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from scipy import optimize
from utils import get_monthly_sip_nav_df
def calculate_xnpv(rate, cashflows):
chron_order = sorted(cashflows, key=lambda x: x[0])
t0 = chron_order[0][0]
return sum([cf/(1+rate)**((t-t0).days/365.0) for (t,cf) in chron_order])
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, 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
cash_flows = []
dates = []
# Add the lumpsum investment at the start
cash_flows.append(-lumpsum_amount)
dates.append(start_date)
# Calculate initial units from lumpsum investment
initial_units = lumpsum_amount / monthly_nav_df['nav'].iloc[0]
total_units = initial_units
# Iterate over each row and record the SIP investments
for _, row in monthly_nav_df.iterrows():
cash_flows.append(-sip_amount) # SIP investment is negative cash flow
dates.append(row['date'])
total_units += sip_amount / row['nav']
# Add the final value as a positive cash flow
final_value = total_units * monthly_nav_df['nav'].iloc[-1]
cash_flows.append(final_value)
dates.append(monthly_nav_df['date'].iloc[-1])
portfolio_XIRR = calculate_xirr(list(zip(dates, cash_flows)))
return portfolio_XIRR * 100
def get_investment_sip_absolute_returns(investment_df, sip_amount, lumpsum_amount, stepup, start_date, end_date, SIP_Date):
# start_date = pd.Timestamp(start_date)
# end_date = pd.Timestamp(end_date)
scheme_df_monthly = get_monthly_sip_nav_df(investment_df, start_date, end_date, SIP_Date)
total_investment = lumpsum_amount
current_sip_amount = sip_amount
# do calculation for upfront investment
units_bought = lumpsum_amount / scheme_df_monthly.iloc[0]['nav']
units_accumulated = units_bought
previous_year = start_date.year
for _, row in scheme_df_monthly.iloc[:-1].iterrows():
# Check if a year has passed and increase SIP amount accordingly
if row['date'].year > previous_year:
current_sip_amount += current_sip_amount * (stepup / 100)
previous_year = row['date'].year
units_bought = current_sip_amount / row['nav']
units_accumulated += units_bought
total_investment += current_sip_amount
final_value = units_accumulated * scheme_df_monthly.iloc[-1]['nav']
total_return = (final_value - total_investment) / total_investment * 100
return total_return, final_value, total_investment |