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JSON and Quandl API Data¶
In [ ]:
pip install quandl
Get Data by Quandl API¶
In [1]:
import quandl
data = quandl.get("BSE/BOM543207", authtoken="x8VdDXzBty3iGsaRLTVs")
data
Out[1]:
| 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 | ||||||||||||
| 2020-05-20 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 151.0 | 1.0 | 1887.0 | 151.0 | 100.00 | 0.00 | 0.00 |
| 2020-05-21 | 13.12 | 13.12 | 13.12 | 13.12 | 13.07 | 15.0 | 1.0 | 196.0 | 15.0 | 100.00 | 0.00 | 0.00 |
| 2020-05-28 | 13.77 | 13.77 | 13.12 | 13.12 | 13.33 | 15.0 | 2.0 | 200.0 | 15.0 | 100.00 | 0.65 | -0.65 |
| 2020-06-01 | 13.12 | 13.12 | 13.12 | 13.12 | 13.12 | 25.0 | 1.0 | 328.0 | 25.0 | 100.00 | 0.00 | 0.00 |
| 2020-06-09 | 13.12 | 13.70 | 13.12 | 13.70 | 13.40 | 20.0 | 2.0 | 268.0 | 20.0 | 100.00 | 0.58 | 0.58 |
| … | … | … | … | … | … | … | … | … | … | … | … | … |
| 2022-07-04 | 5.55 | 5.69 | 5.35 | 5.36 | 5.54 | 23313.0 | 97.0 | 129260.0 | 14293.0 | 61.31 | 0.34 | -0.19 |
| 2022-07-05 | 5.35 | 5.62 | 5.35 | 5.61 | 5.57 | 31991.0 | 72.0 | 178196.0 | 26804.0 | 83.79 | 0.27 | 0.26 |
| 2022-07-06 | 5.75 | 5.89 | 5.37 | 5.82 | 5.76 | 65001.0 | 69.0 | 374272.0 | 56240.0 | 86.52 | 0.52 | 0.07 |
| 2022-07-07 | 5.75 | 5.90 | 5.53 | 5.53 | 5.61 | 57607.0 | 116.0 | 323022.0 | 40746.0 | 70.73 | 0.37 | -0.22 |
| 2022-07-08 | 5.53 | 5.78 | 5.32 | 5.64 | 5.58 | 20970.0 | 91.0 | 116914.0 | 12148.0 | 57.93 | 0.46 | 0.11 |
246 rows × 12 columns
Get data from CSV File online¶
In [2]:
import pandas as pd
data = pd.read_csv('https://data.nasdaq.com/api/v3/datasets/BSE/BOM543207.csv?api_key=x8VdDXzBty3iGsaRLTVs')
data
Out[2]:
| 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 | 2022-07-08 | 5.53 | 5.78 | 5.32 | 5.64 | 5.58 | 20970.0 | 91.0 | 116914.0 | 12148.0 | 57.93 | 0.46 | 0.11 |
| 1 | 2022-07-07 | 5.75 | 5.90 | 5.53 | 5.53 | 5.61 | 57607.0 | 116.0 | 323022.0 | 40746.0 | 70.73 | 0.37 | -0.22 |
| 2 | 2022-07-06 | 5.75 | 5.89 | 5.37 | 5.82 | 5.76 | 65001.0 | 69.0 | 374272.0 | 56240.0 | 86.52 | 0.52 | 0.07 |
| 3 | 2022-07-05 | 5.35 | 5.62 | 5.35 | 5.61 | 5.57 | 31991.0 | 72.0 | 178196.0 | 26804.0 | 83.79 | 0.27 | 0.26 |
| 4 | 2022-07-04 | 5.55 | 5.69 | 5.35 | 5.36 | 5.54 | 23313.0 | 97.0 | 129260.0 | 14293.0 | 61.31 | 0.34 | -0.19 |
| … | … | … | … | … | … | … | … | … | … | … | … | … | … |
