light_logLight Log - A Defensive Programming Library for Pine Script
Overview
The Light Log library transforms Pine Script development by introducing structured logging and defensive programming patterns typically found in enterprise languages like C#. This library addresses a fundamental challenge in Pine Script: the lack of sophisticated error handling and debugging tools that developers expect when building complex trading systems.
At its core, Light Log provides three transformative capabilities that work together to create more reliable and maintainable code. First, it wraps all native Pine Script types in error-aware containers, allowing values to carry validation state alongside their data. Second, it offers a comprehensive logging system with severity levels and conditional rendering. Third, it includes defensive programming utilities that catch errors early and make code self-documenting.
The Philosophy of Errors as Values
Traditional Pine Script error handling relies on runtime errors that halt execution, making it difficult to build resilient systems that can gracefully handle edge cases. Light Log introduces a paradigm shift by treating errors as first-class values that flow through your program alongside regular data.
When you wrap a value using Light Log's type system, you're not just storing data – you're creating a container that can carry both the value and its validation state. For example, when you call myNumber.INT() , you receive an INT object that contains both the integer value and a Log object that can describe any issues with that value. This approach, inspired by functional programming languages, allows errors to propagate through calculations without causing immediate failures.
Consider how this changes error handling in practice. Instead of a calculation failing catastrophically when it encounters invalid input, it can produce a result object that contains both the computed value (which might be na) and a detailed log explaining what went wrong. Subsequent operations can check has_error() to decide whether to proceed or handle the error condition gracefully.
The Typed Wrapper System
Light Log provides typed wrappers for every native Pine Script type: INT, FLOAT, BOOL, STRING, COLOR, LINE, LABEL, BOX, TABLE, CHART_POINT, POLYLINE, and LINEFILL. These wrappers serve multiple purposes beyond simple value storage.
Each wrapper type contains two fields: the value field v holds the actual data, while the error field e contains a Log object that tracks the value's validation state. This dual nature enables powerful programming patterns. You can perform operations on wrapped values and accumulate error information along the way, creating an audit trail of how values were processed.
The wrapper system includes convenient methods for converting between wrapped and unwrapped values. The extension methods like INT() , FLOAT() , etc., make it easy to wrap existing values, while the from_INT() , from_FLOAT() methods extract the underlying values when needed. The has_error() method provides a consistent interface for checking whether any wrapped value has encountered issues during processing.
The Log Object: Your Debugging Companion
The Log object represents the heart of Light Log's debugging capabilities. Unlike simple string concatenation for error messages, the Log object provides a structured approach to building, modifying, and rendering diagnostic information.
Each Log object carries three essential pieces of information: an error type (info, warning, error, or runtime_error), a message string that can be built incrementally, and an active flag that controls conditional rendering. This structure enables sophisticated logging patterns where you can build up detailed diagnostic information throughout your script's execution and decide later whether and how to display it.
The Log object's methods support fluent chaining, allowing you to build complex messages in a readable way. The write() and write_line() methods append text to the log, while new_line() adds formatting. The clear() method resets the log for reuse, and the rendering methods ( render_now() , render_condition() , and the general render() ) control when and how messages appear.
Defensive Programming Made Easy
Light Log's argument validation functions transform how you write defensive code. Instead of cluttering your functions with verbose validation logic, you can use concise, self-documenting calls that make your intentions clear.
The argument_error() function provides strict validation that halts execution when conditions aren't met – perfect for catching programming errors early. For less critical issues, argument_log_warning() and argument_log_error() record problems without stopping execution, while argument_log_info() provides debug visibility into your function's behavior.
These functions follow a consistent pattern: they take a condition to check, the function name, the argument name, and a descriptive message. This consistency makes error messages predictable and helpful, automatically formatting them to show exactly where problems occurred.
Building Modular, Reusable Code
Light Log encourages a modular approach to Pine Script development by providing tools that make functions more self-contained and reliable. When functions validate their inputs and return wrapped values with error information, they become true black boxes that can be safely composed into larger systems.
The void_return() function addresses Pine Script's requirement that all code paths return a value, even in error handling branches. This utility function provides a clean way to satisfy the compiler while making it clear that a particular code path should never execute.
The static log pattern, initialized with init_static_log() , enables module-wide error tracking. You can create a persistent Log object that accumulates information across multiple function calls, building a comprehensive diagnostic report that helps you understand complex behaviors in your indicators and strategies.
Real-World Applications
In practice, Light Log shines when building sophisticated trading systems. Imagine developing a complex indicator that processes multiple data streams, performs statistical calculations, and generates trading signals. With Light Log, each processing stage can validate its inputs, perform calculations, and pass along both results and diagnostic information.
For example, a moving average calculation might check that the period is positive, that sufficient data exists, and that the input series contains valid values. Instead of failing silently or throwing runtime errors, it can return a FLOAT object that contains either the calculated average or a detailed explanation of why the calculation couldn't be performed.
Strategy developers benefit even more from Light Log's capabilities. Complex entry and exit logic often involves multiple conditions that must all be satisfied. With Light Log, each condition check can contribute to a comprehensive log that explains exactly why a trade was or wasn't taken, making strategy debugging and optimization much more straightforward.
Performance Considerations
While Light Log adds a layer of abstraction over raw Pine Script values, its design minimizes performance impact. The wrapper objects are lightweight, containing only two fields. The logging operations only consume resources when actually rendered, and the conditional rendering system ensures that production code can run with logging disabled for maximum performance.
The library follows Pine Script best practices for performance, using appropriate data structures and avoiding unnecessary operations. The var keyword in init_static_log() ensures that persistent logs don't create new objects on every bar, maintaining efficiency even in real-time calculations.
Getting Started
Adopting Light Log in your Pine Script projects is straightforward. Import the library, wrap your critical values, add validation to your functions, and use Log objects to track important events. Start small by adding logging to a single function, then expand as you see the benefits of better error visibility and code organization.
Remember that Light Log is designed to grow with your needs. You can use as much or as little of its functionality as makes sense for your project. Even simple uses, like adding argument validation to key functions, can significantly improve code reliability and debugging ease.
Transform your Pine Script development experience with Light Log – because professional trading systems deserve professional development tools.
Light Log Technical Deep Dive: Advanced Patterns and Architecture
Understanding Errors as Values
The concept of "errors as values" represents a fundamental shift in how we think about error handling in Pine Script. In traditional Pine Script development, errors are events – they happen at a specific moment in time and immediately interrupt program flow. Light Log transforms errors into data – they become information that flows through your program just like any other value.
This transformation has profound implications. When errors are values, they can be stored, passed between functions, accumulated, transformed, and inspected. They become part of your program's data flow rather than exceptions to it. This approach, popularized by languages like Rust with its Result type and Haskell with its Either monad, brings functional programming's elegance to Pine Script.
Consider a practical example. Traditional Pine Script might calculate a momentum indicator like this:
momentum = close - close
If period is invalid or if there isn't enough historical data, this calculation might produce na or cause subtle bugs. With Light Log's approach:
calculate_momentum(src, period)=>
result = src.FLOAT()
if period <= 0
result.e.write("Invalid period: must be positive", true, ErrorType.error)
result.v := na
else if bar_index < period
result.e.write("Insufficient data: need " + str.tostring(period) + " bars", true, ErrorType.warning)
result.v := na
else
result.v := src - src
result.e.write("Momentum calculated successfully", false, ErrorType.info)
result
Now the function returns not just a value but a complete computational result that includes diagnostic information. Calling code can make intelligent decisions based on both the value and its associated metadata.
The Monad Pattern in Pine Script
While Pine Script lacks the type system features to implement true monads, Light Log brings monadic thinking to Pine Script development. The wrapped types (INT, FLOAT, etc.) act as computational contexts that carry both values and metadata through a series of transformations.
The key insight of monadic programming is that you can chain operations while automatically propagating context. In Light Log, this context is the error state. When you have a FLOAT that contains an error, operations on that FLOAT can check the error state and decide whether to proceed or propagate the error.
This pattern enables what functional programmers call "railway-oriented programming" – your code follows a success track when all is well but can switch to an error track when problems occur. Both tracks lead to the same destination (a result with error information), but they take different paths based on the validity of intermediate values.
Composable Error Handling
Light Log's design encourages composition – building complex functionality from simpler, well-tested components. Each component can validate its inputs, perform its calculation, and return a result with appropriate error information. Higher-level functions can then combine these results intelligently.
Consider building a complex trading signal from multiple indicators:
generate_signal(src, fast_period, slow_period, signal_period) =>
log = init_static_log(ErrorType.info)
// Calculate components with error tracking
fast_ma = calculate_ma(src, fast_period)
slow_ma = calculate_ma(src, slow_period)
// Check for errors in components
if fast_ma.has_error()
log.write_line("Fast MA error: " + fast_ma.e.message, true)
if slow_ma.has_error()
log.write_line("Slow MA error: " + slow_ma.e.message, true)
// Proceed with calculation if no errors
signal = 0.0.FLOAT()
if not (fast_ma.has_error() or slow_ma.has_error())
macd_line = fast_ma.v - slow_ma.v
signal_line = calculate_ma(macd_line, signal_period)
if signal_line.has_error()
log.write_line("Signal line error: " + signal_line.e.message, true)
signal.e := log
else
signal.v := macd_line - signal_line.v
log.write("Signal generated successfully")
else
signal.e := log
signal.v := na
signal
This composable approach makes complex calculations more reliable and easier to debug. Each component is responsible for its own validation and error reporting, and the composite function orchestrates these components while maintaining comprehensive error tracking.
The Static Log Pattern
The init_static_log() function introduces a powerful pattern for maintaining state across function calls. In Pine Script, the var keyword creates variables that persist across bars but are initialized only once. Light Log leverages this to create logging objects that can accumulate information throughout a script's execution.
This pattern is particularly valuable for debugging complex strategies where you need to understand behavior across multiple bars. You can create module-level logs that track important events:
// Module-level diagnostic log
diagnostics = init_static_log(ErrorType.info)
// Track strategy decisions across bars
check_entry_conditions() =>
diagnostics.clear() // Start fresh each bar
diagnostics.write_line("Bar " + str.tostring(bar_index) + " analysis:")
if close > sma(close, 20)
diagnostics.write_line("Price above SMA20", false)
else
diagnostics.write_line("Price below SMA20 - no entry", true, ErrorType.warning)
if volume > sma(volume, 20) * 1.5
diagnostics.write_line("Volume surge detected", false)
else
diagnostics.write_line("Normal volume", false)
// Render diagnostics based on verbosity setting
if debug_mode
diagnostics.render_now()
Advanced Validation Patterns
Light Log's argument validation functions enable sophisticated precondition checking that goes beyond simple null checks. You can implement complex validation logic while keeping your code readable:
validate_price_data(open_val, high_val, low_val, close_val) =>
argument_error(na(open_val) or na(high_val) or na(low_val) or na(close_val),
"validate_price_data", "OHLC values", "contain na values")
argument_error(high_val < low_val,
"validate_price_data", "high/low", "high is less than low")
argument_error(close_val > high_val or close_val < low_val,
"validate_price_data", "close", "is outside high/low range")
argument_log_warning(high_val == low_val,
"validate_price_data", "high/low", "are equal (no range)")
This validation function documents its requirements clearly and fails fast with helpful error messages when assumptions are violated. The mix of errors (which halt execution) and warnings (which allow continuation) provides fine-grained control over how strict your validation should be.
Performance Optimization Strategies
While Light Log adds abstraction, careful design minimizes overhead. Understanding Pine Script's execution model helps you use Light Log efficiently.
Pine Script executes once per bar, so operations that seem expensive in traditional programming might have negligible impact. However, when building real-time systems, every optimization matters. Light Log provides several patterns for efficient use:
Lazy Evaluation: Log messages are only built when they'll be rendered. Use conditional logging to avoid string concatenation in production:
if debug_mode
log.write_line("Calculated value: " + str.tostring(complex_calculation))
Selective Wrapping: Not every value needs error tracking. Wrap values at API boundaries and critical calculation points, but use raw values for simple operations:
// Wrap at boundaries
input_price = close.FLOAT()
validated_period = validate_period(input_period).INT()
// Use raw values internally
sum = 0.0
for i = 0 to validated_period.v - 1
sum += close
Error Propagation: When errors occur early, avoid expensive calculations:
process_data(input) =>
validated = validate_input(input)
if validated.has_error()
validated // Return early with error
else
// Expensive processing only if valid
perform_complex_calculation(validated)
Integration Patterns
Light Log integrates smoothly with existing Pine Script code. You can adopt it incrementally, starting with critical functions and expanding coverage as needed.
Boundary Validation: Add Light Log at the boundaries of your system – where user input enters and where final outputs are produced. This catches most errors while minimizing changes to existing code.
Progressive Enhancement: Start by adding argument validation to existing functions. Then wrap return values. Finally, add comprehensive logging. Each step improves reliability without requiring a complete rewrite.
Testing and Debugging: Use Light Log's conditional rendering to create debug modes for your scripts. Production users see clean output while developers get detailed diagnostics:
// User input for debug mode
debug = input.bool(false, "Enable debug logging")
// Conditional diagnostic output
if debug
diagnostics.render_now()
else
diagnostics.render_condition() // Only shows errors/warnings
Future-Proofing Your Code
Light Log's patterns prepare your code for Pine Script's evolution. As Pine Script adds more sophisticated features, code that uses structured error handling and defensive programming will adapt more easily than code that relies on implicit assumptions.
The type wrapper system, in particular, positions your code to take advantage of potential future features or more sophisticated type inference. By thinking in terms of wrapped values and error propagation today, you're building code that will remain maintainable and extensible tomorrow.
Light Log doesn't just make your Pine Script better today – it prepares it for the trading systems you'll need to build tomorrow.
Library "light_log"
A lightweight logging and defensive programming library for Pine Script.
Designed for modular and extensible scripts, this utility provides structured runtime validation,
conditional logging, and reusable `Log` objects for centralized error propagation.
It also introduces a typed wrapping system for all native Pine values (e.g., `INT`, `FLOAT`, `LABEL`),
allowing values to carry errors alongside data. This enables functional-style flows with built-in
validation tracking, error detection (`has_error()`), and fluent chaining.
Inspired by structured logging patterns found in systems like C#, it reduces boilerplate,
enforces argument safety, and encourages clean, maintainable code architecture.
method INT(self, error_type)
Wraps an `int` value into an `INT` struct with an optional log severity.
Namespace types: series int, simple int, input int, const int
Parameters:
self (int) : The raw `int` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: An `INT` object containing the value and a default Log instance.
method FLOAT(self, error_type)
Wraps a `float` value into a `FLOAT` struct with an optional log severity.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : The raw `float` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `FLOAT` object containing the value and a default Log instance.
method BOOL(self, error_type)
Wraps a `bool` value into a `BOOL` struct with an optional log severity.
Namespace types: series bool, simple bool, input bool, const bool
Parameters:
self (bool) : The raw `bool` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `BOOL` object containing the value and a default Log instance.
method STRING(self, error_type)
Wraps a `string` value into a `STRING` struct with an optional log severity.
Namespace types: series string, simple string, input string, const string
Parameters:
self (string) : The raw `string` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `STRING` object containing the value and a default Log instance.
method COLOR(self, error_type)
Wraps a `color` value into a `COLOR` struct with an optional log severity.
Namespace types: series color, simple color, input color, const color
Parameters:
self (color) : The raw `color` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `COLOR` object containing the value and a default Log instance.
method LINE(self, error_type)
Wraps a `line` object into a `LINE` struct with an optional log severity.
Namespace types: series line
Parameters:
self (line) : The raw `line` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LINE` object containing the value and a default Log instance.
method LABEL(self, error_type)
Wraps a `label` object into a `LABEL` struct with an optional log severity.
Namespace types: series label
Parameters:
self (label) : The raw `label` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LABEL` object containing the value and a default Log instance.
method BOX(self, error_type)
Wraps a `box` object into a `BOX` struct with an optional log severity.
Namespace types: series box
Parameters:
self (box) : The raw `box` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `BOX` object containing the value and a default Log instance.
method TABLE(self, error_type)
Wraps a `table` object into a `TABLE` struct with an optional log severity.
Namespace types: series table
Parameters:
self (table) : The raw `table` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `TABLE` object containing the value and a default Log instance.
method CHART_POINT(self, error_type)
Wraps a `chart.point` value into a `CHART_POINT` struct with an optional log severity.
Namespace types: chart.point
Parameters:
self (chart.point) : The raw `chart.point` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `CHART_POINT` object containing the value and a default Log instance.
method POLYLINE(self, error_type)
Wraps a `polyline` object into a `POLYLINE` struct with an optional log severity.
Namespace types: series polyline, series polyline, series polyline, series polyline
Parameters:
self (polyline) : The raw `polyline` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `POLYLINE` object containing the value and a default Log instance.
method LINEFILL(self, error_type)
Wraps a `linefill` object into a `LINEFILL` struct with an optional log severity.
Namespace types: series linefill
Parameters:
self (linefill) : The raw `linefill` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LINEFILL` object containing the value and a default Log instance.
method from_INT(self)
Extracts the integer value from an INT wrapper.
Namespace types: INT
Parameters:
self (INT) : The wrapped INT instance.
Returns: The underlying `int` value.
method from_FLOAT(self)
Extracts the float value from a FLOAT wrapper.
Namespace types: FLOAT
Parameters:
self (FLOAT) : The wrapped FLOAT instance.
Returns: The underlying `float` value.
method from_BOOL(self)
Extracts the boolean value from a BOOL wrapper.
Namespace types: BOOL
Parameters:
self (BOOL) : The wrapped BOOL instance.
Returns: The underlying `bool` value.
method from_STRING(self)
Extracts the string value from a STRING wrapper.
Namespace types: STRING
Parameters:
self (STRING) : The wrapped STRING instance.
Returns: The underlying `string` value.
method from_COLOR(self)
Extracts the color value from a COLOR wrapper.
Namespace types: COLOR
Parameters:
self (COLOR) : The wrapped COLOR instance.
Returns: The underlying `color` value.
method from_LINE(self)
Extracts the line object from a LINE wrapper.
Namespace types: LINE
Parameters:
self (LINE) : The wrapped LINE instance.
Returns: The underlying `line` object.
method from_LABEL(self)
Extracts the label object from a LABEL wrapper.
Namespace types: LABEL
Parameters:
self (LABEL) : The wrapped LABEL instance.
Returns: The underlying `label` object.
method from_BOX(self)
Extracts the box object from a BOX wrapper.
Namespace types: BOX
Parameters:
self (BOX) : The wrapped BOX instance.
Returns: The underlying `box` object.
method from_TABLE(self)
Extracts the table object from a TABLE wrapper.
Namespace types: TABLE
Parameters:
self (TABLE) : The wrapped TABLE instance.
Returns: The underlying `table` object.
method from_CHART_POINT(self)
Extracts the chart.point from a CHART_POINT wrapper.
Namespace types: CHART_POINT
Parameters:
self (CHART_POINT) : The wrapped CHART_POINT instance.
Returns: The underlying `chart.point` value.
method from_POLYLINE(self)
Extracts the polyline object from a POLYLINE wrapper.
Namespace types: POLYLINE
Parameters:
self (POLYLINE) : The wrapped POLYLINE instance.
Returns: The underlying `polyline` object.
method from_LINEFILL(self)
Extracts the linefill object from a LINEFILL wrapper.
Namespace types: LINEFILL
Parameters:
self (LINEFILL) : The wrapped LINEFILL instance.
Returns: The underlying `linefill` object.
method has_error(self)
Returns true if the INT wrapper has an active log entry.
Namespace types: INT
Parameters:
self (INT) : The INT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the FLOAT wrapper has an active log entry.
Namespace types: FLOAT
Parameters:
self (FLOAT) : The FLOAT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the BOOL wrapper has an active log entry.
Namespace types: BOOL
Parameters:
self (BOOL) : The BOOL instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the STRING wrapper has an active log entry.
Namespace types: STRING
Parameters:
self (STRING) : The STRING instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the COLOR wrapper has an active log entry.
Namespace types: COLOR
Parameters:
self (COLOR) : The COLOR instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LINE wrapper has an active log entry.
Namespace types: LINE
Parameters:
self (LINE) : The LINE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LABEL wrapper has an active log entry.
Namespace types: LABEL
Parameters:
self (LABEL) : The LABEL instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the BOX wrapper has an active log entry.
Namespace types: BOX
Parameters:
self (BOX) : The BOX instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the TABLE wrapper has an active log entry.
Namespace types: TABLE
Parameters:
self (TABLE) : The TABLE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the CHART_POINT wrapper has an active log entry.
Namespace types: CHART_POINT
Parameters:
self (CHART_POINT) : The CHART_POINT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the POLYLINE wrapper has an active log entry.
Namespace types: POLYLINE
Parameters:
self (POLYLINE) : The POLYLINE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LINEFILL wrapper has an active log entry.
Namespace types: LINEFILL
Parameters:
self (LINEFILL) : The LINEFILL instance to check.
Returns: True if an error or message is active in the log.
void_return()
Utility function used when a return is syntactically required but functionally unnecessary.
Returns: Nothing. Function never executes its body.
argument_error(condition, function, argument, message)
Throws a runtime error when a condition is met. Used for strict argument validation.
Parameters:
condition (bool) : Boolean expression that triggers the runtime error.
function (string) : Name of the calling function (for formatting).
argument (string) : Name of the problematic argument.
message (string) : Description of the error cause.
Returns: Never returns. Halts execution if the condition is true.
argument_log_info(condition, function, argument, message)
Logs an informational message when a condition is met. Used for optional debug visibility.
Parameters:
condition (bool) : Boolean expression that triggers the log.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Informational message to log.
Returns: Nothing. Logs if the condition is true.
argument_log_warning(condition, function, argument, message)
Logs a warning when a condition is met. Non-fatal but highlights potential issues.
Parameters:
condition (bool) : Boolean expression that triggers the warning.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Warning message to log.
Returns: Nothing. Logs if the condition is true.
argument_log_error(condition, function, argument, message)
Logs an error message when a condition is met. Does not halt execution.
Parameters:
condition (bool) : Boolean expression that triggers the error log.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Error message to log.
Returns: Nothing. Logs if the condition is true.
init_static_log(error_type, message, active)
Initializes a persistent (var) Log object. Ideal for global logging in scripts or modules.
Parameters:
error_type (series ErrorType) : Initial severity level (required).
message (string) : Optional starting message string. Default value of ("").
active (bool) : Whether the log should be flagged active on initialization. Default value of (false).
Returns: A static Log object with the given parameters.
method new_line(self)
Appends a newline character to the Log message. Useful for separating entries during chained writes.
Namespace types: Log
Parameters:
self (Log) : The Log instance to modify.
Returns: The updated Log object with a newline appended.
method write(self, message, flag_active, error_type)
Appends a message to a Log object without a newline. Updates severity and active state if specified.
Namespace types: Log
Parameters:
self (Log) : The Log instance being modified.
message (string) : The text to append to the log.
flag_active (bool) : Whether to activate the log for conditional rendering. Default value of (false).
error_type (series ErrorType) : Optional override for the severity level. Default value of (na).
Returns: The updated Log object.
method write_line(self, message, flag_active, error_type)
Appends a message to a Log object, prefixed with a newline for clarity.
Namespace types: Log
Parameters:
self (Log) : The Log instance being modified.
message (string) : The text to append to the log.
flag_active (bool) : Whether to activate the log for conditional rendering. Default value of (false).
error_type (series ErrorType) : Optional override for the severity level. Default value of (na).
Returns: The updated Log object.
method clear(self, flag_active, error_type)
Clears a Log object’s message and optionally reactivates it. Can also update the error type.
Namespace types: Log
Parameters:
self (Log) : The Log instance being cleared.
flag_active (bool) : Whether to activate the log after clearing. Default value of (false).
error_type (series ErrorType) : Optional new error type to assign. If not provided, the previous type is retained. Default value of (na).
Returns: The cleared Log object.
method render_condition(self, flag_active, error_type)
Conditionally renders the log if it is active. Allows overriding error type and controlling active state afterward.
Namespace types: Log
Parameters:
self (Log) : The Log instance to evaluate and render.
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override. Useful for contextual formatting just before rendering. Default value of (na).
Returns: The updated Log object.
method render_now(self, flag_active, error_type)
Immediately renders the log regardless of `active` state. Allows overriding error type and active flag.
Namespace types: Log
Parameters:
self (Log) : The Log instance to render.
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override. Allows dynamic severity adjustment at render time. Default value of (na).
Returns: The updated Log object.
render(self, condition, flag_active, error_type)
Renders the log conditionally or unconditionally. Allows full control over render behavior.
Parameters:
self (Log) : The Log instance to render.
condition (bool) : If true, renders only if the log is active. If false, always renders. Default value of (false).
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override passed to the render methods. Default value of (na).
Returns: The updated Log object.
Log
A structured object used to store and render logging messages.
Fields:
error_type (series ErrorType) : The severity level of the message (from the ErrorType enum).
message (series string) : The text of the log message.
active (series bool) : Whether the log should trigger rendering when conditionally evaluated.
