SMA vs. EMA

SMA ed EMA sono entrambi indicatori di trend molto usati. La SMA assegna lo stesso peso a tutti i punti dati, mentre l’EMA dà più peso ai dati più recenti. Hai a disposizione alcuni dati di prezzo delle azioni Google e vuoi scegliere quale media mobile usare. Prevedi di calcolare sia la SMA sia l’EMA con lo stesso periodo di lookback e di tracciarle in un unico grafico.

I dati storici giornalieri del titolo Google sono stati caricati in stock_data. Inoltre, talib è già stato importato per te e matplotlib.pyplot è stato importato come plt.

Questo esercizio fa parte del corso

Trading finanziario con Python

Visualizza il corso

Istruzioni dell'esercizio

Calcola una SMA a 50 giorni del prezzo Close e salvala in una nuova colonna SMA.
Calcola una EMA a 50 giorni del prezzo Close e salvala in una nuova colonna EMA.
Traccia SMA ed EMA insieme al prezzo Close.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Calculate the SMA
stock_data['SMA'] = ____(stock_data['____'], ____)
# Calculate the EMA
stock_data['EMA'] = ____(stock_data['____'], ____)

# Plot the SMA, EMA with price
____(stock_data['____'], label='SMA')
____(stock_data['____'], label='EMA')
____(stock_data['____'], label='Close')

# Customize and show the plot
plt.legend()
plt.title('SMA vs EMA')
plt.show()

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

Esercizio in corso

Exercise 4: Indicatore di forza: ADX Exercise 5: Capire l'ADX Exercise 6: Calcolare l'ADX 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!