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Real Stock Data Plotting using Matplotlib

9. Real Stock Data

Quandl API to Plot Real Time Stock Data

The premier source for financial, economic and alternative datasets, serving investment professionals.

In [ ]:
pip install quandl
In [2]:
import quandl
data = quandl.get("BSE/BOM539678", authtoken="x8VdDXzBty3iGsaRLTVs")
data
Out[2]:
Open High Low Close WAP No. of Shares No. of Trades Total Turnover Deliverable Quantity % Deli. Qty to Traded Qty Spread H-L Spread C-O
Date
2017-02-21 254.25 259.50 253.00 257.75 257.17 40544.0 1048.0 10426526.0 9129.0 22.52 6.50 3.50
2017-02-22 257.00 261.00 252.60 253.05 256.18 35603.0 1067.0 9120718.0 8450.0 23.73 8.40 -3.95
2017-02-23 253.30 254.60 249.20 250.55 251.99 26262.0 600.0 6617792.0 13570.0 51.67 5.40 -2.75
2017-02-27 251.40 252.00 244.00 247.50 249.58 28019.0 732.0 6992855.0 11992.0 42.80 8.00 -3.90
2017-02-28 248.50 253.50 247.60 248.50 249.88 66735.0 1514.0 16675820.0 13474.0 20.19 5.90 0.00
2021-07-02 284.55 296.30 284.25 287.95 289.25 308092.0 5109.0 89115468.0 46506.0 15.09 12.05 3.40
2021-07-05 291.30 306.00 284.50 298.05 298.63 288284.0 5557.0 86091075.0 56308.0 19.53 21.50 6.75
2021-07-06 300.00 303.25 290.00 291.10 297.30 96810.0 2219.0 28781348.0 15177.0 15.68 13.25 -8.90
2021-07-07 292.00 296.50 288.35 294.10 292.01 84635.0 2484.0 24713902.0 16887.0 19.95 8.15 2.10
2021-07-08 294.10 295.45 284.30 287.70 289.84 70920.0 2647.0 20555342.0 14016.0 19.76 11.15 -6.40

1084 rows × 12 columns

In [4]:
import matplotlib.pyplot as plt

plt.rc('figure',figsize=(18,5))

plt.plot(data.Open)

plt.savefig('stock.pdf')
plt.savefig('stock.png')
plt.show()

