mutual-funds/Functions at master · projecthawkeye/mutual-funds · GitHub

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import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
def risk_metric(df):

  df['stdev3'] = 0.0
  df['stdev5'] = 0.0
  df['stdev10'] = 0.0
  for i in range(0,len(df)):
    df['stdev3'][i] = np.std([df['Return 1 Year (%) Regular'][i], df['Return 3 Year (%) Regular'][i]])
    df['stdev5'][i] = np.std([df['Return 1 Year (%) Regular'][i], df['Return 3 Year (%) Regular'][i], df['Return 5 Year (%) Regular'][i]])
    df['stdev10'][i] = np.std([df['Return 1 Year (%) Regular'][i], df['Return 3 Year (%) Regular'][i], df['Return 5 Year (%) Regular'][i], df['Return 10 Year (%) Regular'][i]])

  max_3 = df['stdev3'].max()
  max_5 = df['stdev5'].max()
  max_10 = df['stdev10'].max()

  df['Risk Score of 3 Years'] = 0.0
  df['Risk Score of 5 Years'] = 0.0
  df['Risk Score of 10 Years'] = 0.0
  df['Risk'] = 0.0
  df['RiskTag'] = 0.0
  df['Returns'] = 0.0
  df['ReturnsTag'] = 0.0

  for i in range(0,len(df)):
    df['Risk Score of 3 Years'][i] = ((df['stdev3'][i])/max_3)*10.0
    df['Risk Score of 5 Years'][i] = ((df['stdev5'][i])/max_5)*10.0
    df['Risk Score of 10 Years'][i] = ((df['stdev10'][i])/max_10)*10.0
    df['Returns'][i] = (df['Return 1 Year (%) Regular'][i] + df['Return 3 Year (%) Regular'][i] + df['Return 5 Year (%) Regular'][i] + df['Return 10 Year (%) Regular'][i])/4
    df['Risk'][i] = (df['Risk Score of 3 Years'][i]+df['Risk Score of 5 Years'][i]+df['Risk Score of 10 Years'][i])/3

  mean=df['Risk'].mean()
  std=df['Risk'].std()
  mean1=df['Returns'].mean()
  std1=df['Returns'].std()
  for i in range(0,len(df)):
    if(df['Risk'][i]>(mean+0.2*std)):
        df['RiskTag'][i]=3
    if(df['Risk'][i]<(mean-0.2*std)):
        df['RiskTag'][i]=1
    else:
        if(df['Risk'][i]>(mean-0.2*std) and df['Risk'][i]<(mean+0.2*std)):
            df['RiskTag'][i]=2
    if(df['Returns'][i]>(mean1+0.2*std1)):
        df['ReturnsTag'][i]=3
    if(df['Returns'][i]<(mean1-0.2*std1)):
        df['ReturnsTag'][i]=1
    else:
        if(df['Returns'][i]>(mean1-0.2*std1) and df['Returns'][i]<(mean1+0.2*std1)):
            df['ReturnsTag'][i]=2


  Q1=eval(input("Select your goal of investing (1 for WealthGrowth (fixed risk & maximum returns) OR 2 for FixedEarnings (fixed returns & minimum risk))"))

  def RiskFix(df):


        Q2=eval(input("Risk Tolerence (3-High Risk, 2-Moderate Risk, 1-Low Risk)"))
        if (Q2>3 or Q2<1):
            print ("Please Enter 1,2 or 3")
        else:
            test1=(df['RiskTag']==Q2)
            test3=df[test1]
            test4=test3[['Scheme Name','Returns']]
            return test4


  def ReturnFix(df):
        Q3=eval(input("Enter minimum return required (in %)"))

        test2=(df['Returns']>=Q3)
        test3=df[test2]
        test4=test3[['Scheme Name','Risk']]
        return test4


  if(Q1==1):
       r = df.sort_values(by='Returns', ascending=False)
       p=RiskFix(r)
  else:
      if(Q1==2):
            r = df.sort_values(by='Risk', ascending=True)

            p=ReturnFix(r)
      else:
        print("Enter 1 or 2")
  return p
  df = pd.read_csv('1.csv', delimiter=',')
  risk_metric(df)