INT
A wrapped integer type with attached logging for validation or tracing.
Fields:
v (series int) : The underlying `int` value.
e (Log) : Optional log object describing validation status or error context.
FLOAT
A wrapped float type with attached logging for validation or tracing.
Fields:
v (series float) : The underlying `float` value.
e (Log) : Optional log object describing validation status or error context.
BOOL
A wrapped boolean type with attached logging for validation or tracing.
Fields:
v (series bool) : The underlying `bool` value.
e (Log) : Optional log object describing validation status or error context.
STRING
A wrapped string type with attached logging for validation or tracing.
Fields:
v (series string) : The underlying `string` value.
e (Log) : Optional log object describing validation status or error context.
COLOR
A wrapped color type with attached logging for validation or tracing.
Fields:
v (series color) : The underlying `color` value.
e (Log) : Optional log object describing validation status or error context.
LINE
A wrapped line object with attached logging for validation or tracing.
Fields:
v (series line) : The underlying `line` value.
e (Log) : Optional log object describing validation status or error context.
LABEL
A wrapped label object with attached logging for validation or tracing.
Fields:
v (series label) : The underlying `label` value.
e (Log) : Optional log object describing validation status or error context.
BOX
A wrapped box object with attached logging for validation or tracing.
Fields:
v (series box) : The underlying `box` value.
e (Log) : Optional log object describing validation status or error context.
TABLE
A wrapped table object with attached logging for validation or tracing.
Fields:
v (series table) : The underlying `table` value.
e (Log) : Optional log object describing validation status or error context.
CHART_POINT
A wrapped chart point with attached logging for validation or tracing.
Fields:
v (chart.point) : The underlying `chart.point` value.
e (Log) : Optional log object describing validation status or error context.
POLYLINE
A wrapped polyline object with attached logging for validation or tracing.
Fields:
v (series polyline) : The underlying `polyline` value.
e (Log) : Optional log object describing validation status or error context.
LINEFILL
A wrapped linefill object with attached logging for validation or tracing.
Fields:
v (series linefill) : The underlying `linefill` value.
e (Log) : Optional log object describing validation status or error context.
"entry" için komut dosyalarını ara
CVD Divergence & Volume ProfileThis Pine Script indicator, named "CVD Divergence & Volume Profile," is designed to identify potential trading opportunities by combining Cumulative Volume Delta (CVD) divergence with Volume Profile levels and an optional Simple Moving Average (SMA) trend filter. It plots signals directly on the price chart.
Here's a breakdown of what each component does and how to potentially trade with it:
1. Cumulative Volume Delta (CVD) Divergence
What it does: CVD measures the cumulative difference between buying and selling volume. A rising CVD indicates more buying pressure, while a falling CVD indicates more selling pressure. Divergence occurs when the price action contradicts the CVD's direction, suggesting a potential shift in momentum or trend reversal.
Bearish Divergence: The price makes a higher high, but the CVD makes a lower high (or fails to make a new high). This suggests that despite the price increasing, the underlying buying pressure is weakening.
Bullish Divergence: The price makes a lower low, but the CVD makes a higher low (or fails to make a new low). This suggests that despite the price decreasing, the underlying selling pressure is weakening.
Visualization:
Red triangle pointing down on the chart indicates a Bearish Divergence signal.
Green triangle pointing up on the chart indicates a Bullish Divergence signal.
2. Volume Profile Levels (VAH, VAL, POC)
What it does: The indicator calculates simplified Volume Profile levels over a user-defined vp_range (number of candles). These levels represent areas where significant trading activity has occurred:
VAH (Value Area High): The upper boundary of the "Value Area," where 70% of the volume traded.
VAL (Value Area Low): The lower boundary of the "Value Area," where 70% of the volume traded.
POC (Point of Control): The price level within the vp_range where the most volume was traded.
Significance: These levels often act as significant support and resistance zones.
Visualization:
Orange lines for VAH and VAL.
Yellow line for POC.
Zone Proximity (zone_thresh): The indicator only generates divergence signals if the current close price is within a specified percentage zone_thresh of either VAH, VAL, or POC. This filters signals to areas of high liquidity and potential turning points.
3. Trend Filter (SMA)
What it does: This is an optional filter (use_trend_filter) that uses a Simple Moving Average (sma_period, default 200).
Significance: It helps ensure that divergence signals are traded in alignment with the broader market trend, potentially increasing their reliability.
For long signals (bullish divergence), the price (close) must be above the SMA (indicating an uptrend).
For short signals (bearish divergence), the price (close) must be below the SMA (indicating a downtrend).
Visualization: A blue line on the chart representing the SMA.
How to Trade with It (Potential Strategies)
The indicator aims to provide high-probability entry points by combining multiple confirming factors. Here's how you might interpret and trade the signals:
Identify Divergence: Look for the triangle signals on your chart (red for bearish, green for bullish).
Confirm Proximity to Volume Profile Levels: The signal itself confirms that the price is near a significant Volume Profile level (VAH, VAL, or POC). These are areas where price often reacts.
Bullish Signal (Green Triangle): This suggests buying momentum is returning after a price decline, especially when the price is near VAL or POC, which might act as support.
Bearish Signal (Red Triangle): This suggests selling momentum is increasing after a price rally, especially when the price is near VAH or POC, which might act as resistance.
Check Trend Alignment (SMA Filter):
For a long trade: You would ideally want to see a green triangle (bullish divergence) while the price is above the blue SMA line. This indicates a bullish divergence confirming a potential bounce within an existing uptrend.
For a short trade: You would ideally want to see a red triangle (bearish divergence) while the price is below the blue SMA line. This indicates a bearish divergence confirming a potential rejection within an existing downtrend.
Entry and Exit Considerations:
Entry: Consider entering a trade on the candle where the signal appears, or on the subsequent candle for confirmation.
Stop Loss: For a long trade, a logical stop-loss could be placed below the lowest point of the divergence, or below the VAL/POC if the signal occurred near it. For a short trade, above the highest point of the divergence or VAH/POC.
Take Profit: Targets could be set at the opposite Volume Profile level, previous swing highs/lows, or using a fixed risk-reward ratio.
Example Trading Scenario:
Long Trade: You see a green triangle (bullish divergence) printed on the chart. You notice the price is currently at the VAL (orange line). You check the blue SMA line and confirm that the price is above it (uptrend). This confluence of factors (bullish divergence, support at VAL, and uptrend) provides a strong potential long entry signal. You might enter, place your stop loss just below VAL, and target VAH or the next resistance level.
Short Trade: You see a red triangle (bearish divergence). The price is at the VAH (orange line). The price is also below the blue SMA line (downtrend). This suggests a potential short entry. You might enter, place your stop loss just above VAH, and target VAL or the next support level.
Strategy Stats [presentTrading]Hello! it's another weekend. This tool is a strategy performance analysis tool. Looking at the TradingView community, it seems few creators focus on this aspect. I've intentionally created a shared version. Welcome to share your idea or question on this.
█ Introduction and How it is Different
Strategy Stats is a comprehensive performance analytics framework designed specifically for trading strategies. Unlike standard strategy backtesting tools that simply show cumulative profits, this analytics suite provides real-time, multi-timeframe statistical analysis of your trading performance.
Multi-timeframe analysis: Automatically tracks performance metrics across the most recent time periods (last 7 days, 30 days, 90 days, 1 year, and 4 years)
Advanced statistical measures: Goes beyond basic metrics to include Information Coefficient (IC) and Sortino Ratio
Real-time feedback: Updates performance statistics with each new trade
Visual analytics: Color-coded performance table provides instant visual feedback on strategy health
Integrated risk management: Implements sophisticated take profit mechanisms with 3-step ATR and percentage-based exits
BTCUSD Performance
The table in the upper right corner is a comprehensive performance dashboard showing trading strategy statistics.
Note: While this presentation uses Vegas SuperTrend as the underlying strategy, this is merely an example. The Stats framework can be applied to any trading strategy. The Vegas SuperTrend implementation is included solely to demonstrate how the analytics module integrates with a trading strategy.
⚠️ Timeframe Limitations
Important: TradingView's backtesting engine has a maximum storage limit of 10,000 bars. When using this strategy stats framework on smaller timeframes such as 1-hour or 2-hour charts, you may encounter errors if your backtesting period is too long.
Recommended Timeframe Usage:
Ideal for: 4H, 6H, 8H, Daily charts and above
May cause errors on: 1H, 2H charts spanning multiple years
Not recommended for: Timeframes below 1H with long history
█ Strategy, How it Works: Detailed Explanation
The Strategy Stats framework consists of three primary components: statistical data collection, performance analysis, and visualization.
🔶 Statistical Data Collection
The system maintains several critical data arrays:
equityHistory: Tracks equity curve over time
tradeHistory: Records profit/loss of each trade
predictionSignals: Stores trade direction signals (1 for long, -1 for short)
actualReturns: Records corresponding actual returns from each trade
For each closed trade, the system captures:
float tradePnL = strategy.closedtrades.profit(tradeIndex)
float tradeReturn = strategy.closedtrades.profit_percent(tradeIndex)
int tradeType = entryPrice < exitPrice ? 1 : -1 // Direction
🔶 Performance Metrics Calculation
The framework calculates several key performance metrics:
Information Coefficient (IC):
The correlation between prediction signals and actual returns, measuring forecast skill.
IC = Correlation(predictionSignals, actualReturns)
Where Correlation is the Pearson correlation coefficient:
Correlation(X,Y) = (nΣXY - ΣXY) / √
Sortino Ratio:
Measures risk-adjusted return focusing only on downside risk:
Sortino = (Avg_Return - Risk_Free_Rate) / Downside_Deviation
Where Downside Deviation is:
Downside_Deviation = √
R_i represents individual returns, T is the target return (typically the risk-free rate), and n is the number of observations.
Maximum Drawdown:
Tracks the largest percentage drop from peak to trough:
DD = (Peak_Equity - Trough_Equity) / Peak_Equity * 100
🔶 Time Period Calculation
The system automatically determines the appropriate number of bars to analyze for each timeframe based on the current chart timeframe:
bars_7d = math.max(1, math.round(7 * barsPerDay))
bars_30d = math.max(1, math.round(30 * barsPerDay))
bars_90d = math.max(1, math.round(90 * barsPerDay))
bars_365d = math.max(1, math.round(365 * barsPerDay))
bars_4y = math.max(1, math.round(365 * 4 * barsPerDay))
Where barsPerDay is calculated based on the chart timeframe:
barsPerDay = timeframe.isintraday ?
24 * 60 / math.max(1, (timeframe.in_seconds() / 60)) :
timeframe.isdaily ? 1 :
timeframe.isweekly ? 1/7 :
timeframe.ismonthly ? 1/30 : 0.01
🔶 Visual Representation
The system presents performance data in a color-coded table with intuitive visual indicators:
Green: Excellent performance
Lime: Good performance
Gray: Neutral performance
Orange: Mediocre performance
Red: Poor performance
█ Trade Direction
The Strategy Stats framework supports three trading directions:
Long Only: Only takes long positions when entry conditions are met
Short Only: Only takes short positions when entry conditions are met
Both: Takes both long and short positions depending on market conditions
█ Usage
To effectively use the Strategy Stats framework:
Apply to existing strategies: Add the performance tracking code to any strategy to gain advanced analytics
Monitor multiple timeframes: Use the multi-timeframe analysis to identify performance trends
Evaluate strategy health: Review IC and Sortino ratios to assess predictive power and risk-adjusted returns
Optimize parameters: Use performance data to refine strategy parameters
Compare strategies: Apply the framework to multiple strategies to identify the most effective approach
For best results, allow the strategy to generate sufficient trade history for meaningful statistical analysis (at least 20-30 trades).
█ Default Settings
The default settings have been carefully calibrated for cryptocurrency markets:
Performance Tracking:
Time periods: 7D, 30D, 90D, 1Y, 4Y
Statistical measures: Return, Win%, MaxDD, IC, Sortino Ratio
IC color thresholds: >0.3 (green), >0.1 (lime), <-0.1 (orange), <-0.3 (red)
Sortino color thresholds: >1.0 (green), >0.5 (lime), <0 (red)
Multi-Step Take Profit:
ATR multipliers: 2.618, 5.0, 10.0
Percentage levels: 3%, 8%, 17%
Short multiplier: 1.5x (makes short take profits more aggressive)
Stop loss: 20%
CapitalManagementLibrary "CapitalManagement"
TODO: Manage the capital
order_volume(percent_risk, order_entry_price, stop_loss_price)
: Function to calculate order volume according to give risk percent_risk
Parameters:
percent_risk (float)
order_entry_price (float)
stop_loss_price (float)
calculate_takeprofit_price(entry_price, stop_loss_price, risk_reward)
: Function to calculate take profit price according to given risk:reward ratio
Parameters:
entry_price (float)
stop_loss_price (float)
risk_reward (float)
Returns: Return take profit value according to given risk:reward ratio
SL Hunting Detector📌 Step 1: Identify Liquidity Zones
The script plots high-liquidity zones (red) and low-liquidity zones (green).
These are areas where big players target stop-losses before reversing the price.
Example:
If price is near a red liquidity zone, expect a potential stop-loss hunt & reversal downward.
If price is near a green liquidity zone, expect a potential stop-loss hunt & reversal upward.
📌 Step 2: Watch for Stop-Loss Hunts (Fakeouts)
The indicator marks stop-loss hunts with red (bearish) or green (bullish) arrows.
When do stop-loss hunts occur?
✅ A long wick below support (with high volume) = Stop hunt before reversal upward.
✅ A long wick above resistance (with high volume) = Stop hunt before reversal downward.
Confirmation:
Volume must spike (volume > 1.5x the average volume).
ATR-based wicks must be longer than usual (showing a stop-hunt trap).
📌 Step 3: Enter a Trade After a Stop-Hunt
🔹 Bullish Trade (Buying a Dip)
If a green arrow appears (stop-hunt below support):
✅ Enter a long (buy) trade at or just above the wick’s recovery level.
✅ Stop-loss: Below the wick’s low (avoid getting hunted again).
✅ Take-profit: Next resistance level or mid-range of the liquidity zone.
🔹 Bearish Trade (Shorting a Fakeout)
If a red arrow appears (stop-hunt above resistance):
✅ Enter a short (sell) trade at or just below the wick’s rejection level.
✅ Stop-loss: Above the wick’s high (avoid getting stopped out).
✅ Take-profit: Next support level or mid-range of the liquidity zone.
📌 Step 4: Set Alerts & Automate
✅ The indicator triggers alerts when a stop-hunt is detected.
✅ You can set TradingView to notify you instantly when:
A bullish stop-hunt occurs → Look for long entry.
A bearish stop-hunt occurs → Look for short entry.
📌 Example Trade Setup
Example (BTC Long Trade on Stop-Hunt)
BTC is near $40,000 support (green liquidity zone).
A long wick drops to $39,800 with a green arrow (bullish stop-hunt signal).
Volume spikes, and price recovers quickly back above $40,000.
Trade entry: Buy at $40,050.
Stop-loss: Below wick ($39,700).
Take-profit: $41,500 (next resistance).
Result: BTC pumps, stop-loss remains safe, and trade profits.
🔥 Final Tips
Always wait for confirmation (don’t enter blindly on signals).
Use higher timeframes (15m, 1H, 4H) for better accuracy.
Combine with Order Flow tools (like Bookmap) to see real liquidity zones.
🚀 Now try it on TradingView! Let me know if you need adjustments. 📈🔥
Mean Reversion Cloud (Ornstein-Uhlenbeck) // AlgoFyreThe Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator detects mean-reversion opportunities by applying the Ornstein-Uhlenbeck process. It calculates a dynamic mean using an Exponential Weighted Moving Average, surrounded by volatility bands, signaling potential buy/sell points when prices deviate.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Adaptive Mean Calculation
🔸Volatility-Based Cloud
🔸Speed of Reversion (θ)
🔶 FUNCTIONALITY
🔸Dynamic Mean and Volatility Bands
🞘 How it works
🞘 How to calculate
🞘 Code extract
🔸Visualization via Table and Plotshapes
🞘 Table Overview
🞘 Plotshapes Explanation
🞘 Code extract
🔶 INSTRUCTIONS
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
🞘 Understanding What to Look For on the Chart
🞘 Possible Entry Signals
🞘 Possible Take Profit Strategies
🞘 Possible Stop-Loss Levels
🞘 Additional Tips
🔸Customize settings
🔶 CONCLUSION
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🔶 ORIGINALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) is a unique indicator that applies the Ornstein-Uhlenbeck stochastic process to identify mean-reverting behavior in asset prices. Unlike traditional moving average-based indicators, this model uses an Exponentially Weighted Moving Average (EWMA) to calculate the long-term mean, dynamically adjusting to recent price movements while still considering all historical data. It also incorporates volatility bands, providing a "cloud" that visually highlights overbought or oversold conditions. By calculating the speed of mean reversion (θ) through the autocorrelation of log returns, this indicator offers traders a more nuanced and mathematically robust tool for identifying mean-reversion opportunities. These innovations make it especially useful for markets that exhibit range-bound characteristics, offering timely buy and sell signals based on statistical deviations from the mean.
🔸Adaptive Mean Calculation Traditional MA indicators use fixed lengths, which can lead to lagging signals or over-sensitivity in volatile markets. The Mean Reversion Cloud uses an Exponentially Weighted Moving Average (EWMA), which adapts to price movements by dynamically adjusting its calculation, offering a more responsive mean.
🔸Volatility-Based Cloud Unlike simple moving averages that only plot a single line, the Mean Reversion Cloud surrounds the dynamic mean with volatility bands. These bands, based on standard deviations, provide traders with a visual cue of when prices are statistically likely to revert, highlighting potential reversal zones.
🔸Speed of Reversion (θ) The indicator goes beyond price averages by calculating the speed at which the price reverts to the mean (θ), using the autocorrelation of log returns. This gives traders an additional tool for estimating the likelihood and timing of mean reversion, making the signals more reliable in practice.
🔶 FUNCTIONALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator is designed to detect potential mean-reversion opportunities in asset prices by applying the Ornstein-Uhlenbeck stochastic process. It calculates a dynamic mean through the Exponentially Weighted Moving Average (EWMA) and plots volatility bands based on the standard deviation of the asset's price over a specified period. These bands create a "cloud" that represents expected price fluctuations, helping traders to identify overbought or oversold conditions. By calculating the speed of reversion (θ) from the autocorrelation of log returns, the indicator offers a more refined way of assessing how quickly prices may revert to the mean. Additionally, the inclusion of volatility provides a comprehensive view of market conditions, allowing for more accurate buy and sell signals.
Let's dive into the details:
🔸Dynamic Mean and Volatility Bands The dynamic mean (μ) is calculated using the EWMA, giving more weight to recent prices but considering all historical data. This process closely resembles the Ornstein-Uhlenbeck (OU) process, which models the tendency of a stochastic variable (such as price) to revert to its mean over time. Volatility bands are plotted around the mean using standard deviation, forming the "cloud" that signals overbought or oversold conditions. The cloud adapts dynamically to price fluctuations and market volatility, making it a versatile tool for mean-reversion strategies. 🞘 How it works Step one: Calculate the dynamic mean (μ) The Ornstein-Uhlenbeck process describes how a variable, such as an asset's price, tends to revert to a long-term mean while subject to random fluctuations. In this indicator, the EWMA is used to compute the dynamic mean (μ), mimicking the mean-reverting behavior of the OU process. Use the EWMA formula to compute a weighted mean that adjusts to recent price movements. Assign exponentially decreasing weights to older data while giving more emphasis to current prices. Step two: Plot volatility bands Calculate the standard deviation of the price over a user-defined period to determine market volatility. Position the upper and lower bands around the mean by adding and subtracting a multiple of the standard deviation. 🞘 How to calculate Exponential Weighted Moving Average (EWMA)
The EWMA dynamically adjusts to recent price movements:
mu_t = lambda * mu_{t-1} + (1 - lambda) * P_t
Where mu_t is the mean at time t, lambda is the decay factor, and P_t is the price at time t. The higher the decay factor, the more weight is given to recent data.
Autocorrelation (ρ) and Standard Deviation (σ)
To measure mean reversion speed and volatility: rho = correlation(log(close), log(close ), length) Where rho is the autocorrelation of log returns over a specified period.
To calculate volatility:
sigma = stdev(close, length)
Where sigma is the standard deviation of the asset's closing price over a specified length.
Upper and Lower Bands
The upper and lower bands are calculated as follows:
upper_band = mu + (threshold * sigma)
lower_band = mu - (threshold * sigma)
Where threshold is a multiplier for the standard deviation, usually set to 2. These bands represent the range within which the price is expected to fluctuate, based on current volatility and the mean.
🞘 Code extract // Calculate Returns
returns = math.log(close / close )
// Calculate Long-Term Mean (μ) using EWMA over the entire dataset
var float ewma_mu = na // Initialize ewma_mu as 'na'
ewma_mu := na(ewma_mu ) ? close : decay_factor * ewma_mu + (1 - decay_factor) * close
mu = ewma_mu
// Calculate Autocorrelation at Lag 1
rho1 = ta.correlation(returns, returns , corr_length)
// Ensure rho1 is within valid range to avoid errors
rho1 := na(rho1) or rho1 <= 0 ? 0.0001 : rho1
// Calculate Speed of Mean Reversion (θ)
theta = -math.log(rho1)
// Calculate Volatility (σ)
sigma = ta.stdev(close, corr_length)
// Calculate Upper and Lower Bands
upper_band = mu + threshold * sigma
lower_band = mu - threshold * sigma
🔸Visualization via Table and Plotshapes
The table shows key statistics such as the current value of the dynamic mean (μ), the number of times the price has crossed the upper or lower bands, and the consecutive number of bars that the price has remained in an overbought or oversold state.
Plotshapes (diamonds) are used to signal buy and sell opportunities. A green diamond below the price suggests a buy signal when the price crosses below the lower band, and a red diamond above the price indicates a sell signal when the price crosses above the upper band.
The table and plotshapes provide a comprehensive visualization, combining both statistical and actionable information to aid decision-making.
🞘 Code extract // Reset consecutive_bars when price crosses the mean
var consecutive_bars = 0
if (close < mu and close >= mu) or (close > mu and close <= mu)
consecutive_bars := 0
else if math.abs(deviation) > 0
consecutive_bars := math.min(consecutive_bars + 1, dev_length)
transparency = math.max(0, math.min(100, 100 - (consecutive_bars * 100 / dev_length)))
🔶 INSTRUCTIONS
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator can be set up by adding it to your TradingView chart and configuring parameters such as the decay factor, autocorrelation length, and volatility threshold to suit current market conditions. Look for price crossovers and deviations from the calculated mean for potential entry signals. Use the upper and lower bands as dynamic support/resistance levels for setting take profit and stop-loss orders. Combining this indicator with additional trend-following or momentum-based indicators can improve signal accuracy. Adjust settings for better mean-reversion detection and risk management.
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
Adding the Indicator to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Mean Reversion Cloud (Ornstein-Uhlenbeck)" in the indicators list.
Click on the indicator to add it to your chart.
Configuring the Indicator:
Open the indicator settings by clicking on the gear icon next to its name on the chart.
Decay Factor: Adjust the decay factor (λ) to control the responsiveness of the mean calculation. A higher value prioritizes recent data.
Autocorrelation Length: Set the autocorrelation length (θ) for calculating the speed of mean reversion. Longer lengths consider more historical data.
Threshold: Define the number of standard deviations for the upper and lower bands to determine how far price must deviate to trigger a signal.
Chart Setup:
Select the appropriate timeframe (e.g., 1-hour, daily) based on your trading strategy.
Consider using other indicators such as RSI or MACD to confirm buy and sell signals.
🞘 Understanding What to Look For on the Chart
Indicator Behavior:
Observe how the price interacts with the dynamic mean and volatility bands. The price staying within the bands suggests mean-reverting behavior, while crossing the bands signals potential entry points.
The indicator calculates overbought/oversold conditions based on deviation from the mean, highlighted by color-coded cloud areas on the chart.
Crossovers and Deviation:
Look for crossovers between the price and the mean (μ) or the bands. A bullish crossover occurs when the price crosses below the lower band, signaling a potential buying opportunity.
A bearish crossover occurs when the price crosses above the upper band, suggesting a potential sell signal.
Deviations from the mean indicate market extremes. A large deviation indicates that the price is far from the mean, suggesting a potential reversal.
Slope and Direction:
Pay attention to the slope of the mean (μ). A rising slope suggests bullish market conditions, while a declining slope signals a bearish market.
The steepness of the slope can indicate the strength of the mean-reversion trend.
🞘 Possible Entry Signals
Bullish Entry:
Crossover Entry: Enter a long position when the price crosses below the lower band with a positive deviation from the mean.
Confirmation Entry: Use additional indicators like RSI (above 50) or increasing volume to confirm the bullish signal.
Bearish Entry:
Crossover Entry: Enter a short position when the price crosses above the upper band with a negative deviation from the mean.
