Calculate the ADX

The average directional movement index (ADX) was developed by J. Welles Wilder as an indicator of trend strength. It combines two other indicators, the plus directional index (+DI) and minus directional indicator (-DI), and is obtained using lengthy calculations. However, with Python, you can calculate it with one line of code. In this exercise, you will implement your first ADX indicator using daily price data of the Tesla stock.

The historical daily price data has been loaded in stock_data. Also, talib has been imported for you.

This is a part of the course

“Financial Trading in Python”

Exercise instructions

Calculate the ADX using the appropriate function from talib, and the High, Low and Close columns in the stock_data. Save it in a new column ADX_14.
Calculate the ADX . This time change the default time period to 21, and save it in a new column ADX_21.
Print the last five rows of stock_data.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Calculate the ADX with the default time period
stock_data['ADX_14'] = ____(stock_data['____'],
                            stock_data['____'], 
                            stock_data['____'])

# Calculate the ADX with the time period set to 21
stock_data['ADX_21'] = ____

# Print the last five rows
print(stock_data.____())

Edit and Run Code

This exercise is part of the course

Financial Trading in Python

IntermediateSkill Level

4.3+

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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 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!