Calcolare l'ADX

L'Average Directional Movement Index (ADX) è stato sviluppato da J. Welles Wilder come indicatore della forza del trend. Combina due altri indicatori, il plus directional index (+DI) e il minus directional indicator (-DI), ed è ottenuto tramite calcoli piuttosto lunghi. Con Python, però, puoi calcolarlo con una sola riga di codice. In questo esercizio, implementerai il tuo primo indicatore ADX usando i dati giornalieri del titolo Tesla.

I dati storici giornalieri dei prezzi sono stati caricati in stock_data. Inoltre, talib è già stato importato per te.

Questo esercizio fa parte del corso

Trading finanziario con Python

Visualizza il corso

Istruzioni dell'esercizio

Calcola l'ADX usando l'apposita funzione di talib e le colonne High, Low e Close in stock_data. Salvalo in una nuova colonna ADX_14.
Calcola di nuovo l'ADX. Questa volta modifica il periodo predefinito impostandolo a 21 e salvalo in una nuova colonna ADX_21.
Stampa le ultime cinque righe di stock_data.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# 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.____())

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Trading finanziario con Python

IntermediárioNível de habilidade

4.8+

Inizia il corso 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: Medie mobili come indicatori di trend Exercise 2: Calcola e traccia due EMA Exercise 3: SMA vs. EMA Exercise 4: Indicatore di forza: ADX Exercise 5: Capire l'ADX Exercise 6: Calcolare l'ADX

Esercizio in corso

Exercise 7: Visualizza l'ADX Exercise 8: Indicatore di momentum: RSI Exercise 9: Comprendere l'RSI Exercise 10: Calcolare l'RSI Exercise 11: Visualizza l'RSI Exercise 12: Indicatore di volatilità: Bande di Bollinger Exercise 13: Capire le Bande di Bollinger Exercise 14: Implementare le Bande di Bollinger

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!