True digits

In the binary representation of a number.**Exponentiation: Raise one integer to the power of another.*/%Basic arithmetic operators:Multiplication.Division.Remainder (modulo).+-Basic arithmetic operators:Addition.Subtraction.>> Bitwise shift left.Bitwise shift right.>=> Value comparison: Less than or equal to.Greater than or equal to.Less than.Greater than.==!=Equality: Returns true if the operands are equal. Inequality: Returns false if the operands are equal.&Bitwise AND: Multiplies the corresponding digits of two binary values.^Bitwise XOR: Compares the corresponding digits of two binary values and returns 1 if the digits differ.|Bitwise OR: Compares the corresponding digits of two binary values and returns 1 if either of the digits is a 1.&&Logical AND: Returns true if both operands are true.||Logical OR: Returns true if either of the operands is true.expr1 ? expr2 : expr3Conditional operator: If expr1 is true, return expr2. If expr1 is false, return expr3.=, *=, /=, %=, +=, -=, >=, &=, ^=, |=Assignment: Assign a value to a variable.Bash let ExamplesThe let command lets you assign values to variables and perform arithmetic, bitwise, and logical operations. Read the sections below for examples.Assigning a Value to a VariableUse the let command to assign a value to a variable with:let "[variable] = [value]"For instance, assign the value of 5 to the variable var1 and print out the value as the output:let "var1 = 5"; echo $var1Likewise, after setting a variable value, the let command lets you assign the same value to another variable:let "var1 = 5" "var2=var1"; echo $var2Performing Basic Arithmetic OperationsAfter setting variable values, use the let command to perform basic arithmetic operations. For example, set var1 as 6 and var2 as 2 and perform addition, subtraction, multiplication, division, and modulus:let "var1 = 6" "var2 = 2" "var3=var1+var2"; echo $var3let "var1 = 6" "var2 = 2" "var3=var1-var2"; echo $var3let "var1 = 6" "var2 = 2" "var3=var1*var2"; echo $var3let "var1 = 6" "var2

(M2, M3, M4, M6, M7, M8, M9 or M10) Apply template to offline chart (name of the template to apply to the range bars chart) Automatically open offline chart (true/false) Build history for a number of days Re-calculate chart on history updates Minimum chart update interval (defined in milliseconds) == Alerts and Info == Sound signal on a new bar (true/false) Only signal reversal bars (true/false) Use Alert Window to display signal (true/false) Sound file to play on a bullish bar Sound file to play on a bearish bar The color of High threshold indicator The color of Low threshold indicator *) EAs can be used directly on the offline chart however a small tweak is required, so you need to have access to your EA source code. Below is an EA skeleton example that is capable of running directly on the offline chart: #property strictint OnInit(){ EventSetMillisecondTimer(250); return(INIT_SUCCEEDED);}void OnDeinit(const int reason){ EventKillTimer(); }void OnTick(){ Print("OnTick function call - bid = "+(string)Bid);}void OnTimer(){ RefreshRates(); static double prevBid = 0; if(Bid != prevBid) { prevBid = Bid; OnTick(); } } Version 1.24 2021.07.19 Fixed problem with ATR% bars on charts not fully synchronized with the broker's server. Version 1.23 2021.05.19 Added new dynamic renko body size algorithm (price % as used on Definedge TradePoint);Optimized indicator inputs. Version 1.22 2020.12.03 Update "Truncate trailing digits on the first renko" to include truncating digits before the decimal point Version 1.21 2020.11.16 Fixed "Truncate trailing digits on the first renko" bug,Added additional timeframes for ATR based brick size calculation. Version 1.20 2020.09.25 Replaced optional first pip offset by more intuitive "Truncate trailing digits on the first renko" & "Number of digits to truncate" combination. The setting will also be applied on each new trading day if "Reset Open on each trading day" is enabled as well. Version 1.19 2019.06.04 Added option for overwriting the start time of trading day Version 1.18 2019.05.27 Added additional synchronization setting “Build history starting from date”. This setting overrides the value set in “Build history for number of days” and lets you precisely define the chart's anchor point in history

Decimal separator. For example, Format(0.4744, "Percent") returns 47.44%.P, or pDisplays number with thousandths separator multiplied by 100 with a percent sign (%) appended to the right and separated by a single space; always displays two digits to the right of the decimal separator. For example, Format(0.80345, "p") returns 80.35 %.ScientificUses standard scientific notation, providing two significant digits. For example, Format(1234567, "Scientific") returns 1.23E+06.E, or eUses standard scientific notation, providing six significant digits. For example, Format(1234567, "e") returns 1.234567e+006.D, or dDisplays number as a string that contains the value of the number in Decimal (base 10) format. This option is supported for integral types (Byte, Short, Integer, Long) only. For example, Format(&H7F, "d") returns 127.X, or xDisplays number as a string that contains the value of the number in Hexadecimal (base 16) format. This option is supported for integral types (Byte, Short, Integer, Long) only. For example, Format(127, "x") returns 7f.Yes/NoDisplays No if number is 0; otherwise, displays Yes. For example, Format(0, "Yes/No") returns No.True/FalseDisplays False if number is 0; otherwise, displays True. For example, Format(1, "True/False") returns True.On/OffDisplays Off if number is 0; otherwise, displays On. For example, Format(1, "On/Off") returns On.Smart Device Developer NotesThe Yes/No, True/False, and On/Off formats are not supported.User-Defined Numeric FormatsThe following table identifies characters you can use to create user-defined number formats. These may be used to build the Style argument for the Format function:CharacterDescriptionNoneDisplays the number with no formatting.(0)Digit placeholder. Displays a digit or a zero. If the expression has a digit in the

Digits taken from a 1925 census report. Other researchers followed suit in the following two decades, publishing tables of numbers created through methods such as picking digits from the results of logarithm tables up to 20 decimal places. However, many of these early resources contained bias and disproportionate distributions of digits. British statisticians Maurice Kendall and Bernard Babington-Smith in 1938 proposed four tests (including the frequency test and a version of the runs test) to assess the uniformity and independence of a table of numbers and thus to determine whether it was truly random. The pair of statisticians also reportedly created the first table of random numbers using a machine: a rotating cardboard disk divided into 10 sections labeled with a single digit would be illuminated at random, and an observer would record the highlighted number. The advent of computers in the 1940s heralded a revolution in random number generation. In 1947 the RAND Corporation created the first fully automated table of random digits using an electronic device that randomly emitted pulses, which were counted by a computer and punched into cards. (The table was published as a 400-page book, A Million Random Digits with 100,000 Normal Deviates [1955], with 50 lines of 50 digits per page.) Today, most random number generators involve some type of automation in their production process. Two types of generators are used in modern times. The first, true random number generation, is based on a non-deterministic pattern produced by a physical source, such as radio static, voltage fluctuations from thermal motion in an electric circuit, or radioactive decay of an isotope. This unpredictable sequence is then processed and translated by a function to produce random digits. The second method, pseudorandom number generation, uses an algorithm that produces a string of numbers from a predetermined sequence of digits. As a deterministic process, one or more inputs called seeds are used to determine which distribution of values the random digits are drawn from. The reliance on seeding is why the process is said to be “pseudorandom”; if the seed were known, the values could theoretically be predicted