| 241 | 2020-06-09 | 13.12 | 13.70 | 13.12 | 13.70 | 13.40 | 20.0 | 2.0 | 268.0 | 20.0 | 100.00 | 0.58 | 0.58 |
| 242 | 2020-06-01 | 13.12 | 13.12 | 13.12 | 13.12 | 13.12 | 25.0 | 1.0 | 328.0 | 25.0 | 100.00 | 0.00 | 0.00 |
| 243 | 2020-05-28 | 13.77 | 13.77 | 13.12 | 13.12 | 13.33 | 15.0 | 2.0 | 200.0 | 15.0 | 100.00 | 0.65 | -0.65 |
| 244 | 2020-05-21 | 13.12 | 13.12 | 13.12 | 13.12 | 13.07 | 15.0 | 1.0 | 196.0 | 15.0 | 100.00 | 0.00 | 0.00 |
| 245 | 2020-05-20 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 151.0 | 1.0 | 1887.0 | 151.0 | 100.00 | 0.00 | 0.00 |
246 rows × 13 columns
In [3]:
data.set_index('Date',inplace=True)
In [4]:
data = data.sort_index()
data
Out[4]:
| 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 | ||||||||||||
| 2020-05-20 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 151.0 | 1.0 | 1887.0 | 151.0 | 100.00 | 0.00 | 0.00 |
| 2020-05-21 | 13.12 | 13.12 | 13.12 | 13.12 | 13.07 | 15.0 | 1.0 | 196.0 | 15.0 | 100.00 | 0.00 | 0.00 |
| 2020-05-28 | 13.77 | 13.77 | 13.12 | 13.12 | 13.33 | 15.0 | 2.0 | 200.0 | 15.0 | 100.00 | 0.65 | -0.65 |
| 2020-06-01 | 13.12 | 13.12 | 13.12 | 13.12 | 13.12 | 25.0 | 1.0 | 328.0 | 25.0 | 100.00 | 0.00 | 0.00 |
| 2020-06-09 | 13.12 | 13.70 | 13.12 | 13.70 | 13.40 | 20.0 | 2.0 | 268.0 | 20.0 | 100.00 | 0.58 | 0.58 |
| … | … | … | … | … | … | … | … | … | … | … | … | … |
| 2022-07-04 | 5.55 | 5.69 | 5.35 | 5.36 | 5.54 | 23313.0 | 97.0 | 129260.0 | 14293.0 | 61.31 | 0.34 | -0.19 |
| 2022-07-05 | 5.35 | 5.62 | 5.35 | 5.61 | 5.57 | 31991.0 | 72.0 | 178196.0 | 26804.0 | 83.79 | 0.27 | 0.26 |
| 2022-07-06 | 5.75 | 5.89 | 5.37 | 5.82 | 5.76 | 65001.0 | 69.0 | 374272.0 | 56240.0 | 86.52 | 0.52 | 0.07 |
| 2022-07-07 | 5.75 | 5.90 | 5.53 | 5.53 | 5.61 | 57607.0 | 116.0 | 323022.0 | 40746.0 | 70.73 | 0.37 | -0.22 |
| 2022-07-08 | 5.53 | 5.78 | 5.32 | 5.64 | 5.58 | 20970.0 | 91.0 | 116914.0 | 12148.0 | 57.93 | 0.46 | 0.11 |
246 rows × 12 columns
Get Data From JSON¶
In [5]:
import requests
r = requests.get('https://data.nasdaq.com/api/v3/datasets/BSE/BOM543207.json?api_key=x8VdDXzBty3iGsaRLTVs')
r
Out[5]:
<Response [200]>
In [6]:
import json
data = json.loads(r.content)
data
Out[6]:
{'dataset': {'id': 49659786,
'dataset_code': 'BOM543207',
'database_code': 'BSE',
'name': 'Natural Biocon (India) Ltd EOD Prices',
