zipline Function to buy and sell stocks

1 What about this article?

Introducing commands to buy and sell stocks.

2 Contents

Get the csv files for the brands n1570 and n7752 used to run the code get here

2-1 zipline.api.order Purchase the specified number of shares.

test.py


from zipline.api import order, record, symbol,set_benchmark
import pandas as pd
from datetime import datetime
import zipline
import pytz  #timezone settings https://narito.ninja/blog/detail/81/
from trading_calendars import get_calendar #Import the calendar of each exchange
from collections import OrderedDict
import seaborn as sns
import matplotlib.dates as mdates
import matplotlib.pyplot as plt


###### (1)Initial setting#######

HDIR="xxxxxxxxxxxxxxxxxx" #Specify the directory where the brand data csv file is stored.
data=OrderedDict() #Give order to the ordered dictionary.
tickers=["n1570","n7752"]  #Specify the brand name.


###### (2)Read the stock price of a stock from a csv file######

for ticker in tickers:
    DIR=HDIR + ticker +".csv" #Brand to read(csv file)To specify.
    data[ticker]= pd.read_csv(DIR, index_col=0,parse_dates=True) #Read the csv file.

###### (3)Prepare the data set.###########

panel=pd.Panel(data)  #Put the brand data in the 3D array panel.
panel.major_axis=panel.major_axis.tz_localize(pytz.utc) #Set the time to the UTC zone.(An error will occur if the UTC zone is not set for convenience.)


###### (4)Description of trading algorithm#########

def initialize(contect):
    set_benchmark(symbol("n1570")) #Designate brand n1570 as a benchmark.


def handle_data(context,data):
    order(symbol("n1570"),1) #Buy one share every day at the close.
    record(N1570=data.current(symbol("n1570"),"price")) #Record the close value of issue n1570.


###### (5)Perform backtesting#########    
    
#Specify start date and time and end date and time
starttime=datetime(2020,2,4,0,0,0,0,pytz.utc)
endtime=datetime(2020,2,8,0,0,0,0,pytz.utc)    
    
#Run backtest.(Buy one share of brand n1570 at the close every day.)
perf=zipline.run_algorithm(start=starttime,
                            end=endtime,
                            initialize=initialize,
                            capital_base=1000000, #Specify the asset at the start.
                            handle_data= handle_data,
                            data=panel,
                            trading_calendar=get_calendar('XTKS') #Read the Tokyo Stock Exchange calendar
                           )

dat0=pd.DataFrame(perf,columns=["N1570","ending_cash","ending_exposure"])

dat0.to_csv("C:/Users/fdfpy/anaconda3/backtestresult/dat0.csv")
print(dat0)

The execution result is as follows.

python


[4 rows x 38 columns]
                           N1570  ending_cash  ending_exposure
2020-02-04 06:00:00+00:00  21240  1000000.000              0.0
2020-02-05 06:00:00+00:00  21680   978309.159          21680.0
2020-02-06 06:00:00+00:00  22750   955547.783          45500.0
2020-02-07 06:00:00+00:00  22630   932906.467          67890.0

(note)Description of each line
N1570           :CLOSE value of brand N1570
ending_cash     :Cash on hand
ending_exposure :Valuation value of stocks held

2-2 zipline.api.order_percent Purchases stocks within the specified ratio of cash on hand. Of the code posted in 2-1 only, the part "(4) Description of trading algorithm" is posted. (Other parts are the same)

test.py


###### (4)Description of trading algorithm#########

def initialize(contect):
    set_benchmark(symbol("n1570")) #Designate brand n1570 as a benchmark.


def handle_data(context,data):

    zipline.api.order_percent(symbol("n1570"),0.1) #Close every day and percent of all assets(=10%)Buy the stock that corresponds to
    record(N1570=data.current(symbol("n1570"),"price")) #Record the close value of issue n1570.

python


[4 rows x 38 columns]
                           N1570  ending_cash  ending_exposure
2020-02-04 06:00:00+00:00  21240  1000000.000              0.0
2020-02-05 06:00:00+00:00  21680   913236.636          86720.0
2020-02-06 06:00:00+00:00  22750   822191.132         182000.0
2020-02-07 06:00:00+00:00  22630   731625.868         271560.0

(Description)
Daily assets(ending_cash)10 of%I am buying the stock price of the amount of.(Fractions are truncated)

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