Added indicator like sd and sharpe

.gitignore

@@ -0,0 +1,2 @@
+#ignore python cache files
+*.pyc

app.py

@@ -35,8 +35,8 @@ def get_portfolio_report(*args):
     stepup = args[6]
     schemes_df = args[7]
 
+    # Extract scheme names and weights, into a dictionary from the args
     scheme_name_and_weight = {}
-    
     for i in range(8, len(args) - 1, 2):
         if args[i] and args[i+1]:
             scheme_name_and_weight[args[i]] = float(args[i+1])
@@ -71,22 +71,8 @@ def get_portfolio_report(*args):
             months = int(period_parts[0])
             start_date = end_date - DateOffset(months=months)
 
-    portfolio_return_string, inception_dates, scheme_individual_returns = calculate_portfolio_returns(scheme_name_and_weight, sip_amount, lumpsum_amount, stepup, start_date, end_date, SIP_Date, schemes_df)
-
-    inception_warnings = []
-    for scheme_name, inception_date in inception_dates:
-        if start_date < inception_date:
-            inception_warnings.append(f"Warning: {scheme_name} inception date ({inception_date.date()}) is after the chosen start date ({start_date.date()}).")
-
-    result_string = f"""
-    {portfolio_return_string}
-    ------------------------------------
-    Individual Scheme Returns
-    ------------------------------------
-    {''.join(scheme_individual_returns)}
-    {''.join(inception_warnings)}
-    """
-    return result_string
+    portfolio_report_string = calculate_portfolio_returns(scheme_name_and_weight, sip_amount, lumpsum_amount, stepup, start_date, end_date, SIP_Date, schemes_df)
+    return portfolio_report_string
 
 
 def quick_search_schemes(query, schemes_df):
@@ -191,7 +177,7 @@ def create_ui():
             period = gr.Dropdown(choices=["YTD", "1 month","3 months","6 months","1 year", "3 years", "5 years", "7 years", "10 years","15 years","20 years", "Custom"], label="Select Period",value="YTD")
             custom_start_date = gr.Textbox(label="Custom Start Date (YYYY-MM-DD)", visible=False)
             custom_end_date = gr.Textbox(label="Custom End Date (YYYY-MM-DD)", visible=False)
-            SIP_Date = gr.Dropdown(label="Monthly SIP Date", choices=["start","middle","end"],value="end")
+            SIP_Date = gr.Dropdown(label="Monthly SIP Date", choices=["start","middle","end"],value="start")
             with gr.Column():
                 use_inception_date = gr.Checkbox(label="Use Earliest Inception Date", value=False)
                 inception_date_display = gr.Textbox(label="Earliest Inception Date", interactive=False)

indicators.py

@@ -0,0 +1,38 @@
+from utils import get_monthly_sip_nav_df
+import numpy as np
+import pandas as pd
+
+def get_investment_sd(investment_df,start_date, end_date, SIP_date):
+    return_df = pd.DataFrame()
+    investment_monthly_df = get_monthly_sip_nav_df(investment_df, start_date, end_date, SIP_date=SIP_date)
+    return_df['monthly_return'] = investment_monthly_df['nav'].pct_change()
+    return_df['monthly_return'] = return_df['monthly_return'].dropna()
+
+    # calculate annualized standard deviation of monthly returns
+    return (return_df['monthly_return'].std()*np.sqrt(12)) * 100
+
+def get_investment_sharpe_ratio(investment_df, start_date, end_date, SIP_date,risk_free_rate=6.86):
+    return_df = pd.DataFrame()
+    investment_monthly_df = get_monthly_sip_nav_df(investment_df, start_date, end_date, SIP_date=SIP_date)
+    return_df['monthly_return'] = investment_monthly_df['nav'].pct_change()*100
+    return_df['monthly_return'] = return_df['monthly_return'].dropna()
+
+    # calculate annualized standard deviation of monthly returns
+    annualized_sd = return_df['monthly_return'].std()*np.sqrt(12)
+    monthly_mean_return = return_df['monthly_return'].mean()
+    print(monthly_mean_return)
+    annualized_return = ((1+monthly_mean_return/100)**12 - 1)*100
+    print(annualized_return)
+
+    # calculate Sharpe Ratio
+    return ((annualized_return - risk_free_rate) / annualized_sd)
+
+def get_investment_indicator_report(investment_df, start_date,end_date,SIP_date="start",risk_free_rate=6.86):
+    investment_monthly_df = get_monthly_sip_nav_df(investment_df, start_date, end_date, SIP_date)
+    investment_sd = get_investment_sd(investment_monthly_df, start_date, end_date, SIP_date)
+    investment_sharpe_ratio = get_investment_sharpe_ratio(investment_monthly_df, start_date, end_date, SIP_date,risk_free_rate)
+    return (f"""
+    Standard Deviation: {investment_sd}
+    Sharpe Ratio: {investment_sharpe_ratio}""")
+
+

portfolio.py

@@ -1,6 +1,7 @@
 import pandas as pd
 from utils import get_mf_scheme_data
 from returns import get_investment_xirr, get_investment_sip_absolute_returns
+from indicators import get_investment_sd, get_investment_sharpe_ratio, get_investment_indicator_report
 
 def get_portfolio_nav_df(schemes_name_and_weight, start_date, end_date, schemes_df):
     # start_date = pd.to_datetime(start_date)
@@ -41,6 +42,12 @@ def get_invdividual_scheme_returns(scheme_df, scheme_name,scheme_sip_amount, sch
     """
     return (scheme_string)
 
