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[LeonidasCrypto]EMA with Volatility GlowEMA Volatility Glow - Advanced Moving Average with Dynamic Volatility Visualization Overview The EMA Volatility Glow indicator combines dual exponential moving averages with a sophisticated volatility measurement system, enhanced by dynamic visual effects that respond to real-time market conditions. Technical Components Volatility Calculation Engine BB Volatility Curve: Utilizes Bollinger Band width normalized through RSI smoothing Multi-stage Noise Filtering: 3-layer exponential smoothing algorithm reduces market noise Rate of Change Analysis: Dual-timeframe RoC calculation (14/11 periods) processed through weighted moving average Dynamic Normalization: 100-period lookback for relative volatility assessment Moving Average System Primary EMA: Default 55-period exponential moving average with volatility-responsive coloring Secondary EMA: Default 100-period exponential moving average for trend confirmation Trend Analysis: Real-time bullish/bearish determination based on EMA crossover dynamics Visual Enhancement Framework Gradient Band System: Multi-layer volatility bands using Fibonacci ratios (0.236, 0.382, 0.618) Dynamic Color Mapping: Five-tier color system reflecting volatility intensity levels Configurable Glow Effects: Customizable transparency and intensity settings Trend Fill Visualization: Directional bias indication between moving averages Key Features Volatility States: Ultra-Low: Minimal market movement periods Low: Reduced volatility environments Medium: Normal market conditions High: Increased volatility phases Extreme: Exceptional market stress periods Customization Options: Adjustable EMA periods Configurable glow intensity (1-10 levels) Variable transparency controls Toggleable visual components Customizable gradient band width Technical Calculations: ATR-based gradient bands with noise filtering ChartPrime-inspired multi-layer fill system Real-time volatility curve computation Smooth color gradient transitions Applications Trend Identification: Dual EMA system for directional bias assessment Volatility Analysis: Real-time market stress evaluation Risk Management: Visual volatility cues for position sizing decisions Market Timing: Enhanced visual feedback for entry/exit consideration
Indicatore Pine Script®
di LeonidasCrypto
PhenLabs - Market Fluid Dynamics📊 Market Fluid Dynamics - Version: PineScript™ v6 📌 Description The Market Fluid Dynamics - Phen indicator is a new thinking regarding market analysis by modeling price action, volume, and volatility using a fluid system. It attempts to offer traders control over more profound market forces, such as momentum (speed), resistance (thickness), and buying/selling pressure. By visualizing such dynamics, the script allows the traders to decide on the prevailing market flow, its power, likely continuations, and zones of calmness and chaos, and thereby allows improved decision-making. This measure avoids the usual difficulty of reconciling multiple, often contradictory, market indications by including them within a single overarching model. It moves beyond traditional binary indicators by providing a multi-dimensional view of market behavior, employing fluid dynamic analogs to describe complex interactions in an accessible manner. 🚀 Points of Innovation Integrated Fluid Dynamics Model: Combines velocity, viscosity, pressure, and turbulence into a single indicator. Normalized Metrics: Uses ATR and other normalization techniques for consistent readings across different assets and timeframes. Dynamic Flow Visualization: Main flow line changes color and intensity based on direction and strength. Turbulence Background: Visually represents market stability with a gradient background, from calm to turbulent. Comprehensive Dashboard: Provides an at-a-glance summary of key fluid dynamic metrics. Multi-Layer Smoothing: Employs several layers of EMA smoothing for a clearer, more responsive main flow line. 🔧 Core Components Velocity Component: Measures price momentum (first derivative of price), normalized by ATR. It indicates the speed and direction of price changes. Viscosity Component: Represents market resistance to price changes, derived from ATR relative to its historical average. Higher viscosity suggests it’s harder for prices to move. Pressure Component: Quantifies the force created by volume and price range (close - open), normalized by ATR. It reflects buying or selling pressure. Turbulence Detection: Calculates a Reynolds number equivalent to identify market stability, ranging from laminar (stable) to turbulent (chaotic). Main Flow Indicator: Combines the above components, applying sensitivity and smoothing, to generate a primary signal of market direction and strength. 🔥 Key Features Advanced Smoothing Algorithm: Utilizes multiple EMA layers on the raw flow calculation for a fluid and responsive main flow line, reducing noise while maintaining sensitivity. Gradient Flow Coloring: The main flow line dynamically changes color from light to deep blue for bullish flow and light to deep red for bearish flow, with intensity reflecting flow strength. This provides an immediate visual cue of market sentiment and momentum. Turbulence Level Background: The chart background changes color based on calculated turbulence (from calm gray to vibrant orange), offering an intuitive understanding of market stability and potential for erratic price action. Informative Dashboard: A customizable on-screen table displays critical metrics like Flow State, Flow Strength, Market Viscosity, Turbulence, Pressure Force, Flow Acceleration, and Flow Continuity, allowing traders to quickly assess current market conditions. Configurable Lookback and Sensitivity: Users can adjust the base lookback period for calculations and the sensitivity of the flow to viscosity, tailoring the indicator to different trading styles and market conditions. Alert Conditions: Pre-defined alerts for flow direction changes (positive/negative crossover of zero line) and detection of high turbulence states. 🎨 Visualization Main Flow Line: A smoothed line plotted below the main chart, colored blue for bullish flow and red for bearish flow. The intensity of the color (light to dark) indicates the strength of the flow. This line crossing the zero line can signal a change in market direction. Zero Line: A dotted horizontal line at the zero level, serving as a baseline to gauge whether the market flow is positive (bullish) or negative (bearish). Turbulence Background: The indicator pane’s background color changes based on the calculated turbulence level. A calm, almost transparent gray indicates low turbulence (laminar flow), while a more vibrant, semi-transparent orange signifies high turbulence. This helps traders visually assess market stability. Dashboard Table: An optional table displayed on the chart, showing key metrics like ‘Flow State’, ‘Flow Strength’, ‘Market Viscosity’, ‘Turbulence’, ‘Pressure Force’, ‘Flow Acceleration’, and ‘Flow Continuity’ with their current values and qualitative descriptions (e.g., ‘Bullish Flow’, ‘Laminar (Stable)’). 📖 Usage Guidelines Setting Categories Show Dashboard - Default: true; Range: true/false; Description: Toggles the visibility of the Market Fluid Dynamics dashboard on the chart. Enable to see key metrics at a glance. Base Lookback Period - Default: 14; Range: 5 - (no upper limit, practical limits apply); Description: Sets the primary lookback period for core calculations like velocity, ATR, and volume SMA. Shorter periods make the indicator more sensitive to recent price action, while longer periods provide a smoother, slower signal. Flow Sensitivity - Default: 0.5; Range: 0.1 - 1.0 (step 0.1); Description: Adjusts how much the market viscosity dampens the raw flow. A lower value means viscosity has less impact (flow is more sensitive to raw velocity/pressure), while a higher value means viscosity has a greater dampening effect. Flow Smoothing - Default: 5; Range: 1 - 20; Description: Controls the length of the EMA smoothing applied to the main flow line. Higher values result in a smoother flow line but with more lag; lower values make it more responsive but potentially noisier. Dashboard Position - Default: ‘Top Right’; Range: ‘Top Right’, ‘Top Left’, ‘Bottom Right’, ‘Bottom Left’, ‘Middle Right’, ‘Middle Left’; Description: Determines the placement of the dashboard on the chart. Header Size - Default: ‘Normal’; Range: ‘Tiny’, ‘Small’, ‘Normal’, ‘Large’, ‘Huge’; Description: Sets the text size for the dashboard header. Values Size - Default: ‘Small’; Range: ‘Tiny’, ‘Small’, ‘Normal’, ‘Large’; Description: Sets the text size for the metric values in the dashboard. ✅ Best Use Cases Trend Identification: Identifying the dominant market flow (bullish or bearish) and its strength to trade in the direction of the prevailing trend. Momentum Confirmation: Using the flow strength and acceleration to confirm the conviction behind price movements. Volatility Assessment: Utilizing the turbulence metric to gauge market stability, helping to adjust position sizing or avoid choppy conditions. Reversal Spotting: Watching for divergences between price and flow, or crossovers of the main flow line above/below the zero line, as potential reversal signals, especially when combined with changes in pressure or viscosity. Swing Trading: Leveraging the smoothed flow line to capture medium-term market swings, entering when flow aligns with the desired trade direction and exiting when flow weakens or reverses. Intraday Scalping: Using shorter lookback periods and higher sensitivity to identify quick shifts in flow and turbulence for short-term trading opportunities, particularly in liquid markets. ⚠️ Limitations Lagging Nature: Like many indicators based on moving averages and lookback periods, the main flow line can lag behind rapid price changes, potentially leading to delayed signals. Whipsaws in Ranging Markets: During periods of low volatility or sideways price action (high viscosity, low flow strength), the indicator might produce frequent buy/sell signals (whipsaws) as the flow oscillates around the zero line. Not a Standalone System: While comprehensive, it should be used in conjunction with other forms of analysis (e.g., price action, support/resistance levels, other indicators) and not as a sole basis for trading decisions. Subjectivity in Interpretation: While the dashboard provides quantitative values, the interpretation of “strong” flow, “high” turbulence, or “significant” acceleration can still have a subjective element depending on the trader’s strategy and risk tolerance. 💡 What Makes This Unique Fluid Dynamics Analogy: Its core strength lies in translating complex market interactions into an intuitive fluid dynamics framework, making concepts like momentum, resistance, and pressure easier to visualize and understand. Market View: Instead of focusing on a single aspect (like just momentum or just volatility), it integrates multiple factors (velocity, viscosity, pressure, turbulence) to provide a more comprehensive picture of market conditions. Adaptive Visualization: The dynamic coloring of the flow line and the turbulence background provide immediate, adaptive visual feedback that changes with market conditions. 