'description': 'End of Day prices and additional trading information for Natural Biocon (India) Ltd EOD Prices.',
'refreshed_at': '2022-07-08T13:33:24.401Z',
'newest_available_date': '2022-07-08',
'oldest_available_date': '2020-05-20',
'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': '2020-05-20',
'end_date': '2022-07-08',
'data': [['2022-07-08',
5.53,
5.78,
5.32,
5.64,
5.58,
20970.0,
91.0,
116914.0,
12148.0,
57.93,
0.46,
0.11],
['2022-07-07',
5.75,
5.9,
5.53,
5.53,
5.61,
57607.0,
116.0,
323022.0,
40746.0,
70.73,
0.37,
-0.22],
['2022-07-06',
5.75,
5.89,
5.37,
5.82,
5.76,
65001.0,
69.0,
374272.0,
56240.0,
86.52,
0.52,
0.07],
['2022-07-05',
5.35,
5.62,
5.35,
5.61,
5.57,
31991.0,
72.0,
178196.0,
26804.0,
83.79,
0.27,
0.26],
['2022-07-04',
5.55,
5.69,
5.35,
5.36,
5.54,
23313.0,
97.0,
129260.0,
14293.0,
61.31,
0.34,
-0.19],
['2022-07-01',
5.55,
5.84,
5.37,
5.42,
5.48,
24692.0,
84.0,
135354.0,
18515.0,
74.98,
0.47,
-0.13],
['2022-06-30',
5.86,
6.15,
5.61,
5.64,
5.82,
36684.0,
87.0,
213371.0,
30294.0,
82.58,
0.54,
-0.22],
['2022-06-29',
5.36,
5.89,
5.36,
5.86,
5.74,
79609.0,
124.0,
457203.0,
54825.0,
68.87,
0.53,
0.5],
['2022-06-28',
5.5,
5.61,
5.35,
5.61,
5.58,
31922.0,
98.0,
178190.0,
22685.0,
71.06,
0.26,
0.11],
['2022-06-27',
5.15,
5.35,
5.15,
5.35,
5.34,
59253.0,
98.0,
316193.0,
42280.0,
71.36,
0.2,
0.2],
['2022-06-24',
4.9,
5.1,
4.76,
5.1,
5.07,
34864.0,
94.0,
176652.0,
31317.0,
89.83,
0.34,
0.2],
['2022-06-23',
4.9,
5.04,
4.61,
4.86,
4.96,
82920.0,
119.0,
411273.0,
77288.0,
93.21,
0.43,
-0.04],
['2022-06-22',
5.25,
5.25,
4.79,
4.8,
4.84,
37475.0,
110.0,
181461.0,
34516.0,
92.1,
0.46,
-0.45],
['2022-06-21',
4.8,
5.04,
4.57,
5.04,
4.94,
49833.0,
151.0,
246184.0,
42101.0,
84.48,
0.47,
0.24],
['2022-06-20',
5.45,
5.68,
4.8,
4.8,
4.96,
213303.0,
286.0,
1058089.0,
139929.0,
65.6,
0.88,
-0.65],
['2022-06-17',
5.6,
5.96,
5.26,
5.33,
5.38,
113453.0,
191.0,
610540.0,
74928.0,
66.04,
0.7,
-0.27],
['2022-06-16',
6.08,
6.19,
5.6,
5.78,
5.89,
77493.0,
191.0,
456110.0,
69427.0,
89.59,
0.59,
-0.3],
['2022-06-15',
5.85,
5.89,
5.27,
5.86,
5.67,
61884.0,
194.0,
350579.0,
36949.0,
59.71,
0.62,
0.01],
['2022-06-14',
5.8,
5.99,
5.6,
5.64,
5.72,
120990.0,
237.0,
692365.0,
93125.0,
76.97,
0.39,
-0.16],
['2022-06-13',
6.94,
6.94,
6.04,
6.04,
6.11,
290003.0,
283.0,
1772474.0,
205602.0,
70.9,
0.9,