Plot using JSON

In [5]:
import requests
r = requests.get('https://www.quandl.com/api/v3/datasets/BSE/BOM539678.json?api_key=x8VdDXzBty3iGsaRLTVs')
r
Out[5]:
<Response [200]>
In [6]:
import json
data = json.loads(r.content)
data
Out[6]:
{'dataset': {'id': 35121372,
  'dataset_code': 'BOM539678',
  'database_code': 'BSE',
  'name': 'Quick Heal Technologies Ltd EOD Prices',
  'description': 'End of Day prices and additional trading information for Quick Heal Technologies Ltd EOD Prices.',
  'refreshed_at': '2021-07-08T14:15:56.547Z',
  'newest_available_date': '2021-07-08',
  'oldest_available_date': '2017-02-21',
  'column_names': ['Date',
   'Open',
   'High',
   'Low',
   'Close',
   'WAP',
   'No. of Shares',
   'No. of Trades',
   'Total Turnover',
   'Deliverable Quantity',
   '% Deli. Qty to Traded Qty',
   'Spread H-L',
   'Spread C-O'],
  'frequency': 'daily',
  'type': 'Time Series',
  'premium': False,
  'limit': None,
  'transform': None,
  'column_index': None,
  'start_date': '2017-02-21',
  'end_date': '2021-07-08',
  'data': [['2021-07-08',
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    11.15,
    -6.4],
   ['2021-07-07',
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   ['2021-06-29',
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  'collapse': None,
  'order': None,
  'database_id': 3287}}
In [7]:
data['dataset']['data']
Out[7]:
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 ['2017-08-03',
  210.0,
  211.5,
  207.8,
  208.5,
  209.31,
  10174.0,
  256.0,
  2129526.0,
  5054.0,
  49.68,
  3.7,
  -1.5],
 ['2017-08-02',
  214.8,
  214.8,
  211.1,
  212.15,
  212.88,
  14071.0,
  299.0,
  2995464.0,
  6813.0,
  48.42,
  3.7,
  -2.65],
 ['2017-08-01',
  212.05,
  217.0,
  212.05,
  213.45,
  214.46,
  25488.0,
  427.0,
  5466129.0,
  10628.0,
  41.7,
  4.95,
  1.4],
 ['2017-07-31',
  215.0,
  215.0,
  212.0,
  213.05,
  213.42,
  36575.0,
  566.0,
  7805810.0,
  11024.0,
  30.14,
  3.0,
  -1.95],
 ['2017-07-28',
  212.0,
  216.7,
  211.05,
  214.15,
  214.92,
  29790.0,
  520.0,
  6402558.0,
  10127.0,
  33.99,
  5.65,
  2.15],
 ['2017-07-27',
  219.0,
  220.0,
  213.2,
  213.55,
  216.8,
  42871.0,
  803.0,
  9294647.0,
  11875.0,
  27.7,
  6.8,
  -5.45],
 ['2017-07-26',
  220.0,
  221.9,
  217.2,
  217.6,
  220.02,
  45033.0,
  769.0,
  9908241.0,
  10871.0,
  24.14,
  4.7,
  -2.4],
 ['2017-07-25',
  218.0,
  222.25,
  217.0,
  219.25,
  220.14,
  45379.0,
  849.0,
  9989657.0,
  10479.0,
  23.09,
  5.25,
  1.25],
 ['2017-07-24',
  222.0,
  224.7,
  218.35,
  220.1,
  221.82,
  23891.0,
  511.0,
  5299468.0,
  8177.0,
  34.23,
  6.35,
  -1.9],
 ['2017-07-21',
  225.0,
  226.65,
  218.2,
  221.6,
  223.24,
  42397.0,
  862.0,
  9464839.0,
  10784.0,
  25.44,
  8.45,
  -3.4],
 ['2017-07-20',
  228.3,
  228.5,
  224.0,
  224.8,
  226.05,
  47281.0,
  840.0,
  10688105.0,
  8445.0,
  17.86,
  4.5,
  -3.5],
 ['2017-07-19',
  224.25,
  230.2,
  224.2,
  226.15,
  227.46,
  63135.0,
  1040.0,
  14360589.0,
  13774.0,
  21.82,
  6.0,
  1.9],
 ['2017-07-18',
  225.5,
  229.0,
  223.6,
  225.3,
  225.98,
  70911.0,
  1282.0,
  16024167.0,
  24706.0,
  34.84,
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  -0.2],
 ['2017-07-17',
  222.85,
  226.15,
  221.0,
  222.7,
  223.33,
  58940.0,
  1117.0,
  13163330.0,
  10263.0,
  17.41,
  5.15,
  -0.15],
 ['2017-07-14',
  229.65,
  229.65,
  220.8,
  221.65,
  224.15,
  101040.0,
  1629.0,
  22648354.0,
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  -8.0],
 ['2017-07-13',
  224.8,
  231.5,
  222.3,
  228.0,
  226.02,
  119564.0,
  2080.0,
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  19.33,
  9.2,
  3.2],
 ['2017-07-12',
  225.8,
  227.85,
  221.6,
  223.25,
  224.56,
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 ['2017-07-11',
  219.15,
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  215.65,
  224.65,
  225.15,
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  20.85,
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 ['2017-07-10',
  222.7,
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  221.79,
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  -3.1],
 ['2017-07-07',
  204.7,
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  203.9,
  221.0,
  217.85,
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  21.24,
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  16.3],
 ['2017-07-06',
  203.0,
  205.7,
  203.0,
  204.3,
  204.38,
  27628.0,
  572.0,
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 ['2017-07-05',
  206.0,
  206.2,
  201.85,
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  -3.4],
 ['2017-07-04',
  206.6,
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  204.0,
  205.35,
  206.22,
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 ['2017-07-03',
  207.0,
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  206.1,
  207.25,
  207.09,
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  411.0,
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  43.58,
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 ['2017-06-30',
  207.25,
  209.25,
  206.0,
  206.75,
  207.24,
  13950.0,
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  7560.0,
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  -0.5],
 ['2017-06-29',
  208.55,
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  208.0,
  208.35,
  210.3,
  34226.0,
  589.0,
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  6666.0,
  19.48,
  3.9,
  -0.2],
 ['2017-06-28',
  209.95,
  211.5,
  207.15,
  208.2,
  209.94,
  52268.0,
  1059.0,
  10973376.0,
  12241.0,
  23.42,
  4.35,
  -1.75],
 ['2017-06-27',
  209.9,
  209.9,
  205.25,
  206.75,
  207.15,
  17377.0,
  432.0,
  3599653.0,
  6782.0,
  39.03,
  4.65,
  -3.15],
 ...]
In [8]:
import pandas as pd
df = pd.DataFrame(data['dataset']['data'],columns=data['dataset']['column_names'])
df
Out[8]:
Date Open High Low Close WAP No. of Shares No. of Trades Total Turnover Deliverable Quantity % Deli. Qty to Traded Qty Spread H-L Spread C-O
0 2021-07-08 294.10 295.45 284.30 287.70 289.84 70920.0 2647.0 20555342.0 14016.0 19.76 11.15 -6.40
1 2021-07-07 292.00 296.50 288.35 294.10 292.01 84635.0 2484.0 24713902.0 16887.0 19.95 8.15 2.10
2 2021-07-06 300.00 303.25 290.00 291.10 297.30 96810.0 2219.0 28781348.0 15177.0 15.68 13.25 -8.90
3 2021-07-05 291.30 306.00 284.50 298.05 298.63 288284.0 5557.0 86091075.0 56308.0 19.53 21.50 6.75
4 2021-07-02 284.55 296.30 284.25 287.95 289.25 308092.0 5109.0 89115468.0 46506.0 15.09 12.05 3.40
1079 2017-02-28 248.50 253.50 247.60 248.50 249.88 66735.0 1514.0 16675820.0 13474.0 20.19 5.90 0.00
1080 2017-02-27 251.40 252.00 244.00 247.50 249.58 28019.0 732.0 6992855.0 11992.0 42.80 8.00 -3.90
1081 2017-02-23 253.30 254.60 249.20 250.55 251.99 26262.0 600.0 6617792.0 13570.0 51.67 5.40 -2.75
1082 2017-02-22 257.00 261.00 252.60 253.05 256.18 35603.0 1067.0 9120718.0 8450.0 23.73 8.40 -3.95
1083 2017-02-21 254.25 259.50 253.00 257.75 257.17 40544.0 1048.0 10426526.0 9129.0 22.52 6.50 3.50

