from utils import get_monthly_sip_nav_df,get_mf_scheme_data
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()
    # calculate annualized return, as the risk free rate is annualized
    annualized_return = ((1+monthly_mean_return/100)**12 - 1)*100
    # calculate Sharpe Ratio
    return ((annualized_return - risk_free_rate) / annualized_sd)

def get_investment_beta(investment_df, start_date, end_date, SIP_date):
    benchmark_df,_ = get_mf_scheme_data('120716')
    benchmark_monthly_df = get_monthly_sip_nav_df(benchmark_df, start_date, end_date, SIP_date)
    investment_monthly_df = get_monthly_sip_nav_df(investment_df, start_date, end_date, SIP_date)
    return_df = pd.DataFrame()
    return_df['investment_monthly_return'] = investment_monthly_df['nav'].pct_change()*100
    return_df['benchmark_monthly_return'] = benchmark_monthly_df['nav'].pct_change()*100
    return_df = return_df.dropna()
    
    # calculate beta
    cov_matrix = np.cov(return_df['investment_monthly_return'], return_df['benchmark_monthly_return'])
    covariance = cov_matrix[0][1]
    benchmark_variance = cov_matrix[1][1]
    beta = covariance / benchmark_variance
    
    return beta


def get_investment_indicator_report(investment_df, start_date,end_date,SIP_date="start",risk_free_rate=6.55):
    benchmark_df,_ = get_mf_scheme_data('120716')
    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)
    investment_beta = get_investment_beta(investment_monthly_df, start_date, end_date, SIP_date)
    benchmark_sd = get_investment_sd(benchmark_df, start_date, end_date, SIP_date)
    benchmark_sharpe_ratio = get_investment_sharpe_ratio(benchmark_df, start_date, end_date, SIP_date,risk_free_rate)
    benchmark_beta = get_investment_beta(benchmark_df, start_date, end_date, SIP_date)

    return (f"""
    Standard Deviation: {investment_sd}
    Sharpe Ratio: {investment_sharpe_ratio}
    Beta: {investment_beta}
    
    UTI NIFTY50 Standard Deviation: {benchmark_sd}
    UTI NIFTY50 Sharpe Ratio: {benchmark_sharpe_ratio}""")