Bereken de ADX

De Average Directional Movement Index (ADX) is ontwikkeld door J. Welles Wilder als een indicator voor trendsterkte. Hij combineert twee andere indicatoren, de plus directional index (+DI) en de minus directional indicator (-DI), en wordt verkregen via uitgebreide berekeningen. Met Python kun je hem echter in één regel code berekenen. In deze oefening implementeer je je eerste ADX-indicator met dagelijkse prijsgegevens van het aandeel Tesla.

De historische dagelijkse prijsgegevens zijn geladen in stock_data. Ook is talib al voor je geïmporteerd.

Deze oefening maakt deel uit van de cursus

Financieel traden in Python

Cursus bekijken

Oefeninstructies

Bereken de ADX met de juiste functie uit talib, en gebruik de kolommen High, Low en Close in stock_data. Sla het op in een nieuwe kolom ADX_14.
Bereken de ADX. Pas deze keer de standaardperiode aan naar 21 en sla het op in een nieuwe kolom ADX_21.
Print de laatste vijf rijen van stock_data.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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.____())

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Financieel traden in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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: Trendindicator: MA's Exercise 2: Bereken en plot twee EMA's Exercise 3: SMA vs. EMA Exercise 4: Sterkte-indicator: ADX Exercise 5: Begrijp de ADX Exercise 6: Bereken de ADX

Huidige oefening

Exercise 7: Visualiseer de ADX Exercise 8: Momentumindicator: RSI Exercise 9: Begrijp de RSI Exercise 10: Bereken de RSI Exercise 11: Visualiseer de RSI Exercise 12: Volatiliteitsindicator: Bollinger Bands Exercise 13: Bollinger Bands begrijpen Exercise 14: Bollinger Bands implementeren

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!