Data analysis --- analyze the stock using Tushare

 

Demand：

• Use tushare historical market data package to obtain a stock.
• The output of the stock of all closed more than 3% than the date opened up.
• The output of the shares opened 2% of all date than the day before closing down more than.
• If I start from January 1, 2010, the first trading day each month to buy a hand stock, sell all the stock last trading day of each year, to date, how my earnings?

Use tushare get a package of historical stock market data

• pip install tushare

import tushare as ts
df = ts.get_k_data('600519',start='1900-01-01')

df.to_csv('./maotai.csv')

# Outputs the stock closed than opened up to all more than 3% of the date 

DF = pd.read_csv ( ' ./maotai.csv ' )
df.drop(labels='Unnamed: 0',axis=1,inplace=True)

# Verification data type of data in the column data 
type (DF [ ' DATE ' ] [. 9 ])

# The date data is converted into the first row of this type was then time as the original data row index 
DF = pd.read_csv ( ' ./maotai.csv ' , index_col = ' date ' , parse_dates = [ ' date ' ])
df.drop(labels='Unnamed: 0',axis=1,inplace=True)

# (Closing - opening) / opening> 0.03 
(df [ ' use Close ' ] - df [ ' Open ' ]) / df [ ' Open ' ]> 0.03
 # True: meet 
# false: not satisfied

# Returns the row of data needs 
df.loc [(df [ ' Close ' ] - DF [ ' Open ' ]) / DF [ ' Open ' ]> 0.03 ]
 # acquired date to meet the needs of 
df.loc [(df [ ' Close ' ] - DF [ ' Open ' ]) / DF [ ' Open ' ]> 0.03 ] .index

# Conclusion: If the row index acquired a set of Boolean values, Boolean shuffling followed directly as meta data 

(DF [ ' Open ' ] - DF [ ' Close ' ] .shift (. 1)) / DF [ ' Close ' ] .shift (1) <-0.02

# Line data to meet the demand 
df.loc [(DF [ ' Open ' ] - DF [ ' Close ' ] .shift (. 1)) / DF [ ' Close ' ] .shift (. 1) <-0.02 ] .index



df_new = DF [ ' 2010 ' : ' 2019 ' ]
 # resampled data mechanisms (resample): designating data in accordance with specified rules good extraction 

df_monthly = df_new.resample ( ' M ' ) .first ()
 # calculated buy stocks spent a total of how much money 

cost_monry = df_monthly [ ' Open ' ] .sum () * 100 
df_yearly = df_new.resample ( ' a ' ) .last ()

df_yearly = df_yearly[:-1]

recv_monry = df_yearly['open'].sum()*1200
last_price = df.iloc[-1]['close']

cunHuo_price = last_price * 900

# Calculate total revenue 
cunHuo_price + recv_monry-cost_monry