-0.9],
['2022-06-10',
6.52,
6.85,
5.66,
6.71,
6.38,
211772.0,
334.0,
1350862.0,
139459.0,
65.85,
1.19,
0.19],
['2022-06-09',
5.98,
6.45,
5.31,
6.23,
5.65,
332321.0,
520.0,
1877119.0,
167786.0,
50.49,
1.14,
0.25],
['2022-06-08',
6.74,
6.74,
5.85,
5.87,
5.98,
590459.0,
928.0,
3528511.0,
311751.0,
52.8,
0.89,
-0.87],
['2022-06-07',
7.3,
7.86,
6.44,
6.49,
7.09,
629409.0,
716.0,
4460418.0,
301481.0,
47.9,
1.42,
-0.81],
['2022-06-06',
7.64,
7.64,
7.13,
7.15,
7.25,
23895.0,
99.0,
173216.0,
14895.0,
62.34,
0.51,
-0.49],
['2022-06-03',
7.55,
7.55,
7.15,
7.46,
7.39,
14687.0,
71.0,
108595.0,
11010.0,
74.96,
0.4,
-0.09],
['2022-06-02',
7.79,
7.79,
7.2,
7.26,
7.33,
18705.0,
69.0,
137128.0,
14055.0,
75.14,
0.59,
-0.53],
['2022-06-01',
7.49,
7.58,
7.16,
7.52,
7.44,
71620.0,
148.0,
532727.0,
53978.0,
75.37,
0.42,
0.03],
['2022-05-31',
7.05,
7.22,
6.63,
7.22,
7.17,
76319.0,
157.0,
547008.0,
57291.0,
75.07,
0.59,
0.17],
['2022-05-30',
7.25,
7.39,
6.8,
6.88,
7.02,
24873.0,
122.0,
174566.0,
18296.0,
73.56,
0.59,
-0.37],
['2022-05-27',
7.14,
7.14,
6.66,
7.07,
6.9,
19436.0,
82.0,
134033.0,
12946.0,
66.61,
0.48,
-0.07],
['2022-05-26',
7.5,
7.5,
6.82,
6.89,
6.84,
34974.0,
178.0,
239300.0,
26023.0,
74.41,
0.68,
-0.61],
['2022-05-25',
7.75,
7.75,
7.03,
7.17,
7.17,
62994.0,
191.0,
451414.0,
42438.0,
67.37,
0.72,
-0.58],
['2022-05-24',
7.6,
7.6,
7.2,
7.4,
7.41,
14384.0,
96.0,
106599.0,
10261.0,
71.34,
0.4,
-0.2],
['2022-05-23',
7.53,
7.8,
7.07,
7.3,
7.48,
103170.0,
204.0,
771364.0,
78222.0,
75.82,
0.73,
-0.23],
['2022-05-20',
7.39,
7.54,
7.0,
7.44,
7.32,
28872.0,
136.0,
211214.0,
24190.0,
83.78,
0.54,
0.05],
['2022-05-19',
7.0,
7.25,
6.92,
7.19,
6.96,
143250.0,
253.0,
997117.0,
116862.0,
81.58,
0.33,
0.19],
['2022-05-18',
7.5,
7.64,
7.08,
7.28,
7.31,
99202.0,
248.0,
724940.0,
83261.0,
83.93,
0.56,
-0.22],
['2022-05-17',
7.8,
7.8,
7.44,
7.45,
7.45,
96480.0,
235.0,
719115.0,
73109.0,
75.78,
0.36,
-0.35],
['2022-05-16',
8.4,
8.45,
7.8,
7.83,
8.06,
141798.0,
208.0,
1142822.0,
122895.0,
86.67,
0.65,
-0.57],
['2022-05-13',
8.35,
8.35,
8.0,
8.21,
8.22,
38873.0,
139.0,
319351.0,
30597.0,
78.71,
0.35,
-0.14],
['2022-05-12',
7.6,
7.98,
7.3,
7.96,
7.91,
89535.0,
171.0,
708310.0,
82286.0,
91.9,
0.68,
0.36],
['2022-05-11',
8.05,
8.05,
7.51,
7.6,
7.67,
34410.0,
134.0,
263985.0,
22843.0,
66.38,
0.54,
-0.45],
['2022-05-10',