🔬 How It Works Price Velocity Calculation: The indicator first calculates price velocity by measuring the rate of change of the closing price over a given ‘lookback’ period. The raw velocity is then normalized by the Average True Range (ATR) of the same lookback period. Normalization enables comparison of momentum between assets or timeframes by scaling for volatility. This is the direction and speed of initial price movement. Viscosity Calculation: Market ‘viscosity’ or resistance to price movement is determined by looking at the current ATR relative to its longer-term average (SMA of ATR over lookback * 2). The further the current ATR is above its average, the lower the viscosity (less resistance to price movement), and vice-versa. The script inverts this relationship and bounds it so that rising viscosity means more resistance. Pressure Force Measurement: A ‘pressure’ variable is calculated as a function of the ratio of current volume to its simple moving average, multiplied by the price range (close - open) and normalized by ATR. This is designed to measure the force behind price movement created by volume and intraday price thrusts. This pressure is smoothed by an EMA. Turbulence State Evaluation: A equivalent ‘Reynolds number’ is calculated by dividing the absolute normalized velocity by the viscosity. This is the proclivity of the market to move in a chaotic or orderly fashion. This ‘reynoldsValue’ is smoothed with an EMA to get the ‘turbulenceState’, which indicates if the market is laminar (stable), transitional, or turbulent. Main Flow Derivation: The ‘rawFlow’ is calculated by taking the normalized velocity, dampening its impact based on the ‘viscosity’ and user-input ‘sensitivity’, and orienting it by the sign of the smoothed ‘pressureSmooth’. The ‘rawFlow’ is then put through multiple layers of exponential moving average (EMA) smoothing (with ‘smoothingLength’ and derived values) to reach the final ‘mainFlow’ line. The extensive smoothing is designed to give a smooth and clear visualization of the overall market direction and magnitude. Dashboard Metrics Compilation: Additional metrics like flow acceleration (derivative of mainFlow), and flow continuity (correlation between close and volume) are calculated. All primary components (Flow State, Strength, Viscosity, Turbulence, Pressure, Acceleration, Continuity) are then presented in a user-configurable dashboard for ease of monitoring. 💡 Note: The “Market Fluid Dynamics - Phen” indicator is designed to offer a unique perspective on market behavior by applying principles from fluid dynamics. It’s most effective when used to understand the underlying forces driving price rather than as a direct buy/sell signal generator in isolation. Experiment with the settings, particularly the ‘Base Lookback Period’, ‘Flow Sensitivity’, and ‘Flow Smoothing’, to find what best suits your trading style and the specific asset you are analyzing. Always combine its insights with robust risk management practices.
Indicatore Pine Script®
di PhenLabs
2
CAN INDICATORCAN Moving Averages Indicator - Feature Guide 1. Multiple Moving Averages (20 MAs) - Supports up to 20 individual moving averages - Each MA can be independently configured: - Enable/Disable toggle - Length (period) setting - Type selection (SMA, EMA, DEMA, VWMA, RMA, WMA) - Color customization - Individual timeframe settings when global timeframe is disabled Pre-configured MA Settings: 1. MA1-8: SMA type - Lengths: 20, 50, 100, 200, 365, 489, 600, 1460 2. MA9-20: EMA type - Lengths: 30, 60, 120, 240, 300, 400, 500, 700, 800, 900, 1000, 2000 2. Global Timeframe Settings Location: Global Settings group Features: - Use Global Timeframe: Toggle to use one timeframe for all MAs - Global Timeframe: Select the timeframe to apply globally 3. Label Display Options Location: Main Inputs section Controls: - Show MA Type: Display MA type (SMA, EMA, etc.) - Show MA Length: Display period length - Show Resolution: Display timeframe - Label Offset: Adjust label position 4. Cross Alerts System Location: Cross Alerts group Features: 1. Price Crosses: - Alerts when price crosses any selected MA - Select MA to monitor (1-20) - Triggers on crossover/crossunder 2. MA Crosses: - Alerts when one MA crosses another - Select fast MA (1-20) - Select slow MA (1-20) - Triggers on crossover/crossunder 5. Relative Strength (RS) Analysis Location: Relative Strength group Features: - Select any MA to monitor (1-20) - Compares MA to its own average - Adjustable RS Length (default 14) - Visual feedback via background color: - Green: MA above its average (uptrend) - Red: MA below its average (downtrend) - Customizable colors and transparency 6. Moving Average Types Available 1. **SMA** (Simple Moving Average) - Equal weight to all prices 2. **EMA** (Exponential Moving Average) - More weight to recent prices 3. **DEMA** (Double Exponential Moving Average) - Reduced lag compared to EMA 4. **VWMA** (Volume Weighted Moving Average) - Incorporates volume data 5. **RMA** (Running Moving Average) - Smoother than EMA 6. **WMA** (Weighted Moving Average) - Linear weight distribution Usage Tips 1. **For Trend Following:** - Enable longer-period MAs (MA4-MA8) - Use cross alerts between long-term MAs - Monitor RS for trend strength 2. **For Short-term Trading:** - Focus on shorter-period MAs (MA1-MA3, MA9-MA11) - Enable price cross alerts - Use multiple timeframe analysis 3. **For Multiple Timeframe Analysis:** - Disable global timeframe - Set different timeframes for each MA - Compare MA relationships across timeframes 4. **For Performance:** - Disable unused MAs - Limit active alerts to necessary pairs - Use RS selectively on key MAs
Indicatore Pine Script®
di mr_uponly
real_time_candlesIntroduction The Real-Time Candles Library provides comprehensive tools for creating, manipulating, and visualizing custom timeframe candles in Pine Script. Unlike standard indicators that only update at bar close, this library enables real-time visualization of price action and indicators within the current bar, offering traders unprecedented insight into market dynamics as they unfold. This library addresses a fundamental limitation in traditional technical analysis: the inability to see how indicators evolve between bar closes. By implementing sophisticated real-time data processing techniques, traders can now observe indicator movements, divergences, and trend changes as they develop, potentially identifying trading opportunities much earlier than with conventional approaches. Key Features The library supports two primary candle generation approaches: Chart-Time Candles: Generate real-time OHLC data for any variable (like RSI, MACD, etc.) while maintaining synchronization with chart bars. Custom Timeframe (CTF) Candles: Create candles with custom time intervals or tick counts completely independent of the chart's native timeframe. Both approaches support traditional candlestick and Heikin-Ashi visualization styles, with options for moving average overlays to smooth the data. Configuration Requirements For optimal performance with this library: Set max_bars_back = 5000 in your script settings When using CTF drawing functions, set max_lines_count = 500, max_boxes_count = 500, and max_labels_count = 500 These settings ensure that you will be able to draw correctly and will avoid any runtime errors. Usage Examples Basic Chart-Time Candle Visualization // Create real-time candles for RSI float rsi = ta.rsi(close, 14) Candle rsi_candle = candle_series(rsi, CandleType.candlestick) // Plot the candles using Pine's built-in function plotcandle(rsi_candle.Open, rsi_candle.High, rsi_candle.Low, rsi_candle.Close, "RSI Candles", rsi_candle.candle_color, rsi_candle.candle_color) Multiple Access Patterns The library provides three ways to access candle data, accommodating different programming styles: // 1. Array-based access for collection operations Candle candles = candle_array(source) // 2. Object-oriented access for single entity manipulation Candle candle = candle_series(source) float value = candle.source(Source.HLC3) // 3. Tuple-based access for functional programming styles = candle_tuple(source) Custom Timeframe Examples // Create 20-second candles with EMA overlay plot_ctf_candles( source = close, candle_type = CandleType.candlestick, sample_type = SampleType.Time, number_of_seconds = 20, timezone = -5, tied_open = true, ema_period = 9, enable_ema = true ) // Create tick-based candles (new candle every 15 ticks) plot_ctf_tick_candles( source = close, candle_type = CandleType.heikin_ashi, number_of_ticks = 15, timezone = -5, tied_open = true ) Advanced Usage with Custom Visualization // Get custom timeframe candles without automatic plotting CandleCTF my_candles = ctf_candles_array( source = close, candle_type = CandleType.candlestick, sample_type = SampleType.Time, number_of_seconds = 30 ) // Apply custom logic to the candles float ema_values = my_candles.ctf_ema(14) // Draw candles and EMA using time-based coordinates my_candles.draw_ctf_candles_time() ema_values.draw_ctf_line_time(line_color = #FF6D00) Library Components Data Types Candle: Structure representing chart-time candles with OHLC, polarity, and visualization properties CandleCTF: Extended candle structure with additional time metadata for custom timeframes TickData: Structure for individual price updates with time deltas Enumerations CandleType: Specifies visualization style (candlestick or Heikin-Ashi) Source: Defines price components for calculations (Open, High, Low, Close, HL2, etc.) SampleType: Sets sampling method (Time-based or Tick-based) Core Functions get_tick(): Captures current price as a tick data point candle_array(): Creates an array of candles from price updates candle_series(): Provides a single candle based on latest data candle_tuple(): Returns OHLC values as a tuple ctf_candles_array(): Creates custom timeframe candles without rendering Visualization Functions source(): Extracts specific price components from candles candle_ctf_to_float(): Converts candle data to float arrays ctf_ema(): Calculates exponential moving averages for candle arrays draw_ctf_candles_time(): Renders candles using time coordinates draw_ctf_candles_index(): Renders candles using bar index coordinates draw_ctf_line_time(): Renders lines using time coordinates draw_ctf_line_index(): Renders lines using bar index coordinates Technical Implementation Notes This library leverages Pine Script's varip variables for state management, creating a sophisticated real-time data processing system. The implementation includes: Efficient tick capturing: Samples price at every execution, maintaining temporal tracking with time deltas Smart state management: Uses a hybrid approach with mutable updates at index 0 and historical preservation at index 1+ Temporal synchronization: Manages two time domains (chart time and custom timeframe) The tooltip implementation provides crucial temporal context for custom timeframe visualizations, allowing users to understand exactly when each candle formed regardless of chart timeframe. Limitations Custom timeframe candles cannot be backtested due to Pine Script's limitations with historical tick data Real-time visualization is only available during live chart updates Maximum history is constrained by Pine Script's array size limits Applications Indicator visualization: See how RSI, MACD, or other indicators evolve in real-time Volume analysis: Create custom volume profiles independent of chart timeframe Scalping strategies: Identify short-term patterns with precisely defined time windows Volatility measurement: Track price movement characteristics within bars Custom signal generation: Create entry/exit signals based on custom timeframe patterns Conclusion The Real-Time Candles Library bridges the gap between traditional technical analysis (based on discrete