The SMA and RSI functions

Welcome to the chapter on indicators! An indicator is a transformation of market data that is used to generate signals or filter noise. Indicators form the backbone of many trading systems, and the system you will build in this course uses several of them.

The simple moving average (SMA) and relative strength index (RSI) are two classic indicators. As you saw in Chapter 1, the SMA is an arithmetic moving average of past prices, while the RSI is a bounded oscillating indicator that ranges from 0 to 100. Their respective functions SMA() and RSI() both take in a series of prices, denoted by x and price respectively, and a lookback period n, for example:

SMA(x = Cl(GDX), n = 50)
RSI(price = Cl(GDX), n = 50)

In this exercise, you will practice calling the base functions for these indicators. The quantmod and TTR packages and SPY data have been loaded for you.

This exercise is part of the course

Financial Trading in R

Exercise instructions

Create a 200-day SMA of the closing price of SPY. Call this spy_sma.
Create an RSI with a lookback period n of 3 days using the closing price of SPY. Call this spy_rsi.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create a 200-day SMA
spy_sma <- SMA(___)

# Create an RSI with a 3-day lookback period
spy_rsi <- RSI(___)

Edit and Run Code

This exercise is part of the course

Financial Trading in R

IntermediateSkill Level

4.6+

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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: Why do people trade?Exercise 2: Identifying types of trading philosophies - I Exercise 3: Identifying types of trading philosophies - II Exercise 4: Identifying types of trading philosophies - III Exercise 5: Pitfalls of various trading systems Exercise 6: How to prevent overfitting Exercise 7: Getting financial data Exercise 8: Plotting financial data Exercise 9: Adding indicators to financial data Exercise 10: Adding a moving average to financial data

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

Current Exercise

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