Confirmation Entry: Look for RSI (below 50) or decreasing volume to confirm the bearish signal.
Deviation Confirmation:
Enter trades when the deviation from the mean is significant, indicating that the price has strayed far from its expected value and is likely to revert.
🞘 Possible Take Profit Strategies
Static Take Profit Levels:
Set predefined take profit levels based on historical volatility, using the upper and lower bands as guides.
Place take profit orders near recent support/resistance levels, ensuring you're capitalizing on the mean-reversion behavior.
Trailing Stop Loss:
Use a trailing stop based on a percentage of the price deviation from the mean to lock in profits as the trend progresses.
Adjust the trailing stop dynamically along the calculated bands to protect profits as the price returns to the mean.
Deviation-Based Exits:
Exit when the deviation from the mean starts to decrease, signaling that the price is returning to its equilibrium.
🞘 Possible Stop-Loss Levels
Initial Stop Loss:
Place an initial stop loss outside the lower band (for long positions) or above the upper band (for short positions) to protect against excessive deviations.
Use a volatility-based buffer to avoid getting stopped out during normal price fluctuations.
Dynamic Stop Loss:
Move the stop loss closer to the mean as the price converges back towards equilibrium, reducing risk.
Adjust the stop loss dynamically along the bands to account for sudden market movements.
🞘 Additional Tips
Combine with Other Indicators:
Enhance your strategy by combining the Mean Reversion Cloud with momentum indicators like MACD, RSI, or Bollinger Bands to confirm market conditions.
Backtesting and Practice:
Backtest the indicator on historical data to understand how it performs in various market environments.
Practice using the indicator on a demo account before implementing it in live trading.
Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The indicator reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Customize settings 🞘 Decay Factor (λ): Defines the weight assigned to recent price data in the calculation of the mean. A value closer to 1 places more emphasis on recent prices, while lower values create a smoother, more lagging mean.
🞘 Autocorrelation Length (θ): Sets the period for calculating the speed of mean reversion and volatility. Longer lengths capture more historical data, providing smoother calculations, while shorter lengths make the indicator more responsive.
🞘 Threshold (σ): Specifies the number of standard deviations used to create the upper and lower bands. Higher thresholds widen the bands, producing fewer signals, while lower thresholds tighten the bands for more frequent signals.
🞘 Max Gradient Length (γ): Determines the maximum number of consecutive bars for calculating the deviation gradient. This setting impacts the transparency of the plotted bands based on the length of deviation from the mean.
🔶 CONCLUSION
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator offers a sophisticated approach to identifying mean-reversion opportunities by applying the Ornstein-Uhlenbeck stochastic process. This dynamic indicator calculates a responsive mean using an Exponentially Weighted Moving Average (EWMA) and plots volatility-based bands to highlight overbought and oversold conditions. By incorporating advanced statistical measures like autocorrelation and standard deviation, traders can better assess market extremes and potential reversals. The indicator’s ability to adapt to price behavior makes it a versatile tool for traders focused on both short-term price deviations and longer-term mean-reversion strategies. With its unique blend of statistical rigor and visual clarity, the Mean Reversion Cloud provides an invaluable tool for understanding and capitalizing on market inefficiencies.
Ichimoku Crosses_RSI_AITIchimoku Crosser_RSI_AIT
Overview
The "Ichimoku Cloud Crosses_AIT" strategy is a technical trading strategy that combines the Ichimoku Cloud components with the Relative Strength Index (RSI) to generate trade signals. This strategy leverages the crossovers of the Tenkan-sen and Kijun-sen lines of the Ichimoku Cloud, along with RSI levels, to identify potential entry and exit points for long and short trades. This guide explains the strategy components, conditions, and how to use it effectively in your trading.
1. Strategy Parameters
User Inputs
Tenkan-sen Period (tenkanLength): Default value is 21. This is the period used to calculate the Tenkan-sen line (conversion line) of the Ichimoku Cloud.
Kijun-sen Period (kijunLength): Default value is 120. This is the period used to calculate the Kijun-sen line (base line) of the Ichimoku Cloud.
Senkou Span B Period (senkouBLength): Default value is 52. This is the period used to calculate the Senkou Span B line (leading span B) of the Ichimoku Cloud.
RSI Period (rsiLength): Default value is 14. This period is used to calculate the Relative Strength Index (RSI).
RSI Long Entry Level (rsiLongLevel): Default value is 60. This level indicates the minimum RSI value for a long entry signal.
RSI Short Entry Level (rsiShortLevel): Default value is 40. This level indicates the maximum RSI value for a short entry signal.
2. Strategy Components
Ichimoku Cloud
Tenkan-sen: A short-term trend indicator calculated as the simple moving average (SMA) of the highest high and the lowest low over the Tenkan-sen period.
Kijun-sen: A medium-term trend indicator calculated as the SMA of the highest high and the lowest low over the Kijun-sen period.
Senkou Span A: Calculated as the average of the Tenkan-sen and Kijun-sen, plotted 26 periods ahead.
Senkou Span B: Calculated as the SMA of the highest high and lowest low over the Senkou Span B period, plotted 26 periods ahead.
Chikou Span: The closing price plotted 26 periods behind.
Relative Strength Index (RSI)
RSI: A momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is used to identify overbought or oversold conditions.
3. Entry and Exit Conditions
Entry Conditions
Long Entry:
The Tenkan-sen crosses above the Kijun-sen (bullish crossover).
The RSI value is greater than or equal to the rsiLongLevel.
Short Entry:
The Tenkan-sen crosses below the Kijun-sen (bearish crossover).
The RSI value is less than or equal to the rsiShortLevel.
Exit Conditions
Exit Long Position: The Tenkan-sen crosses below the Kijun-sen.
Exit Short Position: The Tenkan-sen crosses above the Kijun-sen.
4. Visual Representation
Tenkan-sen Line: Plotted on the chart. The color changes based on its relation to the Kijun-sen (green if above, red if below) and is displayed with a line width of 2.
Kijun-sen Line: Plotted as a white line with a line width of 1.
Entry Arrows:
Long Entry: Displayed as a yellow triangle below the bar.
Short Entry: Displayed as a fuchsia triangle above the bar.
5. How to Use
Apply the Strategy: Apply the "Ichimoku Cloud Crosses_AIT" strategy to your chart in TradingView.
Configure Parameters: Adjust the strategy parameters (Tenkan-sen, Kijun-sen, Senkou Span B, and RSI settings) according to your trading preferences.
Interpret the Signals:
Long Entry: A yellow triangle appears below the bar when a long entry signal is generated.
Short Entry: A fuchsia triangle appears above the bar when a short entry signal is generated.
Monitor Open Positions: The strategy automatically exits positions based on the defined conditions.
Backtesting and Live Trading: Use the strategy for backtesting and live trading. Adjust risk management settings in the strategy properties as needed.
Conclusion
The "Ichimoku Cloud Crosses_AIT" strategy uses Ichimoku Cloud crossovers and RSI to generate trading signals. This strategy aims to capture market trends and potential reversals, providing a structured way to enter and exit trades. Make sure to backtest and optimize the strategy parameters to suit your trading style and market conditions before using it in a live trading environment.
TRADINGLibrary "TRADING"
This library is a client script for making a webhook signal formatted string to PoABOT server.
entry_message(password, percent, leverage, margin_mode, kis_number)
Create a entry message for POABOT
Parameters:
password (string) : (string) The password of your bot.
percent (float) : (float) The percent for entry based on your wallet balance.
leverage (int) : (int) The leverage of entry. If not set, your levereage doesn't change.
margin_mode (string) : (string) The margin mode for trade(only for OKX). "cross" or "isolated"
kis_number (int) : (int) The number of koreainvestment account. Default 1
Returns: (string) A json formatted string for webhook message.
order_message(password, percent, leverage, margin_mode, kis_number)
Create a order message for POABOT
Parameters:
password (string) : (string) The password of your bot.
percent (float) : (float) The percent for entry based on your wallet balance.
leverage (int) : (int) The leverage of entry. If not set, your levereage doesn't change.
margin_mode (string) : (string) The margin mode for trade(only for OKX). "cross" or "isolated"
kis_number (int) : (int) The number of koreainvestment account. Default 1
Returns: (string) A json formatted string for webhook message.
close_message(password, percent, margin_mode, kis_number)
Create a close message for POABOT
Parameters:
password (string) : (string) The password of your bot.
percent (float) : (float) The percent for close based on your wallet balance.
margin_mode (string) : (string) The margin mode for trade(only for OKX). "cross" or "isolated"
kis_number (int) : (int) The number of koreainvestment account. Default 1
Returns: (string) A json formatted string for webhook message.
exit_message(password, percent, margin_mode, kis_number)
Create a exit message for POABOT
Parameters:
password (string) : (string) The password of your bot.
percent (float) : (float) The percent for exit based on your wallet balance.
margin_mode (string) : (string) The margin mode for trade(only for OKX). "cross" or "isolated"
kis_number (int) : (int) The number of koreainvestment account. Default 1
Returns: (string) A json formatted string for webhook message.
manual_message(password, exchange, base, quote, side, qty, price, percent, leverage, margin_mode, kis_number, order_name)
Create a manual message for POABOT
Parameters:
password (string) : (string) The password of your bot.
exchange (string) : (string) The exchange
base (string) : (string) The base
quote (string) : (string) The quote of order message
side (string) : (string) The side of order messsage
qty (float) : (float) The qty of order message
price (float) : (float) The price of order message
percent (float) : (float) The percent for order based on your wallet balance.
leverage (int) : (int) The leverage of entry. If not set, your levereage doesn't change.
margin_mode (string) : (string) The margin mode for trade(only for OKX). "cross" or "isolated"
kis_number (int) : (int) The number of koreainvestment account.
order_name (string) : (string) The name of order message
Returns: (string) A json formatted string for webhook message.
in_trade(start_time, end_time, hide_trade_line)
Create a trade start line
Parameters:
start_time (int) : (int) The start of time.
end_time (int) : (int) The end of time.
hide_trade_line (bool) : (bool) if true, hide trade line. Default false.
Returns: (bool) Get bool for trade based on time range.
real_qty(qty, precision, leverage, contract_size, default_qty_type, default_qty_value)
Get exchange specific real qty
Parameters:
qty (float) : (float) qty
precision (float) : (float) precision
leverage (int) : (int) leverage
contract_size (float) : (float) contract_size
default_qty_type (string)
default_qty_value (float)
Returns: (float) exchange specific qty.
method set(this, password, start_time, end_time, leverage, initial_capital, default_qty_type, default_qty_value, margin_mode, contract_size, kis_number, entry_percent, close_percent, exit_percent, fixed_qty, fixed_cash, real, auto_alert_message, hide_trade_line)
Set bot object.
Namespace types: bot
Parameters:
this (bot)
password (string) : (string) password for poabot.
start_time (int) : (int) start_time timestamp.
end_time (int) : (int) end_time timestamp.
leverage (int) : (int) leverage.
initial_capital (float)
default_qty_type (string)
default_qty_value (float)
margin_mode (string) : (string) The margin mode for trade(only for OKX). "cross" or "isolated"
contract_size (float)
kis_number (int) : (int) kis_number for poabot.
entry_percent (float) : (float) entry_percent for poabot.
close_percent (float) : (float) close_percent for poabot.
exit_percent (float) : (float) exit_percent for poabot.
fixed_qty (float) : (float) fixed qty.
fixed_cash (float) : (float) fixed cash.
real (bool) : (bool) convert qty for exchange specific.
auto_alert_message (bool) : (bool) convert alert_message for exchange specific.
hide_trade_line (bool) : (bool) if true, Hide trade line. Default false.
Returns: (void)
method print(this, message)
Print message using log table.
Namespace types: bot
Parameters:
this (bot)
message (string)
Returns: (void)
method start_trade(this)
start trade using start_time and end_time
Namespace types: bot
Parameters:
this (bot)
Returns: (void)
method entry(this, id, direction, qty, limit, stop, oca_name, oca_type, comment, alert_message, when)
It is a command to enter market position. If an order with the same ID is already pending, it is possible to modify the order. If there is no order with the specified ID, a new order is placed. To deactivate an entry order, the command strategy.cancel or strategy.cancel_all should be used. In comparison to the function strategy.order, the function strategy.entry is affected by pyramiding and it can reverse market position correctly. If both 'limit' and 'stop' parameters are 'NaN', the order type is market order.
Namespace types: bot
Parameters:
this (bot)
id (string) : (string) A required parameter. The order identifier. It is possible to cancel or modify an order by referencing its identifier.
direction (string) : (string) A required parameter. Market position direction: 'strategy.long' is for long, 'strategy.short' is for short.
qty (float) : (float) An optional parameter. Number of contracts/shares/lots/units to trade. The default value is 'NaN'.
limit (float) : (float) An optional parameter. Limit price of the order. If it is specified, the order type is either 'limit', or 'stop-limit'. 'NaN' should be specified for any other order type.
stop (float) : (float) An optional parameter. Stop price of the order. If it is specified, the order type is either 'stop', or 'stop-limit'. 'NaN' should be specified for any other order type.
oca_name (string) : (string) An optional parameter. Name of the OCA group the order belongs to. If the order should not belong to any particular OCA group, there should be an empty string.
oca_type (string) : (string) An optional parameter. Type of the OCA group. The allowed values are: "strategy.oca.none" - the order should not belong to any particular OCA group; "strategy.oca.cancel" - the order should belong to an OCA group, where as soon as an order is filled, all other orders of the same group are cancelled; "strategy.oca.reduce" - the order should belong to an OCA group, where if X number of contracts of an order is filled, number of contracts for each other order of the same OCA group is decreased by X.
comment (string) : (string) An optional parameter. Additional notes on the order.
alert_message (string) : (string) An optional parameter which replaces the {{strategy.order.alert_message}} placeholder when it is used in the "Create Alert" dialog box's "Message" field.
when (bool) : (bool) An optional parmeter. Condition, deprecated.
Returns: (void)
method order(this, id, direction, qty, limit, stop, oca_name, oca_type, comment, alert_message, when)
It is a command to place order. If an order with the same ID is already pending, it is possible to modify the order. If there is no order with the specified ID, a new order is placed. To deactivate order, the command strategy.cancel or strategy.cancel_all should be used. In comparison to the function strategy.entry, the function strategy.order is not affected by pyramiding. If both 'limit' and 'stop' parameters are 'NaN', the order type is market order.
Namespace types: bot
Parameters:
this (bot)
id (string) : (string) A required parameter. The order identifier. It is possible to cancel or modify an order by referencing its identifier.
direction (string) : (string) A required parameter. Market position direction: 'strategy.long' is for long, 'strategy.short' is for short.
qty (float) : (float) An optional parameter. Number of contracts/shares/lots/units to trade. The default value is 'NaN'.
limit (float) : (float) An optional parameter. Limit price of the order. If it is specified, the order type is either 'limit', or 'stop-limit'. 'NaN' should be specified for any other order type.
stop (float) : (float) An optional parameter. Stop price of the order. If it is specified, the order type is either 'stop', or 'stop-limit'. 'NaN' should be specified for any other order type.
oca_name (string) : (string) An optional parameter. Name of the OCA group the order belongs to. If the order should not belong to any particular OCA group, there should be an empty string.
oca_type (string) : (string) An optional parameter. Type of the OCA group. The allowed values are: "strategy.oca.none" - the order should not belong to any particular OCA group; "strategy.oca.cancel" - the order should belong to an OCA group, where as soon as an order is filled, all other orders of the same group are cancelled; "strategy.oca.reduce" - the order should belong to an OCA group, where if X number of contracts of an order is filled, number of contracts for each other order of the same OCA group is decreased by X.
comment (string) : (string) An optional parameter. Additional notes on the order.
alert_message (string) : (string) An optional parameter which replaces the {{strategy.order.alert_message}} placeholder when it is used in the "Create Alert" dialog box's "Message" field.
when (bool) : (bool) An optional parmeter. Condition, deprecated.
Returns: (void)
method close_all(this, comment, alert_message, immediately, when)
Exits the current market position, making it flat.
Namespace types: bot
Parameters:
this (bot)
comment (string) : (string) An optional parameter. Additional notes on the order.
alert_message (string) : (string) An optional parameter which replaces the {{strategy.order.alert_message}} placeholder when it is used in the "Create Alert" dialog box's "Message" field.
immediately (bool) : (bool) An optional parameter. If true, the closing order will be executed on the tick where it has been placed, ignoring the strategy parameters that restrict the order execution to the open of the next bar. The default is false.
when (bool) : (bool) An optional parmeter. Condition, deprecated.
Returns: (void)
method cancel(this, id, when)
It is a command to cancel/deactivate pending orders by referencing their names, which were generated by the functions: strategy.order, strategy.entry and strategy.exit.
Namespace types: bot
Parameters:
this (bot)
id (string) : (string) A required parameter. The order identifier. It is possible to cancel an order by referencing its identifier.
when (bool) : (bool) An optional parmeter. Condition, deprecated.
Returns: (void)
method cancel_all(this, when)
It is a command to cancel/deactivate all pending orders, which were generated by the functions: strategy.order, strategy.entry and strategy.exit.
Namespace types: bot
Parameters:
this (bot)
when (bool) : (bool) An optional parmeter. Condition, deprecated.
Returns: (void)
method close(this, id, comment, qty, qty_percent, alert_message, immediately, when)
It is a command to exit from the entry with the specified ID. If there were multiple entry orders with the same ID, all of them are exited at once. If there are no open entries with the specified ID by the moment the command is triggered, the command will not come into effect. The command uses market order. Every entry is closed by a separate market order.
Namespace types: bot
Parameters:
this (bot)
id (string) : (string) A required parameter. The order identifier. It is possible to close an order by referencing its identifier.
comment (string) : (string) An optional parameter. Additional notes on the order.
qty (float) : (float) An optional parameter. Number of contracts/shares/lots/units to exit a trade with. The default value is 'NaN'.
qty_percent (float) : (float) Defines the percentage (0-100) of the position to close. Its priority is lower than that of the 'qty' parameter. Optional. The default is 100.
alert_message (string) : (string) An optional parameter which replaces the {{strategy.order.alert_message}} placeholder when it is used in the "Create Alert" dialog box's "Message" field.
immediately (bool) : (bool) An optional parameter. If true, the closing order will be executed on the tick where it has been placed, ignoring the strategy parameters that restrict the order execution to the open of the next bar. The default is false.
when (bool) : (bool) An optional parmeter. Condition, deprecated.
Returns: (void)
ticks_to_price(ticks, from)
Converts ticks to a price offset from the supplied price or the average entry price.
Parameters:
ticks (float) : (float) Ticks to convert to a price.
from (float) : (float) A price that can be used to calculate from. Optional. The default value is `strategy.position_avg_price`.
Returns: (float) A price level that has a distance from the entry price equal to the specified number of ticks.
method exit(this, id, from_entry, qty, qty_percent, profit, limit, loss, stop, trail_price, trail_points, trail_offset, oca_name, comment, comment_profit, comment_loss, comment_trailing, alert_message, alert_profit, alert_loss, alert_trailing, when)
It is a command to exit either a specific entry, or whole market position. If an order with the same ID is already pending, it is possible to modify the order. If an entry order was not filled, but an exit order is generated, the exit order will wait till entry order is filled and then the exit order is placed. To deactivate an exit order, the command strategy.cancel or strategy.cancel_all should be used. If the function strategy.exit is called once, it exits a position only once. If you want to exit multiple times, the command strategy.exit should be called multiple times. If you use a stop loss and a trailing stop, their order type is 'stop', so only one of them is placed (the one that is supposed to be filled first). If all the following parameters 'profit', 'limit', 'loss', 'stop', 'trail_points', 'trail_offset' are 'NaN', the command will fail. To use market order to exit, the command strategy.close or strategy.close_all should be used.
Namespace types: bot
Parameters:
this (bot)
id (string) : (string) A required parameter. The order identifier. It is possible to cancel or modify an order by referencing its identifier.
from_entry (string) : (string) An optional parameter. The identifier of a specific entry order to exit from it. To exit all entries an empty string should be used. The default values is empty string.
qty (float) : (float) An optional parameter. Number of contracts/shares/lots/units to exit a trade with. The default value is 'NaN'.
qty_percent (float) : (float) Defines the percentage of (0-100) the position to close. Its priority is lower than that of the 'qty' parameter. Optional. The default is 100.
profit (float) : (float) An optional parameter. Profit target (specified in ticks). If it is specified, a limit order is placed to exit market position when the specified amount of profit (in ticks) is reached. The default value is 'NaN'.
limit (float) : (float) An optional parameter. Profit target (requires a specific price). If it is specified, a limit order is placed to exit market position at the specified price (or better). Priority of the parameter 'limit' is higher than priority of the parameter 'profit' ('limit' is used instead of 'profit', if its value is not 'NaN'). The default value is 'NaN'.
loss (float) : (float) An optional parameter. Stop loss (specified in ticks). If it is specified, a stop order is placed to exit market position when the specified amount of loss (in ticks) is reached. The default value is 'NaN'.
stop (float) : (float) An optional parameter. Stop loss (requires a specific price). If it is specified, a stop order is placed to exit market position at the specified price (or worse). Priority of the parameter 'stop' is higher than priority of the parameter 'loss' ('stop' is used instead of 'loss', if its value is not 'NaN'). The default value is 'NaN'.
trail_price (float) : (float) An optional parameter. Trailing stop activation level (requires a specific price). If it is specified, a trailing stop order will be placed when the specified price level is reached. The offset (in ticks) to determine initial price of the trailing stop order is specified in the 'trail_offset' parameter: X ticks lower than activation level to exit long position; X ticks higher than activation level to exit short position. The default value is 'NaN'.
trail_points (float) : (float) An optional parameter. Trailing stop activation level (profit specified in ticks). If it is specified, a trailing stop order will be placed when the calculated price level (specified amount of profit) is reached. The offset (in ticks) to determine initial price of the trailing stop order is specified in the 'trail_offset' parameter: X ticks lower than activation level to exit long position; X ticks higher than activation level to exit short position. The default value is 'NaN'.
trail_offset (float) : (float) An optional parameter. Trailing stop price (specified in ticks). The offset in ticks to determine initial price of the trailing stop order: X ticks lower than 'trail_price' or 'trail_points' to exit long position; X ticks higher than 'trail_price' or 'trail_points' to exit short position. The default value is 'NaN'.
oca_name (string) : (string) An optional parameter. Name of the OCA group (oca_type = strategy.oca.reduce) the profit target, the stop loss / the trailing stop orders belong to. If the name is not specified, it will be generated automatically.
comment (string) : (string) Additional notes on the order. If specified, displays near the order marker on the chart. Optional. The default is na.
comment_profit (string) : (string) Additional notes on the order if the exit was triggered by crossing `profit` or `limit` specifically. If specified, supercedes the `comment` parameter and displays near the order marker on the chart. Optional. The default is na.
comment_loss (string) : (string) Additional notes on the order if the exit was triggered by crossing `stop` or `loss` specifically. If specified, supercedes the `comment` parameter and displays near the order marker on the chart. Optional. The default is na.
comment_trailing (string) : (string) Additional notes on the order if the exit was triggered by crossing `trail_offset` specifically. If specified, supercedes the `comment` parameter and displays near the order marker on the chart. Optional. The default is na.
alert_message (string) : (string) Text that will replace the '{{strategy.order.alert_message}}' placeholder when one is used in the "Message" field of the "Create Alert" dialog. Optional. The default is na.
alert_profit (string) : (string) Text that will replace the '{{strategy.order.alert_message}}' placeholder when one is used in the "Message" field of the "Create Alert" dialog. Only replaces the text if the exit was triggered by crossing `profit` or `limit` specifically. Optional. The default is na.
alert_loss (string) : (string) Text that will replace the '{{strategy.order.alert_message}}' placeholder when one is used in the "Message" field of the "Create Alert" dialog. Only replaces the text if the exit was triggered by crossing `stop` or `loss` specifically. Optional. The default is na.
alert_trailing (string) : (string) Text that will replace the '{{strategy.order.alert_message}}' placeholder when one is used in the "Message" field of the "Create Alert" dialog. Only replaces the text if the exit was triggered by crossing `trail_offset` specifically. Optional. The default is na.
when (bool) : (bool) An optional parmeter. Condition, deprecated.
Returns: (void)
percent_to_ticks(percent, from)
Converts a percentage of the supplied price or the average entry price to ticks.
Parameters:
percent (float) : (float) The percentage of supplied price to convert to ticks. 50 is 50% of the entry price.
from (float) : (float) A price that can be used to calculate from. Optional. The default value is `strategy.position_avg_price`.
Returns: (float) A value in ticks.
percent_to_price(percent, from)
Converts a percentage of the supplied price or the average entry price to a price.
Parameters:
percent (float) : (float) The percentage of the supplied price to convert to price. 50 is 50% of the supplied price.
from (float) : (float) A price that can be used to calculate from. Optional. The default value is `strategy.position_avg_price`.
