Transformer.from_stock_engine_date("aapl", "2020-02-01", "2020-12-10", "investing", country = "united states")
port_data = Stock_Transformer.from_stock_engine_period(["aapl", "msft", "amzn"], "1y" , "yahoo")
intra_data = Stock_Transformer.from_stock_engine_intraday("amzn", "2020-12-01", "2020-12-10", "yahoo", interval = "5m")
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("===============================")
print("intra_data: ")
print(to_pandas(intra_data.time_series_data, None, False, None).head())
print("===============================")
.. parsed-literal::
C:\Users\Allen Chiang\anaconda3\lib\site-packages\pandas\core\frame.py:1485: FutureWarning: Using short name for 'orient' is deprecated. Only the options: ('dict', list, 'series', 'split', 'records', 'index') will be used in a future version. Use one of the above to silence this warning.
warnings.warn(
.. parsed-literal::
stock_data:
Date Open High Low Close Volume Currency
0 2020-02-03 76.08 78.37 75.56 77.17 173985600 USD
1 2020-02-04 78.83 79.91 78.41 79.71 136616544 USD
2 2020-02-05 80.88 81.19 79.74 80.36 118826872 USD
3 2020-02-06 80.64 81.31 80.07 81.30 105425536 USD
4 2020-02-07 80.59 80.85 79.50 80.01 117684048 USD
===============================
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
===============================
intra_data:
Datetime Open High Low Close \
0 2020-11-30 16:00:00 3147.530029 3153.000000 3143.055176 3152.020020
1 2020-11-30 16:05:00 3152.274902 3154.577393 3148.370117 3153.360107
2 2020-11-30 16:10:00 3152.070068 3153.949951 3150.100098 3150.370117
3 2020-11-30 16:15:00 3150.070068 3156.979980 3148.000000 3150.929932
4 2020-11-30 16:20:00 3150.446533 3156.379883 3149.679932 3150.750000
Volume Dividends Stock Splits
0 0 0 0
1 38406 0 0
2 38418 0 0
3 50079 0 0
4 26603 0 0
===============================
Technical Indicator
~~~~~~~~~~~~~~~~~~~
Stock_Transformer provides the function of **get_technial_indicator**.
Combining with the *pandas_ta* module, we are able to calculate the
technical indicator in a few lines of code.
.. 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()[25:30])
.. parsed-literal::
Date Open High Low Close Volume Currency MACD_12_26_9 \
25 2020-03-10 69.28 71.61 67.34 71.33 285290080 USD -3.682278
26 2020-03-11 69.35 70.31 67.97 68.86 256379872 USD -3.711825
27 2020-03-12 63.98 67.50 62.00 62.06 418474080 USD -4.235124
28 2020-03-13 66.22 69.98 63.24 69.49 370732128 USD -4.004146
29 2020-03-16 60.49 64.77 60.00 60.55 322423456 USD -4.490712
MACDh_12_26_9 MACDs_12_26_9 EMA_10 BBL BBM BBU
25 -0.372633 -3.309645 72.193387 65.614075 75.2845 84.954925
26 -0.321744 -3.390081 71.587317 64.912182 74.7325 84.552818
27 -0.676034 -3.559090 69.855077 62.992357 73.7455 84.498643
28 -0.356045 -3.648101 69.788700 62.852085 73.1590 83.465915
29 -0.674088 -3.816623 68.108936 61.104089 72.1245 83.144911
Plot
~~~~
In this example, we are going to create the candle plot.
For more advance manipulation on the plot module, please refer to the
**Plot Gallery** example.
.. code:: ipython3
stock_data.plot
.. raw:: html
.. raw:: html
.. parsed-literal::