Rivedi i risultati di rendimento di un backtest

Hai implementato una strategia trend-following usando un indicatore di media mobile semplice. L’hai testata retrospettivamente sui dati storici del prezzo delle azioni Amazon dal 2019 al 2020, e il grafico del risultato è mostrato a destra. Ora vuoi rivedere le statistiche di rendimento del backtest.

Il risultato del backtest bt è stato fornito in bt_result ed è pronto all’uso.

Questo esercizio fa parte del corso

Trading finanziario con Python

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Istruzioni dell'esercizio

Ottieni tutte le statistiche del backtest da bt_result e salvale in resInfo.
Stampa i rendimenti giornalieri, mensili e annuali da resInfo.
Stampa il tasso di crescita annuo composto (CAGR) da resInfo.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Obtain all backtest stats
resInfo = ____

# Get daily, monthly, and yearly returns
print('Daily return: %.4f'% ____)
print('Monthly return: %.4f'% ____)
print('Yearly return: %.4f'% ____)

# Get the compound annual growth rate
print('Compound annual growth rate: %.4f'% ____)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Trading finanziario con Python

IntermediárioNível de habilidade

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 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: Analisi dei ritorni della strategia Exercise 2: Rivedi i risultati di rendimento di un backtest

Esercizio in corso

Exercise 3: Traccia gli istogrammi dei rendimenti di un backtest Exercise 4: Confronta i risultati di rendimento di più strategie Exercise 5: Drawdown Exercise 6: Valuta le performance con i drawdown Exercise 7: Calcola e verifica il rapporto di Calmar Exercise 8: Sharpe ratio e Sortino ratio Exercise 9: Valuta la performance della strategia con il rapporto di Sharpe Exercise 10: Valuta la performance della strategia con il rapporto di Sortino Exercise 11: Congratulazioni!