Returns: (float) A value in the symbol's quote currency (USD for BTCUSD).
bot
Fields:
password (series__string)
start_time (series__integer)
end_time (series__integer)
leverage (series__integer)
initial_capital (series__float)
default_qty_type (series__string)
default_qty_value (series__float)
margin_mode (series__string)
contract_size (series__float)
kis_number (series__integer)
entry_percent (series__float)
close_percent (series__float)
exit_percent (series__float)
log_table (series__table)
fixed_qty (series__float)
fixed_cash (series__float)
real (series__bool)
auto_alert_message (series__bool)
hide_trade_line (series__bool)
[imba]lance algo🟩 INTRODUCTION
Hello, everyone!
Please take the time to review this description and source code to utilize this script to its fullest potential.
🟩 CONCEPTS
This is a trend indicator. The trend is the 0.5 fibonacci level for a certain period of time.
A trend change occurs when at least one candle closes above the level of 0.236 (for long) or below 0.786 (for short). Also it has massive amout of settings and features more about this below.
With good settings, the indicator works great on any market and any time frame!
A distinctive feature of this indicator is its backtest panel. With which you can dynamically view the results of setting up a strategy such as profit, what the deposit size is, etc.
Please note that the profit is indicated as a percentage of the initial deposit. It is also worth considering that all profit calculations are based on the risk % setting.
🟩 FEATURES
First, I want to show you what you see on the chart. And I’ll show you everything closer and in more detail.
1. Position
2. Statistic panel
3. Backtest panel
Indicator settings:
Let's go in order:
1. Strategies
This setting is responsible for loading saved strategies. There are only two preset settings, MANUAL and UNIVERSAL. If you choose any strategy other than MANUAL, then changing the settings for take profits, stop loss, sensitivity will not bring any results.
You can also save your customized strategies, this is discussed in a separate paragraph “🟩HOW TO SAVE A STRATEGY”
2. Sensitive
Responsible for the time period in bars to create Fibonacci levels
3. Start calculating date
This is the time to start backtesting strategies
4. Position group
Show checkbox - is responsible for displaying positions
Fill checkbox - is responsible for filling positions with background
Risk % - is responsible for what percentage of the deposit you are willing to lose if there is a stop loss
BE target - here you can choose when you reach which take profit you need to move your stop loss to breakeven
Initial deposit- starting deposit for profit calculation
5. Stoploss group
Fixed stoploss % checkbox - If choosed: stoploss will be calculated manually depending on the setting below( formula: entry_price * (1 - stoploss percent)) If NOT choosed: stoploss will be ( formula: fibonacci level(0.786/0.236) * (1 + stoploss percent))
6. Take profit group
This group of settings is responsible for how far from the entry point take profits will be and what % of the position to fix
7. RSI
Responsible for configuring the built-in RSI. Suitable bars will be highlighted with crosses above or below, depending on overbought/oversold
8. Infopanels group
Here I think everything is clear, you can hide or show information panels
9. Developer mode
If enabled, all events that occur will be shown, for example, reaching a take profit or stop loss with detailed information about the unfixed balance of the position
🟩 HOW TO USE
Very simple. All you need is to wait for the trend to change to long or short, you will immediately see a stop loss and four take profits, and you will also see prices. Like in this picture:
🟩 ALERTS
There are 3 types of alerts:
1. Long signal
2. Short signal
3. Any alert() function call - will be send to you json with these fields
{
"side": "LONG",
"entry": "64.454",
"tp1": "65.099",
"tp2": "65.743",
"tp3": "66.388",
"tp4": "67.032",
"winrate": "35.42%",
"strategy": "MANUAL",
"beTargetTrigger": "1",
"stop": "64.44"
}
🟩 HOW TO SAVE A STRATEGY
First, you need to make sure that the “MANUAL” strategy is selected in the strategy settings.
After this, you can start selecting parameters that will show the largest profit in the statistics panel.
I have highlighted what you need to pay attention to when choosing a strategy
Let's assume you have set up a strategy. The main question is how to preserve it?
Let’s say the strategy turned out with the following parameters:
Next we need to find this section of code:
// STRATS
selector(string strategy_name) =>
strategy_settings = Strategy_settings.new()
switch strategy_name
"MANUAL" =>
strategy_settings.sensitivity := 18
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"UNIVERSAL" =>
strategy_settings.sensitivity := 20
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
// "NEW STRATEGY" =>
// strategy_settings.sensitivity := 20
// strategy_settings.risk_percent := 1
// strategy_settings.break_even_target := "1"
// strategy_settings.tp1_percent := 1
// strategy_settings.tp1_percent_fix := 40
// strategy_settings.tp2_percent := 2
// strategy_settings.tp2_percent_fix := 30
// strategy_settings.tp3_percent := 3
// strategy_settings.tp3_percent_fix := 20
// strategy_settings.tp4_percent := 4
// strategy_settings.tp4_percent_fix := 10
// strategy_settings.fixed_stop := false
// strategy_settings.sl_percent := 0.0
strategy_settings
// STRATS
Let's uncomment on the latest strategy called "NEW STRATEGY" rename it to "SOL 5m" and change the sensitivity:
// STRATS
selector(string strategy_name) =>
strategy_settings = Strategy_settings.new()
switch strategy_name
"MANUAL" =>
strategy_settings.sensitivity := 18
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"UNIVERSAL" =>
strategy_settings.sensitivity := 20
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
"SOL 5m" =>
strategy_settings.sensitivity := 15
strategy_settings.risk_percent := 1
strategy_settings.break_even_target := "1"
strategy_settings.tp1_percent := 1
strategy_settings.tp1_percent_fix := 40
strategy_settings.tp2_percent := 2
strategy_settings.tp2_percent_fix := 30
strategy_settings.tp3_percent := 3
strategy_settings.tp3_percent_fix := 20
strategy_settings.tp4_percent := 4
strategy_settings.tp4_percent_fix := 10
strategy_settings.fixed_stop := false
strategy_settings.sl_percent := 0.0
strategy_settings
// STRATS
Now let's find this code:
strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта")
And let's add our new strategy there, it turned out like this:
strategy_input = input.string(title = "STRATEGY", options = , defval = "MANUAL", tooltip = "EN:\nTo manually configure the strategy, select MANUAL otherwise, changing the settings won't have any effect\nRU:\nЧтобы настроить стратегию вручную, выберите MANUAL в противном случае изменение настроек не будет иметь никакого эффекта")
That's all. Our new strategy is now saved! It's simple! Now we can select it in the list of strategies:
Price Pivots for NASDQ 100 StocksPrice Pivots for NASDQ 100 Stocks
What is this Indicator?
• This indicator calculates the price range a Stock can move in a Day.
Advantages of this Indicator
• This is a Leading indicator, not Dynamic or Repaint.
• Helps to identify the tight range of price movement.
• Can easily identify the Options strike price.
• Develops a discipline in placing Targets.
Disadvantages of this Indicator
• The indicator is specifically made for NASDQ 100 stocks. The levels won't work for other stocks.
• The indicator shows nothing for other indexes and stocks other than above mentioned.
• The data need to be entered manually.
Who to use?
Highly beneficial for Day Traders, it can be used for Swing and Positions as well.
What timeframe to use?
• Any timeframe.
• The highlighted levels in Red and Green will not show correct levels in 1 minute timeframe.
• 5min is recommended for Day Traders.
When to use?
• Wait for proper swing to form.
• Recommended to avoid 1st 1 hour or market open, that is 9.15am to 10.15 or 10.30am.
• Within this time a proper swing will be formed.
What are the Lines?
• The concept is the price will move from one pivot to another.
• Entry and Exit can be these levels as Reversal or Retracement.
Gray Lines:
• Every lines with price labels are the Strike Prices in the Option Chain.
• Price moves from 1 Strike Price level to another.
• The dashed lines are average levels of 2 Strike Prices.
Red & Green Lines:
• The Red and Green Lines will appear only after the first 1 hour.
• The levels are calculated based on the 1st 1 hour.
• Red Lines are important Resistance levels, these are strong Bearish reversal points. It is also a breakout level, this need to be figured out from the past levels, trend, percentage change and consolidation.
• Green Lines are important Support levels, these are strong Bullish reversal points. It is also a breakdown level, this need to be figured out from the past levels, trend, percentage change and consolidation.
What are the Labels?
• First Number: Price of that level.
• Numbers in (): Percentage change and Change of price from LTP (Last Traded Price) to that Level.
How to use?
Entry:
• Enter when price is closer to the Red or Green lines.
• Enter after considering previous Swing and Trend.
• Note the 50% of previous Swing.
• Enter Short when price reverse from each level.
• If 50% of swing and the pivot level is closer it can be a good entry.
Exit:
• Use the logic of Entry, each level can be a target.
• Exit when price is closer to the Red or Green lines.
Indicator Menu
Source
• Custom: Enter the price manually after choosing the Source as Custom to show the Pivots at that price.
• LTP: Pivot is calculated based on Last Traded Price.
• Day Open: Pivot is calculated based on current day opening price.
• PD Close: Pivot is calculated based on previous day closing price.
• PD HL2: Pivot is calculated based on previous day average of High and Low.
• PD HLC3: Pivot is calculated based on previous day average of High, Low and Close.
"Time (Vertical Lines)"
• This is a marker of every 1 hour.
• Usually major price movement happen between previous day last 1 hour to today first 1 hour.
• Two swings can happen between first 2 hour of current day.
• At the end of the day last 1 hour another important movement will happen.
• Usually rest of the time won't show any interesting movement.
To the Users
• Certain symbols may show the levels as a single line. For such symbols choose a different Source or Timeframe from the indicator menu.
• Please inform if any of the Symbol's price levels don't react to the pivots , include the Symbol a well.
• Also inform if you notice any wrong values, errors or abnormal behavior in the indicator.
• Feel free to suggest or adding new features and options.
General Tips
• It is good if Stock trend is same as that of Index trend.
• Lots of indicators creates lots of confusion.
• Keep the chart simple and clean.
• Buy Low and Sell High.
• Master averages or 50%.
• Previous Swing High and Swing Low are crucial.
Important Note
• Currently the levels are in testing stage.
• Eventually the levels of certain symbols will be corrected after each update and test.
Price Pivots for NSE Index & F&O StocksPrice Pivots for NSE Index & F&O Stocks
What is this Indicator?
• This indicator calculates the price range a Stock or Index can move in a Day, Week or Month.
Advantages of this Indicator
• This is a Leading indicator, not Dynamic or Repaint.
• Helps to identify the tight range of price movement.
• Can easily identify the Options strike price.
• The levels are more reliable and authentic than Gann Square of 9 Levels.
• Develops a discipline in placing Targets.
Disadvantages of this Indicator
• The indicator is specifically made for National Stock Exchange of India (NSE) listed index and stocks.
• The indicator is calculated only for index NIFTY, BANKNIFTY, FINNIFTY, MIDCPNIFTY and Stocks listed in Futures and Options.
• The indicator shows nothing for other indexes and stocks other than above mentioned.
• The data need to be entered manually.
• The data need to be updated manually when the F&O listed stocks are updated.
Who to use?
Highly beneficial for Day Traders, it can be used for Swing and Positions as well.
What timeframe to use?
• Any timeframe.
• The highlighted levels in Red and Green will not show correct levels in 1 minute timeframe.
• 5min is recommended for Day Traders.
When to use?
• Wait for proper swing to form.
• Recommended to avoid 1st 1 hour or market open, that is 9.15am to 10.15 or 10.30am.
• Within this time a proper swing will be formed.
How to use?
Entry
• Enter when the Price reach closer to the Blue line.
• Enter Long when the Price takes a pullback or breakout at the Red lines.
Exit
• Exit position when the Price reach closer to the Red lines in Long positions.
What are the Lines?
Gray Lines:
• Every lines with price labels are the Strike Prices in the Option Chain from NSE website.
• Price moves from 1 Strike Price level to another.
• The dashed lines are average levels of 2 Strike Prices.
Red & Green Lines:
• The Red and Green Lines will appear only after the first 1 hour.
• The levels are calculated based on the 1st 1 hour.
• Red Lines are important Resistance levels, these are strong Bearish reversal points. It is also a breakout level, this need to be figured out from the past levels, trend, percentage change and consolidation.
• Green Lines are important Support levels, these are strong Bullish reversal points. It is also a breakdown level, this need to be figured out from the past levels, trend, percentage change and consolidation.
What are the Labels?
• First Number: Price of that level.
• Numbers in (): Percentage change and Change of price from LTP(Last Traded Price) to that Level.
How to use?
Entry:
• Enter when price is closer to the Red or Green lines.
• Enter after considering previous Swing and Trend.
• Note the 50% of previous Swing.
• Enter Short when price reverse from each level.
• If 50% of swing and the pivot level is closer it can be a good entry.
Exit:
• Use the logic of Entry, each level can be a target.
• Exit when price is closer to the Red or Green lines.
Indicator Menu
Source
• Custom: Enter the price manually after choosing the Source as Custom to show the Pivots at that price.
• LTP: Pivot is calculated based on Last Traded Price.
• Day Open: Pivot is calculated based on current day opening price.
• PD Close: Pivot is calculated based on previous day closing price.
• PD HL2: Pivot is calculated based on previous day average of High and Low.
• PD HLC3: Pivot is calculated based on previous day average of High, Low and Close.
"Time (IST) (Vertical)"
• This is a marker of every 1 hour.
• Usually major price movement happen between previous day last 1 hour (2:15 pm) to today first 1 hour (10:15 pm).
• Two swings can happen between first 2 hour of current day.
• At the end of the day last 1 hour from 2.15 pm another important movement will happen.
• Usually rest of the time won't show any interesting movement.
To the Users
• Certain symbols may show the levels as a single line. For such symbols choose a different Source or Timeframe from the indicator menu.
• Please inform if any of the Symbol's price levels don't react to the pivots, include the Symbol a well.
• Also inform if you notice any wrong values, errors or abnormal behavior in the indicator.
• Feel free to suggest or adding new features and options.
General Tips
• It is good if Stock trend is same as that of NIFTY trend.
• Lots of indicators creates lots of confusion.
• Keep the chart simple and clean.
• Buy Low and Sell High.
• Master averages or 50%.
• Previous Swing High and Swing Low are crucial.
taLibrary "ta"
█ OVERVIEW
This library holds technical analysis functions calculating values for which no Pine built-in exists.
Look first. Then leap.
█ FUNCTIONS
cagr(entryTime, entryPrice, exitTime, exitPrice)
It calculates the "Compound Annual Growth Rate" between two points in time. The CAGR is a notional, annualized growth rate that assumes all profits are reinvested. It only takes into account the prices of the two end points — not drawdowns, so it does not calculate risk. It can be used as a yardstick to compare the performance of two instruments. Because it annualizes values, the function requires a minimum of one day between the two end points (annualizing returns over smaller periods of times doesn't produce very meaningful figures).
Parameters:
entryTime : The starting timestamp.
entryPrice : The starting point's price.
exitTime : The ending timestamp.
exitPrice : The ending point's price.
Returns: CAGR in % (50 is 50%). Returns `na` if there is not >=1D between `entryTime` and `exitTime`, or until the two time points have not been reached by the script.
█ v2, Mar. 8, 2022
Added functions `allTimeHigh()` and `allTimeLow()` to find the highest or lowest value of a source from the first historical bar to the current bar. These functions will not look ahead; they will only return new highs/lows on the bar where they occur.
allTimeHigh(src)
Tracks the highest value of `src` from the first historical bar to the current bar.
Parameters:
src : (series int/float) Series to track. Optional. The default is `high`.
Returns: (float) The highest value tracked.
allTimeLow(src)
Tracks the lowest value of `src` from the first historical bar to the current bar.
Parameters:
src : (series int/float) Series to track. Optional. The default is `low`.
Returns: (float) The lowest value tracked.
█ v3, Sept. 27, 2022
This version includes the following new functions:
aroon(length)
Calculates the values of the Aroon indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: ( [float, float ]) A tuple of the Aroon-Up and Aroon-Down values.
coppock(source, longLength, shortLength, smoothLength)
Calculates the value of the Coppock Curve indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
longLength (simple int) : (simple int) Number of bars for the fast ROC value (length).
shortLength (simple int) : (simple int) Number of bars for the slow ROC value (length).
smoothLength (simple int) : (simple int) Number of bars for the weigted moving average value (length).
Returns: (float) The oscillator value.
dema(source, length)
Calculates the value of the Double Exponential Moving Average (DEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `source`.
dema2(src, length)
An alternate Double Exponential Moving Average (Dema) function to `dema()`, which allows a "series float" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `src`.
dm(length)
Calculates the value of the "Demarker" indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
donchian(length)
Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.
Parameters:
length (int) : (series int) Number of bars (length).
Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively.
ema2(src, length)
An alternate ema function to the `ta.ema()` built-in, which allows a "series float" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int/float) Number of bars (length).
Returns: (float) The exponentially weighted moving average of the `src`.
eom(length, div)
Calculates the value of the Ease of Movement indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
div (simple int) : (simple int) Divisor used for normalzing values. Optional. The default is 10000.
Returns: (float) The oscillator value.
frama(source, length)
The Fractal Adaptive Moving Average (FRAMA), developed by John Ehlers, is an adaptive moving average that dynamically adjusts its lookback period based on fractal geometry.
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The fractal adaptive moving average of the `source`.
ft(source, length)
Calculates the value of the Fisher Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
ht(source)
Calculates the value of the Hilbert Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
Returns: (float) The oscillator value.
ichimoku(conLength, baseLength, senkouLength)
Calculates values of the Ichimoku Cloud indicator, including tenkan, kijun, senkouSpan1, senkouSpan2, and chikou. NOTE: offsets forward or backward can be done using the `offset` argument in `plot()`.
Parameters:
conLength (int) : (series int) Length for the Conversion Line (Tenkan). The default is 9 periods, which returns the mid-point of the 9 period Donchian Channel.
baseLength (int) : (series int) Length for the Base Line (Kijun-sen). The default is 26 periods, which returns the mid-point of the 26 period Donchian Channel.
senkouLength (int) : (series int) Length for the Senkou Span 2 (Leading Span B). The default is 52 periods, which returns the mid-point of the 52 period Donchian Channel.
Returns: ( [float, float, float, float, float ]) A tuple of the Tenkan, Kijun, Senkou Span 1, Senkou Span 2, and Chikou Span values. NOTE: by default, the senkouSpan1 and senkouSpan2 should be plotted 26 periods in the future, and the Chikou Span plotted 26 days in the past.
ift(source)
Calculates the value of the Inverse Fisher Transform indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
Returns: (float) The oscillator value.
kvo(fastLen, slowLen, trigLen)
Calculates the values of the Klinger Volume Oscillator.
Parameters:
fastLen (simple int) : (simple int) Length for the fast moving average smoothing parameter calculation.
slowLen (simple int) : (simple int) Length for the slow moving average smoothing parameter calculation.
trigLen (simple int) : (simple int) Length for the trigger moving average smoothing parameter calculation.
Returns: ( [float, float ]) A tuple of the KVO value, and the trigger value.
pzo(length)
Calculates the value of the Price Zone Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
rms(source, length)
Calculates the Root Mean Square of the `source` over the `length`.
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The RMS value.
rwi(length)
Calculates the values of the Random Walk Index.
Parameters:
length (simple int) : (simple int) Lookback and ATR smoothing parameter length.
Returns: ( [float, float ]) A tuple of the `rwiHigh` and `rwiLow` values.
stc(source, fast, slow, cycle, d1, d2)
Calculates the value of the Schaff Trend Cycle indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
fast (simple int) : (simple int) Length for the MACD fast smoothing parameter calculation.
slow (simple int) : (simple int) Length for the MACD slow smoothing parameter calculation.
cycle (simple int) : (simple int) Number of bars for the Stochastic values (length).
d1 (simple int) : (simple int) Length for the initial %D smoothing parameter calculation.
d2 (simple int) : (simple int) Length for the final %D smoothing parameter calculation.
Returns: (float) The oscillator value.
stochFull(periodK, smoothK, periodD)
Calculates the %K and %D values of the Full Stochastic indicator.
Parameters:
periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).
smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).
periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).
Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values.
stochRsi(lengthRsi, periodK, smoothK, periodD, source)
Calculates the %K and %D values of the Stochastic RSI indicator.
Parameters:
lengthRsi (simple int) : (simple int) Length for the RSI smoothing parameter calculation.
periodK (simple int) : (simple int) Number of bars for Stochastic calculation. (length).
smoothK (simple int) : (simple int) Number of bars for smoothing of the %K value (length).
periodD (simple int) : (simple int) Number of bars for smoothing of the %D value (length).
source (float) : (series int/float) Series of values to process. Optional. The default is `close`.
Returns: ( [float, float ]) A tuple of the slow %K and the %D moving average values.
supertrend(factor, atrLength, wicks)
Calculates the values of the SuperTrend indicator with the ability to take candle wicks into account, rather than only the closing price.
Parameters:
factor (float) : (series int/float) Multiplier for the ATR value.
atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.
wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is false.
Returns: ( [float, int ]) A tuple of the superTrend value and trend direction.
szo(source, length)
Calculates the value of the Sentiment Zone Oscillator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
t3(source, length, vf)
Calculates the value of the Tilson Moving Average (T3).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
vf (simple float) : (simple float) Volume factor. Affects the responsiveness.
Returns: (float) The Tilson moving average of the `source`.
t3Alt(source, length, vf)
An alternate Tilson Moving Average (T3) function to `t3()`, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
vf (simple float) : (simple float) Volume factor. Affects the responsiveness.
Returns: (float) The Tilson moving average of the `source`.
tema(source, length)
Calculates the value of the Triple Exponential Moving Average (TEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The triple exponentially weighted moving average of the `source`.
tema2(source, length)
An alternate Triple Exponential Moving Average (TEMA) function to `tema()`, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The triple exponentially weighted moving average of the `source`.
trima(source, length)
Calculates the value of the Triangular Moving Average (TRIMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (int) : (series int) Number of bars (length).
Returns: (float) The triangular moving average of the `source`.
trima2(src, length)
An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a "series int" length argument.
Parameters:
src : (series int/float) Series of values to process.
length : (series int) Number of bars (length).
Returns: (float) The triangular moving average of the `src`.
trix(source, length, signalLength, exponential)
Calculates the values of the TRIX indicator.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
signalLength (simple int) : (simple int) Length for smoothing the signal line.
exponential (simple bool) : (simple bool) Condition to determine whether exponential or simple smoothing is used. Optional. The default is `true` (exponential smoothing).
Returns: ( [float, float, float ]) A tuple of the TRIX value, the signal value, and the histogram.
uo(fastLen, midLen, slowLen)
Calculates the value of the Ultimate Oscillator.
Parameters:
fastLen (simple int) : (series int) Number of bars for the fast smoothing average (length).
midLen (simple int) : (series int) Number of bars for the middle smoothing average (length).
slowLen (simple int) : (series int) Number of bars for the slow smoothing average (length).
Returns: (float) The oscillator value.
vhf(source, length)
Calculates the value of the Vertical Horizontal Filter.
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Number of bars (length).
Returns: (float) The oscillator value.
vi(length)
Calculates the values of the Vortex Indicator.
Parameters:
length (simple int) : (simple int) Number of bars (length).
Returns: ( [float, float ]) A tuple of the viPlus and viMinus values.
vzo(length)
Calculates the value of the Volume Zone Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
williamsFractal(period)
Detects Williams Fractals.
Parameters:
period (int) : (series int) Number of bars (length).
Returns: ( [bool, bool ]) A tuple of an up fractal and down fractal. Variables are true when detected.
wpo(length)
Calculates the value of the Wave Period Oscillator.
Parameters:
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The oscillator value.
█ v7, Nov. 2, 2023
This version includes the following new and updated functions:
atr2(length)
An alternate ATR function to the `ta.atr()` built-in, which allows a "series float" `length` argument.
Parameters:
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The ATR value.
changePercent(newValue, oldValue)
Calculates the percentage difference between two distinct values.
Parameters:
newValue (float) : (series int/float) The current value.
oldValue (float) : (series int/float) The previous value.
Returns: (float) The percentage change from the `oldValue` to the `newValue`.
donchian(length)
Calculates the values of a Donchian Channel using `high` and `low` over a given `length`.
Parameters:
length (int) : (series int) Number of bars (length).
Returns: ( [float, float, float ]) A tuple containing the channel high, low, and median, respectively.
highestSince(cond, source)
Tracks the highest value of a series since the last occurrence of a condition.
Parameters:
cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the highest `source`.
source (float) : (series int/float) Series of values to process. Optional. The default is `high`.
Returns: (float) The highest `source` value since the last time the `cond` was `true`.
lowestSince(cond, source)
Tracks the lowest value of a series since the last occurrence of a condition.
Parameters:
cond (bool) : (series bool) A condition which, when `true`, resets the tracking of the lowest `source`.
source (float) : (series int/float) Series of values to process. Optional. The default is `low`.
