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Editor to share with you how to use Python to analyze the recent stock market. I hope you will get something after reading this article. Let's discuss it together.

I. data acquisition

The range of data obtained is from January 1, 2022 to February 25, 2022. The data obtained are Russian gold, silver, oil, banking and natural gas:

# Import module import numpy as npimport pandas as pdimport yfinance as yf# GC=F Gold, SI=F Silver, ROSN.ME Rosneft, SBER.ME Russian Bank Natural gas tickerSymbols = ['GC=F',' SI=F', 'ROSN.ME',' SBER.ME','NG=F'] # get the data of these codes MSFT = yf.Ticker (tickerSymbols [0]) TSLA = yf.Ticker (tickerSymbols [1]) AAPL = yf.Ticker (tickerSymbols [2]) AMZN = yf.Ticker (tickerSymbols [3]) GOOG = yf.Ticker (tickerSymbols [4]) # get the historical price of the code MSFT_df = MSFT.history (period='1d', start='2022-1-1' End='2022-2-25') TSLA_df = TSLA.history (period='1d', start='2022-1-1, end='2022-1-25') AAPL_df = AAPL.history (period='1d', start='2022-1-1, end='2022-1-25') AMZN_df = AMZN.history (period='1d', start='2022-1-1, end='2022-1-25') GOOG_df = GOOG.history (period='1d', start='2022-1-1') End='2022-1-25') # such as natural gas GOOG_df.head ()

As follows:

2. Merge data # save the closing column of each stock in the new variable MSFT = MSFT_df ['Close'] TSLA = TSLA_df [' Close'] AAPL = AAPL_df ['Close'] AMZN = AMZN_df [' Close'] GOOG = GOOG_df ['Close'] # Concatenate all stocks close columns into one data framestocks_df = pd.concat ([MSFT, TSLA, AAPL, AMZN, GOOG], axis='columns' Join='inner') # Rename the data frame columns with their corresponding tickers symbolsstocks_df.columns = ['gold',' silver', 'oil',' bank', 'gas'] # Visualize the new data framestocks_df.head ()

As follows:

Third, chart the daily percentage changes of stocks # next, let's calculate the daily percentage changes of stocks and plot them to visually analyze their changing behavior over the past month or so. # get daily percentage change stocks_df = stocks_df.pct_change (). Dropna () # Visualization new data box stocks_df.head () # draw daily percentage change stocks_df.plot (figsize= (20,10), title= "Daily Returns")

As follows:

From the picture, you can also see which change is big and which is stable.) look at it with your own eyes.

IV. Box diagram

The picture above really doesn't look good, so it's more intuitive to draw a box diagram:

# Box line diagram # calculate cumulative return cumulative_returns = (1 + stocks_df). Cumprod () # draw cumulative return cumulative_returns.plot (figsize= (20,10), title= "Cumulative Returns"); # Box chart visually shows risk stocks_df.plot.box (figsize= (20,10), title= "Portfolio Risk")

Cumulative return chart:

Box diagram:

Natural gas is clearly the most profitable; the wider the box and the longer the whisker, the more volatile stocks become. Oil is the most stable, natural gas fluctuates a little too much.

5. Calculate monthly Sharpe ratio # calculate monthly Sharpe ratio sharpe_ratios = (stocks_df.mean () * 30) / (stocks_df.std () * np.sqrt (30)) sharpe_ratios = sharpe_ratios.sort_values (ascending=False) sharpe_ratios

As follows:

# visualize the Sharpe ratio as a bar chart sharpe_ratios.plot (figsize= (20,10), kind= "bar", title= "Sharpe Ratios")

As follows:

VI. Conclusion

Based on the above results, it is recommended to buy gold, followed by silver, natural gas, Russian banks and oil are not recommended. Note: the above analysis data are from January 1, 2022 to February 5, 2022. Did not write the prediction part, I think there is too much turbulence, there is no need to predict, buy a stable bar, prediction is no longer meaningful.

After reading this article, I believe you have a certain understanding of "how to use Python to analyze the recent stock market". If you want to know more about it, you are welcome to follow the industry information channel. Thank you for reading!

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