1084 rows × 13 columns

In [9]:
df.set_index('Date',inplace=True)
In [10]:
df = df.sort_index()
df
Out[10]:
Open High Low Close WAP No. of Shares No. of Trades Total Turnover Deliverable Quantity % Deli. Qty to Traded Qty Spread H-L Spread C-O
Date
2017-02-21 254.25 259.50 253.00 257.75 257.17 40544.0 1048.0 10426526.0 9129.0 22.52 6.50 3.50
2017-02-22 257.00 261.00 252.60 253.05 256.18 35603.0 1067.0 9120718.0 8450.0 23.73 8.40 -3.95
2017-02-23 253.30 254.60 249.20 250.55 251.99 26262.0 600.0 6617792.0 13570.0 51.67 5.40 -2.75
2017-02-27 251.40 252.00 244.00 247.50 249.58 28019.0 732.0 6992855.0 11992.0 42.80 8.00 -3.90
2017-02-28 248.50 253.50 247.60 248.50 249.88 66735.0 1514.0 16675820.0 13474.0 20.19 5.90 0.00
2021-07-02 284.55 296.30 284.25 287.95 289.25 308092.0 5109.0 89115468.0 46506.0 15.09 12.05 3.40
2021-07-05 291.30 306.00 284.50 298.05 298.63 288284.0 5557.0 86091075.0 56308.0 19.53 21.50 6.75
2021-07-06 300.00 303.25 290.00 291.10 297.30 96810.0 2219.0 28781348.0 15177.0 15.68 13.25 -8.90
2021-07-07 292.00 296.50 288.35 294.10 292.01 84635.0 2484.0 24713902.0 16887.0 19.95 8.15 2.10
2021-07-08 294.10 295.45 284.30 287.70 289.84 70920.0 2647.0 20555342.0 14016.0 19.76 11.15 -6.40

1084 rows × 12 columns

In [11]:
df.index = pd.to_datetime(df.index, dayfirst=True)
In [12]:
import matplotlib.pyplot as plt

plt.rc('figure',figsize=(18,5))

plt.plot(df.Open)

plt.savefig('stock.pdf')
plt.savefig('stock.png')
plt.show()