# How I Made An RSI Trading Strategy Using Python (Step-By-Step)

Last Updated on June 3, 2023

Python is the most popular programming language to backtest trading strategies. However, most people are afraid of it or don’t know where to start, while others think it’s difficult to learn.

**Today we will show you how to backtest an RSI trading strategy with a few lines of code, step by step, for beginners. As you will see, you don’t need to have a computer science degree to do it!**

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In this article, we will learn how to download historical stock data from Yahoo Finance, calculate the RSI indicator, generate trading signals, and plot the returns of the strategy, all using Python.

## Download historical data using Python

*yfinance* allows us to download historical data from Yahoo Finance for free and also includes fundamental data such as income statements, trading multiples, and dividends, among many others.

We are going to create a Python notebook to run our code. A Python notebook is a web-based environment to create and edit Python scripts (for example, *Jupyter* notebook or *Google Colaboratory). They are easier to use and understand than other Python* programs to write and run our code.

In our Python notebook, we are going to first import the *yfinance* library as well as the *pandas* library, which will allow us to manipulate the data frame later. Then we define a variable, which we named data, and download the dataframe of the historical prices of the SPY using a *yfinance* function called *yf.download()*.

If we print the data, here is the output:

Now we have the daily open, high, low, close, adjusted close, and volume values for the SPY since 1993!

## Calculating the RSI indicator

The formula to calculate the relative strength index is not difficult, but there is a library in Python that can do it for us. *Pandas_ta is an easy-to-use* library that leverages the Pandas package with hundreds of technical indicators – all for free.

As we did before with *yfinance* and *pandas*, we need to import *pandas_ta* into our notebook. The function to calculate the RSI is called *pta.rsi(). Inside the parenthesis goes two inputs: the daily closing price of the SPY and the length of the RSI we want, in this case, two* days.

So to our data frame named data, we are going to add a new column called â€™rsiâ€™ and use the formula mentioned above to calculate the RSI.

## Generating the trading signals

The trading rules of the strategy are pretty simple:

- We buy the SPY at the close when the RSI(2) is under 10
- We sell when the RSI(2) crosses above 60

In order to generate the trading signals, we are going to have to do a loop through the data frame using the *for* function and checking for conditions with the *if* function.

This strategy has two different signals, one for buying and the other for selling. This means that we are going to scan every row in the data frame one by one (*for* function), checking for two conditions in each signal (*if *function):

- For the buy signal, the RSI must be below ten and z equal to 1 (see z described below)
- For the sell signal, RSI must be above 60 and z be equal to 0

We created another column in the data frame called *regime* and put one if the buy signal is triggered and -1 when the sell signal is flashed.

What is z? z is a variable we set equal to 1, so if, for example, there are two consecutive days where the RSI(2) is below 10, the signal is only generated on the first day because after that, z value changes to 0. The two conditions are no longer met. The same goes for the sell signal.

It is also important to note that we put 1 in the following row(i+1) where the buy signal is generated because if the signal is triggered at the close of the day, meaning that we didnâ€™t gain or lose any money that day. If we weren’t to put i+1 we would consider the return of the SPY the day we bought it, even though we bought it at the close.

Now, to make it easier to calculate the returns, we are going to fill with 1â€™s the regime columns between where the buy signals are generated and when the sell signals are triggered (before this, the value in these rows was 0).

In other words, if the previous row equals 1, put a 1 in the current column. Eventually, we will reach a point where the previous column is -1, and the condition is no longer met.

Now here is an example of how the data frame would look when a buy and sell signal is triggered:

## Calculating and plotting the returns of the strategy

Finally, to calculate the returns and performance of the strategy, we need to make one final loop through the data frame (we promise it is the last!).

Before that, we are going to create a new column called *change*, where we are going to calculate the daily change of the SPY. In order to do this, we will use the *pct_change()* function and sum 1 in the end.

Then we will* *create another column called *returns* where if the value of the regime does not equal zero, we put the daily change of the SPY, if not, we put just 1.

To plot the cumulative returns of the strategy, we will use the *cumprod()* function followed by the *.plot() *function to make a graph showing the compounded returns. Inside the parentheses, we add a label to differentiate the market returns from the strategy returns.

And that’s it! You just backtested a trading strategy using Python.

## 3 RSI trading strategies video

## How I made an RSI trading strategy using Python – conclusion

To sum up, today we backtested an RSI trading strategy from scratch. You learned some basic Python code and hopefully found out how easy it is to use Python to backtest trading strategies.