Unlocking Strategic Insights: A Comprehensive Analysis of Key Trading Metrics with Python

This article is beneficial for generating metrics to evaluate and select trading strategies. The parameters encompass a range from profit to losses, risks involved, graphical representation, and more.

By executing the provided code, you can obtain key information on various metrics such as:

1. Cumulative returns
2. CAGR (Compound Annual Growth Rate)
3. Sharpe Ratio
4. Max Drawdown
5. Skewness and Kurtosis
6. Max consecutive wins and losses
7. Gain/Pain Ratio
8. Best and worst day
9. Expected Shortfall
10. Strategy monthly returns

The detailed analysis in the attachment focuses on Nifty performance from September 2007 to September 2023. Please refer to the attachment for an in-depth exploration of all the parameters.

#Python code 

# importing necessary libraries #

import yfinance as yf

import warnings
warnings.filterwarnings("ignore")
import quantstats as qs

# Download data from yfinance and caculating returns #

df1 = yf.download('^NSEI', '2000-01-01', '2023-09-30')
df = df1[['Adj Close']]
returns = df['Adj Close'].pct_change().dropna()

# using quantstas to get the full analysis of the returns #

qs.reports.full(
    returns,
    mode = 'full'

)

Output of the above code