Returns: (float) The lowest `source` value since the last time the `cond` was `true`.
relativeVolume(length, anchorTimeframe, isCumulative, adjustRealtime)
Calculates the volume since the last change in the time value from the `anchorTimeframe`, the historical average volume using bars from past periods that have the same relative time offset as the current bar from the start of its period, and the ratio of these volumes. The volume values are cumulative by default, but can be adjusted to non-accumulated with the `isCumulative` parameter.
Parameters:
length (simple int) : (simple int) The number of periods to use for the historical average calculation.
anchorTimeframe (simple string) : (simple string) The anchor timeframe used in the calculation. Optional. Default is "D".
isCumulative (simple bool) : (simple bool) If `true`, the volume values will be accumulated since the start of the last `anchorTimeframe`. If `false`, values will be used without accumulation. Optional. The default is `true`.
adjustRealtime (simple bool) : (simple bool) If `true`, estimates the cumulative value on unclosed bars based on the data since the last `anchor` condition. Optional. The default is `false`.
Returns: ( [float, float, float ]) A tuple of three float values. The first element is the current volume. The second is the average of volumes at equivalent time offsets from past anchors over the specified number of periods. The third is the ratio of the current volume to the historical average volume.
rma2(source, length)
An alternate RMA function to the `ta.rma()` built-in, which allows a "series float" `length` argument.
Parameters:
source (float) : (series int/float) Series of values to process.
length (float) : (series int/float) Length for the smoothing parameter calculation.
Returns: (float) The rolling moving average of the `source`.
supertrend2(factor, atrLength, wicks)
An alternate SuperTrend function to `supertrend()`, which allows a "series float" `atrLength` argument.
Parameters:
factor (float) : (series int/float) Multiplier for the ATR value.
atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.
wicks (simple bool) : (simple bool) Condition to determine whether to take candle wicks into account when reversing trend, or to use the close price. Optional. Default is `false`.
Returns: ( [float, int ]) A tuple of the superTrend value and trend direction.
vStop(source, atrLength, atrFactor)
Calculates an ATR-based stop value that trails behind the `source`. Can serve as a possible stop-loss guide and trend identifier.
Parameters:
source (float) : (series int/float) Series of values that the stop trails behind.
atrLength (simple int) : (simple int) Length for the ATR smoothing parameter calculation.
atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.
Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool".
vStop2(source, atrLength, atrFactor)
An alternate Volatility Stop function to `vStop()`, which allows a "series float" `atrLength` argument.
Parameters:
source (float) : (series int/float) Series of values that the stop trails behind.
atrLength (float) : (series int/float) Length for the ATR smoothing parameter calculation.
atrFactor (float) : (series int/float) The multiplier of the ATR value. Affects the maximum distance between the stop and the `source` value. A value of 1 means the maximum distance is 100% of the ATR value. Optional. The default is 1.
Returns: ( [float, bool ]) A tuple of the volatility stop value and the trend direction as a "bool".
Removed Functions:
allTimeHigh(src)
Tracks the highest value of `src` from the first historical bar to the current bar.
allTimeLow(src)
Tracks the lowest value of `src` from the first historical bar to the current bar.
trima2(src, length)
An alternate Triangular Moving Average (TRIMA) function to `trima()`, which allows a
"series int" length argument.
ColorSchemeLibrary "ColorScheme"
A color scheme generator.
init() Initiate the array data registry that will hold the color profile. Returns: tuple with 2 arrays (string array, color array)
check_registry_integrity(key_registry, color_registry) Checks the integrity of the registers.
Parameters:
key_registry : string array, key data holder array.
color_registry : color array, color value data holder array.
Returns: void.
add(key_registry, color_registry, key, value) Add new (key, color) entry to the registry.
Parameters:
key_registry : string array, key data holder array.
color_registry : color array, color value data array.
key : string, the unique key to reference the value.
value : color, the color value of the specified key.
Returns: void.
get_color(key_registry, color_registry, key) Get a (key, color) entry from the registry.
Parameters:
key_registry : string array, key data holder array.
color_registry : color array, color value data array.
key : string, the unique key to reference the value.
Returns: void.
edit_key(key_registry, color_registry, key, new_key) Edit a (key, color) entry in the registry.
Parameters:
key_registry : string array, key data holder array.
color_registry : color array, color value data array.
key : string, the unique key to reference the value.
new_key : string, the unique key to reference the value.
Returns: void.
edit_color(key_registry, color_registry, key, new_value) Edit a (key, color) entry in the registry.
Parameters:
key_registry : string array, key data holder array.
color_registry : color array, color value data array.
key : string, the unique key to reference the value.
new_value : color, the color value of the specified key.
Returns: void.
delete(key_registry, color_registry, key) Delete a (key, color) entry from the registry.
Parameters:
key_registry : string array, key data holder array.
color_registry : color array, color value data array.
key : string, the unique key to reference the value.
Returns: void.
delete_all(key_registry, color_registry) Delete all (key, color) entrys from the registry.
Parameters:
key_registry : string array, key data holder array.
color_registry : color array, color value data array.
Returns: void.
model(index) Enumerate models available to profile colors.
Parameters:
index : int, index of model. (1:'monochromatic', 2:'analog', 3:'triadic', 4:'tetradic', 5:'square', anything else:'monochromatic')
Returns: string.
generate_scheme(key_registry, color_registry, primary, model) Generate a multi color scheme.
Parameters:
key_registry : string array, key data holder array.
color_registry : color array, color value data array.
primary : color, the origin color to base the profile.
model : string, default='monochromatic', options=('monochromatic', 'triadic near', 'triadic far', 'tetradic')
Returns: void.
Linear Regression Market State IndexStandard Deviation Market Structure Indicator
A Comprehensive Multi-Timeframe Market Analysis Tool
🎯 Overview
The Standard Deviation Market Structure (SDMS) indicator is a sophisticated technical analysis tool that integrates multiple proven methodologies to identify market structure, trend direction, and potential reversal zones. By combining price action, statistical analysis, and momentum indicators across multiple timeframes, SDMS provides traders with a comprehensive view of market dynamics.
✨ Key Features
Multi-Timeframe Integration
Primary analysis on current timeframe
1-hour statistical confirmation for support/resistance levels
Order block extension across 500 future bars
Comprehensive Technical Suite
RSI with Deviation Analysis
Dynamic Order Block Detection
Gaussian Filter Channels
Linear Regression with Statistical Bands
Standard deviation to detect price outliers
Directional Movement Index (DMI/ADX)
Bollinger Band % Analysis
Support/Resistance Line System
Visual Clarity
Color-coded signals and zones
Automatic level management
Clean, intuitive display
📊 Core Components Explained
1. Order Block System
What Are Order Blocks?
Order blocks are price zones where institutional activity has occurred, creating future support or resistance levels. SDMS automatically detects these critical zones.
Detection Logic:
Bullish Order Blocks: Form when price breaks above recent highs following bearish candles
Bearish Order Blocks: Form when price breaks below recent lows following bullish candles
Visual Identification:
Green boxes with "BuOB" labels (support zones)
Red boxes with "BeOB" labels (resistance zones)
Each block shows its boundary price for easy reference
Dynamic Management:
Automatically extends 300 bars into the future
Self-cleaning: removes blocks when price breaches their boundaries
Real-time adjustment to changing market structure
2. Statistical Support/Resistance System
How It Works:
SDMS creates support and resistance lines based on statistical extremes confirmed on the 1-hour timeframe.
Trigger Conditions:
Support Lines (Green): Trigger when 1H Bollinger Band % crosses above 0 and bearish momentum subsides.
Resistance Lines (Red): Trigger when 1H Bollinger Band % crosses below 1 and bullish momentum subsides
The Science Behind BB%:
BB% = (Price - Lower Band) / (Upper Band - Lower Band)
BB% <= 0: Price at statistical oversold extreme; also indicated by white candles.
BB% > 1: Price at statistical overbought extreme; also indicated by white candles.
Line Management:
Maximum of 15 active lines
Oldest lines automatically removed
Lines extend across chart for ongoing reference
3. Trend Analysis Suite
Hull Moving Average (HMA):
55-period smoothed trend indicator
Color-coded: Green = bullish, Red = bearish
Visual band shows trend acceleration/deceleration
Gaussian Channel:
Advanced filtering of market noise
Dynamic channel based on true range volatility
Helps identify mean reversion opportunities
Form a yellow band when price is overbought or oversold zones.
Linear Regression System:
Statistical price modeling
Multiple standard deviation bands (up to 3SD)
Regression-based candlestick visualization
Candles turn white when in overbought zones. Yellow candles indicate extremely overbought zones. Blue candles indicate a bullish trend with high volume.
Bearish candles are bluish-purple when volume is high and red when the volume is within normal ranges or low.
4. Momentum & Oscillator Integration
RSI with Deviation Tracking:
21-period RSI with 30-period smoothing
Tracks deviation from moving average based off linear regression
Identifies momentum divergences
Directional Movement Index:
Multi-period DMI/ADX analysis
Used to detect overbought and oversold zones within the indicator calculations.
Combines with RSI for enhanced signals
Momentum confirmation for all entries/exits
🎯 Trading Signals & Alerts
Buy Signals (Yellow "Buy" Labels)
Multi-Condition Confirmation Required:
RSI Oversold Reversal: RSI crosses above 30
Trend Alignment: HMA showing bullish structure
Momentum Confirmation: DMI alignment
Statistical Support: Price at or near support zones
Risk Management: Multiple confirming indicators
Strong Buy Conditions:
Confluence of order block support + BB% support line
Multiple timeframe alignment
Volume confirmation at key levels
Sell Signals (Red/Yellow "Sell" Labels)
Multi-Condition Confirmation Required:
RSI Overbought Reversal: RSI crosses below 70
Trend Exhaustion: HMA showing bearish structure
Momentum Divergence: DMI bearish alignment
Statistical Resistance: Price at or near resistance zones
Timeframe Confirmation: 1H BB% bearish signals
Strong Sell Conditions:
Confluence of order block resistance + BB% resistance line
Multiple timeframe distribution
Volume surge at resistance
Additional Alerts
RSI Divergence Signals: Triangles showing momentum shifts
Extreme Price Alerts: Circles at statistical extremes
Structure Breaks: Visual cues for order block violations
🎨 Visual System Guide
Color Coding System
Green: Bullish conditions, support zones, rising trends
Red: Bearish conditions, resistance zones, falling trends
Blue: Statistical channels, neutral zones
Yellow: Alert conditions, extreme signals
White: Transition zones, neutral signals
Zone Identification
Buying Pressure Zones: Green/blue tinted areas below price or white candles with white dots within the moving average center line
Selling Pressure Zones: Red tinted areas above price with white dots within the moving average center line
Standard Deviation Zones: Gradient colors showing statistical extremes
⚙️ Customization Options
Adjustable Parameters
RSI Settings: Period, oversold/overbought levels, sensitivity
Order Block Detection: Lookback period, ATR multiplier, extension
Statistical Settings: Gaussian filter poles, regression periods
Support/Resistance: Maximum lines, BB% settings
Visual Preferences: Colors, band displays, alert styles
Input Groups
RSI Trading Strategy
Order Block Configuration
Gaussian Channel Settings
Linear Regression Parameters
DMI/ADX Configuration
Bollinger Band % Settings
📈 Practical Trading Applications
For Swing Traders
Identify Key Levels: Use order blocks + BB% lines for entry/exit planning
Trend Confirmation: HMA + Gaussian channel for trend direction
Risk Management: Standard deviation bands for stop placement
Timing Entries: RSI/DMI alignment for optimal entry timing
For Day Traders
Intraday Levels: Order blocks provide immediate S/R for day trading
Momentum Signals: Real-time RSI/DMI signals for quick moves
Statistical Edges: Gaussian channel for mean reversion plays
Breakout Confirmation: Order block breaks with volume
For Position Traders
Higher Timeframe Structure: 1H BB% lines for major levels
Trend Persistence: HMA for long-term trend identification
Accumulation/Distribution Zones: Order blocks show institutional activity
Multi-Timeframe Alignment: Confirmation across timeframes
🔍 How to Use SDMS Effectively
Step 1: Market Structure Assessment
Identify active order blocks (green/red boxes)
Note BB% support/resistance lines (horizontal lines)
Assess HMA and moving average trend direction (color)
Check Gaussian channel position (preferably outside 2SD)
Step 2: Signal Confirmation
Wait for multiple indicator alignment
look for doji candles.
Confirm with green (bullish) or red (bearish) candles
Confirm with volume if available
Check for confluence of levels
Assess risk/reward based on nearby levels
Step 3: Trade Management
Enter at confirmed support/resistance
Place stops beyond opposite levels
Take profits at next statistical level
Monitor for structure changes
Step 4: Risk Management
Use standard deviation bands for volatility assessment
Never risk more than 1-2% per trade
Adjust position size based on confluence strength
Have predefined exit rules
💡 Advanced Strategies
Strategy 1: Confluence Trading
Setup: Order block + BB% line at same level
Entry: Price tests confluence zone with RSI signal
Stop: Beyond the confluence zone
Target: Next statistical level
Strategy 2: Breakout Trading
Setup: Price approaching order block boundary
Entry: Break with volume + RSI/DMI confirmation
Stop: Re-entry into order block
Target: Next BB% line extension
Strategy 3: Mean Reversion
Setup: Price at Gaussian channel extremes
Entry: RSI reversal signal at channel boundary
Stop: Beyond channel extreme
Target: Channel midline or opposite boundary
⚠️ Important Considerations
Best Market Conditions
Trending Markets: Excellent performance in clear trends
Breakout Scenarios: Strong identification of break levels
Range Markets: Works well with defined ranges
Limitations
Choppy Markets: May give false signals in consolidation
News Events: Fundamental shocks can override technical levels
Timeframe Specific: Optimal on 15-minute to daily charts
Risk Management Rules
Always use stops
Never rely on single signals
Consider market context
Adjust for volatility changes
Keep position sizes consistent
🔧 Technical Specifications
Maximum Lines: 500
Maximum Bars Back: 1000
Maximum Boxes: 500
Calculation Efficiency: Optimized for real-time use
🏆 Why SDMS Stands Out
Unique Advantages
Integrated Approach: Combines multiple methodologies into one tool
Self-Adjusting: Automatically adapts to market changes
Multi-Timeframe: Provides both immediate and higher timeframe context
Visual Clarity: Clean, intuitive display of complex data
Professional Grade: Institutional-level analysis accessible to all traders
Educational Value: Learn how different indicators interact
Understand market structure development
See institutional order flow patterns
Develop disciplined trading habits
📚 Learning Resources
Recommended Study Approach
Start Simple: Focus on order blocks and BB% lines first
Add Complexity: Gradually incorporate other indicators
Paper Trade: Practice without risk
Keep Journal: Document setups and outcomes
Review Regularly: Analyze both wins and losses
Common Pitfalls to Avoid
Overtrading: Wait for high-quality setups
Ignoring Context: Consider overall market conditions
Chasing Signals: Enter at planned levels, not after moves
Risk Mismanagement: Always know your risk before entering
Confirmation Bias: Be objective about signals
🤝 Community & Support
Getting the Most from SDMS
Start with Defaults: Use default settings initially
Adjust Gradually: Make small changes as you understand the tool
Combine with Fundamentals: Use for timing within fundamental context
Stay Disciplined: Follow your trading plan consistently
Continuous Improvement
SDMS is designed for continuous learning. As you use the indicator, you'll develop insights into:
Market microstructure
Institutional trading patterns
Statistical edge identification
Risk management optimization
Risk management is more important than signal accuracy
Patience is required for high-quality setups
Success Factors
Discipline: Following your plan consistently
Patience: Waiting for proper setups
Risk Management: Protecting your capital
Continuous Learning: Improving your skills over time
🌟 Final Thoughts
The Standard Deviation Market Structure indicator represents a sophisticated approach to technical analysis, combining the best elements of price action, statistical analysis, and momentum indicators. While powerful, remember that no indicator guarantees success. SDMS is a tool – your skill, discipline, and risk management determine your trading results.
Use SDMS as part of a comprehensive trading plan, combine it with proper risk management, and continue developing your trading skills. The markets are always teaching – stay humble, stay disciplined, and trade well.
Disclaimer: This indicator is for educational purposes only. Past performance does not guarantee future results. Trading involves risk of loss. Always consult with a qualified financial professional before making investment decisions.
PVSRA Dashboard PRO [Customized]# 📘 OPERATING MANUAL: Institutional Volume Suite (v1.0)
**Integrated Systems:** PVSRA Dashboard PRO + SR High Volume Boxes + Massive Order Spike Detector
---
## 1. SYSTEM PHILOSOPHY
This ecosystem tracks **Institutional Order Flow**. The core principle is that "Smart Money" leaves undeniable footprints through abnormal volume (Spikes) and specific price zones (High Volume Boxes). The system filters retail noise to identify where "Whales" are accumulating or distributing positions.
---
## 2. VISUAL DICTIONARY & SIGNALS
### A. PVSRA & Candles (Institutional Sentiment)
| Candle Color | Signal Type | Operational Meaning |
| :--- | :--- | :--- |
| 🟢 **Bright Green** | **Bull Climax** | Maximum Volume. Strong institutional buying or "Blow-off top". |
| 🟣 **Magenta** | **Bear Climax** | Maximum Volume. Strong institutional selling or "Selling climax". |
| 🔵 **Blue** | **Bull Rising** | Above-average volume. Professional buying interest. |
| 🔴 **Red/Orange** | **Bear Rising** | Above-average volume. Professional selling interest. |
| ⚪ **Grey** | **Normal** | Retail volume. Low institutional participation. |
### B. SR Boxes & Spike Detector (The Triggers)
* **Teal Boxes:** High Volume Support (Demand Zone).
* **Red Boxes:** High Volume Resistance (Supply Zone).
* **Triangles (▲/▼):** "Massive Order Spike". Statistical confirmation of heavy entry.
* **Diamonds (◆):** Real-time confirmation that a level (Box) is "Holding."
---
## 3. THE PRO DASHBOARD (Confluence Matrix)
Always consult the top-right dashboard before executing a trade:
1. **Momentum (9/20):** Short-term direction (Green Cloud = Long, Red = Short).
2. **Trend (20/50):** Health of the intermediate trend.
3. **Inst. Trend (200):** The master filter. Above SMA 200, look for Longs only; below, Shorts only.
4. **Delta Pressure:** Shows if the actual money flow is positive (BUY) or negative (SELL).
5. **CONFLUENCE PRO:** The final verdict. "STRONG BUY/SELL" means all parameters are aligned.
---
## 4. OPERATIONAL PROTOCOL (STRATEGY)
### **Phase 1: Zone Identification**
Identify where the price is relative to the **High Volume Boxes**.
- *Long Setup:* Price enters a Teal Box or tests a dashed "Support-Flip" line.
- *Short Setup:* Price enters a Red Box or tests a dashed "Resistance-Flip" line.
### **Phase 2: The Trigger (Action)**
Wait for the coordinated appearance of signals:
1. **PVSRA Color:** The candle must turn Climax (Green/Magenta).
2. **Order Spike:** The Triangle confirmation must appear.
3. **Level Confirmation:** The Diamond (◆) appears, indicating a bounce/rejection from the zone.
### **Phase 3: Execution**
- **ENTRY:** Enter when the Dashboard shows "STRONG BUY/SELL" coinciding with Phase 2 signals.
- **STOP LOSS:** Placed behind the opposite limit of the Box or the Climax candle wick.
- **TAKE PROFIT:** Use the **Dashed Recovery Lines** or the opposite High Volume Box.
---
## 5. RECOMMENDED TECHNICAL CONFIGURATION
| Parameter | Value | Notes |
| :--- | :--- | :--- |
| **PVSRA Climax** | 2.7 | Captures only the most significant institutional moves. |
| **Spike Multiplier** | 4.0 | Filters out statistical noise. |
| **Inst. SMA** | 200 | Blue (
Multi-indicator Signal Builder [Skyrexio]Overview
Multi-Indicator Signal Builder is a versatile, all-in-one script designed to streamline your trading workflow by combining multiple popular technical indicators under a single roof.
It features a single-entry, single-exit logic, intrabar stop-loss/take-profit handling, an optional time filter, a visually accessible condition table, and a built-in statistics label.
Traders can choose any combination of 12+ indicators (RSI, Ultimate Oscillator, Bollinger %B, Moving Averages, ADX, Stochastic, MACD, PSAR, MFI, CCI, Heikin Ashi, and a “TV Screener” placeholder) to form entry or exit conditions.
This script aims to simplify strategy creation and analysis , making it a powerful toolkit for technical traders.
Indicators Overview
RSI (Relative Strength Index)
Measures recent price changes to evaluate overbought or oversold conditions on a 0–100 scale.
Ultimate Oscillator (UO)
Uses weighted averages of three different timeframes, aiming to confirm price momentum while avoiding false divergences.
Bollinger %B
Expresses price relative to Bollinger Bands, indicating whether price is near the upper band (overbought) or lower band (oversold).
Moving Average (MA)
Smooths price data over a specified period. The script supports both SMA and EMA to help identify trend direction and potential crossovers.
ADX (Average Directional Index)
Gauges the strength of a trend (0–100). Higher ADX signals stronger momentum, while lower ADX indicates a weaker trend.
Stochastic
Compares a closing price to a price range over a given period to identify momentum shifts and potential reversals.
MACD (Moving Average Convergence/Divergence)
Tracks the difference between two EMAs plus a signal line, commonly used to spot momentum flips through crossovers.
PSAR (Parabolic SAR)
Plots a trailing stop-and-reverse dot that moves with the trend. Often used to signal potential reversals when price crosses PSAR.
MFI (Money Flow Index)
Similar to RSI but incorporates volume data. A reading above 80 can suggest overbought conditions, while below 20 may indicate oversold.
CCI (Commodity Channel Index)
Identifies cyclical trends or overbought/oversold levels by comparing current price to an average price over a set timeframe.
Heikin Ashi
A type of candlestick charting that filters out market noise. The script uses a streak-based approach (multiple consecutive bullish or bearish bars) to gauge mini-trends.
TV Screener
A placeholder condition designed to integrate external buy/sell logic (like a TradingView “Buy” or “Sell” rating). Users can override or reference external signals if desired.
Unique Features
Multi-Indicator Entry and Exit
You can selectively enable any subset of 12+ classic indicators, each with customizable parameters and conditions. A position opens only if all enabled entry conditions are met, and it closes only when all enabled exit conditions are satisfied, helping reduce false triggers.
Single-Entry / Single-Exit with Intrabar SL/TP
The script supports a single position at a time. Once a position is open, it monitors intrabar to see if the price hits your stop-loss or take-profit levels before the bar closes, making results more realistic for fast-moving markets.
Time Window Filter
Users may specify a start/end date range during which trades are allowed, making it convenient to focus on specific market cycles for backtesting or live trading.
Condition Table and Statistics
A table at the bottom of the chart lists all active entry/exit indicators. Upon each closed trade, an integrated statistics label displays net profit, total trades, win/loss count, average and median PnL, etc.
Seamless Alerts and Automation
• Configure alerts in TradingView using “Any alert() function call.”
• The script sends JSON alert messages you can route to your own webhook.
• The indicator can be integrated with Skyrexio alert bots to automate execution on major cryptocurrency exchanges.
Optional MA/PSAR Plots
For added visual clarity, optionally plot the chosen moving averages or PSAR on the chart to confirm signals without stacking multiple indicators.
Methodology
Multi-Indicator Entry Logic
When multiple entry indicators are enabled (e.g., RSI + Stochastic + MACD), the script requires all signals to align before generating an entry. Each indicator can be set for crossovers, crossunders, thresholds (above/below), etc. This “AND” logic aims to filter out low-confidence triggers.
Single-Entry Intrabar SL/TP
• One Position At a Time: Once an entry signal triggers, a trade opens at the bar’s close.
• Intrabar Checks: Stop-loss and take-profit levels (if enabled) are monitored on every tick. If either is reached, the position closes immediately, without waiting for the bar to end.
Exit Logic
All Conditions Must Agree: If the trade is still open (SL/TP not triggered), then all enabled exit indicators must confirm a closure before the script exits on the bar’s close.
Time Filter
Optional Trading Window: You can activate a date/time range to constrain entries and exits strictly to that interval.
Justification of Methodology
Indicator Confluence: Combining multiple tools (RSI, MACD, etc.) can reduce noise and false signals.
Intrabar SL/TP: Capturing real-time spikes or dips provides a more precise reflection of typical live trading scenarios.
Single-Entry Model: Straightforward for both manual and automated tracking (especially important in bridging to bots).
Custom Date Range: Helps refine backtesting for specific market conditions or to avoid known irregular data periods.
How to Use
Add the Script to Your Chart
• In TradingView, open Indicators , search for “Multi-indicator Signal Builder” .
• Click to add it to your chart.
Configure Inputs
• Time Filter: Set a start and end date for trades.
• Alerts Messages: Input any JSON or text payload needed by your external service or bot.
• Entry Conditions: Enable and configure any indicators (e.g., RSI, MACD) for a confluence-based entry.
• Close Conditions: Enable exit indicators, along with optional SL (negative %) and TP (positive %) levels.
Set Up Alerts
• In TradingView, select “Create Alert” → Condition = “Any alert() function call” → choose this script.
