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Course

Financial Trading in R

IntermediateSkill Level

4.8+

Updated 09/2024

This course covers the basics of financial trading and how to use quantstrat to build signal-based trading strategies.

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RApplied Finance5 hr20 videos65 Exercises5,050 XP22,613Statement of Accomplishment

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Course Description

This course will cover the basics on financial trading and will give you an overview of how to use quantstrat to build signal-based trading strategies in R. It will teach you how to set up a quantstrat strategy, apply transformations of market data called indicators, create signals based on the interactions of those indicators, and even simulate orders. Lastly, it will explain how to analyze your results both from statistical and visual perspectives.

Prerequisites

Intermediate R for Finance

1

Trading basics

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.

Why do people trade?

Identifying types of trading philosophies - I

Identifying types of trading philosophies - II

Identifying types of trading philosophies - III

Pitfalls of various trading systems

How to prevent overfitting

Getting financial data

Plotting financial data

Adding indicators to financial data

Adding a moving average to financial data

2

A boilerplate for quantstrat strategies

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.

Setting up a strategy I

Understanding initialization settings - I

Understanding initialization settings - II

Setting up a strategy II

Understanding initialization settings - III

Understanding initialization settings - IV

3

Indicators

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.

Introduction to indicators

The SMA and RSI functions

Visualize an indicator and guess its purpose - I

Visualize an indicator and guess its purpose - II

Indicator mechanics

Implementing an indicator - I

Implementing an indicator - II

Implementing an indicator - III

Indicator structure review

Code your own indicator - I

Code your own indicator - II

Apply your own indicator

4

Signals

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.

Introduction to signals

Signal or not? - I

Signal or not? - II

sigComparison and sigCrossover

Using sigComparison

Using sigCrossover

sigThreshold

Using sigThreshold - I

Using sigThreshold() - II

Using sigFormula()

Combining signals - I

Combining signals - II

5

Rules

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.

Introduction to rules

Using add.rule() to implement an exit rule

Specifying sigcol in add.rule()

Specifying sigval in add.rule()

More rule mechanics

Specifying orderqty in add.rule()

Specifying ordertype in add.rule()

Specifying orderside in add.rule()

More rule mechanics II

Specifying replace in add.rule()

Specifying prefer in add.rule()

Using add.rule() to implement an entry rule

Order sizing functions

Implementing a rule with an order sizing function

6

Analyzing results

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.

Analyzing your strategy

Running your strategy

Profit factor

Percent positive

Visualizing your strategy

Using chart.Posn()

Adding an indicator to a chart.Posn() chart

Additional analytics

Cash Sharpe ratio

Returns Sharpe ratio in quantstrat

Financial Trading in R

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Excellent course easy to understand and very useful.

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explained extremely well

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FAQs

What R package is used to build trading strategies in this course?

You use the quantstrat package to set up signal-based trading strategies, including defining indicators, creating signals, writing trading rules, and simulating orders.

Do I need prior trading experience to take this course?

No. Chapter 1 introduces trading basics including momentum and oscillation philosophies, common pitfalls, and overfitting before you begin building strategies in quantstrat.

What are the four types of signals covered in this course?

You learn sigComparison, sigCrossover, sigThreshold, and sigFormula, which define how market data interacts with indicators and how indicators interact with each other.

How is strategy performance evaluated?

Chapter 6 teaches you to read trade statistics, view performance over time, and calculate the Sharpe ratio, a key reward-to-risk metric, using two different methods.

How long is this course compared to other DataCamp courses?

It is larger than average with 6 chapters and 67 exercises. The median completion time is about 2.7 hours, covering trading basics through full strategy analysis.

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