+def get_inception_date_warnings(inception_dates, start_date):
+    warnings = []
+    for scheme_name, scheme_inception_date in inception_dates:
+        if scheme_inception_date > start_date:
+            warnings.append(f"{scheme_name} has an inception date of {scheme_inception_date} which is after the start date of the portfolio.")
+    return warnings
 
 
 def calculate_portfolio_returns(scheme_name_and_weight, sip_amount, lumpsum_amount, stepup, start_date, end_date, SIP_date, schemes_df):
@@ -48,7 +55,19 @@ def calculate_portfolio_returns(scheme_name_and_weight, sip_amount, lumpsum_amou
     scheme_individual_returns = []
     portfolio_df = get_portfolio_nav_df(scheme_name_and_weight, start_date, end_date,schemes_df)
     portfolio_absolute_return, portfolio_final_value, portfolio_invested_value = get_portfolio_absolute_returns(portfolio_df, sip_amount, lumpsum_amount, stepup, start_date, end_date, SIP_date)
-    portfolio_return_string = f"""
+    portfolio_indicators_string  = get_investment_indicator_report(portfolio_df, start_date, end_date)
+
+    for scheme_name, scheme_weight in scheme_name_and_weight.items():
+        scheme_code = schemes_df[schemes_df['schemeName'] == scheme_name]['schemeCode'].values[0]
+        scheme_df, scheme_inception_date = get_mf_scheme_data(scheme_code)
+        inception_dates.append((scheme_name, scheme_inception_date))
+        scheme_sip_amount = sip_amount * scheme_weight / 100
+        scheme_lumpsum_amount = lumpsum_amount * scheme_weight / 100
+        scheme_individual_returns.append(get_invdividual_scheme_returns(scheme_df, scheme_name, scheme_sip_amount, scheme_lumpsum_amount, stepup, start_date, end_date, SIP_date))
+
+    inception_date_warnings = get_inception_date_warnings(inception_dates, start_date)
+
+    portfolio_report_string = f"""
     Portfolio
     ------------------------------------
     SIP Amount: {sip_amount}
@@ -59,21 +78,20 @@ def calculate_portfolio_returns(scheme_name_and_weight, sip_amount, lumpsum_amou
     SIP Date: {SIP_date}
     Total Investment: {portfolio_invested_value}
     ------------------------------------
-    Portfolio Returns
+    Portfolio Report
     XIRR = {get_portfolio_xirr_returns(portfolio_df, start_date, end_date, SIP_date, lumpsum_amount, sip_amount)}%
     Absolute Returns = {portfolio_absolute_return}%
     Portfolio Final Value = {portfolio_final_value}
+    {portfolio_indicators_string}
+    ------------------------------------
+    Individual Scheme Returns
+    ------------------------------------
+    {''.join(scheme_individual_returns)}
+    ------------------------------------
+    Warnings
+    {''.join(inception_date_warnings)}
     """
-
-    for scheme_name, scheme_weight in scheme_name_and_weight.items():
-        scheme_code = schemes_df[schemes_df['schemeName'] == scheme_name]['schemeCode'].values[0]
-        scheme_df, scheme_inception_date = get_mf_scheme_data(scheme_code)
-        inception_dates.append((scheme_name, scheme_inception_date))
-        scheme_sip_amount = sip_amount * scheme_weight / 100
-        scheme_lumpsum_amount = lumpsum_amount * scheme_weight / 100
-        scheme_individual_returns.append(get_invdividual_scheme_returns(scheme_df, scheme_name, scheme_sip_amount, scheme_lumpsum_amount, stepup, start_date, end_date, SIP_date))
-
-    return portfolio_return_string, inception_dates, scheme_individual_returns
+    return portfolio_report_string
     
 
 

returns.py

@@ -10,8 +10,11 @@ def calculate_xnpv(rate, cashflows):
 def calculate_xirr(cashflows, guess=0.1):
     return optimize.newton(lambda r: calculate_xnpv(r,cashflows), guess)
 
-def get_investment_xirr(investment_df, start_date, end_date, SIP_date, lumpsum_amount=0, sip_amount=1000):
+def get_investment_xirr(investment_df, start_date, end_date, SIP_date, lumpsum_amount, sip_amount):
     # Get the monthly NAVs
+    if(sip_amount == 0):
+        sip_amount = 1000
+
     monthly_nav_df = get_monthly_sip_nav_df(investment_df, start_date, end_date, SIP_date)
     
     # Initialize lists to store cash flows and their corresponding dates