• Entry Alert: Triggers on the script’s entry signal.
• Close Alert: Triggers on the script’s close signal (or if SL/TP is hit).
• Skyrexio Alert Bots: You can route these alerts via webhook to Skyrexio alert bots to automate order execution on major crypto exchanges (or any other supported broker).
Visual Reference
• A condition table at the bottom summarizes active signals.
• Statistics Label updates automatically as trades are closed, showing PnL stats and distribution metrics.
Backtesting Guidelines
Symbol/Timeframe: Works on multiple assets and timeframes; always do thorough testing.
Realistic Costs: Adjust commissions and potential slippage to match typical exchange conditions.
Risk Management: If using the built-in stop-loss/take-profit, set percentages that reflect your personal risk tolerance.
Longer Test Horizons: Verify performance across diverse market cycles to gauge reliability.
Example of statistic calculation
Test Period: 2023-01-01 to 2025-12-31
Initial Capital: $1,000
Commission: 0.1%, Slippage ~5 ticks
Trade Count: 680 (varies by strategy conditions)
Win rate: 75.44% (varies by strategy conditions)
Net Profit: +90.14% (varies by strategy conditions)
Disclaimer
This indicator is provided strictly for informational and educational purposes.
It does not constitute financial or trading advice.
Past performance never guarantees future results.
Always test thoroughly in demo environments before using real capital.
Enjoy exploring the Multi-Indicator Signal Builder! Experiment with different indicator combinations and adjust parameters to align with your trading preferences, whether you trade manually or link your alerts to external automation services. Happy trading and stay safe!
Delta Volume EMA Strategy
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DELTA VOLUME EMA STRATEGY - STRATEGY GUIDE 📊
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💡 COLLABORATION & SUPPORT
---------------------------
If you want to collaborate, have an idea for a strategy, or need help writing
or customizing code, send an email to burdytrader@gmail.com or send me a
message. Suggestions, ideas, and comments are always welcome! 🤝
================================================================================
⚠️ IMPORTANT: INSTRUMENT SELECTION 📈
-------------------------------------
This strategy performs BEST with instruments that have a centralized data flow,
such as Futures contracts. Centralized markets provide more accurate and
reliable volume data, which is essential for Volume Delta analysis to work
effectively.
Why Futures? 🎯
- Centralized exchange = Accurate volume data
- All trades flow through a single exchange
- Volume reflects true buying/selling pressure
- Better correlation between volume and price movements
While the strategy can work with other instruments (stocks, forex, etc.),
volume data quality may vary, which can affect the reliability of Volume Delta
signals. For optimal performance, use Futures contracts or other instruments
with centralized, high-quality volume data.
================================================================================
WHAT DOES THIS STRATEGY DO? 🎯
---------------------------
This strategy uses Volume Delta analysis combined with Exponential Moving
Averages (EMA) to identify high-probability trading opportunities. The Volume
Delta measures the difference between buying and selling pressure, helping to
identify when strong institutional or smart money movements occur. The strategy
automatically enters trades when volume delta reaches extreme levels, indicating
potential trend continuation or reversal points.
HOW IT WORKS? ⚙️
---------------
1. VOLUME DELTA CALCULATION 📈
The strategy calculates the Volume Delta using the following formula:
- Volume Ratio (v) = Current Volume / Previous Volume
- EMA of Close (mac) = EMA(Close, MA Length) × Volume Ratio
- EMA of Open (mao) = EMA(Open, MA Length) × Volume Ratio
- Volume Delta (vd) = mac - mao
The Volume Delta shows:
- Positive values (green) = Buying pressure (buyers are more active)
- Negative values (red) = Selling pressure (sellers are more active)
2. VOLUME DELTA MOVING AVERAGE 📊
The strategy calculates an EMA of the Volume Delta (vdma) to smooth out
fluctuations and identify the overall trend of buying/selling pressure:
- vdma = EMA(Volume Delta, EMA Length)
- When vdma is above zero = Overall buying pressure
- When vdma is below zero = Overall selling pressure
3. PERCENTILE-BASED ENTRY CONDITIONS 🎲
Instead of using fixed thresholds, the strategy uses percentile analysis to
identify extreme volume delta movements:
For LONG entries:
- Analyzes seller volumes (negative volume delta) over the lookback period
- Calculates the percentile threshold (default: 80th percentile)
- Enters LONG when volume delta becomes positive AND exceeds the threshold
- This indicates a strong shift from selling to buying pressure
For SHORT entries:
- Analyzes buyer volumes (positive volume delta) over the lookback period
- Calculates the percentile threshold (default: 80th percentile)
- Enters SHORT when volume delta becomes negative AND exceeds the threshold
- This indicates a strong shift from buying to selling pressure
4. POSITION SIZING 💰
The strategy offers two position sizing methods:
a) RISK VALUE (Fixed Risk in Dollars):
- Calculates position size based on a fixed dollar risk amount
- Formula: Position Size = Risk Amount / (Entry Price × Stop Loss %)
- Ensures consistent risk per trade regardless of price level
b) LOTS SIZE:
- Uses a fixed lot size for all trades
- Simple and straightforward approach
- Useful when you want consistent position sizes
5. TAKE PROFIT & STOP LOSS SETTINGS 🎯
The strategy offers flexible TP/SL configuration in three modes:
a) PERCENTAGE (%):
- TP/SL calculated as a percentage of entry price
- Example: 2% TP means entry price × 1.02 (for LONG) or × 0.98 (for SHORT)
- Adapts automatically to different price levels
b) CURRENCY:
- TP/SL set as a fixed currency amount
- Example: $100 TP means entry price + $100 (for LONG) or - $100 (for SHORT)
- Useful for instruments with consistent price movements
c) PIPS:
- TP/SL set as a fixed number of pips
- Automatically converts pips to price using the instrument's minimum tick
- Ideal for forex and other pip-based instruments
6. AUTOMATIC TRADE EXECUTION ⚡
When entry conditions are met:
- Opens a position (LONG or SHORT) at market price
- Automatically sets Take Profit and Stop Loss based on selected mode
- Sends an alert with all trade information
- Only one position at a time (waits for current position to close)
AVAILABLE PARAMETERS ⚙️
----------------------
1. MA LENGTH (Default: 10)
- Length of the Exponential Moving Average used for close and open prices
- Lower values = More sensitive to recent price action
- Higher values = More smoothed, less sensitive
2. EMA LENGTH (Default: 20)
- Length of the EMA applied to Volume Delta
- Controls the smoothing of the volume delta signal
- Lower values = Faster signals, more trades
- Higher values = Slower signals, fewer but potentially more reliable trades
3. POSITION SIZE MODE
- "Risk Value": Calculate position size based on fixed dollar risk
- "Lots Size": Use fixed lot size for all trades
4. FIXED RISK IN $ (Default: 50)
- Only used when Position Size Mode = "Risk Value"
- The dollar amount you're willing to risk per trade
- Strategy calculates position size automatically
5. LOT SIZE (Default: 0.01)
- Only used when Position Size Mode = "Lots Size"
- Fixed lot size for all trades
6. TAKE PROFIT MODE
- "%": Percentage of entry price
- "Currency": Fixed currency amount
- "Pips": Fixed number of pips
7. STOP LOSS MODE
- "%": Percentage of entry price
- "Currency": Fixed currency amount
- "Pips": Fixed number of pips
8. TAKE PROFIT / STOP LOSS VALUES
- Different input fields appear based on selected mode
- Configure TP and SL independently
9. VOLUME LOOKBACK PERIOD (Default: 20)
- Number of bars used to calculate percentile thresholds
- Lower values = More sensitive, adapts faster to recent conditions
- Higher values = More stable, uses longer-term statistics
10. PERCENTILE THRESHOLD (Default: 80%)
- The percentile level used to identify extreme volume delta movements
- 80% means: only enter when volume delta exceeds 80% of recent values
- Higher values = Fewer but potentially stronger signals
- Lower values = More frequent signals
VISUALIZATION 📊
---------------
The strategy displays on the chart:
1. VOLUME DELTA COLUMNS
- Green columns = Positive volume delta (buying pressure)
- Red columns = Negative volume delta (selling pressure)
- Height represents the magnitude of buying/selling pressure
2. VOLUME DELTA MA AREA
- Two overlapping area plots showing the smoothed volume delta
- Black area (base layer) for overall visualization
- Green area (when positive) = Overall buying pressure trend
- Red area (when negative) = Overall selling pressure trend
- Helps identify the dominant market sentiment
3. ZERO LINE
- Horizontal line at zero
- Helps visualize when buying/selling pressure crosses the neutral point
ALERTS 🔔
--------
When enabled, the strategy sends alerts when a trade is opened. The alert
message includes:
- Direction: "Buy" for LONG positions or "Sell" for SHORT positions
- Entry Price: The price at which the position was opened
- TP (Take Profit): The target profit price
- SL (Stop Loss): The stop loss price
Example alert message:
"Buy | Entry: 1.2050 | TP: 1.2250 | SL: 1.1950"
Alerts can be configured in TradingView to send notifications via email,
SMS, webhooks, or other platforms.
RECOMMENDED SETTINGS 🎯
-----------------------
To get started, you can use these settings:
STRATEGY PARAMETERS:
- MA Length: 10 (default)
- EMA Length: 20 (default)
- Volume Lookback Period: 20 (default)
- Percentile Threshold: 80% (default)
POSITION SIZING:
- Position Size Mode: "Risk Value" (for risk management)
- Fixed Risk in $: Adjust based on your account size (e.g., 1-2% of account)
- OR use "Lots Size" with 0.01 lots for small accounts
TAKE PROFIT & STOP LOSS:
- TP Mode: "%" (recommended for most instruments)
- SL Mode: "%" (recommended for most instruments)
- Take Profit (%): 2.0% (adjust based on your risk/reward preference)
- Stop Loss (%): 1.0% (adjust based on your risk tolerance)
For Forex:
- Consider using "Pips" mode for TP/SL
- Typical values: 20-50 pips TP, 10-30 pips SL
For Stocks/Indices:
- Use "%" mode for TP/SL
- Typical values: 2-5% TP, 1-2% SL
PRACTICAL EXAMPLE 📝
-------------------
Scenario: LONG Entry on EUR/USD
1. Market conditions:
- Price: 1.1000
- Volume Delta becomes strongly positive
- Volume Delta exceeds 80th percentile of recent seller volumes
2. Strategy calculates:
- Entry Price: 1.1000 (current close)
- Position Size Mode: "Risk Value"
- Fixed Risk: $50
- Stop Loss Mode: "%"
- Stop Loss: 1.0%
- Position Size = $50 / (1.1000 × 0.01) = 4.55 lots
3. Strategy opens position:
- Direction: LONG (Buy)
- Entry: 1.1000
- Take Profit: 1.1220 (2% above entry)
- Stop Loss: 1.0890 (1% below entry)
- Alert sent: "Buy | Entry: 1.1000 | TP: 1.1220 | SL: 1.0890"
4. Outcome scenarios:
- If price rises to 1.1220 → Take Profit hit (profit)
- If price falls to 1.0890 → Stop Loss hit (loss limited to $50)
IMPORTANT NOTE ⚠️
-----------------
This strategy is a technical analysis tool based on volume delta analysis.
Like all trading strategies, it does NOT guarantee profits. Trading involves
significant risks and you can lose money, including your entire investment.
Past performance does not guarantee future results.
Always:
- Use appropriate risk management
- Never risk more than you can afford to lose
- Test the strategy on historical data (backtesting) before using real money
- Start with small position sizes or paper trading
- Understand that no strategy works 100% of the time
- Consider market conditions, news events, and other factors
- Keep a trading journal to learn and improve
The author and contributors are NOT responsible for any losses incurred from
using this strategy. Trading decisions are your own responsibility. Profits
are NOT guaranteed, and losses are possible.
LICENSE 📄
---------
This code is open source and available for modification. You are free to use,
modify, and distribute this strategy. If you republish or share a modified
version, please kindly mention the original author.
================================================================================
CVD Zones & Divergence [Pro]# CVD Zones & Divergence
**Complete CVD order flow toolkit** - Divergences, POC, Profile, and Supply/Demand zones all in one professional indicator.
## 🎯 What It Does
Combines **four powerful order flow tools** into a single, cohesive indicator:
1. **CVD Divergences** - Early warnings + confirmed signals
2. **Point of Control (POC)** - Fair value equilibrium line
3. **CVD Profile** - Visual distribution histogram
4. **Supply/Demand Zones** - Real absorption-based S/R levels
All based on **Cumulative Volume Delta (CVD)** - actual buying/selling pressure, not approximations.
## ✨ Key Features
### 🔄 CVD Divergences (Dual Mode)
**Confirmed Divergences** (High Accuracy)
- Solid lines (customizable colors)
- 🔻 Bear / 🔺 Bull labels
- Win rate: ~70-80%
- Best for swing traders
**Early Warning Mode** ⚡ (Fast Signals)
- Dashed lines (default purple)
- ⚠️ Early Bear / ⚠️ Early Bull labels
- Fires 6+ bars earlier
- Win rate: ~55-65%
- Best for scalpers/day traders
### 🎯 Point of Control (POC)
- **Independent lookback** (300 bars default)
- Yellow line showing fair value
- Where most CVD activity occurred
- Acts as dynamic support/resistance
- Resets and recalculates continuously
### 📊 CVD Profile Histogram
- **Visual CVD distribution** over lookback period
- **Split buy/sell** (blue/orange bars)
- **Value Area** (70% CVD zone highlighted)
- Position: Right/Left/Current (your choice)
- Shows where actual order flow happened
### 📦 Supply/Demand Zones
- **Absorption-based** detection (not guesses!)
- Green = Demand (buyers absorbed 2:1+)
- Red = Supply (sellers absorbed 2:1+)
- Shows **real** institutional levels
- Auto-sorted by strength
- Displays top 8 zones
## 📊 What You See on Chart
```
Your Chart:
├─ 🔴 Red lines (bearish divergences)
├─ 🟢 Green lines (bullish divergences)
├─ 🟣 Purple dashed (early warnings)
├─ 🟡 Yellow POC line (fair value)
├─ 📊 Blue/Orange profile (right side)
├─ 🟢 Green boxes (demand zones)
└─ 🔴 Red boxes (supply zones)
```
## ⚙️ Recommended Settings
### 15m Day Trading (Most Popular)
```
📊 Profile:
- Lookback: 150 bars
- Profile Rows: 24
- Position: Right
🎯 POC:
- POC Lookback: 300 bars
- Show POC: ON
📦 Zones:
- Min Absorption Ratio: 2.0
- HVN Threshold: 1.5
- Max Zones: 8
🔄 Divergences:
- Pivot L/R: 9
- Early Warning: ON
- Early Right Bars: 3
- Min Bars Between: 40
- Min CVD Diff: 5%
```
### 5m Scalping
```
Profile Lookback: 100
POC Lookback: 200
Pivot L/R: 7
Early Warning Right: 2
Min Bars Between: 60
```
### 1H Swing Trading
```
Profile Lookback: 200
POC Lookback: 400-500
Pivot L/R: 12-14
Early Warning Right: 4-5
Min Bars Between: 30
Min CVD Diff: 8%
```
## 💡 How to Trade
### Setup 1: Divergence at Zone ⭐ (BEST - 75%+ win rate)
**Entry:**
- Price hits demand/supply zone
- Divergence appears (early or confirmed)
- Double confluence = high probability
**Example (Long):**
```
1. Price drops into green demand zone
2. ⚠️ Early bullish divergence fires
3. Enter long with tight stop below zone
4. Target: POC or next supply zone
```
**Risk/Reward:** 1:3 to 1:5
---
### Setup 2: POC Bounce/Rejection
**Entry:**
- Price approaches POC line
- Wait for reaction (bounce or rejection)
- Enter in direction of reaction
**Long Setup:**
```
1. Price pulls back to POC from above
2. POC acts as support
3. Bullish divergence appears (confirmation)
4. Enter long, stop below POC
```
**Short Setup:**
```
1. Price rallies to POC from below
2. POC acts as resistance
3. Bearish divergence appears
4. Enter short, stop above POC
```
**Risk/Reward:** 1:2 to 1:4
---
### Setup 3: Zone + Profile Confluence
**Entry:**
- Supply/demand zone aligns with thick profile bar
- Shows high CVD activity at that level
- Triple confluence = very high probability
**Example:**
```
1. Supply zone at 26,100
2. Profile shows heavy selling at 26,100
3. Price rallies to 26,100
4. Bearish divergence appears
5. Enter short
```
**Risk/Reward:** 1:4 to 1:6
---
### Setup 4: Early Warning Scalp ⚡
**Entry (Aggressive):**
- ⚠️ Early warning fires
- Price at zone or POC
- Enter immediately
- Tight stop (1-2 ATR)
**Management:**
```
- Take 50% profit at 1:1
- Move stop to breakeven
- 🔻 Confirmed signal → Trail stop
- Exit rest at target
```
**Risk/Reward:** 1:1.5 to 1:2
**Trades/day:** 3-8
---
### Setup 5: Multi-Timeframe (Advanced)
**Confirmation Required:**
```
Higher TF (1H):
- Confirmed divergence
- At major POC or zone
Lower TF (15m):
- Early warning triggers
- Entry with better timing
```
**Benefits:**
- HTF gives direction
- LTF gives entry
- Best of both worlds
**Risk/Reward:** 1:3 to 1:5
---
## 📊 Component Details
### CVD Profile
**What the colors mean:**
- **Blue bars** = Buying CVD (demand)
- **Orange bars** = Selling CVD (supply)
- **Lighter shade** = Value Area (70% CVD)
- **Thicker bar** = More volume at that price
**How to use:**
- Thick bars = Support/Resistance
- Profile shape shows market structure
- Balanced profile = range
- Skewed profile = trend
---
### Supply/Demand Zones
**How they're detected:**
1. High Volume Node (1.5x average)
2. CVD buy/sell ratio calculated
3. Ratio ≥ 2.0 → Zone created
4. Sorted by strength (top 8 shown)
**Zone labels show:**
- Type: "Demand" or "Supply"
- Ratio: "2.8:1" = strength
**Not like other indicators:**
- ❌ Other tools use price action alone
- ✅ This uses actual CVD absorption
- Shows WHERE limit orders defended levels
---
### Point of Control (POC)
**What it shows:**
- Price with highest CVD activity
- Market's "fair value"
- Dynamic S/R level
**How to use:**
- Price above POC = bullish bias
- Price below POC = bearish bias
- POC retest = trading opportunity
- POC cross = trend change signal
**Independent lookback:**
- Profile: 150 bars (short-term)
- POC: 300 bars (longer-term context)
- Gives stable, relevant POC
---
## 🔧 Settings Explained
### 📊 Profile Settings
**Lookback Bars** (150 default)
- How many bars for profile calculation
- Lower = more recent, reactive
- Higher = more historical, stable
**Profile Rows** (24 default)
- Granularity of distribution
- Lower = coarser (faster)
- Higher = finer detail (slower)
**Profile Position**
- Right: After current price
- Left: Before lookback period
- Current: At lookback start
**Value Area** (70% default)
- Highlights main CVD concentration
- 70% is standard
- Higher % = wider zone
---
### 🎯 POC Settings
**POC Lookback** (300 default)
- Independent from profile
- Longer = more stable POC
- Shorter = more reactive POC
**Show POC Line/Label**
- Toggle visibility
- Customize color/width
---
### 📦 Zone Settings
**Min Absorption Ratio** (2.0 default)
- Buy/Sell threshold for zones
- 2.0 = 2:1 ratio minimum
- Higher = fewer, stronger zones
**HVN Threshold** (1.5 default)
- Volume must be 1.5x average
- Higher = stricter filtering
- Lower = more zones
**Max Zones** (8 default)
- Limits display clutter
- Shows strongest N zones only
---
### 🔄 Divergence Settings
**Pivot Left/Right** (9/9 default)
- Bars to confirm pivot
- Higher = slower, more confirmed
- Lower = faster, less confirmed
**Early Warning**
- ON = Show early signals
- Early Right Bars (3 default)
- 3 = 6 bars faster than confirmed
**Filters:**
- Min Bars Between (40): Prevents spam
- Min CVD Diff % (5): Filters weak signals
**Visual:**
- Line styles: Solid/Dashed/Dotted
- Colors: Customize all 4 types
- Labels: Toggle ON/OFF
---
## 🎨 Color Customization
**Divergences:**
- Bullish Confirmed: Green (default)
- Bearish Confirmed: Red (default)
- Early Bullish: Purple (default)
- Early Bearish: Purple (default)
**Zones & Profile:**
- Bull/Demand: Green
- Bear/Supply: Red
- Buy CVD Profile: Blue
- Sell CVD Profile: Orange
- Value Area Up/Down: Lighter blue/orange
**POC:**
- POC Color: Yellow (default)
All customizable to your preference!
---
## 🔔 Alerts Available
**6 Alert Types:**
1. 🔻 Bearish Divergence (confirmed)
2. 🔺 Bullish Divergence (confirmed)
3. ⚠️ Early Bearish Warning
4. ⚠️ Early Bullish Warning
5. (Manual: POC cross)
6. (Manual: Zone touch)
**Setup:**
1. Click Alert (⏰)
2. Choose "CVD Zones & Divergence"
3. Select alert type
4. Configure notification
5. Create!
---
## 💎 Pro Tips
### From Experienced Traders:
**"Use zones with divergences for best setups"**
- Zone alone: 60% win rate
- Divergence alone: 65% win rate
- Both together: 75%+ win rate
**"POC is your friend"**
- Price tends to revert to POC
- Great target for counter-trend trades
- POC cross = potential trend change
**"Profile tells the story"**
- Thick bars = institutional levels
- Balanced profile = range-bound
- Skewed high = distribution (top)
- Skewed low = accumulation (bottom)
**"Early warnings for entries, confirmed for confidence"**
- Early = better entry price
- Confirmed = validation
- Use both in scale-in strategy
**"Filter by timeframe"**
- 1m-5m: Very fast, many signals
- 15m: Sweet spot for most traders
- 1H-4H: High quality, fewer signals
---
## 🔧 Tuning Guide
### Too Cluttered?
**Simplify:**
```
✅ Show Divergences: ON
✅ Show POC: ON
❌ Show Zones: OFF (or reduce to 4-5)
❌ Show Value Area: OFF
❌ Divergence Labels: OFF
→ Clean chart with just lines + POC
```
### Missing Opportunities?
**More Signals:**
```
↓ Pivot Right: 6-7
↓ Early Warning Right: 2
↓ Min Bars Between: 25-30
↓ Min CVD Diff: 2-3%
↓ Min Absorption Ratio: 1.8
```
### Too Many False Signals?
**Stricter Filters:**
```
↑ Pivot Right: 12-15
↑ Min Bars Between: 60
↑ Min CVD Diff: 8-10%
↑ Min Absorption Ratio: 2.5
↓ Max Zones: 4-5
```
### POC Not Making Sense?
**Adjust POC Lookback:**
```
If too high: Increase to 400-500
If too low: Increase to 400-500
If jumping around: Increase to 500+
→ Longer lookback = more stable POC
```
---
## ❓ FAQ
**Q: Difference from CVD Divergence (standalone)?**
A: This is the **complete package**:
- Divergence tool = divergences only
- This = divergences + POC + profile + zones
- Use divergence tool for clean charts
- Use this for full analysis
**Q: Too slow/laggy?**
A: Reduce computational load:
```
Profile Rows: 18 (from 24)
Lookback: 100 (from 150)
Max Zones: 5 (from 8)
```
**Q: No volume data error?**
A: Symbol has no volume
- Works: Futures, stocks, crypto
- Maybe: Forex (broker-dependent)
- Doesn't work: Some forex pairs
**Q: Can I use just some features?**
A: Absolutely! Toggle what you want:
```
Zones only: Turn off divergences + POC
POC only: Turn off zones + divergences
Divergences only: Turn off zones + POC + profile
Mix and match as needed!
```
**Q: Best timeframe?**
A:
- **1m-5m**: Scalping (busy, many signals)
- **15m**: Day trading ⭐ (recommended)
- **1H-4H**: Swing trading (quality signals)
- **Daily**: Position trading (very selective)
**Q: Works on crypto/forex/stocks?**
A:
- ✅ Futures: Excellent
- ✅ Stocks: Excellent
- ✅ Crypto: Very good (major pairs)
- ⚠️ Forex: Depends on broker volume
---
## 📈 Performance Expectations
### Realistic Win Rates
| Strategy | Win Rate | Avg R/R | Trades/Week |
|----------|----------|---------|-------------|
| Early warnings only | 55-65% | 1:1.5 | 15-30 |
| Confirmed only | 70-80% | 1:2 | 8-15 |
| Divergence + Zone | 75-85% | 1:3 | 5-12 |
| Full confluence (all 4) | 80-90% | 1:4+ | 3-8 |
**Keys to success:**
- Don't trade every signal
- Wait for confluence
- Proper risk management
- Trade what you see, not what you think
---
## 🚀 Quick Start
**New User (5 minutes):**
1. ✅ Add to 15m chart
2. ✅ Default settings work well
3. ✅ Watch for 1 week (don't trade yet!)
