best_five_cases.py

Results=pd.DataFrame([])
sns.set(font_scale=1.2)
for i in matrix:
    results1=pd.read_csv('Results'+str(i)+'.csv')
    Positive_Fees=(i[0]+100)*0.00075+100*0.00075
    Negative_Fees=(i[1]+100)*0.00075+100*0.00075
    No_Outcome_Fees=200*0.00075
    results1['Return']=(i[0]-Positive_Fees)*results1['Success rate']*results1['Quantity']+(i[1]-Positive_Fees)*(results1['Quantity']-results1['Success rate']*results1['Quantity'])+((i[0]+i[1])/7-No_Outcome_Fees)*results1['Quantity']*results1['no_outcome']
    results1['Combination']=str(i[0])+', '+str(i[1])
    results1.drop(results1.columns[0],inplace=True,axis=1)
    Results=Results.append(results1,ignore_index=True)
selected_lines=Results.sort_values(by='Return',ascending=False)['Combination'].unique()[0:5]
from celluloid import Camera
camera = Camera(plt.figure(figsize=(11,7)))
plt.title('Models Return')
plt.xlabel('Models Return')
plt.ylabel('Models Return')

for i in range(75):
    print(i)
    line=Results[(Results['Combination'].isin(selected_lines)) & (Results['P(p)>']<0.16+i/100)]
    if(i==1):
        sns.lineplot(data=line,x='P(p)>',y='Return',hue='Combination')
    else:
        sns.lineplot(data=line,x='P(p)>',y='Return',hue='Combination',legend=False,lw=0.5)
        sns.scatterplot(data=line,x='P(p)>',y='Return',hue='Combination',legend=False)
        #plt.plot('P(p)>','Return',data=line[line['Combination']==j],lw=0.5)
        

    camera.snap()
print('a')
anim = camera.animate(blit=True,interval=100)
print('a')
anim.save('scatter'+str(i)+'.gif')