OHLC bars) and the continuous nature of market movement. By making indicators more responsive to real-time price action, it gives traders a significant edge in timing and decision-making, particularly in fast-moving markets where waiting for bar close could mean missing important opportunities. Whether you're building custom indicators, researching price patterns, or developing trading strategies, this library provides the foundation for sophisticated real-time analysis in Pine Script. Implementation Details & Advanced Guide Core Implementation Concepts The Real-Time Candles Library implements a sophisticated event-driven architecture within Pine Script's constraints. At its heart, the library creates what's essentially a reactive programming framework handling continuous data streams. Tick Processing System The foundation of the library is the get_tick() function, which captures price updates as they occur: export get_tick(series float source = close, series float na_replace = na)=> varip float price = na varip int series_index = -1 varip int old_time = 0 varip int new_time = na varip float time_delta = 0 // ... This function: Samples the current price Calculates time elapsed since last update Maintains a sequential index to track updates The resulting TickData structure serves as the fundamental building block for all candle generation. State Management Architecture The library employs a sophisticated state management system using varip variables, which persist across executions within the same bar. This creates a hybrid programming paradigm that's different from standard Pine Script's bar-by-bar model. For chart-time candles, the core state transition logic is: // Real-time update of current candle candle_data := Candle.new(Open, High, Low, Close, polarity, series_index, candle_color) candles.set(0, candle_data) // When a new bar starts, preserve the previous candle if clear_state candles.insert(1, candle_data) price.clear() // Reset state for new candle Open := Close price.push(Open) series_index += 1 This pattern of updating index 0 in real-time while inserting completed candles at index 1 creates an elegant solution for maintaining both current state and historical data. Custom Timeframe Implementation The custom timeframe system manages its own time boundaries independent of chart bars: bool clear_state = switch settings.sample_type SampleType.Ticks => cumulative_series_idx >= settings.number_of_ticks SampleType.Time => cumulative_time_delta >= settings.number_of_seconds This dual-clock system synchronizes two time domains: Pine's execution clock (bar-by-bar processing) The custom timeframe clock (tick or time-based) The library carefully handles temporal discontinuities, ensuring candle formation remains accurate despite irregular tick arrival or market gaps. Advanced Usage Techniques 1. Creating Custom Indicators with Real-Time Candles To develop indicators that process real-time data within the current bar: // Get real-time candles for your data Candle rsi_candles = candle_array(ta.rsi(close, 14)) // Calculate indicator values based on candle properties float signal = ta.ema(rsi_candles.first().source(Source.Close), 9) // Detect patterns that occur within the bar bool divergence = close > close and rsi_candles.first().Close < rsi_candles.get(1).Close 2. Working with Custom Timeframes and Plotting For maximum flexibility when visualizing custom timeframe data: // Create custom timeframe candles CandleCTF volume_candles = ctf_candles_array( source = volume, candle_type = CandleType.candlestick, sample_type = SampleType.Time, number_of_seconds = 60 ) // Convert specific candle properties to float arrays float volume_closes = volume_candles.candle_ctf_to_float(Source.Close) // Calculate derived values float volume_ema = volume_candles.ctf_ema(14) // Create custom visualization volume_candles.draw_ctf_candles_time() volume_ema.draw_ctf_line_time(line_color = color.orange) 3. Creating Hybrid Timeframe Analysis One powerful application is comparing indicators across multiple timeframes: // Standard chart timeframe RSI float chart_rsi = ta.rsi(close, 14) // Custom 5-second timeframe RSI CandleCTF ctf_candles = ctf_candles_array( source = close, candle_type = CandleType.candlestick, sample_type = SampleType.Time, number_of_seconds = 5 ) float fast_rsi_array = ctf_candles.candle_ctf_to_float(Source.Close) float fast_rsi = fast_rsi_array.first() // Generate signals based on divergence between timeframes bool entry_signal = chart_rsi < 30 and fast_rsi > fast_rsi_array.get(1) Final Notes This library represents an advanced implementation of real-time data processing within Pine Script's constraints. By creating a reactive programming framework for handling continuous data streams, it enables sophisticated analysis typically only available in dedicated trading platforms. The design principles employed—including state management, temporal processing, and object-oriented architecture—can serve as patterns for other advanced Pine Script development beyond this specific application. ------------------------ Library "real_time_candles" A comprehensive library for creating real-time candles with customizable timeframes and sampling methods. Supports both chart-time and custom-time candles with options for candlestick and Heikin-Ashi visualization. Allows for tick-based or time-based sampling with moving average overlay capabilities. get_tick(source, na_replace)   Captures the current price as a tick data point   Parameters:      source (float) : Optional - Price source to sample (defaults to close)      na_replace (float) : Optional - Value to use when source is na   Returns: TickData structure containing price, time since last update, and sequential index candle_array(source, candle_type, sync_start, bullish_color, bearish_color)   Creates an array of candles based on price updates   Parameters:      source (float) : Optional - Price source to sample (defaults to close)      candle_type (simple CandleType) : Optional - Type of candle chart to create (candlestick or Heikin-Ashi)      sync_start (simple bool) : Optional - Whether to synchronize with the start of a new bar      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles   Returns: Array of Candle objects ordered with most recent at index 0 candle_series(source, candle_type, wait_for_sync, bullish_color, bearish_color)   Provides a single candle based on the latest price data   Parameters:      source (float) : Optional - Price source to sample (defaults to close)      candle_type (simple CandleType) : Optional - Type of candle chart to create (candlestick or Heikin-Ashi)      wait_for_sync (simple bool) : Optional - Whether to wait for a new bar before starting      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles   Returns: A single Candle object representing the current state candle_tuple(source, candle_type, wait_for_sync, bullish_color, bearish_color)   Provides candle data as a tuple of OHLC values   Parameters:      source (float) : Optional - Price source to sample (defaults to close)      candle_type (simple CandleType) : Optional - Type of candle chart to create (candlestick or Heikin-Ashi)      wait_for_sync (simple bool) : Optional - Whether to wait for a new bar before starting      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles   Returns: Tuple representing current candle values method source(self, source, na_replace)   Extracts a specific price component from a Candle   Namespace types: Candle   Parameters:      self (Candle)      source (series Source) : Type of price data to extract (Open, High, Low, Close, or composite values)      na_replace (float) : Optional - Value to use when source value is na   Returns: The requested price value from the candle method source(self, source)   Extracts a specific price component from a CandleCTF   Namespace types: CandleCTF   Parameters:      self (CandleCTF)      source (simple Source) : Type of price data to extract (Open, High, Low, Close, or composite values)   Returns: The requested price value from the candle as a varip method candle_ctf_to_float(self, source)   Converts a specific price component from each CandleCTF to a float array   Namespace types: array   Parameters:      self (array)      source (simple Source) : Optional - Type of price data to extract (defaults to Close)   Returns: Array of float values extracted from the candles, ordered with most recent at index 0 method ctf_ema(self, ema_period)   Calculates an Exponential Moving Average for a CandleCTF array   Namespace types: array   Parameters:      self (array)      ema_period (simple float) : Period for the EMA calculation   Returns: Array of float values representing the EMA of the candle data, ordered with most recent at index 0 method draw_ctf_candles_time(self, sample_type, number_of_ticks, number_of_seconds, timezone)   Renders custom timeframe candles using bar time coordinates   Namespace types: array   Parameters:      self (array)      sample_type (simple SampleType) : Optional - Method for sampling data (Time or Ticks), used for tooltips      number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks), used for tooltips      number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time), used for tooltips      timezone (simple int) : Optional - Timezone offset from UTC (-12 to +12), used for tooltips   Returns: void - Renders candles on the chart using time-based x-coordinates method draw_ctf_candles_index(self, sample_type, number_of_ticks, number_of_seconds, timezone)   Renders custom timeframe candles using bar index coordinates   Namespace types: array   Parameters:      self (array)      sample_type (simple SampleType) : Optional - Method for sampling data (Time or Ticks), used for tooltips      number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks), used for tooltips      number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time), used for tooltips      timezone (simple int) : Optional - Timezone offset from UTC (-12 to +12), used for tooltips   Returns: void - Renders candles on the chart using index-based x-coordinates method draw_ctf_line_time(self, source, line_size, line_color)   Renders a line representing a price component from the candles using time coordinates   Namespace types: array   Parameters:      self (array)      source (simple Source) : Optional - Type of price data to extract (defaults to Close)      line_size (simple int) : Optional - Width of the line      line_color (simple color) : Optional - Color of the line   Returns: void - Renders a connected line on the chart using time-based x-coordinates method draw_ctf_line_time(self, line_size, line_color)   Renders a line from a varip float array using time coordinates   Namespace types: array   Parameters:      self (array)      line_size (simple int) : Optional - Width of the line, defaults to 2      line_color (simple color) : Optional - Color of the line   Returns: void - Renders a connected line on the chart using time-based x-coordinates method draw_ctf_line_index(self, source, line_size, line_color)   Renders a line representing a price component from the candles using index coordinates   Namespace types: array   Parameters:      self (array)      source (simple Source) : Optional - Type of price data to extract (defaults to Close)      line_size (simple int) : Optional - Width of the line      line_color (simple color) : Optional - Color of the line   Returns: void - Renders a connected line on the chart using index-based x-coordinates method draw_ctf_line_index(self, line_size, line_color)   Renders