【Application(13)】Momentum select
along for the ride
Highlights
- Difficulty：★★☆☆☆
- Advice: This article adopts TEJ database to select the stock. Before reading this, 【Quant(15)】 Momentum trade is recommended to explore to better understand the basic procedure of backtesting.
Preface
we introduce the Momentum trade last time ,now we are going to teach you how to find this stock ,if we are not highly involved the marker,we often found in the news which already price in ,so what we want to do is find the stock automatic.It also can save our time .
we write the function more flexible.you can change the parameter by yourself ! Following is the way to our set the parameter :
- when the trading volume is 5 times more than the average of previous 4 days.
- stock price rise at least 3%
- the trade volume is more than 500
The Editing Environment and Modules Required
Window10 Spyder(anaconda31)
Database
Security Transaction Data Table：Listed securities with unadjusted price and index. Code is ‘TWN/EWPRCD’
Securities attribute information Data Table : Listed securities industry category and name， Code is 'TWN/ANPRCSTD'
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
#################TEJ
import tejapi
tejapi.ApiConfig.api_key = 'Your Key'
tejapi.ApiConfig.ignoretz = True
###############draw k line
from mplfinance.original_flavor import candlestick_ohlc
from matplotlib.dates import date2num ,num2date
import matplotlib.ticker as ticker
import matplotlib.datesData Selection
data=tejapi.get('TWN/EWNPRCSTD' ,chinese_column_name=True )
select=data["上市別"].unique()
select=select[1:3]
condition =(data["上市別"].isin(select)) & ( data["證券種類名稱"]=="普通股" )
data=data[condition] ######設條件
twid=data["證券碼"].to_list()select [‘coid’,’open_d’,’close_d’,’high_d’,’low_d’ ,’mdate’, ‘volume’, ‘close_adj’ ]
opts={'columns': ['coid','open_d','close_d','high_d','low_d' ,'mdate', 'volume', 'close_adj']}
start='2022-1-01'
end="2022-03-8"
tw=tejapi.get('TWN/EWPRCD',coid=twid,
mdate={'gt':start,'lt':end},
paginate=True,
chinese_column_name=True,
opts=opts
)
a=tw
a=tw.groupby(by=["證券碼"])
b=list(a) #######ˇuse the pandas groupby function , and we have to put there into a list ,so we can observe the data
we should open it
Data Processing
Step 1. set the indicator
def selectstock(b,number,minnum,num) : # b為用groupby做出來的list
output=[]
for i in range(len(b)):
a=b[i][1]
a["五日均量"]=a["成交量(千股)"].rolling(5).sum()
a["五日均價"]=a["收盤價-除權息"].rolling(5).mean()
a["前幾日平均"]=(a["五日均量"]-a["成交量(千股)"]) / 4
a["成交量"+str(number)+"倍喔"]=a["成交量(千股)"]-a['前幾日平均'] *number
a.drop('五日均量',axis=1)
if a["成交量(千股)"].mean() > minnum :
output.append(a)
stockineed=[]for j in output:
j.reset_index(drop=True,inplace=True)
if j["成交量"+str(number)+"倍喔"][len(j)-1] > 0 :
if j["收盤價-除權息"][len(j)-1] > j["收盤價-除權息"][len(j)-2]*num :
stock=j["證券碼"][0]
stockineed.append(stock)
return stockineed
this function have 3 parameter (number,minnum,num)
number : select 5 times more than previous four day
minnum : the trade volume minium we select ”500"
num : stock price growth percentage we select 3 %
stockineed=selectstock(b,5,500,1.03)
there is 8 stocks confrom to our conditions
condition=tw["證券碼"].isin(stockineed)
tw1=tw[condition]
a=tw1.groupby("證券碼")
a=list(a)
for i in a:
i[1].set_index("日期",inplace=True) ###set date to indexuse the groupby function again and we can see each group of the stcok
Step 2 visualize the stocks
we try to get the stock name and the catagory in other database ,so we can combine all of them in one picture
opts={'columns': ['coid','stk_name','mkt','tejindnm',]}
data1=tejapi.get('TWN/ANPRCSTD' ,
opts=opts,
chinese_column_name=True,paginate=True,
coid=stockineed)
def getplot(finaldata):for i in range(len(finaldata)):
out=finaldata[i][1]
name=data1[data1["證券碼"]==finaldata[i][0]]
outputname0=name.loc[i,'證券名稱']##為了得到標題
outputname1=name.loc[i,'上市別'] ##
outputname2=name.loc[i,"TEJ產業名"]##
# out.reset_index(drop=True,inplace=True)
if outputname1 == "TSE":
outputname1 ="上市"
else :
outputname1= "上櫃"
fig = plt.figure(figsize=(6,6))
grid = plt.GridSpec(3, 3, wspace=0.4, hspace=0.3)
ax1=plt.subplot(grid[:2, :])
ax2=plt.subplot(grid[2, :])
date= [i for i in range(len(out))]
##### 獲取 0開始的順序 因為如果使用原日期假日會有空缺
out_index= [tuple([date[i],out.開盤價[i],out.最高價[i],out.最低價[i],out.收盤價[i]]) for i in range(len(out))]
candlestick_ohlc(ax1, out_index, width=0.6, colorup='r', colordown='g', alpha=0.75)ax1.set_xticks(range(0, len(out.index), 10))
ax1.set_xticklabels(out.index[::10])
ax1.set_title([str(out["證券碼"][0]),outputname0,outputname1,outputname2])
ax1.set_ylabel('Price')
ax1.grid(linestyle="--",alpha=0.8)
red_pred = np.where(out["收盤價"] >= out["開盤價"],out["成交量(千股)"], 0)
blue_pred = np.where(out["收盤價"] < out["開盤價"], out["成交量(千股)"], 0)
out1=out.reset_index(drop=True )
ax2.bar(out1.index,red_pred, facecolor="red")
ax2.bar(out1.index,blue_pred,facecolor="green")
ax2.set_xticks(range(0, len(out.index), 5))
ax2.set_xticklabels(out.index[::5])
ax2.set_ylabel('vol')plt.legend(loc='best')
fig.autofmt_xdate()
fig.tight_layout()
plt.show()
in this finction we can plot the stocks which have their price and volumes
getplot(a) ###前面聚合後的List(a)
here is the stock~ maybe we can add some ma line to make the picture more abundant,and you can use this to seclct stock everyday~
Conclusion
By using this function we still can find the strong stock in this period,so maybe this is the way to find the stocks.We can also match the strategy that we introduce last time . Maybe can bring high rate of return!
After all, the application of technical indicator varies from person to person. As a result, if readers are interested in diverse trading backtesting, welcome to purchase the plan offered in TEJ E-Shop. Construct trading strategies fitting you with high quality database.
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