This post shows how to read prices of stock prices with a list of symbols as a string using Python. Splitting data by price types or symbols are illustrated as examples.
Python Jupyter notebook code
The following code downloads collection of stock prices.
import yfinance as yf import pandas as pd symbols = ['^GSPC','^VIX', '^FTSE', '^N225', '^HSI'] data = yf.download(symbols, start='2022-12-01', end ='2022-12-06') print(data)
[*********************100%***********************] 5 of 5 completed
Adj Close \
^FTSE ^GSPC ^HSI ^N225 ^VIX
Date
2022-12-01 7558.500000 4076.570068 18736.439453 28226.080078 19.840000
2022-12-02 7556.200195 4071.699951 18675.349609 27777.900391 19.059999
2022-12-05 7567.500000 3998.840088 19518.289062 27820.400391 20.750000
Close \
^FTSE ^GSPC ^HSI ^N225 ^VIX
Date
2022-12-01 7558.500000 4076.570068 18736.439453 28226.080078 19.840000
2022-12-02 7556.200195 4071.699951 18675.349609 27777.900391 19.059999
2022-12-05 7567.500000 3998.840088 19518.289062 27820.400391 20.750000
... Open \
... ^FTSE ^GSPC ^HSI ^N225
Date ...
2022-12-01 ... 7573.100098 4087.139893 19058.900391 28273.130859
2022-12-02 ... 7558.500000 4040.169922 18785.279297 27983.179688
2022-12-05 ... 7556.200195 4052.020020 19221.679688 27752.990234
Volume
^VIX ^FTSE ^GSPC ^HSI ^N225 ^VIX
Date
2022-12-01 20.830000 642843000 4527130000 4262000300 71400000 0
2022-12-02 20.420000 540219900 4012620000 3757394000 79400000 0
2022-12-05 20.299999 509145400 4280820000 4890142300 63900000 0
[3 rows x 30 columns]
We can split data by type of prices such as Adj Close, Close, Open, and the like.
# Splitting the downloaded data into separate DataFrames
adj_close_df = data['Adj Close']
close_df = data['Close']
high_df = data['High']
low_df = data['Low']
open_df = data['Open']
volume_df = data['Volume']
# Printing the separate DataFrames
print("Adj Close:"); print(adj_close_df.round(2))
print("\nClose:"); print(close_df.round(2))
print("\nHigh:"); print(high_df.round(2))
print("\nLow:"); print(low_df.round(2))
print("\nOpen:"); print(open_df.round(2))
print("\nVolume:"); print(volume_df)
Adj Close:
^FTSE ^GSPC ^HSI ^N225 ^VIX
Date
2022-12-01 7558.5 4076.57 18736.44 28226.08 19.84
2022-12-02 7556.2 4071.70 18675.35 27777.90 19.06
2022-12-05 7567.5 3998.84 19518.29 27820.40 20.75
Close:
^FTSE ^GSPC ^HSI ^N225 ^VIX
Date
2022-12-01 7558.5 4076.57 18736.44 28226.08 19.84
2022-12-02 7556.2 4071.70 18675.35 27777.90 19.06
2022-12-05 7567.5 3998.84 19518.29 27820.40 20.75
High:
^FTSE ^GSPC ^HSI ^N225 ^VIX
Date
2022-12-01 7599.7 4100.51 19237.45 28423.46 21.06
2022-12-02 7570.5 4080.48 18841.22 27983.18 20.96
2022-12-05 7598.2 4052.45 19539.60 27854.11 21.29
Low:
^FTSE ^GSPC ^HSI ^N225 ^VIX
Date
2022-12-01 7552.3 4050.87 18679.35 28226.08 19.80
2022-12-02 7508.0 4026.63 18530.82 27662.12 18.95
2022-12-05 7547.8 3984.49 19035.14 27700.86 19.78
Open:
^FTSE ^GSPC ^HSI ^N225 ^VIX
Date
2022-12-01 7573.1 4087.14 19058.90 28273.13 20.83
2022-12-02 7558.5 4040.17 18785.28 27983.18 20.42
2022-12-05 7556.2 4052.02 19221.68 27752.99 20.30
Volume:
^FTSE ^GSPC ^HSI ^N225 ^VIX
Date
2022-12-01 642843000 4527130000 4262000300 71400000 0
2022-12-02 540219900 4012620000 3757394000 79400000 0
2022-12-05 509145400 4280820000 4890142300 63900000 0
We can also split data by each symbols.
# Create a MultiIndex from the columns
data.columns = data.columns.swaplevel(0, 1)
data.sort_index(axis=1, level=0, inplace=True)
# Split the data based on symbols
symbol_dfs = {}
for symbol in symbols:
# Create a copy of the DataFrame
symbol_dfs[symbol] = data[symbol].copy()
# Divide 'Volume' column by 1000
symbol_dfs[symbol]['Volume'] /= 1000000
symbol_dfs[symbol] = symbol_dfs[symbol].round(0)
# Print the separate DataFrames
for symbol, df in symbol_dfs.items():
print(f"Data for symbol: {symbol}")
print(df)
Data for symbol: ^GSPC
Adj Close Close High Low Open Volume
Date
2022-12-01 4077.0 4077.0 4101.0 4051.0 4087.0 4527.0
2022-12-02 4072.0 4072.0 4080.0 4027.0 4040.0 4013.0
2022-12-05 3999.0 3999.0 4052.0 3984.0 4052.0 4281.0
Data for symbol: ^VIX
Adj Close Close High Low Open Volume
Date
2022-12-01 20.0 20.0 21.0 20.0 21.0 0.0
2022-12-02 19.0 19.0 21.0 19.0 20.0 0.0
2022-12-05 21.0 21.0 21.0 20.0 20.0 0.0
Data for symbol: ^FTSE
Adj Close Close High Low Open Volume
Date
2022-12-01 7558.0 7558.0 7600.0 7552.0 7573.0 643.0
2022-12-02 7556.0 7556.0 7570.0 7508.0 7558.0 540.0
2022-12-05 7568.0 7568.0 7598.0 7548.0 7556.0 509.0
Data for symbol: ^N225
Adj Close Close High Low Open Volume
Date
2022-12-01 28226.0 28226.0 28423.0 28226.0 28273.0 71.0
2022-12-02 27778.0 27778.0 27983.0 27662.0 27983.0 79.0
2022-12-05 27820.0 27820.0 27854.0 27701.0 27753.0 64.0
Data for symbol: ^HSI
Adj Close Close High Low Open Volume
Date
2022-12-01 18736.0 18736.0 19237.0 18679.0 19059.0 4262.0
2022-12-02 18675.0 18675.0 18841.0 18531.0 18785.0 3757.0
2022-12-05 19518.0 19518.0 19540.0 19035.0 19222.0 4890.0
Originally posted on SHLee AI Financial Model blog.
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