Backtesting
11_1 momentum strategy (nifty50)

LINK TO GOOGLE COLAB NOTEBOOK
LINK TO GITHUB

# COMBINED CODE

import yfinance as yf

import numpy as np

import pandas as pd

stocks = pd.read_html('https://en.wikipedia.org/wiki/NIFTY_50')[2].Symbol

stocks = stocks + '.NS'

df = yf.download(stocks.to_list(), start = '2014-01-01')

print(df.head())

df_dret = df_close.pct_change()

df_mret = (df_dret +1).resample('M').prod()

df_mret_11 = df_mret.rolling(11).apply(np.prod).dropna()

def top_performers(date):

  top = df_mret_11.loc[date].nlargest(10)

  port_mret = df_mret[date:][1:2][top.index].mean(axis=1).values[0]

  return port_mret

monthly_ret = []

i = 0

for date in df_mret_11.index[:-1]:

  monthly_ret.append(top_performers(date))

  i = i+1

  print('month',i,'th return : ',monthly_ret[i-1])

# total return of our portfolio in last 10 years

print("\nPortfolio's total return for last ",i,"months is ",

      pd.Series(monthly_ret).prod())

Result:

month 109 th return :  1.0857253482991316

month 110 th return :  1.08806616450063

month 111 th return :  1.0372182316394796

month 112 th return :  1.0230539660690603

month 113 th return :  1.0166232830470263

month 114 th return :  1.0076408858342756

Portfolio's total return for last  114 months is  6.995240771091297 or around 600% return

import yfinance as yf

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

# Get NIFTY 50 stock symbols

stocks = pd.read_html('https://en.wikipedia.org/wiki/NIFTY_50')[2].Symbol

stocks = stocks + '.NS'

# Download historical data

df = yf.download(stocks.to_list(), start='2014-01-01')

df_close = df['Adj Close']

df_dret = df_close.pct_change()

df_mret = (df_dret + 1).resample('M').prod() - 1

# Calculate 11-month rolling returns

df_mret_11 = df_mret.rolling(11).apply(lambda x: (x + 1).prod() - 1).dropna()

# Function to calculate top performers for a given date

def top_performers(date):

    top = df_mret_11.loc[date].nlargest(10)

    port_mret = df_mret[date:][1:2][top.index].mean(axis=1).values[0]

    return port_mret

# Calculate monthly returns of the strategy

monthly_ret = []

for date in df_mret_11.index[:-1]:

    monthly_ret.append(top_performers(date))

# Calculate portfolio value over time

portfolio_value = [1]

for r in monthly_ret:

    portfolio_value.append(portfolio_value[-1] * (1 + r))

# Create a DataFrame for portfolio value

portfolio_df = pd.DataFrame({

    'Date': df_mret_11.index[1:],

    'Portfolio Value': portfolio_value[1:]

})

portfolio_df.set_index('Date', inplace=True)

# Calculate drawdown

portfolio_df['Drawdown'] = portfolio_df['Portfolio Value'] / portfolio_df['Portfolio Value'].cummax() - 1

# Calculate Sharpe ratio

annualized_return = np.prod([1 + r for r in monthly_ret]) ** (12 / len(monthly_ret)) - 1

annualized_std = np.std(monthly_ret) * np.sqrt(12)

sharpe_ratio = annualized_return / annualized_std

# Calculate Beta

# Use NIFTY 50 index as benchmark

nifty50 = yf.download('^NSEI', start='2014-01-01')['Adj Close']

nifty50_mret = nifty50.pct_change().resample('M').apply(lambda x: (x + 1).prod() - 1).dropna()

cov_matrix = np.cov(nifty50_mret[df_mret_11.index[1]:], monthly_ret) #structure of monthly return?

beta = cov_matrix[0, 1] / cov_matrix[0, 0]

print(f"Annualized Return: {annualized_return:.2%}")

print(f"Annualized Volatility: {annualized_std:.2%}")

print(f"Sharpe Ratio: {sharpe_ratio:.2f}")

print(f"Beta: {beta:.2f}")

print(f"Maximum Drawdown: {portfolio_df['Drawdown'].min():.2%}")

# Plot portfolio value over time

plt.figure(figsize=(10, 5))

plt.plot(portfolio_df['Portfolio Value'], label='Portfolio Value')

plt.title('Portfolio Value Over Time')

plt.xlabel('Date')

plt.ylabel('Portfolio Value')

plt.legend()

plt.grid(True)

plt.show()

# Plot drawdown over time

plt.figure(figsize=(10, 5))

plt.plot(portfolio_df['Drawdown'], label='Drawdown', color='red')

plt.title('Drawdown Over Time')

plt.xlabel('Date')

plt.ylabel('Drawdown')

plt.legend()

plt.grid(True)

plt.show()

Result: