Momentum indicator: RSI

1. Momentum indicator: RSI

In this lesson, let's discuss a popular momentum indicator called Relative Strength Index or RSI.

2. What is RSI?

RSI stands for Relative Strength Index. It was also developed by Welles Wilder. RSI has been the most popular indicator used to measure momentum, which is the speed of rising or falling in prices. The RSI oscillates between 0 and 100. Traditionally an RSI over 70 indicates an overbought market condition, which means the asset is overvalued and the price may reverse. An RSI below 30 suggests an oversold market condition, which means the asset is undervalued and the price may rally.

3. How is RSI calculated?

The RSI calculation follows a straightforward formula. RS, or Relative Strength, is the average of the upward price changes over a chosen n periods, divided by the average of downward price changes over those n periods. The formula is constructed in such a way that an RSI is bounded between 0 and 100.

4. Implementing RSI in Python

RSI can be implemented in Python by calling talib dot RSI and passing the price column. Similar to the ADX, Welles Wilder used a 14-period lookback window for RSI calculations, which became the industry standard. You can change the default period with the timeperiod parameter. Note the longer the lookback window, the less sensitive the RSI is to the price fluctuations. Traders may want to change the default time period to suit their specific trading time horizons, or as a variable input for testing different trading strategies. In this sample code, we calculated the RSI and saved it in a new DataFrame column. We can use dot tail to check the last 5 rows.

5. Plotting RSI

Similar to the ADX, it is helpful to plot the price and the RSI one above another. In this sample code, we created two subplots, the top plot shows the price, and the bottom plot shows the RSI. Notice in the chart, when the RSI is falling near 30, the price bottoms out and gradually recovers, and when the RSI value is approaching 70, the price reaches new highs and is more likely to pull back.

6. Let's practice!

Great! Now let's practice!

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This exercise is part of the course

Financial Trading in Python

IntermediateSkill Level

4.8+

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What is financial trading, why do people trade, and what’s the difference between technical trading and value investing? This chapter answers all these questions and more. You’ll also learn useful tools to explore trading data, generate plots, and how to implement and backtest a simple trading strategy in Python.

Exercise 1: What is financial trading Exercise 2: The concept of trading Exercise 3: Plot a time series line chart Exercise 4: Plot a candlestick chart Exercise 5: Getting familiar with your trading data Exercise 6: Resample the data Exercise 7: Plot a return histogram Exercise 8: Calculate and plot SMAs Exercise 9: Financial trading with bt Exercise 10: The bt process Exercise 11: Define and backtest a simple strategy

Let's dive into the world of technical indicators—a useful tool for constructing trading signals and building strategies. You’ll get familiar with the three main indicator groups, including moving averages, ADX, RSI, and Bollinger Bands. By the end of this chapter, you’ll be able to calculate, plot, and understand the implications of indicators in Python.

Exercise 1: Trend indicator MAs Exercise 2: Calculate and plot two EMAs Exercise 3: SMA vs. EMA Exercise 4: Strength indicator: ADX Exercise 5: Understand the ADX Exercise 6: Calculate the ADX Exercise 7: Visualize the ADX Exercise 8: Momentum indicator: RSI

Current Exercise

Exercise 9: Understand the RSI Exercise 10: Calculate the RSI Exercise 11: Visualize the RSI Exercise 12: Volatility indicator: Bollinger Bands Exercise 13: Understand Bollinger Bands Exercise 14: Implement Bollinger Bands

You’re now ready to construct signals and use them to build trading strategies. You’ll get to know the two main styles of trading strategies: trend following and mean reversion. Working with real-life stock data, you’ll gain hands-on experience in implementing and backtesting these strategies and become more familiar with the concepts of strategy optimization and benchmarking.

Exercise 1: Trading signals Exercise 2: Understand trading signals Exercise 3: Build an SMA-based signal strategy Exercise 4: Build an EMA-based signal strategy Exercise 5: Trend-following strategies Exercise 6: Construct an EMA crossover signal Exercise 7: Build and backtest a trend-following strategy Exercise 8: Mean reversion strategy Exercise 9: Match the signals with the strategies Exercise 10: Construct an RSI based signal Exercise 11: Build and backtest a mean reversion strategy Exercise 12: Strategy optimization and benchmarking Exercise 13: Conduct a strategy optimization Exercise 14: Perform a strategy benchmarking

How is your trading strategy performing? Now it’s time to take a look at the detailed statistics of the strategy backtest result. You’ll gain knowledge of useful performance metrics, such as returns, drawdowns, Calmar ratio, Sharpe ratio, and Sortino ratio. You’ll then tie it all together by learning how to obtain these ratios from the backtest results and evaluate the strategy performance on a risk-adjusted basis.

Exercise 1: Strategy return analysis Exercise 2: Review return results of a backtest Exercise 3: Plot return histograms of a backtest Exercise 4: Compare return results of multiple strategies Exercise 5: Drawdown Exercise 6: Review performance with drawdowns Exercise 7: Calculate and review the Calmar ratio Exercise 8: Sharpe ratio and Sortino ratio Exercise 9: Evaluate strategy performance by Sharpe ratio Exercise 10: Evaluate strategy performance by Sortino ratio Exercise 11: Congratulations!