Time-Series-Momentum-Strategy

In this strategy, we will use a lookback period of 12 months and a holding period of 1 month.

1. Import libraries and Read S&P500 data

First, we will import the necessary libraries and then, we will read the csv file with the S&P500 OHLC data using the 'read_csv' method of pandas.

2. Convert daily frequency to monthly frequency

DataFrame.asfreq() function is used to convert timeseries to specified frequency. Here, we use BM to get the last business day of the month.

3. Calculate yearly returns

DataFrame.pct_change() is used to calculate the percentage returns of the prices. Since the frequency is monthly we will use 12 as a lookback period to specify percentage change calculation over 12 months.

4. Calculate holding period returns

Now, we will calculate the holding period returns which is 1 month.

DataFrame.pct_change() is used to calculate the percentage returns of the prices.

Dataframe.shift() function shift index by desired number of periods. Use 1 to shift the index axis by 1 period backward and -1 for 1 period forward.

5. Define strategy logic

To backtest this strategy, we need to generate trading signals. Whenever the yearly returns are greater than 0, we buy; otherwise, we sell. We use where() function from NumPy to do this.

6. Calculate Strategy Returns

To calculate strategy returns, multiply future_1_month_returns with positions. Then, we use cumprod() function to calculate cumulative strategy returns.

7. Analyse Strategy Performance

7.1 Calculate total/absolute returns

The total returns from strategy 1,372.32%

7.2 Calculate annualised returns

The CAGR from strategy is 11.16%

7.3 Calculate annualised volatility

The annalised volatility from strategy is 14.43%

7.4 Calculate Sharpe ratio

The Sharpe ratio is 0.81

7.5 Calculate maximum drawdown

The maximum drawdown is -33.13%

jackmin97/Time-Series-Momentum-Strategy