a line from a varip float array using index coordinates   Namespace types: array   Parameters:      self (array)      line_size (simple int) : Optional - Width of the line, defaults to 2      line_color (simple color) : Optional - Color of the line   Returns: void - Renders a connected line on the chart using index-based x-coordinates plot_ctf_tick_candles(source, candle_type, number_of_ticks, timezone, tied_open, ema_period, bullish_color, bearish_color, line_width, ema_color, use_time_indexing)   Plots tick-based candles with moving average   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Type of candle chart to display      number_of_ticks (simple int) : Number of ticks per candle      timezone (simple int) : Timezone offset from UTC (-12 to +12)      tied_open (simple bool) : Whether to tie open price to close of previous candle      ema_period (simple float) : Period for the exponential moving average      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles      line_width (simple int) : Optional - Width of the moving average line, defaults to 2      ema_color (color) : Optional - Color of the moving average line      use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index   Returns: void - Creates visual candle chart with EMA overlay plot_ctf_tick_candles(source, candle_type, number_of_ticks, timezone, tied_open, bullish_color, bearish_color, use_time_indexing)   Plots tick-based candles without moving average   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Type of candle chart to display      number_of_ticks (simple int) : Number of ticks per candle      timezone (simple int) : Timezone offset from UTC (-12 to +12)      tied_open (simple bool) : Whether to tie open price to close of previous candle      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles      use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index   Returns: void - Creates visual candle chart without moving average plot_ctf_time_candles(source, candle_type, number_of_seconds, timezone, tied_open, ema_period, bullish_color, bearish_color, line_width, ema_color, use_time_indexing)   Plots time-based candles with moving average   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Type of candle chart to display      number_of_seconds (simple float) : Time duration per candle in seconds      timezone (simple int) : Timezone offset from UTC (-12 to +12)      tied_open (simple bool) : Whether to tie open price to close of previous candle      ema_period (simple float) : Period for the exponential moving average      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles      line_width (simple int) : Optional - Width of the moving average line, defaults to 2      ema_color (color) : Optional - Color of the moving average line      use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index   Returns: void - Creates visual candle chart with EMA overlay plot_ctf_time_candles(source, candle_type, number_of_seconds, timezone, tied_open, bullish_color, bearish_color, use_time_indexing)   Plots time-based candles without moving average   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Type of candle chart to display      number_of_seconds (simple float) : Time duration per candle in seconds      timezone (simple int) : Timezone offset from UTC (-12 to +12)      tied_open (simple bool) : Whether to tie open price to close of previous candle      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles      use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index   Returns: void - Creates visual candle chart without moving average plot_ctf_candles(source, candle_type, sample_type, number_of_ticks, number_of_seconds, timezone, tied_open, ema_period, bullish_color, bearish_color, enable_ema, line_width, ema_color, use_time_indexing)   Unified function for plotting candles with comprehensive options   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Optional - Type of candle chart to display      sample_type (simple SampleType) : Optional - Method for sampling data (Time or Ticks)      number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks)      number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time)      timezone (simple int) : Optional - Timezone offset from UTC (-12 to +12)      tied_open (simple bool) : Optional - Whether to tie open price to close of previous candle      ema_period (simple float) : Optional - Period for the exponential moving average      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles      enable_ema (bool) : Optional - Whether to display the EMA overlay      line_width (simple int) : Optional - Width of the moving average line, defaults to 2      ema_color (color) : Optional - Color of the moving average line      use_time_indexing (simple bool) : Optional - When true the function will plot with xloc.time, when false it will plot using xloc.bar_index   Returns: void - Creates visual candle chart with optional EMA overlay ctf_candles_array(source, candle_type, sample_type, number_of_ticks, number_of_seconds, tied_open, bullish_color, bearish_color)   Creates an array of custom timeframe candles without rendering them   Parameters:      source (float) : Input price source to sample      candle_type (simple CandleType) : Type of candle chart to create (candlestick or Heikin-Ashi)      sample_type (simple SampleType) : Method for sampling data (Time or Ticks)      number_of_ticks (simple int) : Optional - Number of ticks per candle (used when sample_type is Ticks)      number_of_seconds (simple float) : Optional - Time duration per candle in seconds (used when sample_type is Time)      tied_open (simple bool) : Optional - Whether to tie open price to close of previous candle      bullish_color (color) : Optional - Color for bullish candles      bearish_color (color) : Optional - Color for bearish candles   Returns: Array of CandleCTF objects ordered with most recent at index 0 Candle   Structure representing a complete candle with price data and display properties   Fields:      Open (series float) : Opening price of the candle      High (series float) : Highest price of the candle      Low (series float) : Lowest price of the candle      Close (series float) : Closing price of the candle      polarity (series bool) : Boolean indicating if candle is bullish (true) or bearish (false)      series_index (series int) : Sequential index identifying the candle in the series      candle_color (series color) : Color to use when rendering the candle      ready (series bool) : Boolean indicating if candle data is valid and ready for use TickData   Structure for storing individual price updates   Fields:      price (series float) : The price value at this tick      time_delta (series float) : Time elapsed since the previous tick in milliseconds      series_index (series int) : Sequential index identifying this tick CandleCTF   Structure representing a custom timeframe candle with additional time metadata   Fields:      Open (series float) : Opening price of the candle      High (series float) : Highest price of the candle      Low (series float) : Lowest price of the candle      Close (series float) : Closing price of the candle      polarity (series bool) : Boolean indicating if candle is bullish (true) or bearish (false)      series_index (series int) : Sequential index identifying the candle in the series      open_time (series int) : Timestamp marking when the candle was opened (in Unix time)      time_delta (series float) : Duration of the candle in milliseconds      candle_color (series color) : Color to use when rendering the candle
Libreria Pine Script®
di The_Peaceful_Lizard
30
Smart Money Index + True Strength IndexThe Smart Money Index + True Strength Index indicator is a combination of two popular technical analysis indicators: the Smart Money Index (SMI) and the True Strength Index (TSI). This combined indicator helps traders identify potential entry points for long and short positions based on signals from both indexes. Main Components: Smart Money Index (SMI): The SMI measures the difference between the closing and opening price of a candle multiplied by the trading volume over a certain period of time. This allows you to assess the activity of large players ("smart money") in the market. If the SMI value is above a certain threshold (smiThreshold), it may indicate a bullish trend, and if lower, it may indicate a bearish trend. True Strength Index (TSI): The TSI is an oscillator that measures the strength of a trend by comparing the price change of the current bar with the previous bar. It uses two exponential moving averages (EMAS) to smooth the data. TSI values can fluctuate around zero, with values above the overbought level indicating a possible downward correction, and values below the oversold level signaling a possible upward correction. Parameters: SMI Length: Defines the number of candles used to calculate the average SMI value. The default value is 14. SMI Threshold: A threshold value that is used to determine a buy or sell signal. The default value is 0. Length of the first TSI smoothing (tsiLength1): The length of the first EMA for calculating TSI. The default value is 25. Second TSI smoothing length (tsiLength2): The length of the second EMA for additional smoothing of TSI values. The default value is 13. TSI Overbought level: The level at which the market is considered to be overbought. The default value is 25. Oversold level TSI: The level at which it is considered that the market is in an oversold state. The default value is -25. Logic of operation: SMI calculation: First, the difference between the closing and opening price of each candle (close - open) is calculated. This difference is then multiplied by the trading volume. The resulting product is averaged using a simple moving average (SMA) over a specified period (smiLength). Calculation of TSI: The price change relative to the previous bar is calculated (close - close ). The first EMA with the length tsiLength1 is applied. Next, a second EMA with a length of tsiLength2 is applied to obtain the final TSI value. The absolute value of price changes is calculated in the same way, and two emas are also applied. The final TSI index is calculated as the ratio of these two values multiplied by 100. Graphical representation: The SMI and TSI lines are plotted on the graph along with their respective thresholds. For SMI, the line is drawn in orange, and the threshold level is dotted in gray. For the TSI, the line is plotted in blue, the overbought and oversold levels are indicated by red and green dotted lines, respectively. Conditions for buy/sell signals: A buy (long) signal is generated when: SMI is greater than the threshold (smi > smiThreshold) TSI crosses the oversold level from bottom to top (ta.crossover(tsi, oversold)). A sell (short) signal is generated when: SMI is less than the threshold (smi < smiThreshold) TSI crosses the overbought level from top to bottom (ta.crossunder(tsi, overbought)). Signal display: When the conditions for a long or short are met, labels labeled "LONG" or "SHORT" appear on the chart. The label for the long is located under the candle and is colored green, and for the short it is above the candle and is colored red. Notification generation: The indicator also supports notifications via the TradingView platform. Notifications are sent when conditions arise for a long or short position. This combined indicator provides the trader with the opportunity to use both SMI and TSI signals simultaneously, which can improve the accuracy of trading decisions.