8.43,
8.43,
7.67,
7.88,
7.86,
70244.0,
193.0,
551833.0,
39767.0,
56.61,
0.76,
-0.55],
['2022-05-09',
8.07,
8.46,
7.7,
8.05,
8.0,
198528.0,
200.0,
1587792.0,
149741.0,
75.43,
0.76,
-0.02],
['2022-05-06',
8.0,
8.1,
7.6,
8.06,
7.96,
19309.0,
122.0,
153738.0,
15458.0,
80.06,
0.5,
0.06],
['2022-05-05',
8.25,
8.82,
7.99,
7.99,
8.28,
112613.0,
235.0,
932404.0,
70622.0,
62.71,
0.83,
-0.26],
['2022-05-04',
8.76,
8.94,
8.33,
8.41,
8.69,
55118.0,
173.0,
478963.0,
44205.0,
80.2,
0.61,
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['2022-05-02',
8.85,
8.85,
8.39,
8.76,
8.56,
114880.0,
186.0,
983804.0,
72516.0,
63.12,
0.46,
-0.09],
['2022-04-29',
9.3,
9.3,
8.64,
8.66,
8.75,
122242.0,
212.0,
1069373.0,
95001.0,
77.72,
0.66,
-0.64],
['2022-04-28',
8.93,
9.27,
8.58,
9.09,
8.96,
124151.0,
209.0,
1112363.0,
111239.0,
89.6,
0.69,
0.16],
['2022-04-27',
9.0,
9.3,
8.93,
8.93,
9.03,
94666.0,
217.0,
854928.0,
63478.0,
67.05,
0.37,
-0.07],
['2022-04-26',
10.05,
10.07,
9.39,
9.39,
9.42,
116860.0,
265.0,
1100543.0,
97140.0,
83.13,
0.68,
-0.66],
['2022-04-25',
10.25,
10.25,
9.5,
9.88,
9.85,
269783.0,
414.0,
2658360.0,
173688.0,
64.38,
0.75,
-0.37],
['2022-04-22',
9.4,
10.04,
9.3,
10.0,
9.97,
324541.0,
370.0,
3234496.0,
224339.0,
69.12,
0.74,
0.6],
['2022-04-21',
9.3,
9.57,
9.06,
9.57,
9.54,
109226.0,
105.0,
1042034.0,
65693.0,
60.14,
0.51,
0.27],
['2022-04-20',
9.8,
9.8,
9.0,
9.12,
9.2,
45779.0,
149.0,
421262.0,
30140.0,
65.84,
0.8,
-0.68],
['2022-04-19',
9.3,
9.74,
9.3,
9.43,
9.42,
56251.0,
128.0,
529949.0,
29838.0,
53.04,
0.44,
0.13],
['2022-04-18',
9.6,
9.89,
9.17,
9.35,
9.25,
80896.0,
182.0,
748619.0,
44437.0,
54.93,
0.72,
-0.25],
['2022-04-13',
10.2,
10.2,
9.51,
9.65,
9.64,
41914.0,
159.0,
403927.0,
24579.0,
58.64,
0.69,
-0.55],
['2022-04-12',
9.9,
10.25,
9.69,
10.01,
9.99,
14158.0,
94.0,
141493.0,
10957.0,
77.39,
0.56,
0.11],
['2022-04-11',
9.88,
10.18,
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'collapse': None,
'order': None,
'database_id': 3287}}
In [7]:
col = data['dataset']['column_names']
col
Out[7]:
['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']
In [8]:
df = pd.DataFrame(data['dataset']['data'],columns=col)
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 | 2022-07-08 | 5.53 | 5.78 | 5.32 | 5.64 | 5.58 | 20970.0 | 91.0 | 116914.0 | 12148.0 | 57.93 | 0.46 | 0.11 |