4. ✅ Note which setups work best
5. ✅ Backtest on 50+ signals
6. ✅ Start with small size
7. ✅ Scale up slowly
**First Trade Checklist:**
- Divergence + Zone/POC = confluence
- Clear S/R level nearby
- Risk/reward minimum 1:2
- Position size = 1% risk max
- Stop loss placed
- Target identified
- Journal entry ready
---
## 📊 What Makes This Special?
**Most indicators:**
- Use RSI/MACD divergences (lagging)
- Guess at S/R zones (subjective)
- Don't show actual order flow
**This indicator:**
- Uses real CVD (actual volume delta)
- Absorption-based zones (real orders)
- Profile shows distribution (real activity)
- POC shows equilibrium (real fair value)
- All from one data source (coherent)
**Result:**
- Everything aligns
- No conflicting signals
- True order flow analysis
- Professional-grade toolkit
---
## 🎯 Trading Philosophy
**Remember:**
- Indicator shows you WHERE to look
- YOU decide whether to trade
- Quality over quantity always
- Risk management is #1
- Patience beats aggression
**Best trades have:**
- ✅ Multiple confluences
- ✅ Clear risk/reward
- ✅ Obvious invalidation point
- ✅ Aligned with trend/context
**Worst trades have:**
- ❌ Single signal only
- ❌ Poor location (middle of nowhere)
- ❌ Unclear stop placement
- ❌ Counter to all context
---
## ⚠️ Risk Disclaimer
**Important:**
- Past performance ≠ future results
- All trading involves risk
- Only risk what you can afford to lose
- This is a tool, not financial advice
- Use proper position sizing
- Keep a trading journal
- Consider professional advice
**Your responsibility:**
- Which setups to trade
- Position size
- Entry/exit timing
- Risk management
- Emotional control
**Success = Tool + Strategy + Discipline + Risk Management**
---
## 📝 Version History
**v1.0** - Current Release
- CVD divergences (confirmed + early warning)
- Point of Control (independent lookback)
- CVD profile histogram
- Supply/demand absorption zones
- Value area visualization
- 6 alert types
- Full customization
---
## 💬 Community
**Questions?** Drop a comment below
**Success story?** Share with the community
**Feature request?** Let me know
**Bug report?** Provide details in comments
---
**Happy Trading! 🚀📊**
*Professional order flow analysis in one indicator.*
**Like this?** ⭐ Follow for more quality tools!
Account GuardianAccount Guardian: Dynamic Risk/Reward Overlay
Introduction
Account Guardian is an open-source indicator for TradingView designed to help traders evaluate trade setups before entering positions. It automatically calculates Risk-to-Reward ratios based on market structure, displays visual Stop Loss and Take Profit zones, and provides real-time position sizing recommendations.
The indicator addresses a fundamental question every trader should ask before entering a trade: "Does this setup make mathematical sense?" Account Guardian answers this question visually and numerically, helping traders avoid impulsive entries with poor risk profiles.
Core Functionality
Account Guardian performs four primary functions:
Detects swing highs and swing lows to identify logical stop loss placement levels
Calculates Risk-to-Reward ratios for both long and short setups in real-time
Displays visual SL/TP zones on the chart for immediate trade planning
Computes position sizing based on your account size and risk tolerance
The goal is to provide traders with instant feedback on whether a potential trade meets their minimum risk/reward criteria before committing capital.
How It Works
Swing Detection
The indicator uses pivot point detection to identify recent swing highs and swing lows on the chart. These swing points serve as logical areas for stop loss placement:
For Long Trades: The most recent swing low becomes the stop loss level. Price breaking below this level would invalidate the bullish thesis.
For Short Trades: The most recent swing high becomes the stop loss level. Price breaking above this level would invalidate the bearish thesis.
The swing detection lookback period is configurable, allowing you to adjust sensitivity based on your trading timeframe and style.
It automatically adjusts the tp and sl when it is applied to your chart so it is always moving up and down!
Risk/Reward Calculation
Once swing levels are identified, the indicator calculates:
Entry Price: Current close price (where you would enter)
Stop Loss: Recent swing low (for longs) or swing high (for shorts)
Risk: Distance from entry to stop loss
Take Profit: Entry plus (Risk × Target Multiplier)
R:R Ratio: Reward divided by Risk
The R:R ratio is then evaluated against your configured thresholds to determine if the setup is valid, marginal, or poor.
Visual Elements
SL/TP Zones
When enabled, the indicator draws colored boxes on the chart showing:
Red Zone: Stop Loss area - the region between your entry and stop loss
Green/Gold/Red Zone: Take Profit area - colored based on R:R quality
The color coding provides instant visual feedback:
Green: R:R meets or exceeds your "Good R:R" threshold (default 3:1)
Gold: R:R meets minimum threshold but below "Good" (between 2:1 and 3:1)
Red: R:R below minimum threshold - setup should be avoided
Swing Point Markers
Small circles mark detected swing points on the chart:
Green circles: Swing lows (potential support / long SL levels)
Red circles: Swing highs (potential resistance / short SL levels)
Dashboard Panel
The dashboard in the top-right corner displays comprehensive trade planning information:
R:R Row: Current Risk-to-Reward ratio for long and short setups
Status Row: VALID, OK, BAD, or N/A based on R:R thresholds
Stop Loss Row: Exact price level for stop loss placement
Take Profit Row: Exact price level for take profit placement
Pos Size Row: Recommended position size based on your risk parameters
Risk $ Row: Dollar amount at risk per trade
Position Sizing Logic
The indicator calculates position size using the formula:
Position Size = Risk Amount / Risk per Unit
Where:
Risk Amount = Account Size × (Risk Percentage / 100)
Risk per Unit = Entry Price - Stop Loss Price
For example, with a $10,000 account risking 1% per trade ($100), if your entry is at 100 and stop loss at 98 (risk of 2 per unit), your position size would be 50 units.
Input Parameters
Swing Detection:
Swing Lookback: Number of bars to look back for pivot detection (default: 10). Higher values find more significant swing points but may be slower to update.
Target Multiplier: Multiplier applied to risk to calculate take profit distance (default: 2). A value of 2 means TP is 2× the distance of SL from entry.
Risk/Reward Thresholds:
Minimum R:R: Minimum acceptable Risk-to-Reward ratio (default: 2.0). Setups below this show as "BAD" in red.
Good R:R: Threshold for excellent setups (default: 3.0). Setups at or above this show as "VALID" in green.
Account Settings:
Account Size ($): Your trading account size in dollars (default: 10,000). Used for position sizing calculations.
Risk Per Trade (%): Percentage of account to risk per trade (default: 1.0%). Professional traders typically risk 0.5-2% per trade.
Display:
Show SL/TP Zones: Toggle visibility of the colored zone boxes on chart (default: enabled)
Show Dashboard: Toggle visibility of the information panel (default: enabled)
Analyze Direction: Choose to analyze Long only, Short only, or Both directions (default: Both)
How to Use This Indicator
Basic Workflow:
Add the indicator to your chart
Configure your account size and risk percentage in the settings
Set your minimum and good R:R thresholds based on your trading rules
Look at the dashboard to see current R:R for potential long and short entries
Only consider trades where the status shows "VALID" or at minimum "OK"
Use the displayed SL and TP levels for your order placement
Use the position size recommendation to determine lot/contract size
Interpreting the Dashboard:
VALID (Green): Excellent setup - R:R meets your "Good" threshold. This is the ideal scenario for taking a trade.
OK (Gold): Acceptable setup - R:R meets minimum but isn't optimal. Consider taking if other confluence factors align.
BAD (Red): Poor setup - R:R below minimum threshold. Avoid this trade or wait for better entry.
N/A (Gray): Cannot calculate - usually means no valid swing point detected yet.
Best Practices:
Use this indicator as a filter, not a signal generator. It tells you IF a trade makes sense, not WHEN to enter.
Combine with your existing entry strategy - use Account Guardian to validate setups from other analysis.
Adjust the swing lookback based on your timeframe. Lower timeframes may need smaller lookback values.
Be honest with your account size input - accurate position sizing requires accurate inputs.
Consider the target multiplier carefully. Higher multipliers mean larger potential reward but lower probability of hitting TP.
Alerts
The indicator includes four alert conditions:
Good Long Setup: Triggers when long R:R reaches or exceeds your "Good R:R" threshold
Good Short Setup: Triggers when short R:R reaches or exceeds your "Good R:R" threshold
Bad Long Setup: Triggers when long R:R falls below your minimum threshold
Bad Short Setup: Triggers when short R:R falls below your minimum threshold
These alerts can help you monitor multiple charts and get notified when favorable setups appear.
Technical Implementation
The indicator is built using Pine Script v6 and includes:
Pivot-based swing detection using ta.pivothigh() and ta.pivotlow()
Dynamic box drawing for visual SL/TP zones
Table-based dashboard for clean information display
Color-coded visual feedback system
Persistent variable tracking for swing levels
Code Structure:
// Swing Detection
float swingHi = ta.pivothigh(high, swingLen, swingLen)
float swingLo = ta.pivotlow(low, swingLen, swingLen)
// R:R Calculation for Long
float longSL = recentSwingLo
float longRisk = entry - longSL
float longTP = entry + (longRisk * targetMult)
float longRR = (longTP - entry) / longRisk
// Position Sizing
float riskAmount = accountSize * (riskPct / 100)
float posSize = riskAmount / longRisk
Limitations
The indicator uses historical swing points which may not always represent optimal SL placement for your specific strategy
Position sizing assumes you can trade fractional units - adjust accordingly for instruments with minimum lot sizes
R:R calculations assume linear price movement and don't account for gaps or slippage
The indicator doesn't predict price direction - it only evaluates the mathematical viability of a setup
Swing detection has inherent lag due to the lookback period required for pivot confirmation
Recommended Settings by Trading Style
Scalping (1-5 minute charts):
Swing Lookback: 5-8
Target Multiplier: 1-2
Minimum R:R: 1.5
Good R:R: 2.0
Day Trading (15-60 minute charts):
Swing Lookback: 8-12
Target Multiplier: 2
Minimum R:R: 2.0
Good R:R: 3.0
Swing Trading (4H-Daily charts):
Swing Lookback: 10-20
Target Multiplier: 2-3
Minimum R:R: 2.5
Good R:R: 4.0
Why Risk/Reward Matters
Many traders focus solely on win rate, but profitability depends on the combination of win rate AND risk/reward ratio. Consider these scenarios:
50% win rate with 1:1 R:R = Breakeven (before costs)
50% win rate with 2:1 R:R = Profitable
40% win rate with 3:1 R:R = Profitable
60% win rate with 1:2 R:R = Losing money
Account Guardian helps ensure you only take trades where the math works in your favor, even if you're wrong more often than you're right.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not intended as financial, investment, trading, or any other type of advice or recommendation.
Trading involves substantial risk of loss and is not suitable for all investors. The calculations provided by this indicator are based on historical price data and mathematical formulas that may not accurately predict future price movements.
Position sizing recommendations are estimates based on user inputs and should be verified before placing actual trades. Always consider factors such as leverage, margin requirements, and broker-specific rules when determining actual position sizes.
The Risk-to-Reward ratios displayed are theoretical calculations based on swing point detection. Actual trade outcomes will vary based on market conditions, execution quality, and other factors not captured by this indicator.
Past performance does not guarantee future results. Users should thoroughly test any trading approach in a demo environment before risking real capital. The authors and publishers of this indicator are not responsible for any losses or damages arising from its use.
Always consult with a qualified financial advisor before making investment decisions.
Elliott Wave Full Fractal System v2.0Elliott Wave Full Fractal System v2.0 – Q.C. FINAL (Guaranteed R/R)
Elliott Wave Full Fractal System is a multi-timeframe wave engine that automatically labels Elliott impulses and ABC corrections, then builds a rule-based, ATR-driven risk/reward framework around the “W3–W4–W5” leg.
“Guaranteed R/R” here means every order is placed with a predefined stop-loss and take-profit that respect a minimum Reward:Risk ratio – it does not mean guaranteed profits.
Core Idea
This strategy turns a full fractal Elliott Wave labelling engine into a systematic trading model.
It scans fractal pivots on three wave degrees (Primary, Intermediate, Minor) to detect 5-wave impulses and ABC corrections.
A separate “Trading Degree” pivot stream, filtered by a 200-EMA trend filter and ATR-based dynamic pivots, is then used to find W4 pullback entries with a minimum, user-defined Reward:Risk ratio.
Default Properties & Risk Assumptions
The backtest uses realistic but conservative defaults:
// Default properties used for backtesting
strategy(
"Elliott Wave Full Fractal System - Q.C. FINAL (Guaranteed R/R)",
overlay = true,
initial_capital = 10000, // realistic account size
default_qty_type = strategy.percent_of_equity,
default_qty_value = 1, // 1% risk per trade
commission_type = strategy.commission.cash_per_contract,
commission_value = 0.005, // example stock commission
slippage = 0 // see notes below
)
Account size: 10,000 (can be changed to match your own account).
Position sizing: 1% of equity per trade to keep risk per idea sustainable and aligned with TradingView’s recommendations.
Commission: 0.005 cash per contract/share as a realistic example for stock trading.
Slippage: set to 0 in code for clarity of “pure logic” backtesting. Real-life trading will experience slippage, so users should adjust this according to their market and broker.
Always re-run the backtest after changing any of these values, and avoid using high risk fractions (5–10%+) as that is rarely sustainable.
1. Full Fractal Wave Engine
The script builds and maintains four pivot streams using ATR-adaptive fractals:
Primary Degree (Macro Trend):
Captures the large swings that define the major trend. Labels ①–⑤ and ⒶⒷⒸ using blue “Circle” labels and thicker lines.
Intermediate Degree (Trading Degree):
Captures the medium swings (swing-trading horizon). Uses teal labels ( (1)…(5), (A)(B)(C) ).
Minor Degree (Micro Structure):
Tracks short-term swings inside the larger waves. Uses red roman numerals (i…v, a b c).
ABC Corrections (Optional):
When enabled, the engine tries to detect standard A–B–C corrective structures that follow a completed 5-wave impulse and plots them with dashed lines.
Each degree uses a dynamic pivot lookback that expands when ATR is above its EMA, so the system naturally requires “stronger” pivots in volatile environments and reacts faster in quiet conditions.
2. Theory Rules & Strict Mode
Normal Mode: More permissive detection. Designed to show more wave structures for educational / exploratory use.
Strict Mode: Enforces key Elliott constraints:
Wave 3 not shorter than waves 1 and 5.
No invalid W4 overlap with W1 (for standard impulses).
ABC Logic: After a confirmed bullish impulse, the script expects a down-up-down corrective pattern (A,B,C). After a bearish impulse, it looks for up-down-up.
3. Trend Filter & Pivots
EMA Trend Filter: A configurable EMA (default 200) is used as a non-wave trend filter.
Price above EMA → Only long setups are considered.
Price below EMA → Only short setups are considered.
ATR-Adaptive Pivots: The pivot engine scales its left/right bars based on current ATR vs ATR EMA, making waves and trading pivots more robust in volatile regimes.
4. Dynamic Risk Management (Guaranteed R/R Engine)
The trading engine is designed around risk, not just pattern recognition:
ATR-Based Stop:
Stop-loss is placed at:
Entry ± ATR × Multiplier (user-configurable, default 2.0).
This anchors risk to current volatility.
Minimum Reward:Risk Ratio:
For each setup, the script:
Computes the distance from entry to stop (risk).
Projects a take-profit target at risk × min_rr_ratio away from entry.
Only accepts the setup if risk is positive and the required R:R ratio is achievable.
Result: Every order is created with both TP and SL at a predefined distance, so each trade starts with a known, minimum Reward:Risk profile by design.
“Guaranteed R/R” refers exclusively to this order placement logic (TP/SL geometry), not to win-rate or profitability.
5. Trading Logic – W3–W4–W5 Pattern
The Trading pivot stream (separate from visual wave degrees) looks for a simple but powerful pattern:
Bullish structure:
Sequence of pivots forms a higher-high / higher-low pattern.
Price is above the EMA trend filter.
A strong “W3” leg is confirmed with structure rules (optionally stricter in Strict mode).
Entry (Long – W4 Pullback):
The “height” of W3 is measured.
Entry is placed at a configurable Fibonacci pullback (default 50%) inside that leg.
ATR-based stop is placed below entry.
Take-profit is projected to satisfy min Reward:Risk.
Bearish structure:
Mirrored logic (lower highs/lows, price below EMA, W3 down, W4 retrace up, W5 continuation down).
Once a valid setup is found, the script draws a colored box around the entry zone and a label describing the type of signal (“LONG SETUP” or “SHORT SETUP”) with the suggested limit price.
6. Orders & Execution
Entry Orders: The strategy uses limit orders at the computed W4 level (“Sniper Long” or “Sniper Short”).
Exits: A single strategy.exit() is attached to each entry with:
Take-profit at the projected minimum R:R target.
Stop-loss at ATR-based level.
One Trade at a Time: New setups are only used when there is no open position (strategy.opentrades == 0) to keep the logic clear and risk contained.
7. Visual Guide on the Chart
Wave Labels:
Primary: ①,②,③,④,⑤, ⒶⒷⒸ
Intermediate: (1)…(5), (A)(B)(C)
Minor: i…v, a b c
Trend EMA: Single blue EMA showing the dominant trend.
Setup Boxes:
Green transparent box → long entry zone.
Red transparent box → short entry zone.
Labels: “LONG SETUP / SHORT SETUP” labels mark the proposed limit entry with price.
8. How to Use This Strategy
Attach the strategy to your chart
Choose your market (stocks, indices, FX, crypto, futures, etc.) and timeframe (for example 1h, 4h, or Daily). Then add the strategy to the chart from your Scripts list.
Start with the default settings
Leave all inputs on their defaults first. This lets you see the “intended” behaviour and the exact properties used for the published backtest (account size, 1% risk, commission, etc.).
Study the wave map
Zoom in and out and look at the three wave degrees:
Blue circles → Primary degree (big picture trend).
Teal (1)…(5) → Intermediate degree (swing structure).
Red i…v → Minor degree (micro waves).
Use this to understand how the engine is interpreting the Elliott structure on your symbol.
Watch for valid setups
Look for the coloured boxes and labels:
Green box + “LONG SETUP” label → potential W4 pullback long in an uptrend.
Red box + “SHORT SETUP” label → potential W4 pullback short in a downtrend.
Only trades in the direction of the EMA trend filter are allowed by the strategy.
Check the Reward:Risk of each idea
For each setup, inspect:
Limit entry price.
ATR-based stop level.
Projected take-profit level.
Make sure the minimum Reward:Risk ratio matches your own rules before you consider trading it.
Backtest and evaluate
Open the Strategy Tester:
Verify you have a decent sample size (ideally 100+ trades).
Check drawdowns, average trade, win-rate and R:R distribution.
Change markets and timeframes to see where the logic behaves best.
Adapt to your own risk profile
If you plan to use it live:
Set Initial Capital to your real account size.
Adjust default_qty_value to a risk level you are comfortable with (often 0.5–2% per trade).
Set commission and slippage to realistic broker values.
Re-run the backtest after every major change.
Use as a framework, not a signal machine
Treat this as a structured Elliott/R:R framework:
Filter signals by higher-timeframe trend, major S/R, volume, or fundamentals.
Optionally hide some wave degrees or ABC labels if you want a cleaner chart.
Combine the system’s structure with your own trade management and discretion.
Best Practices & Limitations
This is an approximate Elliott Wave engine based on fractal pivots. It does not replace a full discretionary Elliott analysis.
All wave counts are algorithmic and can differ from a manual analyst’s interpretation.
Like any backtest, results depend heavily on:
Symbol and timeframe.
Sample size (more trades are better).
Realistic commission/slippage settings.
The 0-slippage default is chosen only to show the “raw logic”. In real markets, slippage can significantly impact performance.
No strategy wins all the time. Losing streaks and drawdowns will still occur even with a strict R:R framework.
Disclaimer
This script is for educational and research purposes only and does not constitute financial advice or a recommendation to buy or sell any security. Past performance, whether real or simulated, is not indicative of future results. Always test on multiple symbols/timeframes, use conservative risk, and consult your financial advisor before trading live capital.
VV Moving Average Convergence Divergence # VMACDv3 - Volume-Weighted MACD with A/D Divergence Detection
## Overview
**VMACDv3** (Volume-Weighted Moving Average Convergence Divergence Version 3) is a momentum indicator that applies volume-weighting to traditional MACD calculations on price, while using the Accumulation/Distribution (A/D) line for divergence detection. This hybrid approach combines volume-weighted price momentum with volume distribution analysis for comprehensive market insight.
## Key Features
- **Volume-Weighted Price MACD**: Traditional MACD calculation on price but weighted by volume for earlier signals
- **A/D Divergence Detection**: Identifies when A/D trend diverges from MACD momentum
- **Volume Strength Filtering**: Distinguishes high-volume confirmations from low-volume noise
- **Color-Coded Histogram**: 4-color system showing momentum direction and volume strength
- **Real-Time Alerts**: Background colors and alert conditions for bullish/bearish divergences
## Difference from ACCDv3
| Aspect | VMACDv3 | ACCDv3 |
|--------|---------|---------|
| **MACD Input** | **Price (Close)** | **A/D Line** |
| **Volume Weighting** | Applied to price | Applied to A/D line |
| **Primary Signal** | Volume-weighted price momentum | Volume distribution momentum |
| **Use Case** | Price momentum with volume confirmation | Volume flow and accumulation/distribution |
| **Sensitivity** | More responsive to price changes | More responsive to volume patterns |
| **Best For** | Trend following, breakouts | Volume analysis, smart money tracking |
**Key Insight**: VMACDv3 shows *where price is going* with volume weight, while ACCDv3 shows *where volume is accumulating/distributing*.