Indicatore Pine Script®
di Zdorman
2
RoGr75 Adaptive EMA CrossDescription: The RoGr75 Adaptive EMA Cross indicator dynamically combines exponential moving averages (EMAs) with ATR-based volatility buffers to generate buy and sell signals across multiple timeframes. This script uses customizable settings for short and long EMAs, ATR, and volume filters, ensuring that signals are both volatility-adjusted and timeframe-aware. It includes features such as adaptive buffers, distinct price level filters for buying and selling, and a reset mechanism to prevent redundant signals. Additionally, the indicator manages signal labels efficiently to keep your chart uncluttered. Warning: This script is provided for testing and educational purposes only. It is not intended as financial advice, and past performance does not guarantee future results. Use at your own risk. Always conduct your own research before making any trading decisions. Higher Timeframe Input: Choose a specific timeframe for the indicator’s calculations; leave blank to use the chart’s current timeframe. Signal Distance: Sets the distance of signal labels from the candles as a multiple of the ATR. Exact Value Offset: Adjusts the secondary marker’s position for precision on the chart. ATR Length: Defines the period used to calculate the Average True Range for volatility measurement. EMA Lengths: Specify the periods for the short and long exponential moving averages. Buy/Sell Buffer ATR Multipliers: Dynamically adjust the trigger distance beyond the EMA crossovers based on volatility. Price Level Filter: Activates a filter so that buy signals only occur above a set price and sell signals only occur below that price (0 disables the filter). Volume Filter: Optionally requires current volume to exceed a set multiple of a 20-period average for signal confirmation. Reset Period: Resets the last signal memory after a specified number of bars to avoid suppressing valid repeat signals. EMA Colors & Line Width: Customize the appearance of the short and long EMAs. Label Colors & Styles: Choose colors, text colors, and styles for the buy and sell signal labels. Background Highlighting: Optionally colors the background when a buy or sell signal occurs. Label Management: Automatically removes the oldest labels when a set maximum is reached to keep the chart clean. Alerts: Predefined conditions allow you to set TradingView alerts when buy or sell signals are generated. Warning: This indicator is for testing purposes only and is not financial advice. Use it at your own risk.
Indicatore Pine Script®
di Vallentuna2
DT Bollinger BandsIndicator Overview Purpose: The script calculates and plots Bollinger Bands, a technical analysis tool that shows price volatility by plotting: A central moving average (basis line). Upper and lower bands representing price deviation from the moving average. Additional bands for a higher deviation threshold (3 standard deviations). Customization: Users can customize: The length of the moving average. The type of moving average (e.g., SMA, EMA). The price source (e.g., close price). Standard deviation multipliers for the bands. Fixed Time Frame: The script can use a fixed time frame (e.g., daily) for calculations, regardless of the chart's time frame. Key Features Moving Average Selection: The user can select the type of moving average for the basis line: Simple Moving Average (SMA) Exponential Moving Average (EMA) Smoothed Moving Average (SMMA/RMA) Weighted Moving Average (WMA) Volume Weighted Moving Average (VWMA) Standard Deviation Multipliers: Two multipliers are used: Standard (default = 2.0): For the original Bollinger Bands. Larger (default = 3.0): For additional bands. Bands Calculation: Basis Line: The selected moving average. Upper Band: Basis + Standard Deviation. Lower Band: Basis - Standard Deviation. Additional Bands: Representing ±3 Standard Deviations. Plots: Plots the basis, upper, and lower bands. Fills the area between the bands for visual clarity. Plots and fills additional bands for ±3 Standard Deviations with lighter colors. Alerts: Generates an alert when the price enters the range between the 2nd and 3rd standard deviation bands. The alert can be used to notify when price volatility increases significantly. Background Highlighting: Colors the chart background based on alert conditions: Green if the price is above the basis line. Red if the price is below the basis line. Offset: Adds an optional horizontal offset to the plots for fine-tuning their alignment. How It Works Input Parameters: The user specifies settings such as moving average type, length, multipliers, and fixed time frame. Calculations: The script computes the basis (moving average) and standard deviations on the fixed time frame. Bands are calculated using the basis and multipliers. Plotting: The basis line and upper/lower bands are plotted with distinct colors. Additional 3 StdDev bands are plotted with lighter colors. Alerts: An alert condition is created when the price moves between the 2nd and 3rd standard deviation bands. Visual Enhancements: Chart background changes color dynamically based on the price’s position relative to the basis line and alert conditions. Usage This script is useful for traders who: Want a detailed visualization of price volatility. Use Bollinger Bands to identify breakout or mean-reversion trading opportunities. Need alerts when the price enters specific volatility thresholds.
Indicatore Pine Script®
di azhafpv
1
Hybrid Adaptive Double Exponential Smoothing🙏🏻 This is HADES (Hybrid Adaptive Double Exponential Smoothing) : fully data-driven & adaptive exponential smoothing method, that gains all the necessary info directly from data in the most natural way and needs no subjective parameters & no optimizations. It gets applied to data itself -> to fit residuals & one-point forecast errors, all at O(1) algo complexity. I designed it for streaming high-frequency univariate time series data, such as medical sensor readings, orderbook data, tick charts, requests generated by a backend, etc. The HADES method is: fit & forecast = a + b * (1 / alpha + T - 1) T = 0 provides in-sample fit for the current datum, and T + n provides forecast for n datapoints. y = input time series a = y, if no previous data exists b = 0, if no previous data exists otherwise: a = alpha * y + (1 - alpha) * a b = alpha * (a - a ) + (1 - alpha) * b alpha = 1 / sqrt(len * 4) len = min(ceil(exp(1 / sig)), available data) sig = sqrt(Absolute net change in y / Sum of absolute changes in y) For the start datapoint when both numerator and denominator are zeros, we define 0 / 0 = 1 ... The same set of operations gets applied to the data first, then to resulting fit absolute residuals to build prediction interval, and finally to absolute forecasting errors (from one-point ahead forecast) to build forecasting interval: prediction interval = data fit +- resoduals fit * k forecasting interval = data opf +- errors fit * k where k = multiplier regulating intervals width, and opf = one-point forecasts calculated at each time t ... How-to: 0) Apply to your data where it makes sense, eg. tick data; 1) Use power transform to compensate for multiplicative behavior in case it's there; 2) If you have complete data or only the data you need, like the full history of adjusted close prices: go to the next step; otherwise, guided by your goal & analysis, adjust the 'start index' setting so the calculations will start from this point; 3) Use prediction interval to detect significant deviations from the process core & make decisions according to your strategy; 4) Use one-point forecast for nowcasting; 5) Use forecasting intervals to ~ understand where the next datapoints will emerge, given the data-generating process will stay the same & lack structural breaks. I advise k = 1 or 1.5 or 4 depending on your goal, but 1 is the most natural one. ... Why exponential smoothing at all? Why the double one? Why adaptive? Why not Holt's method? 1) It's O(1) algo complexity & recursive nature allows it to be applied in an online fashion to high-frequency streaming data; otherwise, it makes more sense to use other methods; 2) Double exponential smoothing ensures we are taking trends into account; also, in order to model more complex time series patterns such as seasonality, we need detrended data, and this method can be used to do it; 3) The goal of adaptivity is to eliminate the window size question, in cases where it doesn't make sense to use cumulative moving typical value; 4) Holt's method creates a certain interaction between level and trend components, so its results lack symmetry and similarity with other non-recursive methods such as quantile regression or linear regression. Instead, I decided to base my work on the original double exponential smoothing method published by Rob Brown in 1956, here's the original source , it's really hard to find it online. This cool dude is considered the one who've dropped exponential smoothing to open access for the first time🤘🏻 R&D; log & explanations If you wanna read this, you gotta know, you're taking a great responsability for this long journey, and it gonna be one hell of a trip hehe Machine learning, apprentissage automatique, машинное обучение, digital signal processing, statistical learning, data mining, deep learning, etc., etc., etc.: all these are just artificial categories created by the local population of this wonderful world, but what really separates entities globally in the Universe is solution complexity / algorithmic complexity. In order to get the game a lil better, it's gonna be useful to read the HTES script description first. Secondly, let me guide you through the whole R&D; process. To discover (not to invent) the fundamental universal principle of what exponential smoothing really IS, it required the review of the whole concept, understanding that many things don't add up and don't make much sense in currently available mainstream info, and building it all from the beginning while avoiding these very basic logical & implementation flaws. Given a complete time t, and yet, always growing time series population that can't be logically separated into subpopulations, the very first question is, 'What amount of data do we need to utilize at time t?'