sigFormula() gebruiken

De laatste signaalfunctie is wat vrijer van vorm. De functie sigFormula() gebruikt string-evaluatie en biedt daardoor enorme flexibiliteit om allerlei indicatoren en signalen die je al aan je strategie hebt toegevoegd te combineren tot samengestelde signalen. Hoewel zo’n catch-all-functionaliteit in het begin ingewikkeld kan lijken, blijkt een sigFormula-signaal met een goede implementatie en naamgeving eigenlijk de eenvoudigste logische programmeerinstructie, verpakt in wat quantstrat-syntaxis.

In deze oefening proef je wat de functie sigFormula kan door de logica handmatig te doorlopen. Je moet de functies applyIndicators() en applySignals() gebruiken.

Deze oefening maakt deel uit van de cursus

Financieel traden in R

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Oefeninstructies

Gebruik applyIndicators() met de open, high, low en close van SPY om een gegevenssetobject met de naam test_init te genereren.
Gebruik applySignals() met test_init om de signalen toe te passen die je in dit hoofdstuk hebt geschreven. Sla deze nieuwe gegevensset op als test.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create your dataset: test
test_init <- applyIndicators(strategy.st, mktdata = OHLC(___))
test <- applySignals(strategy = strategy.st, mktdata = ___)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Financieel traden in R

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Begin de cursus 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: 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 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: Introductie tot signals Exercise 2: Signaal of niet? - I Exercise 3: Signaal of niet? - II Exercise 4: sigComparison en sigCrossover Exercise 5: sigComparison gebruiken Exercise 6: sigCrossover gebruiken Exercise 7: sigThreshold Exercise 8: sigThreshold gebruiken - I Exercise 9: sigThreshold() gebruiken - II Exercise 10: sigFormula Exercise 11: sigFormula() gebruiken

Huidige oefening

Exercise 12: Signalen combineren - I Exercise 13: Signalen combineren - 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