Bir strateji optimizasyonu yap

Hisse senedi alım satımı için SMA tabanlı bir sinyal stratejin var. Ancak, stratejinin performansını optimize edecek SMA hesaplama bakış geriye (lookback) süresinin ne olması gerektiğinden emin değilsin. Farklı girdi parametreleriyle birden çok geriye dönük test (backtest) çalıştırmayı planlıyorsun. Ayrıca stratejiyi farklı hisselerde veya farklı tarihsel dönemlerde değerlendirme esnekliğine sahip olmak istiyorsun.

bt paketi senin için içe aktarıldı. Ayrıca Tesla hissesinin tarihsel fiyat verileri price_data içinde önceden yüklendi.

Bu egzersiz

Python ile Finansal Alım Satım

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Finansal Alım Satım

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Alım satım sinyalleri Exercise 2: İşlem sinyallerini anla Exercise 3: SMA tabanlı bir sinyal stratejisi oluştur Exercise 4: EMA tabanlı bir sinyal stratejisi oluştur Exercise 5: Trendi izleyen stratejiler Exercise 6: Bir EMA kesişim sinyali oluştur Exercise 7: Trend-following bir strateji oluştur ve geriye dönük test et Exercise 8: Ortalama dönüş stratejisi Exercise 9: Sinyalleri stratejilerle eşleştir Exercise 10: RSI tabanlı bir sinyal oluştur Exercise 11: Ortalama dönüş stratejisi oluştur ve geriye dönük test et Exercise 12: Strateji optimizasyonu ve kıyaslama Exercise 13: Bir strateji optimizasyonu yap

Geçerli Egzersiz

Exercise 14: Bir strateji kıyaslaması (benchmarking) yap

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!