. Two answers: 1 and all. You can't really gain much info from 1 datum, so go for the second answer: we need the whole dataset. So, given the sequential & incremental nature of time series, the very first and basic thing we can do on the whole dataset is to calculate a cumulative , such as cumulative moving mean or cumulative moving median. Now we need to extend this logic to exponential smoothing, which doesn't use dataset length info directly, but all cool it can be done via a formula that quantifies the relationship between alpha (smoothing parameter) and length. The popular formulas used in mainstream are: alpha = 1 / length alpha = 2 / (length + 1) The funny part starts when you realize that Cumulative Exponential Moving Averages with these 2 alpha formulas Exactly match Cumulative Moving Average and Cumulative (Linearly) Weighted Moving Average, and the same logic goes on: alpha = 3 / (length + 1.5) , matches Cumulative Weighted Moving Average with quadratic weights, and alpha = 4 / (length + 2) , matches Cumulative Weighted Moving Average with cubic weghts, and so on... It all just cries in your shoulder that we need to discover another, native length->alpha formula that leverages the recursive nature of exponential smoothing, because otherwise, it doesn't make sense to use it at all, since the usual CMA and CMWA can be computed incrementally at O(1) algo complexity just as exponential smoothing. From now on I will not mention 'cumulative' or 'linearly weighted / weighted' anymore, it's gonna be implied all the time unless stated otherwise. What we can do is to approach the thing logically and model the response with a little help from synthetic data, a sine wave would suffice. Then we can think of relationships: Based on algo complexity from lower to higher, we have this sequence: exponential smoothing @ O(1) -> parametric statistics (mean) @ O(n) -> non-parametric statistics (50th percentile / median) @ O(n log n). Based on Initial response from slow to fast: mean -> median Based on convergence with the real expected value from slow to fast: mean (infinitely approaches it) -> median (gets it quite fast). Based on these inputs, we need to discover such a length->alpha formula so the resulting fit will have the slowest initial response out of all 3, and have the slowest convergence with expected value out of all 3. In order to do it, we need to have some non-linear transformer in our formula (like a square root) and a couple of factors to modify the response the way we need. I ended up with this formula to meet all our requirements: alpha = sqrt(1 / length * 2) / 2 which simplifies to: alpha = 1 / sqrt(len * 8) ^^ as you can see on the screenshot; where the red line is median, the blue line is the mean, and the purple line is exponential smoothing with the formulas you've just seen, we've met all the requirements. Now we just have to do the same procedure to discover the length->alpha formula but for double exponential smoothing, which models trends as well, not just level as in single exponential smoothing. For this comparison, we need to use linear regression and quantile regression instead of the mean and median. Quantile regression requires a non-closed form solution to be solved that you can't really implement in Pine Script, but that's ok, so I made the tests using Python & sklearn: paste.pics ^^ on this screenshot, you can see the same relationship as on the previous screenshot, but now between the responses of quantile regression & linear regression. I followed the same logic as before for designing alpha for double exponential smoothing (also considered the initial overshoots, but that's a little detail), and ended up with this formula: alpha = sqrt(1 / length) / 2 which simplifies to: alpha = 1 / sqrt(len * 4) Btw, given the pattern you see in the resulting formulas for single and double exponential smoothing, if you ever want to do triple (not Holt & Winters) exponential smoothing, you'll need len * 2 , and just len * 1 for quadruple exponential smoothing. I hope that based on this sequence, you see the hint that Maybe 4 rounds is enough. Now since we've dealt with the length->alpha formula, we can deal with the adaptivity part. Logically, it doesn't make sense to use a slower-than-O(1) method to generate input for an O(1) method, so it must be something universal and minimalistic: something that will help us measure consistency in our data, yet something far away from statistics and close enough to topology. There's one perfect entity that can help us, this is fractal efficiency. The way I define fractal efficiency can be checked at the very beginning of the post, what matters is that I add a square root to the formula that is not typically added. As explained in the description of my metric QSFS , one of the reasons for SQRT-transformed values of fractal efficiency applied in moving window mode is because they start to closely resemble normal distribution, yet with support of (0, 1). Data with this interesting property (normally distributed yet with finite support) can be modeled with the beta distribution. Another reason is, in infinitely expanding window mode, fractal efficiency of every time series that exhibits randomness tends to infinitely approach zero, sqrt-transform kind of partially neutralizes this effect. Yet another reason is, the square root might better reflect the dimensional inefficiency or degree of fractal complexity, since it could balance the influence of extreme deviations from the net paths. And finally, fractals exhibit power-law scaling -> measures like length, area, or volume scale in a non-linear way. Adding a square root acknowledges this intrinsic property, while connecting our metric with the nature of fractals. --- I suspect that, given analogies and connections with other topics in geometry, topology, fractals and most importantly positive test results of the metric, it might be that the sqrt transform is the fundamental part of fractal efficiency that should be applied by default. Now the last part of the ballet is to convert our fractal efficiency to length value. The part about inverse proportionality is obvious: high fractal efficiency aka high consistency -> lower window size, to utilize only the last data that contain brand new information that seems to be highly reliable since we have consistency in the first place. The non-obvious part is now we need to neutralize the side effect created by previous sqrt transform: our length values are too low, and exponentiation is the perfect candidate to fix it since translating fractal efficiency into window sizes requires something non-linear to reflect the fractal dynamics. More importantly, using exp() was the last piece that let the metric shine, any other transformations & formulas alike I've tried always had some weird results on certain data. That exp() in the len formula was the last piece that made it all work both on synthetic and on real data. ^^ a standalone script calculating optimal dynamic window size Omg, THAT took time to write. Comment and/or text me if you need ... "Versace Pip-Boy, I'm a young gun coming up with no bankroll" 👻 ∞
Indicatore Pine Script®
di gorx1
4
Elite By Ashu4750Inside Bar Detection: The script identifies inside bars, which are candles where the high is lower and the low is higher than the previous bar. It tracks the high and low of the mother candle (the candle preceding the inside bars) and plots the ranges on the chart using lines and labels. Exponential Moving Averages (EMA): Three EMAs are calculated and plotted (with default periods of 9, 21, and 50). This is a classic trend-following technique used to smooth price data and identify the direction of the market. Bollinger Bands (BB): The script includes a Bollinger Band calculation using the simple moving average (SMA) with a standard deviation multiplier. The bands help visualize volatility and potential overbought or oversold conditions. The user can configure settings like the length of the SMA and the multiplier for the upper and lower bands. Volume Weighted Average Price (VWAP): The VWAP is plotted on the chart and reset based on user-defined timeframes (e.g., session, week, month). VWAP is a popular indicator for institutional trading, as it shows the average price weighted by volume and can act as support or resistance. Crossover Signals (Buy/Sell): A combination of crossovers between VWAP, EMAs, and Bollinger Bands triggers buy and sell signals. Specifically: Buy signal is generated when VWAP crosses over the 9 EMA, the close crosses over the Bollinger Band line, and VWAP crosses over the Bollinger Band. Sell signal is triggered when VWAP crosses under the 9 EMA, and similar conditions exist for the other indicators. These signals are plotted with a green "Buy" or red "Sell" marker below the bars, and alerts are set up for both buying and selling. Additional Bollinger Band Configuration: The script provides more flexibility in Bollinger Bands by allowing the user to select between SMA, EMA, or SMMA for the moving average. The user can also choose the standard deviation multiplier and whether to display the bands. Alerts: Buy and sell conditions are linked to alert conditions, allowing the user to be notified when a signal is triggered, based on the defined crossover logic. Technical Breakdown: Inside Bar Logic: Tracks inside bars and plots lines representing the high and low of the mother candle. The line and label functions are used to draw these on the chart, which provides a visual representation of the range. EMA and VWAP Crossovers: The 9, 21, and 50-period EMAs are calculated and used in crossover logic with VWAP. Crossovers between VWAP and EMAs are a common method for identifying potential trend changes. Bollinger Bands: The Bollinger Band component allows for volatility analysis by calculating the upper and lower bands based on the moving average's standard deviation. Alert System: Alerts are set for crossover signals, allowing for real-time notifications of potential buy and sell opportunities. Visualization: The script plots the EMAs, VWAP, and Bollinger Bands on the price chart. It highlights inside bar patterns and displays buy/sell markers on the chart when the specified conditions are met. These visual cues make it easier to follow the market’s movements and spot trading opportunities. Customizability: The script is highly customizable with inputs for: EMA periods. VWAP settings. Bollinger Band parameters (moving average type, length, standard deviation). Candle color options for inside bars. In this traders looking for multiple indicators to analyze market trends, volatility, and price action.
