Relative strength is a measure of how strong a stock is compared to an index, sector, industry or another stock. Relative strength can be calculated by taking the stock's price change over a period and comparing it with the price change of another asset during the same period. The other asset could be the stock's industry index, the S&P 500 index or a competitor. Three lines of code are necessary to implemented this indicator in QuantShare programming language, but our goal here is to create a custom function so that we can easily use the relative strength indicator when backtesting trading systems for example. p = 250; a = GetSeries('^GSPC', close); // Get S&P 500 price series b = perf(close, p) - perf(a, p); To avoid typing the above formula each time you want to create a portfolio or backtest a strategy, I will show you how to create a custom indicator and reference the strength index with a single line of code: Example: a = RS("^GSPC", 20); Before continuing, have a look at our previous post: The custom trading indicators tool explained . You will learn the basics and the different steps required to create a custom trading indicator. Now that you know how to create a custom indicator item; create one then name it "RS". In the rest of this article, I will show you two different ways to implement the relative strength indicator. Relative Strength Version 1 The first implementation consists of compiling and executing a QuantShare formula and then getting a time-series to return it (or further process it). The "cFunctions" variable contains two functions that we can use here: "CompileFormula" and "CreateFormula". CompileFormula: Creates then compiles a QuantShare formula CreateFormula: Creates a QuantShare formula The second function should be used in case you want to pass a non-fixed parameter to the underlying formula. In the case of the relative strength, the asset symbol name and the period are fixed and therefore we can directly compile our formula. QSFormula formula = cFunctions.CompileFormula("p = 250; a = GetSeries('^GSPC', close); b = perf(close, p) - perf(a, p);"); Add two parameters to this indicator by clicking on "Add a parameter" button twice (In "Create Functions" control). Under "Variables" panel, type "symbol" in the first cell of the first row and then type "period" in the first cell of the second row. Change the parameter type to "String" in the first row. Here is the complete formula: QSFormula formula = cFunctions.CompileFormula("p = " + period[0] + "; a = GetSeries('" + symbol[0] + "', close); b = perf(close, p) - perf(a, p);"); result = formula.GetVectorDouble("b"); In the first line, I have replaced the static values (250 and ^GSPC) with the values provided in the indicator parameters (period and symbol). These parameters are vectors and therefore in order to get a numeric and text values, I have used "[0]" to obtain the first value of each one of these vectors. In the second line, we obtain the relative strength indicator by getting the vector stored in the variable "b". Relative Strength Version 2: This version also requires that we add two parameters; follow the previous section to learn how to add a parameter. The second implementation is based on the "cFunctions" and "TA" variables. The variable "TA" contains a list of build-in indicator functions. How to get another asset or index price series: VectorCustomDouble v = cFunctions.GetTimeframeData(symbol[0], cFunctions.Timeframe, "close"); Param 1: We request data for the asset (symbol[0]) Param 2: We request data for the default time frame Param 3: We request the "close" price series "VectorCustomDouble" item is different from the "VectorD" item in that it may contain more than one element/value in a specific bar. For example, in case we are working with weekly data and we use "GetTimeframeData" function to reference a daily period, the "VectorCustomDouble" variable will get five values for each trading bar (Trading data for Monday, Tuesday, Wednesday, Thursday and Friday). We can convert a "VectorCustomDouble" variable to "VectorD" by calling the "ToVectorD" method. Here is the complete formula: VectorCustomDouble v = cFunctions.GetTimeframeData(symbol[0], cFunctions.Timeframe, "close"); VectorD a = v.ToVectorD(); result = TA.Perf(cFunctions.Close, period) - TA.Perf(a, period); If you ask which method is the most efficient, I will tell you that the second method is the most efficient one in terms of performance and memory consumption.
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