ASX shares find out best performance shares

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https://steemit.com/invest/@chenlocus/asx-shares-find-out-best-performance-shares

In this post, I am going to pick up those companies whose share price increased at least 10%. I will figured out the percentage of these companies in their own industries.

import matplotlib.pyplot as plt
import pandas as pd
import csv
import numpy as np

def DrawBar(df,title_msg):
plt.figure(figsize=(8,4))
label = df['industry']
cmpcount = df['Company Count']
index = np.arange(len(label))
plt.bar(index, cmpcount,0.35)
plt.xlabel('Industry', fontsize=20)
plt.ylabel('Number of Companies', fontsize=20)
plt.xticks(index, label, fontsize=20, rotation=90)
plt.title(title_msg)
plt.show()


def DrawPie(slice_list,labels,colors,title_msg):
plt.style.use('ggplot')
plt.figure(figsize=(8,4))
#explode = (0,0.05,0.08,0.10,0,0,0)
plt.pie(slicelist,
#explode=explode,
labels=labels,colors=colors,
labeldistance = 1.15,autopct = '%3.1f%%',shadow = False,
startangle = 90,pctdistance = 0.6,counterclock = False)
plt.axis('equal')
plt.title(titlemsg,y = 1.08)
plt.show()

​

dataFile = './shares_likely_jump_cashburn_filter.csv'
df =pd.read_csv(dataFile,header=0,index_col=0)
df.rename(columns={"Asx Code.1": "Asx Code"},inplace=True)
print(df.columns)

df_category = df.groupby('industry').agg({'Company Name':'count'}).rename(columns={'Company Name':'Company Count'}).reset_index()
print('all shares is',df.shape[0])
print(df_category[df_category['industry']=='Banks'])

df10 = df[(df['pricediff']>=10)]

df0_neg20 = df[(df['pricediff']<=-20)]

df10_group = df10.groupby('industry').agg({'Company Name':'count'}).rename(columns={'Company Name':'Company Count'}).reset_index()

df_merged = pd.merge(df10_group, df_category, how='inner', on=['industry'])

df_merged['Company Count'] = df_merged['Company Count_x']/df_merged['Company Count_y']*100

df_merged.drop(['Company Count_x', 'Company Count_y'], axis=1,inplace=True)

print(df_merged)

"""
We are going to draw bar chart to indicate percentage of companies with outstanding price changes in the industries.
"""

DrawBar(df_merged,'The Distribution of Companies ( percentage in their own industries) with Share Price climing more than 10% in the next Year')

This is all those industries and the percentage of companies in these area:


industry Company Count
0 Automobiles & Components 33.333333
1 Capital Goods 21.052632
2 Commercial & Professional Services 35.294118
3 Consumer Services 33.333333
4 Diversified Financials 22.727273
5 Energy 32.352941
6 Food, Beverage & Tobacco 28.571429
7 Health Care Equipment & Services 36.363636
8 Household & Personal Products 25.000000
9 Insurance 50.000000
10 Materials 42.647059
11 Media 10.000000
12 Not Applic 21.428571
13 Pharmaceuticals, Biotechnology & Life Sciences 26.470588
14 Real Estate 30.303030
15 Retailing 38.461538
16 Semiconductors & Semiconductor Equipment 100.000000
17 Software & Services 26.153846
18 Technology Hardware & Equipment 23.076923
19 Telecommunication Services 22.222222
20 Transportation 100.000000




We can see there are two industries in which all companies inside had good share price performance, let's see who they are and their price trend in the market:

df10[df['industry']=='Semiconductors & Semiconductor Equipment']

| Asx Code | Company Name | Finacial Year | Revenue Increase % | industry | pricediff |
| -------- | ------------------ | ------------- | ------------------ | ---------------------------------------- | --------- |
| 4DS | 4DS MEMORY LIMITED | 6/30/2016 | 1053815.0 | Semiconductors & Semiconductor Equipment | 13.725100 |
| BLG | BLUGLASS LIMITED | 6/30/2017 | 86.0 | Semiconductors & Semiconductor Equipment | 24.898013 |





df10[df['industry']=='Transportation']

| Asx Code | Company Name | Finacial Year | Revenue Increase % | industry | pricediff |
| -------- | ------------- | ------------- | ------------------ | -------------- | --------- |
| ALX | ATLAS ARTERIA | 12/31/2016 | 48.0 | Transportation | 13.94639 |