İki EMA'yı hesapla ve görselleştir

12 dönemlik EMA ve 26 dönemlik EMA, MACD (Hareketli Ortalama Yakınsama Iraksama) adı verilen daha karmaşık bir göstergenin hesaplanmasında kullanılan iki hareketli ortalamadır. MACD, kısa dönemli EMA'dan uzun dönemli EMA'yı çıkararak iki EMA'yı bir momentum göstergesine dönüştürür. MACD hakkında daha fazla bilgiye geçmeden önce, önce bileşenlerine aşina olmak istiyorsun. Google'ın günlük hisse fiyatlarını kullanarak iki EMA hesaplamaya ve bunları tek bir grafikte çizmeye karar verdin.

Google hissesinin günlük tarihsel fiyat verileri stock_data içine yüklendi. Ayrıca talib senin için içe aktarıldı ve matplotlib.pyplot plt olarak içe aktarıldı.

Bu egzersiz

Python ile Finansal Alım Satım

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Close fiyatının 12 günlük EMA'sını hesapla ve yeni bir EMA_12 sütununda sakla.
Close fiyatının 26 günlük EMA'sını hesapla ve yeni bir EMA_26 sütununda sakla.
12 günlük EMA ve 26 günlük EMA'yı Close fiyatıyla birlikte görselleştir.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Calculate 12-day EMA
stock_data['EMA_12'] = ____(stock_data['____'], ____)
# Calculate 26-day EMA
stock_data['EMA_26'] = ____

# Plot the EMAs with price
____(stock_data['____'], label='EMA_12')
____(stock_data['____'], label='EMA_26')
____(stock_data['____'], label='Close')

# Customize and show the plot
plt.legend()
plt.title('EMAs')
plt.show()

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 göstergesi Hareketli Ortalamalar (MA)Exercise 2: İki EMA'yı hesapla ve görselleştir

Geçerli Egzersiz

Exercise 3: SMA vs. EMA Exercise 4: Güç göstergesi: ADX Exercise 5: ADX'i Anlamak Exercise 6: ADX'i hesapla Exercise 7: ADX'i görselleştir Exercise 8: Momentum göstergesi: RSI Exercise 9: RSI'yı anla Exercise 10: RSI'ı hesapla Exercise 11: RSI'yi görselleştir Exercise 12: Volatilite göstergesi: Bollinger Bantları Exercise 13: Bollinger Bantlarını Anla Exercise 14: Bollinger Bantlarını Uygula

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!