Please check all Performance Metrics and let me know is it correct or not ?
import pandas as pd
import numpy as np
import talib as ta
# Data reading
columns = ['index','Date','Open','High','Low','Close','Volume','OI']
df = pd.read_csv(r'D:/Algorithmic Trading Proejct/Project/stock_data/downloaded_data/nifty_bank_10minute.txt', names=columns, index_col='Date')
df.drop(['index','Volume','OI'], axis=1, inplace=True)
df = pd.DataFrame(df)
#---------------------- DEMA Indicators ---------------------#
df['DEMA21'] = ta.DEMA(df['Close'], 21)
df['DEMA42'] = ta.DEMA(df['Close'], 42)
#---------------------- Creat Buy and Sell Postion ---------------------#
account_size= 1000000
buy_teade_Postion={}
sell_teade_Postion={}
buy_postion = 0
sell_postion = 0
postion_size = 1
for i in range(1,len(df)):
# Buy
if df['DEMA21'][i] > df['DEMA42'][i] and buy_postion == 0:
buy_postion = 1
buy_teade_Postion[i]= {
'Date': df.index[i],
'Single': 'Buy',
'postion_size' :postion_size,
'Enter_Price' : df['Close'][i],
'Exit_Price' : 0,
}
# exit Buy
elif df['Close'][i] < df['DEMA42'][i] and buy_postion == 1:
buy_postion = 0
buy_teade_Postion[i]= {
'Date': df.index[i],
'Single': 'SL',
'postion_size' :postion_size,
'Enter_Price' : 0,
'Exit_Price' : df['Close'][i],
}
# Sell
elif df['DEMA21'][i] < df['DEMA42'][i] and sell_postion == 0:
sell_postion = 1
sell_teade_Postion[i]= {
'Date': df.index[i],
'Single': 'Sell',
'postion_size' :postion_size,
'Enter_Price' : df['Close'][i],
'Exit_Price' : 0,
}
# exit Sell
elif df['Close'][i] > df['DEMA42'][i] and sell_postion == 1:
sell_postion = 0
sell_teade_Postion[i]= {
'Date': df.index[i],
'Single': 'Sl',
'postion_size' :postion_size,
'Enter_Price' : 0,
'Exit_Price' : df['Close'][i],
}
#buy strategy returns
buy_teade_Postion = pd.DataFrame.from_dict(buy_teade_Postion, orient='index')
buy_teade_Postion.set_index(buy_teade_Postion['Date'], inplace = True)
buy_teade_Postion['buy_strategy_returns'] = (buy_teade_Postion['Exit_Price'] - buy_teade_Postion['Enter_Price'].shift(1)) * postion_size
# Sell strategy returns
sell_teade_Postion = pd.DataFrame.from_dict(sell_teade_Postion, orient='index')
sell_teade_Postion.set_index(sell_teade_Postion['Date'], inplace = True)
sell_teade_Postion['sell_strategy_returns'] = (sell_teade_Postion['Enter_Price'].shift(1) - sell_teade_Postion['Exit_Price']) * postion_size
# Creat total teade Postion
total_teade_Postion = buy_teade_Postion.append(sell_teade_Postion)
# Remove NaN Value
total_teade_Postion['sell_strategy_returns'] = total_teade_Postion['sell_strategy_returns'].fillna(0)
total_teade_Postion['buy_strategy_returns'] = total_teade_Postion['buy_strategy_returns'].fillna(0)
#Total strategy returns
total_teade_Postion['strategy_returns'] = total_teade_Postion['buy_strategy_returns'] + total_teade_Postion['sell_strategy_returns']
# create df strategy_returns for Buy and Sell
df['strategy_returns'] = total_teade_Postion['strategy_returns']
df['strategy_returns'] = df['strategy_returns'].fillna(0)
#Total strategy returns
df['returns'] = df['strategy_returns'].cumsum() + account_size
# Calculate Total Profit
def Total_Profit_percentage (df):
FirstValue = df['returns'].iloc[0]
LastValue = df['returns'].iloc[-1]
total = ((LastValue - FirstValue) / FirstValue )* 100
return total
# Calculate compounded annual growth rate
def get_CAGR(df, price_col_name = "Adj Close"):
intraday_trading_hours = 6
days = len(df)/intraday_trading_hours
return "{0:.2f}%".format((df[price_col_name].iloc[-1]**(252/days)-1)*100)
# Calculate annualised volatility
def get_annualised_volatility(df, price_col_name = "Adj Close"):
return "{0:.2f}%".format(df[price_col_name].std()*np.sqrt(252) * 100)
# Calculate Sharpe ratio
def get_sharpe_ratio(df, price_col_name = "Adj Close"):
risk_free_rate = 0.02/252
return np.sqrt(252)*(np.mean(df[price_col_name]) - (risk_free_rate))/np.std(df[price_col_name])
# Calculate the maximum drawdown
def get_max_drawdown(df, price_col_name = "Adj Close"):
df['Peak'] = df[price_col_name].cummax()
df['Drawdown'] = ((df[price_col_name]- df['Peak'])/df['Peak'])
return "{0:.2f}%".format((df['Drawdown'].min())*100)
def print_strategy_summary(df, price_col_name = "Adj Close"):
print("Total Profit:", Total_Profit_percentage(df))
print("CAGR:", get_CAGR(df, price_col_name = price_col_name))
print("annualised volatility:", get_annualised_volatility(df, price_col_name = price_col_name))
print("Sharpe ratio:", get_sharpe_ratio(df, price_col_name = price_col_name))
print("Maximum drawdown:", get_max_drawdown(df, price_col_name = price_col_name))
print(print_strategy_summary(df, price_col_name = "returns"))Total Profit: 4.808780000000005
CAGR: 71.47%
annualised volatility: 25121290.81%
Sharpe ratio: 1021.9114652415966
Maximum drawdown: -0.57%