Indicatore Pine Script®
di Ashu4750
1
MCDX+RSI+SMA[THANHCONG]### Detailed Analysis of the MCDX+RSI+SMA Indicator The MCDX+RSI+SMA indicator is designed to help investors conduct a deeper analysis of market trends by combining multiple technical factors into a single chart. This integration of popular indicators such as RSI, SMA, and Stochastic RSI provides investors with a comprehensive view of market movements, particularly in distinguishing between "Banker" and "Hot Money"—representing large and small capital flows. #### Key Components of the Indicator: 1. **RSI for Banker and Hot Money:** - **RSI (Relative Strength Index)** is a momentum oscillator that measures the speed and change of price movements, indicating overbought or oversold conditions. In this indicator, there are two distinct RSI lines configured for Banker (large capital) and Hot Money (small capital). - Investors can adjust parameters like the RSI calculation period, baseline levels, and sensitivity for each type of capital flow, providing flexibility to adapt to varying market conditions. 2. **Moving Average (MA) of RSI:** - The indicator employs two common types of Moving Averages: **SMA (Simple Moving Average)** and **EMA (Exponential Moving Average)**. These help smooth the RSI signals for Banker, offering a clearer view of the long-term trend of large capital in the market. - Investors can select the type and period of the MA, allowing them to optimize the indicator for their trading style. 3. **Stochastic RSI:** - The **Stochastic RSI** is incorporated to monitor overbought and oversold conditions over a specified timeframe. Parameters related to %K and %D of the Stochastic can also be adjusted to refine the accuracy of market signal analysis. - A notable feature is the normalization of %K and %D on a 0-20 scale, making these lines compatible with other RSI charts, thus providing consistency in evaluating market strength. 4. **Overbought and Oversold Levels:** - The indicator includes reference lines for overbought and oversold levels, aiding investors in identifying potential reversal zones in the market. This helps to avoid buying at excessively high prices or selling at excessively low prices. #### Benefits for Investors: - **Comprehensive View:** The indicator combines insights from both large (Banker) and small (Hot Money) capital flows, enabling investors to analyze not just trends but also the participation of each type of capital in the market. - **Enhanced Technical Analysis:** By integrating multiple technical indicators within a single chart, investors can track important factors such as market momentum, overbought/oversold conditions, and capital flow shifts without needing to switch between various charts. - **Flexibility and Customization:** The indicator allows adjustment of key parameters like the RSI period, sensitivity, type of MA, and Stochastic RSI settings, enabling investors to tailor the indicator to their trading strategy and timeframe. - **Higher Reliability:** The combination of indicators like RSI, Stochastic RSI, and MA helps investors confirm trading signals more confidently. For instance, when both RSI and Stochastic RSI indicate overbought conditions, the likelihood of a reversal may be higher, reducing risk for investors. #### Unique Features of the Indicator: The MCDX+RSI+SMA indicator is a unique tool that integrates various market analysis factors into a single framework. This not only provides investors with a complete view of capital flows but also aids in optimizing decision-making based on multiple market aspects. Furthermore, its customizable parameters make it suitable for various trading strategies, from short-term to long-term.
Indicatore Pine Script®
di CongTrader_
5
Support and resistance levels (Day, Week, Month) + EMAs + SMAs(ENG): This Pine 5 script provides various tools for configuring and displaying different support and resistance levels, as well as moving averages (EMA and SMA) on charts. Using these tools is an essential strategy for determining entry and exit points in trades. Support and Resistance Levels Daily, weekly, and monthly support and resistance levels play a key role in analyzing price movements: Daily levels: Represent prices where a cryptocurrency has tended to bounce within the current trading day. Weekly levels: Reflect strong prices that hold throughout the week. Monthly levels: Indicate the most significant levels that can influence price movement over the month. When trading cryptocurrencies, traders use these levels to make decisions about entering or exiting positions. For example, if a cryptocurrency approaches a weekly resistance level and fails to break through it, this may signal a sell opportunity. If the price reaches a daily support level and starts to bounce up, it may indicate a potential long position. Market context and trading volumes are also important when analyzing support and resistance levels. High volume near a level can confirm its significance and the likelihood of subsequent price movement. Traders often combine analysis across different time frames to get a more complete picture and improve the accuracy of their trading decisions. Moving Averages Moving averages (EMA and SMA) are another important tool in the technical analysis of cryptocurrencies: EMA (Exponential Moving Average): Gives more weight to recent prices, allowing it to respond more quickly to price changes. SMA (Simple Moving Average): Equally considers all prices over a given period. Key types of moving averages used by traders: EMA 50 and 200: Often used to identify trends. The crossing of the 50-day EMA with the 200-day EMA is called a "golden cross" (buy signal) or a "death cross" (sell signal). SMA 50, 100, 150, and 200: These periods are often used to determine long-term trends and support/resistance levels. Similar to the EMA, the crossings of these averages can signal potential trend changes. Settings Groups: EMA Golden Cross & Death Cross: A setting to display the "golden cross" and "death cross" for the EMA. EMA 50 & 200: A setting to display the 50-day and 200-day EMA. Support and Resistance Levels: Includes settings for daily, weekly, and monthly levels. SMA 50, 100, 150, 200: A setting to display the 50, 100, 150, and 200-day SMA. SMA Golden Cross & Death Cross: A setting to display the "golden cross" and "death cross" for the SMA. Components: Enable/disable the display of support and resistance levels. Show level labels. Parameters for adjusting offset, display of EMA and SMA, and their time intervals. Parameters for configuring EMA and SMA Golden Cross & Death Cross. EMA Parameters: Enable/disable the display of 50 and 200-day EMA. Color and style settings for EMA. Options to use bar gaps and the "LookAhead" function. SMA Parameters: Enable/disable the display of 50, 100, 150, and 200-day SMA. Color and style settings for SMA. Options to use bar gaps and the "LookAhead" function. Effective use of support and resistance levels, as well as moving averages, requires an understanding of technical analysis, discipline, and the ability to adapt the strategy according to changing market conditions. (RUS) Данный Pine 5 скрипт предоставляет разнообразные инструменты для настройки и отображения различных уровней поддержки и сопротивления, а также скользящих средних (EMA и SMA) на графиках. Использование этих инструментов является важной стратегией для определения точек входа и выхода из сделок. Уровни поддержки и сопротивления Дневные, недельные и месячные уровни поддержки и сопротивления играют ключевую роль в анализе движения цен: Дневные уровни: Представляют собой цены, на которых криптовалюта имела тенденцию отскакивать в течение текущего торгового дня. Недельные уровни: Отражают сильные цены, которые сохраняются в течение недели. Месячные уровни: Указывают на наиболее значимые уровни, которые могут влиять на движение цены в течение месяца. При торговле криптовалютами трейдеры используют эти уровни для принятия решений о входе в позицию или закрытии сделки. Например, если криптовалюта приближается к недельному уровню сопротивления и не удается его преодолеть, это может стать сигналом для продажи. Если цена достигает дневного уровня поддержки и начинает отскакивать вверх, это может указывать на возможность открытия длинной позиции. Контекст рынка и объемы торговли также важны при анализе уровней поддержки и сопротивления. Высокий объем при приближении к уровню может подтвердить его значимость и вероятность последующего движения цены. Трейдеры часто комбинируют анализ различных временных рамок для получения более полной картины и улучшения точности своих торговых решений. Скользящие средние Скользящие средние (EMA и SMA) являются еще одним важным инструментом в техническом анализе криптовалют: EMA (Exponential Moving Average): Экспоненциальная скользящая средняя, которая придает большее значение последним ценам. Это позволяет более быстро реагировать на изменения в ценах. SMA (Simple Moving Average): Простая скользящая средняя, которая равномерно учитывает все цены в заданном периоде. Основные виды скользящих средних, которые используются трейдерами: EMA 50 и 200: Часто используются для выявления трендов. Пересечение 50-дневной EMA с 200-дневной EMA называется "золотым крестом" (сигнал на покупку) или "крестом смерти" (сигнал на продажу). SMA 50, 100, 150 и 200: Эти периоды часто используются для определения долгосрочных трендов и уровней поддержки/сопротивления. Аналогично EMA, пересечения этих средних могут сигнализировать о возможных изменениях тренда. Группы настроек: EMA Golden Cross & Death Cross: Настройка для отображения "золотого креста" и "креста смерти" для EMA. EMA 50 & 200: Настройка для отображения 50-дневной и 200-дневной EMA. Уровни поддержки и сопротивления: Включает настройки для дневных, недельных и месячных уровней. SMA 50, 100, 150, 200: Настройка для отображения 50, 100, 150 и 200-дневных SMA. SMA Golden Cross & Death Cross: Настройка для отображения "золотого креста" и "креста смерти" для SMA. Компоненты: Включение/отключение отображения уровней поддержки и сопротивления. Показ ярлыков уровней. Параметры для настройки смещения, отображения EMA и SMA, а также их временных интервалов. Параметры для настройки EMA и SMA Golden Cross & Death Cross. Параметры EMA: Включение/отключение отображения 50 и 200-дневных EMA. Настройки цвета и стиля для EMA. Опции для использования разрыва баров и функции "LookAhead". Параметры SMA: Включение/отключение отображения 50, 100, 150 и 200-дневных SMA. Настройки цвета и стиля для SMA. Опции для использования разрыва баров и функции "LookAhead". Эффективное использование уровней поддержки и сопротивления, а также скользящих средних, требует понимания технического анализа, дисциплины и умения адаптировать стратегию в зависимости от изменяющихся условий рынка.
Indicatore Pine Script®
di expert-x
7
Strong Support and Resistance with EMAs @viniciushadek ### Strategy for Using Continuity Points with 20 and 9 Period Exponential Moving Averages, and Support and Resistance This strategy involves using two exponential moving averages (EMA) - one with a 20-period and another with a 9-period - along with identifying support and resistance levels on the chart. Combining these tools can help determine trend continuation points and potential entry and exit points in market operations. ### 1. Setting Up the Exponential Moving Averages - **20-Period EMA**: This moving average provides a medium-term trend view. It helps smooth out price fluctuations and identify the overall market direction. - **9-Period EMA**: This moving average is more sensitive and reacts more quickly to price changes, providing short-term signals. ### 2. Identifying Support and Resistance - **Support**: Price levels where demand is strong enough to prevent the price from falling further. These levels are identified based on previous lows. - **Resistance**: Price levels where supply is strong enough to prevent the price from rising further. These levels are identified based on previous highs. ### 3. Continuity Points The strategy focuses on identifying trend continuation points using the interaction between the EMAs and the support and resistance levels. ### 4. Buy Signals - When the 9-period EMA crosses above the 20-period EMA. - Confirm the entry if the price is near a support level or breaking through a resistance level. ### 5. Sell Signals - When the 9-period EMA crosses below the 20-period EMA. - Confirm the exit if the price is near a resistance level or breaking through a support level. ### 6. Risk Management - Use appropriate stops below identified supports for buy operations. - Use appropriate stops above identified resistances for sell operations. ### 7. Validating the Trend - Check if the trend is validated by other technical indicators, such as the Relative Strength Index (RSI) or Volume. ### Conclusion This strategy uses the combination of exponential moving averages and support and resistance levels to identify continuity points in the market trend. It is crucial to confirm the signals with other technical analysis tools and maintain proper risk management to maximize results and minimize losses. Implementing this approach can provide a clearer view of market movements and help make more informed trading decisions.