## Components
### 1. Volume-Weighted MACD on Price
Unlike standard MACD that uses simple price EMAs, VMACDv3 weights each price by its corresponding volume:
```
Fast Line = EMA(Price × Volume, 12) / EMA(Volume, 12)
Slow Line = EMA(Price × Volume, 26) / EMA(Volume, 26)
MACD = Fast Line - Slow Line
```
**Benefits of Volume Weighting**:
- High-volume price movements have greater impact
- Filters out low-volume noise and false moves
- Provides earlier trend change signals
- Better reflects institutional activity
### 2. Accumulation/Distribution (A/D) Line
Used for divergence detection, measuring buying/selling pressure:
```
A/D = Σ ((2 × Close - Low - High) / (High - Low)) × Volume
```
- **Rising A/D**: Accumulation (buying pressure)
- **Falling A/D**: Distribution (selling pressure)
- **Doji Handling**: When High = Low, contribution is zero
### 3. Signal Lines
- **MACD Line** (Blue, #2962FF): The fast-slow difference showing momentum
- **Signal Line** (Orange, #FF6D00): EMA or SMA smoothing of MACD
- **Zero Line**: Reference for bullish (above) vs bearish (below) bias
### 4. Histogram Color System
The histogram uses 4 distinct colors based on **direction** and **volume strength**:
| Condition | Color | Meaning |
|-----------|-------|---------|
| Rising + High Volume | **Dark Green** (#1B5E20) | Strong bullish momentum with volume confirmation |
| Rising + Low Volume | **Light Teal** (#26A69A) | Bullish momentum but weak volume (less reliable) |
| Falling + High Volume | **Dark Red** (#B71C1C) | Strong bearish momentum with volume confirmation |
| Falling + Low Volume | **Light Pink** (#FFCDD2) | Bearish momentum but weak volume (less reliable) |
Additional shading:
- **Light Cyan** (#B2DFDB): Positive but not rising (momentum stalling)
- **Bright Red** (#FF5252): Negative and accelerating down
### 5. Divergence Detection
VMACDv3 compares A/D trend against volume-weighted price MACD:
#### Bullish Divergence (Green Background)
- **Condition**: A/D is trending up BUT MACD is negative and trending down
- **Interpretation**: Volume is accumulating while price momentum appears weak
- **Signal**: Smart money accumulation, potential bullish reversal
- **Action**: Look for long entries, especially at support levels
#### Bearish Divergence (Red Background)
- **Condition**: A/D is trending down BUT MACD is positive and trending up
- **Interpretation**: Volume is distributing while price momentum appears strong
- **Signal**: Smart money distribution, potential bearish reversal
- **Action**: Consider exits, avoid new longs, watch for breakdown
## Parameters
| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| **Source** | Close | OHLC/HLC3/etc | Price source for MACD calculation |
| **Fast Length** | 12 | 1-50 | Period for fast EMA (shorter = more sensitive) |
| **Slow Length** | 26 | 1-100 | Period for slow EMA (longer = smoother) |
| **Signal Smoothing** | 9 | 1-50 | Period for signal line (MACD smoothing) |
| **Signal Line MA Type** | EMA | SMA/EMA | Moving average type for signal calculation |
| **Volume MA Length** | 20 | 5-100 | Period for volume average (strength filter) |
## Usage Guide
### Reading the Indicator
1. **MACD Lines (Blue & Orange)**
- **Blue Line (MACD)**: Volume-weighted price momentum
- **Orange Line (Signal)**: Smoothed trend of MACD
- **Crossovers**: Blue crosses above orange = bullish, below = bearish
- **Distance**: Wider gap = stronger momentum
- **Zero Line Position**: Above = bullish bias, below = bearish bias
2. **Histogram Colors**
- **Dark Green (#1B5E20)**: Strong bullish move with high volume - **most reliable buy signal**
- **Light Teal (#26A69A)**: Bullish but low volume - wait for confirmation
- **Dark Red (#B71C1C)**: Strong bearish move with high volume - **most reliable sell signal**
- **Light Pink (#FFCDD2)**: Bearish but low volume - may be temporary dip
3. **Background Divergence Alerts**
- **Green Background**: A/D accumulating while price weak - potential bottom
- **Red Background**: A/D distributing while price strong - potential top
- Most powerful at key support/resistance levels
### Trading Strategies
#### Strategy 1: Volume-Confirmed Trend Following
1. Wait for MACD to cross above zero line
2. Look for **dark green** histogram bars (high volume confirmation)
3. Enter long on second consecutive dark green bar
4. Hold while histogram remains green
5. Exit when histogram turns light green or red appears
6. Set stop below recent swing low
**Example**:
```
Price: 26,400 → 26,450 (rising)
MACD: -50 → +20 (crosses zero)
Histogram: Light teal → Dark green → Dark green
Volume: 50k → 75k → 90k (increasing)
```
#### Strategy 2: Divergence Reversal Trading
1. Identify divergence background (green = bullish, red = bearish)
2. Confirm with price structure (support/resistance, chart patterns)
3. Wait for MACD to cross signal line in divergence direction
4. Enter on first **dark colored** histogram bar after divergence
5. Set stop beyond divergence area
6. Target previous swing high/low
**Example - Bullish Divergence**:
```
Price: Making lower lows (26,350 → 26,300 → 26,250)
A/D: Rising (accumulation)
MACD: Below zero but starting to curve up
Background: Green shading appears
Entry: MACD crosses signal line + dark green bar
Stop: Below 26,230
Target: 26,450 (previous high)
```
#### Strategy 3: Momentum Scalping
1. Trade only in direction of MACD zero line (above = long, below = short)
2. Enter on dark colored bars only
3. Exit on first light colored bar or opposite color
4. Quick in and out (1-5 minute holds)
5. Tight stops (0.2-0.5% depending on instrument)
#### Strategy 4: Histogram Pattern Trading
**V-Bottom Reversal (Bullish)**:
- Red histogram bars start rising (becoming less negative)
- Forms "V" shape at the bottom
- Transitions to light red → light teal → **dark green**
- Entry: First dark green bar
- Signal: Momentum reversal with volume
**Λ-Top Reversal (Bearish)**:
- Green histogram bars start falling (becoming less positive)
- Forms inverted "V" at the top
- Transitions to light green → light pink → **dark red**
- Entry: First dark red bar
- Signal: Momentum exhaustion with volume
### Multi-Timeframe Analysis
**Recommended Approach**:
1. **Higher Timeframe (15m/1h)**: Identify overall trend direction
2. **Trading Timeframe (5m)**: Time entries using VMACDv3 signals
3. **Lower Timeframe (1m)**: Fine-tune entry prices
**Example Setup**:
```
15-minute: MACD above zero (bullish bias)
5-minute: Dark green histogram appears after pullback
1-minute: Enter on break of recent high with volume
```
### Volume Strength Interpretation
The volume filter compares current volume to 20-period average:
- **Volume > Average**: Dark colors (green/red) - high confidence signals
- **Volume < Average**: Light colors (teal/pink) - lower confidence signals
**Trading Rules**:
- ✓ **Aggressive**: Take all dark colored signals
- ✓ **Conservative**: Only take dark colors that follow 2+ light colors of same type
- ✗ **Avoid**: Trading light colored signals during high volatility
- ✗ **Avoid**: Ignoring volume context during news events
## Technical Details
### Volume-Weighted Calculation
```pine
// Volume-weighted fast EMA
fast_ma = ta.ema(src * volume, fast_length) / ta.ema(volume, fast_length)
// Volume-weighted slow EMA
slow_ma = ta.ema(src * volume, slow_length) / ta.ema(volume, slow_length)
// MACD is the difference
macd = fast_ma - slow_ma
// Signal line smoothing
signal = ta.ema(macd, signal_length) // or ta.sma() if SMA selected
// Histogram
hist = macd - signal
```
### Divergence Detection Logic
```pine
// A/D trending up if above its 5-period SMA
ad_trend = ad > ta.sma(ad, 5)
// MACD trending up if above zero
macd_trend = macd > 0
// Divergence when trends oppose each other
divergence = ad_trend != macd_trend
// Specific conditions for alerts
bullish_divergence = ad_trend and not macd_trend and macd < 0
bearish_divergence = not ad_trend and macd_trend and macd > 0
```
### Histogram Coloring Logic
```pine
hist_color = (hist >= 0
? (hist < hist
? (vol_strength ? #1B5E20 : #26A69A) // Rising: dark/light green
: #B2DFDB) // Positive but falling: cyan
: (hist < hist
? (vol_strength ? #B71C1C : #FFCDD2) // Rising (less negative): dark/light red
: #FF5252)) // Falling more: bright red
```
## Alerts
Built-in alert conditions for divergence detection:
### Bullish Divergence Alert
- **Trigger**: A/D trending up, MACD negative and trending down
- **Message**: "Bullish Divergence: A/D trending up but MACD trending down"
- **Use Case**: Potential reversal or continuation after pullback
- **Action**: Look for long entry setups
### Bearish Divergence Alert
- **Trigger**: A/D trending down, MACD positive and trending up
- **Message**: "Bearish Divergence: A/D trending down but MACD trending up"
- **Use Case**: Potential top or trend reversal
- **Action**: Consider exits or short entries
### Setting Up Alerts
1. Click "Create Alert" in TradingView
2. Condition: Select "VMACDv3"
3. Choose alert type: "Bullish Divergence" or "Bearish Divergence"
4. Configure: Email, SMS, webhook, or popup
5. Set frequency: "Once Per Bar Close" recommended
## Comparison Tables
### VMACDv3 vs Standard MACD
| Feature | Standard MACD | VMACDv3 |
|---------|---------------|---------|
| **Price Weighting** | Equal weight all bars | Volume-weighted |
| **Sensitivity** | Fixed | Adaptive to volume |
| **False Signals** | More during low volume | Fewer (volume filter) |
| **Divergence** | Price vs MACD | A/D vs MACD |
| **Volume Analysis** | None | Built-in |
| **Color System** | 2 colors | 4+ colors |
| **Best For** | Simple trend following | Volume-confirmed trading |
### VMACDv3 vs ACCDv3
| Aspect | VMACDv3 | ACCDv3 |
|--------|---------|--------|
| **Focus** | Price momentum | Volume distribution |
| **Reactivity** | Faster to price moves | Faster to volume shifts |
| **Best Markets** | Trending, breakouts | Accumulation/distribution phases |
| **Signal Type** | Where price + volume going | Where smart money positioning |
| **Divergence Meaning** | Volume vs price disagreement | A/D vs momentum disagreement |
| **Use Together?** | ✓ Yes, complementary | ✓ Yes, different perspectives |
## Example Trading Scenarios
### Scenario 1: Strong Bullish Breakout
```
Time: 9:30 AM (market open)
Price: Breaks above 26,400 resistance
MACD: Crosses above zero line
Histogram: Dark green bars (#1B5E20)
Volume: 2x average (150k vs 75k avg)
A/D: Rising (no divergence)
Action: Enter long at 26,405
Stop: 26,380 (below breakout)
Target 1: 26,450 (risk:reward 1:2)
Target 2: 26,500 (risk:reward 1:4)
Result: High probability setup with volume confirmation
```
### Scenario 2: False Breakout (Avoided)
```
Time: 2:30 PM (slow period)
Price: Breaks above 26,400 resistance
MACD: Slightly positive
Histogram: Light teal bars (#26A69A)
Volume: 0.5x average (40k vs 75k avg)
A/D: Flat/declining
Action: Avoid trade
Reason: Low volume, no conviction, potential false breakout
Outcome: Price reverses back below 26,400 within 10 minutes
Saved: Avoided losing trade due to volume filter
```
### Scenario 3: Bullish Divergence Bottom
```
Time: 11:00 AM
Price: Making lower lows (26,350 → 26,300 → 26,280)
MACD: Below zero but curving upward
Histogram: Red bars getting shorter (V-bottom forming)
Background: Green shading (divergence alert)
A/D: Rising despite price falling
Volume: Increasing on down bars
Setup:
1. Divergence appears at 26,280 (green background)
2. Wait for MACD to cross signal line
3. First dark green bar appears at 26,290
4. Enter long: 26,295 (next bar open)
5. Stop: 26,265 (below divergence low)
6. Target: 26,350 (previous swing high)
Result: +55 points (30 point risk, 1.8:1 reward)
Key: Divergence + volume confirmation = high probability reversal
```
### Scenario 4: Bearish Divergence Top
```
Time: 1:45 PM
Price: Making higher highs (26,500 → 26,520 → 26,540)
MACD: Positive but flattening
Histogram: Green bars getting shorter (Λ-top forming)
Background: Red shading (bearish divergence)
A/D: Declining despite rising price
Volume: Decreasing on up bars
Setup:
1. Bearish divergence at 26,540 (red background)
2. MACD crosses below signal line
3. First dark red bar appears at 26,535
4. Enter short: 26,530
5. Stop: 26,555 (above divergence high)
6. Target: 26,475 (support level)
Result: +55 points (25 point risk, 2.2:1 reward)
Key: Distribution while price rising = smart money exiting
```
### Scenario 5: V-Bottom Reversal
```
Downtrend in progress
MACD: Deep below zero (-150)
Histogram: Series of dark red bars
Pattern Development:
Bar 1: Dark red, hist = -80, falling
Bar 2: Dark red, hist = -95, falling
Bar 3: Dark red, hist = -100, falling (extreme)
Bar 4: Light pink, hist = -98, rising!
Bar 5: Light pink, hist = -90, rising
Bar 6: Light teal, hist = -75, rising (crosses to positive momentum)
Bar 7: Dark green, hist = -55, rising + volume
Action: Enter long on Bar 7
Reason: V-bottom confirmed with volume
Stop: Below Bar 3 low
Target: Zero line on histogram (mean reversion)
```
## Best Practices
### Entry Rules
✓ **Wait for dark colors**: High-volume confirmation is key
✓ **Confirm divergences**: Use with price support/resistance
✓ **Trade with zero line**: Long above, short below for best odds
✓ **Multiple timeframes**: Align 1m, 5m, 15m signals
✓ **Watch for patterns**: V-bottoms and Λ-tops are reliable
### Exit Rules
✓ **Partial profits**: Take 50% at first target
✓ **Trail stops**: Use histogram color changes
✓ **Respect signals**: Exit on opposite dark color
✓ **Time stops**: Close positions before major news
✓ **End of day**: Square up before close
### Avoid
✗ **Don't chase light colors**: Low volume = low confidence
✗ **Don't ignore divergence**: Early warning system
✗ **Don't overtrade**: Wait for clear setups
✗ **Don't fight the trend**: Zero line dictates bias
✗ **Don't skip stops**: Always use risk management
## Risk Management
### Position Sizing
- **Dark green/red signals**: 1-2% account risk
- **Light signals**: 0.5% account risk or skip
- **Divergence plays**: 1% account risk (higher uncertainty)
- **Multiple confirmations**: Up to 2% account risk
### Stop Loss Placement
- **Trend trades**: Below/above recent swing (20-30 points typical)
- **Breakout trades**: Below/above breakout level (15-25 points)
- **Divergence trades**: Beyond divergence extreme (25-40 points)
- **Scalp trades**: Tight stops at 10-15 points
### Profit Targets
- **Minimum**: 1.5:1 reward to risk ratio
- **Scalps**: 15-25 points (quick in/out)
- **Swing**: 50-100 points (hold through pullbacks)
- **Runners**: Trail with histogram color changes
## Timeframe Recommendations
| Timeframe | Trading Style | Typical Hold | Advantages | Challenges |
|-----------|---------------|--------------|------------|------------|
| **1-minute** | Scalping | 1-5 minutes | Fast profits, many setups | Noisy, high false signals |
| **5-minute** | Intraday | 15-60 minutes | Balance of speed/clarity | Still requires quick decisions |
| **15-minute** | Swing | 1-4 hours | Clearer trends, less noise | Fewer opportunities |
| **1-hour** | Position | 4-24 hours | Strong signals, less monitoring | Wider stops required |
**Recommendation**: Start with 5-minute for best balance of signal quality and opportunity frequency.
## Combining with Other Indicators
### VMACDv3 + ACCDv3
- **Use**: Confirm volume flow with price momentum
- **Signal**: Both showing dark green = highest conviction long
- **Divergence**: VMACDv3 bullish + ACCDv3 bearish = examine price action
### VMACDv3 + RSI
- **Use**: Overbought/oversold with momentum confirmation
- **Signal**: RSI < 30 + dark green VMACD = strong reversal
- **Caution**: RSI > 70 + light green VMACD = potential false breakout
### VMACDv3 + Elder Impulse
- **Use**: Bar coloring + histogram confirmation
- **Signal**: Green Elder bars + dark green VMACD = aligned momentum
- **Exit**: Blue Elder bars + light colors = momentum stalling
## Limitations
- **Requires volume data**: Will not work on instruments without volume feed
- **Lagging indicator**: MACD inherently follows price (2-3 bar delay)
- **Consolidation noise**: Generates false signals in tight ranges
- **Gap handling**: Large gaps can distort volume-weighted values
- **Not standalone**: Should combine with price action and support/resistance
## Troubleshooting
**Problem**: Too many light colored signals
**Solution**: Increase Volume MA Length to 30-40 for stricter filtering
**Problem**: Missing entries due to waiting for dark colors
**Solution**: Lower Volume MA Length to 10-15 for more signals (accept lower quality)
**Problem**: Divergences not appearing
**Solution**: Verify volume data available; check if A/D line is calculating
**Problem**: Histogram colors not changing
**Solution**: Ensure real-time data feed; refresh indicator
## Version History
- **v3**: Removed traditional MACD, using volume-weighted MACD on price with A/D divergence
- **v2**: Added A/D divergence detection, volume strength filtering, enhanced histogram colors
- **v1**: Basic volume-weighted MACD on price
## Related Indicators
**Companion Tools**:
- **ACCDv3**: Volume-weighted MACD on A/D line (distribution focus)
- **RSIv2**: RSI with A/D divergence detection
- **DMI**: Directional Movement Index with A/D divergence
- **Elder Impulse**: Bar coloring system using volume-weighted MACD
**Use Together**: VMACDv3 (momentum) + ACCDv3 (distribution) + Elder Impulse (bar colors) = complete volume-based trading system
---
*This indicator is for educational purposes. Past performance does not guarantee future results. Always practice proper risk management and never risk more than you can afford to lose.*
MTC – Multi-Timeframe Trend Confirmator V2MTC – Multi-Timeframe Trend Confirmator V2
A comprehensive trend analysis indicator that systematically combines six technical indicators across three customizable timeframes, using a weighted scoring system to identify high-probability trend conditions.
ORIGINALITY AND CONCEPT
This indicator is original in its approach to multi-timeframe trend confirmation. Rather than relying on a single indicator or timeframe, it creates a composite score by evaluating six different technical conditions simultaneously across three timeframes. The scoring system weighs certain indicators more heavily based on their reliability in trend identification. The visual gauge provides an at-a-glance view of trend alignment across timeframes, making it easier to identify when multiple timeframes agree - a condition that typically produces stronger, more reliable trends.
HOW IT WORKS - DETAILED SCORING METHODOLOGY
The indicator evaluates six technical conditions on each timeframe. Each condition contributes to a composite score:
EMA 200 (Weight: 1 point)
Bullish: Price closes above EMA 200 (+1)
Bearish: Price closes below EMA 200 (-1)
Rationale: Long-term trend direction
SMA 50/200 Crossover (Weight: 1 point)
Bullish: SMA 50 above SMA 200 (+1)
Bearish: SMA 50 below SMA 200 (-1)
Rationale: Golden/Death cross confirmation
RSI 14 (Weight: 1 point)
Bullish: RSI above 55 (+1)
Bearish: RSI below 45 (-1)
Neutral: RSI between 45-55 (0)
Rationale: Momentum filter with buffer zone to avoid chop
MACD (12,26,9) (Weight: 1 point)
Bullish: MACD line above signal line (+1)
Bearish: MACD line below signal line (-1)
Rationale: Trend momentum confirmation
ADX 14 (Weight: 2 points - DOUBLE WEIGHTED)
Requires ADX above 25 to activate
Bullish: DI+ above DI- and ADX > 25 (+2)
Bearish: DI- above DI+ and ADX > 25 (-2)
Neutral: ADX below 25 (0)
Rationale: Trend strength filter - only counts when a strong trend exists. Double weighted because ADX is specifically designed to measure trend strength, making it more reliable than oscillators.
Supertrend (Factor: 3.0, ATR Period: 10) (Weight: 2 points - DOUBLE WEIGHTED)
Bullish: Direction indicator = -1 (+2)
Bearish: Direction indicator = +1 (-2)
Rationale: Dynamic support/resistance that adapts to volatility. Double weighted because Supertrend provides clear, objective trend signals with built-in stop-loss levels.
COMPOSITE SCORE CALCULATION:
Total possible score range: -10 to +10 points
Score interpretation:
Score > 2: UPTREND (majority of indicators bullish, especially weighted ones)
Score < -2: DOWNTREND (majority of indicators bearish, especially weighted ones)
Score between -2 and +2: NEUTRAL/RANGING (mixed signals or weak trend)
The threshold of +/- 2 was chosen because it requires more than just basic agreement - it typically means at least 3-4 indicators align, or that the heavily-weighted indicators (ADX, Supertrend) confirm the direction.
MULTI-TIMEFRAME LOGIC:
The indicator calculates the composite score independently for three timeframes:
Higher Timeframe (default: 4H) - Major trend direction
Mid Timeframe (default: 1H) - Intermediate trend
Lower Timeframe (default: 15min) - Entry timing
Main Trend Confirmation Rule:
The indicator only signals a confirmed trend when BOTH the higher timeframe AND mid timeframe scores agree (both > 2 for uptrend, or both < -2 for downtrend). This dual-timeframe confirmation significantly reduces false signals during choppy or ranging markets.
HOW TO USE IT
Setup:
Add indicator to chart
Customize timeframes based on your trading style:
Scalpers: 15min, 5min, 1min
Day traders: 4H, 1H, 15min (default)
Swing traders: Daily, 4H, 1H
Toggle individual indicators on/off based on your preference
Adjust Supertrend parameters if needed for your instrument's volatility
Reading the Gauge (Top Right Corner):
Each row shows one timeframe
Left column: Timeframe label
Middle column: Visual strength bars (10 bars = maximum score)
Green bars = Bullish score
Red bars = Bearish score
Yellow bars = Neutral/ranging
More filled bars = stronger trend
Right column: Numerical score
Trading Signals:
Entry Signals:
Long Entry: Wait for upward triangle arrow (appears when higher + mid TF both bullish)
Confirm gauge shows green bars on higher and mid timeframes
Lower timeframe should ideally turn green for entry timing
Chart background tints light green
Short Entry: Wait for downward triangle arrow (appears when higher + mid TF both bearish)
Confirm gauge shows red bars on higher and mid timeframes
Lower timeframe should ideally turn red for entry timing
Chart background tints light red
Position Management:
Stay in position while higher and mid timeframes remain aligned
Consider reducing position size when mid timeframe score weakens
Exit when higher timeframe trend reverses (daily label changes)
Avoiding False Signals:
Ignore signals when gauge shows mixed colors across timeframes
Avoid trading when scores are close to threshold (+/- 2 to +/- 4 range)
Best trades occur when all three timeframes align (all green or all red in gauge)
Use the numerical scores: higher absolute values (7-10) indicate stronger, more reliable trends
Practical Examples:
Example 1 - Strong Uptrend Entry:
Higher TF: +8 (strong green bars)
Mid TF: +6 (strong green bars)
Lower TF: +4 (moderate green bars)
Action: Look for long entries on lower timeframe pullbacks
Background is tinted green, upward arrow appears
Example 2 - Ranging Market (Avoid):
Higher TF: +3 (weak green)
Mid TF: -1 (weak red)
Lower TF: +2 (neutral yellow)
Action: Stay out, wait for alignment
Example 3 - Trend Reversal Warning:
Higher TF: +7 (still green)
Mid TF: -3 (turned red)
Lower TF: -5 (strong red)
Action: Consider exiting longs, prepare for potential higher TF reversal
Customization Options:
Timeframes: Adjust all three to match your trading horizon
Indicator Toggles: Disable indicators that don't suit your instrument:
Disable RSI for highly volatile crypto markets
Disable SMA crossover for range-bound instruments
Keep ADX and Supertrend enabled for trending markets
Visual Preferences:
Arrow size: 5 options from Tiny to Huge
Gauge size: Small/Medium/Large for different screen sizes
Toggle arrows on/off if you only want the gauge
Alert Setup:
Right-click chart, "Add Alert"
Condition: MTC v6 - UPTREND or DOWNTREND
Get notified when multi-timeframe confirmation occurs
Best Practices:
Use with Price Action: The indicator works best when combined with support/resistance levels, chart patterns, and volume analysis
Risk Management: Even with multi-timeframe confirmation, always use stop losses
Market Context: Works best in trending markets; less reliable in strong consolidation
Backtesting: Test the default settings on your specific instrument and timeframe before live trading
Patience: Wait for full multi-timeframe alignment rather than taking premature signals
Technical Notes:
All calculations use Pine Script's security function to fetch data from multiple timeframes
Prevents repainting by using confirmed bar data
Gauge updates in real-time on the last bar
Daily labels mark at the open of each new daily candle
Works on all instruments and timeframes
This indicator is ideal for traders who want objective, systematic trend identification without the complexity of analyzing multiple indicators manually across different timeframes.
-NATANTIA
ParabolicSAR+EMA[TS_Indie]🚀 EMA + Parabolic SAR Reversal Trading Strategy
This trading system effectively combines the use of Exponential Moving Averages (EMA) with the Parabolic SAR to identify both price trends and key reversal points. The EMA Fast is used to signal the primary short-term trend, while the EMA Slow acts as a filter for the long-term trend direction. The Parabolic SAR then helps to confirm the reversal signals.
🛠️ Tools Used
1. EMA Fast – Primary Short-Term Trend
2. EMA Slow – Long-Term Trend Filter
3. Parabolic SAR – Reversal Confirmation
🎯 Entry Rules
📈 Buy Setup
1. Trend Filter: EMA Fast > EMA Slow → Uptrend
2. Pullback: Price pulls back and closes below the EMA Fast line.
3. Reversal: Price reverses/pulls back up and closes above the EMA Fast line.
4. SAR Confirmation: The previous Parabolic SAR dot is above the high, and the dot in the current candle is below the low → Reversal signal confirmed.
5. Entry: Enter Buy immediately.
📉 Sell Setup
1. Trend Filter: EMA Fast < EMA Slow → Downtrend
2. Pullback: Price pulls back and closes above the EMA Fast line.
3. Reversal: Price reverses/pulls back down and closes below the EMA Fast line.
4. SAR Confirmation: The previous Parabolic SAR dot is below the low, and the dot in the current candle is above the high → Reversal signal confirmed.
5. Entry: Enter Sell immediately.
💰 Exit Management (Entry, Stop Loss, Take Profit)
1. Entry: Enter the order at the closing price of the signal candle.
2. Stop Loss (SL): Set the Stop Loss at the Parabolic SAR dot.
3. Take Profit (TP): Calculated from the Entry and Stop Loss points, multiplied by the Risk Reward Ratio.
⚙️ Optional Parameters
➭ Custom Risk/Reward Ratio for Take Profit.
➭ Option to add an ATR buffer to the Stop Loss.
➭ Adjustable EMA Fast period.
➭ Adjustable EMA Slow period.
➭ Adjustable Parabolic SAR parameters.
➭ Option to enable Long-only / Short-only positions.
➭ Customizable Backtest start and end date.
➭ Customizable trading session time.
🔔 Alert Function
Alerts display:
➭ Entry Price
➭ Stop Loss Price
➭ Take Profit Price
💡 This strategy allows for many parameter adjustments, such as the MA type, adding/subtracting from the Stop Loss using ATR, and selecting specific sessions for backtesting. If you find interesting or profitable results after adjusting the parameters, please share your comments with other traders!
⚠️ Disclaimer
This indicator is designed for educational and research purposes only. It does not guarantee profits and should not be considered financial advice. Trading in financial markets involves significant risk , including the potential loss of capital.
