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  3. Financial Trading in R
  • 1

    Trading basics

    Gratuito

    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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

Initializing

Exercicio

Code your own indicator - II

While the RSI is decent, it is somewhat outdated as far as indicators go. In this exercise, you will code a simplified version of another indicator from scratch. The indicator is called the David Varadi Oscillator (DVO), originated by David Varadi, a quantitative research director.

The purpose of this oscillator is similar to something like the RSI in that it attempts to find opportunities to buy a temporary dip and sell in a temporary uptrend. In addition to obligatory market data, an oscillator function takes in two lookback periods.

First, the function computes a ratio between the closing price and average of high and low prices. Next, it applies an SMA to that quantity to smooth out noise, usually on a very small time frame, such as two days. Finally, it uses the runPercentRank() function to take a running percentage rank of this average ratio, and multiplies it by 100 to convert it to a 0-100 quantity.

Think about the way that students get percentile scores after taking a standardized test (that is, if a student got an 800 on her math section, she might be in the 95th percentile nationally). runPercentRank() does the same thing, except over time. This indicator provides the rank for the latest observation when taken in the context over some past period that the user specifies. For example, if something has a runPercentRank value of .90 when using a lookback period of 126, it means it's in the 90th percentile when compared to itself and the past 125 observations.

Your job is to implement this indicator and save it as DVO. Some of the necessary code has been provided, and the quantstrat, TTR, and quantmod packages are loaded into your workspace.

Instruções

100 XP
  • Create and name a function, DVO, for the indicator described above. The three arguments for your function will be HLC, navg (default to 2), and percentlookback (default to 126).
  • The ratio of the close (Cl()) of HLC divided by the average of the high (Hi()) and low (Lo()) prices is computed for you.
  • Use SMA() to implement a moving average of this ratio, parameterized by the navg argument. Save this as avgratio.
  • Use runPercentRank() to implement a percentage ranking system for avgratio.