Menambahkan moving average ke data keuangan

Salah satu indikator paling populer untuk ditambahkan ke strategi trading adalah simple moving average (SMA) 200 hari. Ini adalah indikator teknikal yang merepresentasikan rata-rata harga penutupan suatu saham selama 200 hari terakhir. Moving average lainnya dapat memiliki panjang berbeda, seperti 50 hari, 100 hari, dan seterusnya.

Setiap kali harga berada di atas moving average 200 hari, biasanya berbagai hal baik terjadi, seperti aset yang terapresiasi harganya, volatilitas rendah, dan sebagainya. Visualisasi jangka panjang dapat membantu menjelaskan mengapa indikator ini sering disebut.

Paket TTR memiliki fungsi untuk menghitung moving average, yaitu SMA(), yang menerima deret harga x dan menghitung mean aritmetika selama n hari. Pemanggilan SMA() dengan jendela lookback 50 hari dapat terlihat seperti berikut:

SMA(Cl(GDX), n = 50)

Dalam latihan ini, Anda akan menggunakan fungsi SMA(). Paket quantmod dan TTR sudah dimuat ke dalam workspace Anda, begitu pula objek SPY.

Latihan ini adalah bagian dari kursus

Perdagangan Finansial dengan R

Petunjuk latihan

Buat plot harga penutupan SPY.
Gunakan fungsi lines() untuk menambahkan SMA 200 hari dari harga penutupan SPY. Warnai garisnya merah dengan menyetel argumen col ke "red".

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Plot the closing prices of SPY
___(___(___))

# Add a 200-day SMA using lines()
lines(___(___(___), n = ___), col = ___)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Perdagangan Finansial dengan R

SkillTag.level.intermediateSkillTag.label

4.9+

Mulai Kursus Gratis

In this chapter, you will learn the definition of trading, the philosophies of trading, and the pitfalls that exist in trading. This chapter covers both momentum and oscillation trading, along with some phrases to identify these types of philosophies. You will learn about overfitting and how to avoid it, obtaining and plotting financial data, and using a well-known indicator in trading.

Exercise 1: Mengapa orang melakukan perdagangan?Exercise 2: Mengidentifikasi jenis filosofi trading - I Exercise 3: Mengidentifikasi jenis filosofi trading - II Exercise 4: Mengidentifikasi jenis filosofi trading - III Exercise 5: Kelemahan berbagai sistem perdagangan Exercise 6: Cara mencegah overfitting Exercise 7: Mengambil data keuangan Exercise 8: Memplot data keuangan Exercise 9: Menambahkan indikator pada data keuangan Exercise 10: Menambahkan moving average ke data keuangan

Latihan Saat Ini

Before building a strategy, the quantstrat package requires you to initialize some settings. In this chapter you will learn how this is done. You will cover a series of functions that deal with initializing a time zone, currency, the instruments you'll be working with, along with quantstrat's various frameworks that will allow it to perform analytics. Once this is done, you will have the knowledge to set up a quantstrat initialization file, and know how to change it.

Exercise 1: Setting up a strategy I Exercise 2: Understanding initialization settings - I Exercise 3: Understanding initialization settings - II Exercise 4: Setting up a strategy II Exercise 5: Understanding initialization settings - III Exercise 6: Understanding initialization settings - IV

Indicators are crucial for your trading strategy. They are transformations of market data that allow a clearer understanding of its overall behavior, usually in exchange for lagging the market behavior. Here, you will be working with both trend types of indicators as well as oscillation indicators. You will also learn how to use pre-programmed indicators available in other libraries as well as implement one of your own.

Exercise 1: Introduction to indicators Exercise 2: The SMA and RSI functions Exercise 3: Visualize an indicator and guess its purpose - I Exercise 4: Visualize an indicator and guess its purpose - II Exercise 5: Indicator mechanics Exercise 6: Implementing an indicator - I Exercise 7: Implementing an indicator - II Exercise 8: Implementing an indicator - III Exercise 9: Indicator structure review Exercise 10: Code your own indicator - I Exercise 11: Code your own indicator - II Exercise 12: Apply your own indicator

When constructing a quantstrat strategy, you want to see how the market interacts with indicators and how indicators interact with each other. In this chapter you'll learn how indicators can generate signals in quantstrat. Signals are interactions of market data with indicators, or indicators with other indicators. There are four types of signals in quantstrat: sigComparison, sigCrossover, sigThreshold, and sigFormula. By the end of this chapter, you'll know all about these signals, what they do, and how to use them.

Exercise 1: Introduction to signals Exercise 2: Signal or not? - I Exercise 3: Signal or not? - II Exercise 4: sigComparison and sigCrossover Exercise 5: Using sigComparison Exercise 6: Using sigCrossover Exercise 7: sigThreshold Exercise 8: Using sigThreshold - I Exercise 9: Using sigThreshold() - II Exercise 10: sigFormula Exercise 11: Using sigFormula()Exercise 12: Combining signals - I Exercise 13: Combining signals - II

In this chapter, you'll learn how to shape your trading transaction once you decide to execute on a signal. This chapter will cover a basic primer on rules, and how to enter and exit positions. You'll also learn how to send inputs to order-sizing functions. By the end of this chapter, you'll learn the gist of how rules function, and where you can continue learning about them.

Exercise 1: Introduction to rules Exercise 2: Using add.rule() to implement an exit rule Exercise 3: Specifying sigcol in add.rule()Exercise 4: Specifying sigval in add.rule()Exercise 5: More rule mechanics Exercise 6: Specifying orderqty in add.rule()Exercise 7: Specifying ordertype in add.rule()Exercise 8: Specifying orderside in add.rule()Exercise 9: More rule mechanics II Exercise 10: Specifying replace in add.rule()Exercise 11: Specifying prefer in add.rule()Exercise 12: Using add.rule() to implement an entry rule Exercise 13: Order sizing functions Exercise 14: Implementing a rule with an order sizing function

After a quantstrat strategy has been constructed, it's vital to know how to actually analyze the strategy's performance. This chapter details just that. You will learn how to read vital trade statistics, and view the performance of your trading strategy over time. You will also learn how to get a reward to risk ratio called the Sharpe ratio in two different ways. This is the last chapter.

Exercise 1: Analyzing your strategy Exercise 2: Running your strategy Exercise 3: Profit factor Exercise 4: Percent positive Exercise 5: Visualizing your strategy Exercise 6: Using chart.Posn()Exercise 7: Adding an indicator to a chart.Posn() chart Exercise 8: Additional analytics Exercise 9: Cash Sharpe ratio Exercise 10: Returns Sharpe ratio in quantstrat