The "Mass Rules" feature of the rules analyzer is a tool that lets you create thousands of trading rules very quickly. You simply define masks and add timeseries, such as indicators, fundamental ratios, composites, and the mass rules generates a lot of trading rules. Once you have your list of rules, you can do the followings: 1/ Rules Analyzer  Backtest these rules using the rules analyzer  Choose the best ones to include in your trading system 2/ Simulator  Use the "ApplyRule" function to create a huge optimizable trading system  Use the simulator optimizer or the genetic algorithm or PBIL optimizer to create a profitable trading strategy Read the following blog posts for more information on the "ApplyRule" function: How to turn any ordinary trading strategy into a very profitable one How to choose which technical indicators to use in your trading system 3/ Market Timing  Use the "ApplyRule" function to create a market indicator (using the "comp" function)  Backtest the trading system based on an index (S&P 500 for example)  Analyze the market timing strategy 4/ Genetic Algorithm Optimizer  Use the GA or PBIL optimizer to find the best combination of rules (AI > Optimizer) How to use "Mass Rules" feature  In QuantShare main menu, select "Analysis" then "Rules Manager"  Create a new list of rules by clicking on "Create"  Type a name then click on "OK"  Select the new list then select "Mass rules" from the top menu  In the "Time Series" panel, add indicators using the "Add" button or click on "Add from TimeSeries Builder" to quickly add several indicators  In "Masks" panel, type a mask formula then click on "Create"  When done, click on "Create Rules" to add all generated rules in your list of rules More information can be found here: How to create and backtest thousands of trading rules in less than 10 minutes 10 masks to create thousands of trading rules 1 Moving Average The formula to create a 10bar simple moving average is: sma(close, 10) "close" variable (which refers to the security close price) can be replaced by any timeseries or indicator. Mask Formulas: mask > sma(mask, 10) mask < sma(mask, 10) 2 Two Moving Averages This mask uses two moving averages instead of one. Mask Formulas: sma(mask, 20) > sma(mask, 10) sma(mask, 20) < sma(mask, 10) You can create several variations by optimizing the moving average parameters. Steps:  Type the following mask: sma(mask, a) > sma(mask, b)  Click on "Create"  Select the mask rule  Type the "min", "max" and "step" values for each variable ("a" and "b") Example: "a" varies from 10 to 20 with a step of 10 "b" varies from 30 to 40 with a step of 10 3 Rate of Return The Rate of return is calculated using the "perf" function. Mask Formulas: perf(mask, 10) > 50 perf(mask, 10) < 50 The above rule compares the 10bar rate of return of an indicator with the 50 and 50 levels. 4 Increase, Decrease To detect an increase in a timeseries or indicator value, you must compare the current value with the previous one using the "ref" function. The "ref" function references past values of a variable. Mask Formulas: mask > ref(mask, 1) mask < ref(mask, 1) 5 Percent Rank The percent rank function calculates the percentage of observations or values, within a lookback period, that are below the current value of a given function. This trading indicator can be downloaded here: Percentile  Percent Rank of a Trading Indicator. It returns a value between 0 and 100, where a value of 100 means that the current bar value is higher than all the previous values (Period defined in the second parameter of the "PercentRank" function). Mask Formulas: percentrank(mask, 30) >= 90 percentrank(mask, 30) <= 10 6 Volatility There are several ways to calculate the volatility of an asset (stock, currency pair, ETF, futures...). These same methods can be applied to calculate the volatility of an indicator. Volatility measure the dispersion of a data points, it does not measure the direction of price or indicator changes. A widely used method to measure the volatility of a timeseries is the standard deviation. The standard deviation of a variable is calculated using the "stddev" function. Mask Formulas: percentrank(stddev(mask, 30), 30) >= 90 percentrank(stddev(mask, 30), 30) <= 10 Because the standard deviation values are likely to be very different from one indicator to another, I have used the "percentrank" function. See above for more information on this function (Percent Rank). 7 Threshold This mask simply compares the value of a variable with a threshold. It is the simplest mask rule you can create. Mask Formulas: mask > 70 mask < 30 This resulting rule will mainly work for indicators or timeseries that oscillate between 0 and 100. 8 Highest and Lowest "HHv" and "LLv" functions return the highest and lowest values of a security price or indicator over a given period. The next function returns 1 if the relative strength index makes a new 20bar high a = rsi(14) == hhv(rsi(14), 20); Mask Formulas: mask == hhv(mask, 20) mask == llv(mask, 20) 9 Crossover This mask signals crossovers between two timeseries. The "cross" function can be used to detect a crossover. Mask Formulas: cross(mask, sma(mask, 10)) The above rule returns a signal when an indicator crosses above its 10bar moving average 10 Linear Regression In the financial markets, the linear regression is an approach used to forecast future prices by estimating the trend given a previous set of data. The "LinearReg_Angle" function returns, for each trading bar, the angle of the line that represents the trend. The higher the angle, the bullish is the trend and the lower the angle, the bearish is the trend. Mask Formulas: LinearReg_Angle(mask, 20) > 50 LinearReg_Angle(mask, 20) < 50 Create even more rules for your trading system Beside the "mask" variable that will be replaced each time by the variable specified in "Time Series" panel of the "Mass Rules" control, you can also use the "mask1" variable. By combining "mask" and "mask1", you can use two different indicators or timeseries in the same rule. Example: TimeSeries: sma(10) sma(20) sma(30) Mask Formula: mask > mask1 Instead of creating three trading rules, this will generate six rules, which are: sma(10) > sma(20) sma(10) > sma(30) sma(20) > sma(10) sma(20) > sma(30) sma(30) > sma(10) sma(30) > sma(20) By adding 100 timeseries, you can create 9900 trading rules by simply using the above mask formula.