| 1 | 2022-07-07 | 5.75 | 5.90 | 5.53 | 5.53 | 5.61 | 57607.0 | 116.0 | 323022.0 | 40746.0 | 70.73 | 0.37 | -0.22 |
| 2 | 2022-07-06 | 5.75 | 5.89 | 5.37 | 5.82 | 5.76 | 65001.0 | 69.0 | 374272.0 | 56240.0 | 86.52 | 0.52 | 0.07 |
| 3 | 2022-07-05 | 5.35 | 5.62 | 5.35 | 5.61 | 5.57 | 31991.0 | 72.0 | 178196.0 | 26804.0 | 83.79 | 0.27 | 0.26 |
| 4 | 2022-07-04 | 5.55 | 5.69 | 5.35 | 5.36 | 5.54 | 23313.0 | 97.0 | 129260.0 | 14293.0 | 61.31 | 0.34 | -0.19 |
| … | … | … | … | … | … | … | … | … | … | … | … | … | … |
| 241 | 2020-06-09 | 13.12 | 13.70 | 13.12 | 13.70 | 13.40 | 20.0 | 2.0 | 268.0 | 20.0 | 100.00 | 0.58 | 0.58 |
| 242 | 2020-06-01 | 13.12 | 13.12 | 13.12 | 13.12 | 13.12 | 25.0 | 1.0 | 328.0 | 25.0 | 100.00 | 0.00 | 0.00 |
| 243 | 2020-05-28 | 13.77 | 13.77 | 13.12 | 13.12 | 13.33 | 15.0 | 2.0 | 200.0 | 15.0 | 100.00 | 0.65 | -0.65 |
| 244 | 2020-05-21 | 13.12 | 13.12 | 13.12 | 13.12 | 13.07 | 15.0 | 1.0 | 196.0 | 15.0 | 100.00 | 0.00 | 0.00 |
| 245 | 2020-05-20 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 151.0 | 1.0 | 1887.0 | 151.0 | 100.00 | 0.00 | 0.00 |
246 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 | ||||||||||||
| 2020-05-20 | 12.50 | 12.50 | 12.50 | 12.50 | 12.50 | 151.0 | 1.0 | 1887.0 | 151.0 | 100.00 | 0.00 | 0.00 |
| 2020-05-21 | 13.12 | 13.12 | 13.12 | 13.12 | 13.07 | 15.0 | 1.0 | 196.0 | 15.0 | 100.00 | 0.00 | 0.00 |
| 2020-05-28 | 13.77 | 13.77 | 13.12 | 13.12 | 13.33 | 15.0 | 2.0 | 200.0 | 15.0 | 100.00 | 0.65 | -0.65 |
| 2020-06-01 | 13.12 | 13.12 | 13.12 | 13.12 | 13.12 | 25.0 | 1.0 | 328.0 | 25.0 | 100.00 | 0.00 | 0.00 |
| 2020-06-09 | 13.12 | 13.70 | 13.12 | 13.70 | 13.40 | 20.0 | 2.0 | 268.0 | 20.0 | 100.00 | 0.58 | 0.58 |
| … | … | … | … | … | … | … | … | … | … | … | … | … |
| 2022-07-04 | 5.55 | 5.69 | 5.35 | 5.36 | 5.54 | 23313.0 | 97.0 | 129260.0 | 14293.0 | 61.31 | 0.34 | -0.19 |
| 2022-07-05 | 5.35 | 5.62 | 5.35 | 5.61 | 5.57 | 31991.0 | 72.0 | 178196.0 | 26804.0 | 83.79 | 0.27 | 0.26 |
| 2022-07-06 | 5.75 | 5.89 | 5.37 | 5.82 | 5.76 | 65001.0 | 69.0 | 374272.0 | 56240.0 | 86.52 | 0.52 | 0.07 |
| 2022-07-07 | 5.75 | 5.90 | 5.53 | 5.53 | 5.61 | 57607.0 | 116.0 | 323022.0 | 40746.0 | 70.73 | 0.37 | -0.22 |
| 2022-07-08 | 5.53 | 5.78 | 5.32 | 5.64 | 5.58 | 20970.0 | 91.0 | 116914.0 | 12148.0 | 57.93 | 0.46 | 0.11 |
246 rows × 12 columns