Lakukan optimisasi strategi

Anda memiliki strategi sinyal berbasis SMA untuk memperdagangkan saham. Namun, Anda belum yakin periode lookback mana yang harus digunakan untuk menghitung SMA agar dapat mengoptimalkan kinerja strategi. Anda berencana menjalankan beberapa backtest dengan parameter masukan yang berbeda. Selain itu, Anda menginginkan kemampuan untuk menilai strategi pada saham yang berbeda atau berdasarkan periode historis yang berbeda.

Paket bt telah diimpor untuk Anda. Selain itu, data harga historis saham Tesla telah dimuat sebelumnya dalam price_data.

Latihan ini adalah bagian dari kursus

Perdagangan Finansial dengan Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

def signal_strategy(price_data, period, name):
    # Calculate SMA
    sma = price_data.____
    # Define the signal-based Strategy
    bt_strategy = bt.Strategy(name, 
                              [____,
                               bt.algos.WeighEqually(),
                               bt.algos.Rebalance()])
    # Return the backtest
    return ____(bt_strategy, price_data)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Perdagangan Finansial dengan Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: 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: Sinyal perdagangan Exercise 2: Memahami sinyal perdagangan Exercise 3: Bangun strategi sinyal berbasis SMA Exercise 4: Bangun strategi sinyal berbasis EMA Exercise 5: Strategi mengikuti tren Exercise 6: Menyusun sinyal persilangan EMA Exercise 7: Bangun dan backtest strategi trend-following Exercise 8: Strategi mean reversion Exercise 9: Cocokkan sinyal dengan strateginya Exercise 10: Membangun sinyal berbasis RSI Exercise 11: Bangun dan uji balik strategi mean reversion Exercise 12: Optimisasi strategi dan pembandingan Exercise 13: Lakukan optimisasi strategi

Latihan Saat Ini

Exercise 14: Lakukan benchmarking strategi

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!