Indicatore Pine Script®
di traderhadek
Moving Average Bands with Signals [UAlgo]The "Moving Average Bands with Signals combines various moving average types with ATR-based bands to help traders identify potential support and resistance levels. It plots moving average bands with upper and lower support/resistance levels based on the Average True Range (ATR) and user-defined settings.Additionally, the script generates buy/sell signals based on price crossing above or below the bands. 🔶 Key Features Multiple Moving Average Types: Supports various moving average calculations including Arnaud Legoux Moving Average (ALMA), Exponential Moving Average (EMA), Double Exponential Moving Average (DEMA), Triple Exponential Moving Average (TEMA), Kaufman Adaptive Moving Average (KAMA), Hull Moving Average (HMA), Least Squares Moving Average (LSMA), Simple Moving Average (SMA), Triangular Moving Average (TMA), Volume-Weighted Moving Average (VWMA), Weighted Moving Average (WMA), and Zero-Lag Moving Average (ZLMA). Customizable ATR Bands: Integrates the Average True Range (ATR) to calculate dynamic support and resistance bands around the moving average. The multiplier for the bands is user-adjustable, allowing for finer control over the sensitivity and width of the bands. Signal Generation: Provides visual signals on the chart when the price interacts with the support or resistance bands. Users can choose between using the wick or the close price to generate these signals, adding an extra layer of customization based on their trading style. Flexible Input Parameters: Allows users to input parameters for moving average length, ATR length, band multiplier, and signal type. Additional settings are available for specific moving average types, such as ALMA's offset and sigma, KAMA's fast and slow periods, and LSMA's offset. 🔶 Disclaimer This script is provided for educational purposes only and should not be considered financial advice. Trading financial instruments involves substantial risk and can result in significant financial losses. The script’s performance in the past is not indicative of future results, and no guarantees are made regarding its accuracy, reliability, or performance.
Indicatore Pine Script®
di UAlgo
1
Support Resistance & Ema The "Support Resistance & Ema" indicator combines various strategies to assist traders in identifying significant support and resistance levels on the chart and in following trends through exponential moving averages (EMA). This script is designed to be versatile and useful in different trading strategies. Key Features: Support and Resistance: It utilizes pivot highs and lows to pinpoint support and resistance levels. These levels are plotted on the chart with lines that change color based on trend reversals. Trend Identification: The indicator follows trends using four conditions: _hh: Higher highs and higher lows, indicating an uptrend. _ll: Lower highs and lower lows, indicating a downtrend. _hl: Higher highs and lower lows, indicating weakening uptrend or an impending reversal. _lh: Lower highs and higher lows, indicating weakening downtrend or an impending reversal. Exponential Moving Averages (EMA): It also displays various EMAs (9, 21, 50, 100, 200) on the chart to provide further insights into the trend direction. Usage: Support and Resistance: Support and resistance lines are automatically plotted on the chart. Trend reversals are highlighted by changing the color of the lines. Trend Identification: The _hh, _ll, _hl, _lh conditions help identify trend changes. When one of these conditions is met, it indicates a particular configuration of highs and lows that might suggest a trading opportunity. Exponential Moving Averages (EMA): The EMAs are plotted on the chart and can be used to confirm trends identified by the main indicator. To use this script, you need to add it as an indicator to your trading chart. Once applied, the support, resistance lines, and EMAs will be visible on the chart, providing traders with valuable information to make informed trading decisions. In summary, this script offers a comprehensive way to identify significant support and resistance levels, spot market trends, and confirm those trends through the use of exponential moving averages.
Indicatore Pine Script®
di GP17
Twin Range Filter VisualizedVisulaized version of @colinmck's Twin Range Filter version on TradingView. On @colinmck's Twin Range Filter version, you can only see Long and Short signals on the chart. But in this version of TRF, users can visually see the BUY and SELL signals on the chart with an added line of TRF. TRF is an average of two smoothed Exponential Moving Averages, fast one has 27 bars of length and the slow one has 55 bars. The purpose is to obtain two ranges that price fluctuates between (upper and lower range) and have LONG AND SHORT SIGNALS when close price crosses above the upper range and conversely crosses below lower range. I personally combine the upper and lower ranges on one line to see the long and short signals with my own eyes so, -BUY when price is higher or equal to the upper range level and the indicator line turns to draw the lower range to follow the price just under the bars as a trailing stop loss indicator like SuperTrend. -SELL when price is lower or equal to the lower range levelline under the bars and then the indicator line turns to draw the upper range to follow the price just over the bars in that same trailing stop loss logic. There are also two coefficients that adjusts the trailing line distance levels from the price multiplying the effect of the faster and slower moving averages. The default values of the multipliers: Fast range multiplier of Fast Moving Average(27): 1.6 Slow range multiplier of fSlow Moving Average(55): 2 Remember that if you enlarge these multipliers you will enlarge the ranges and have less but lagging signals. Conversely, decreasing the multipliers will have small ranges (line will get closer to the price and more signals will occur)
Indicatore Pine Script®
di KivancOzbilgic
25
ChartRage - ELMAELMA - Exponential Logarithmic Moving Average This is a new kind of moving average that is using exponential normalization of a logarithmic formula. The exponential function is used to average the weight on the moving average while the logarithmic function is used to calculate the overall price effect. Features and Settings: ◻️ Following rate of change instead of absolute levels ◻️ Choose input source of the data ◻️ Real time signals through price interaction ◻️ Change ELMA length ◻️ Change the exponential decay rate ◻️ Customize base color and signal color Equation of the ELMA: This formula calculates a weighted average of the logarithm of prices, where more recent prices have a higher weight. The result is then exponentiated to return the ELMA value. This approach emphasizes the relative changes in price, making the ELMA sensitive to the % rate of change rather than absolute price levels. The decay rate can be adjusted in the settings. Comparison EMA vs ELMA: In this image we see the differences to the Exponential Moving Average. Price Interaction and earlier Signals: In this image we have added the bars, so we can see that the ELMA provides different signals of resistance and support zones and highlights them, by changing to the color yellow, when prices interact with the ELMA. Strategy by trading Support and Resistance Zones: The ELMA helps to evaluate trends and find entry points in bullish market conditions, and exit points in bearish conditions. When prices drop below the ELMA in a bull market, it is considered a buying signal. Conversely, in a bear market, it serves as an exit signal when prices trade above the ELMA. Volatile Markets: The ELMA works on all timeframes and markets. In this example we used the default value for Bitcoin. The ELMA clearly shows support and resistance zones. Depending on the asset, the length and the decay rate should be adjusted to provide the best results. Real Time Signals: Signals occur not after a candle closes but when price interacts with the ELMA level, providing real time signals by shifting color. (default = yellow) Disclaimer* All analyses, charts, scripts, strategies, ideas, or indicators developed by us are provided for informational and educational purposes only. We do not guarantee any future results based on the use of these tools or past data. Users should trade at their own risk. This work is licensed under Attribution-NonCommercial-ShareAlike 4.0 International creativecommons.org
Indicatore Pine Script®
di ChartRage
Composite Trend Oscillator [ChartPrime]CODE DUELLO: Have you ever stopped to wonder what the underlying filters contained within complex algorithms are actually providing for you? Wouldn't it be nice to actually visually inspect for that? Those would require some kind of wild west styled quick draw duel or some comparison method as a proper 'code duello'. Then it can be determined which filter can 'draw' the quickest from it's computational holster with the least amount of lag and smoothness. In Pine we can do so, discovering how beneficial that would be. This can be accomplished by quickly switching from one filter to another by input() back and forth, requiring visual memory. A better way could be done by placing two indicators added to the chart and then eventually placed into one indicator pane on top of each other. By adding a filter() helper function that calls other moving average functions chosen for comparison, it can put to the test which moving average is the best drawing filter suited to our expected needs. PhiSmoother was formerly debuted and now it is utilized in a more complex environment in a multitude of ways along side other commonly utilized filters. Now, you the reader, get to judge for yourself... FILTER VERSATILITY: Having the capability to adjust between various smoothing methods such as PhiSmoother, TEMA, DEMA, WMA, EMA, and SMA on historical market data within the code provides an advantage. Each of these filter methods offers distinct advantages and hinderances. PhiSmoother stands out often by having superb noise rejection, while also being able to manipulate the fine-tuning of the phase or lag of the indicator, enhancing responsiveness to price movements. The following are more well-known classic filters. TEMA (Triple Exponential Moving Average) and DEMA (Double Exponential Moving Average) offer reduced transient response times to price changes fluctuations. WMA (Weighted Moving Average) assigns more weight to recent data points, making it particularly useful for reduced lag. EMA (Exponential Moving Average) strikes a balance between responsiveness and computational efficiency, making it a popular choice. SMA (Simple Moving Average) provides a straightforward calculation based on the arithmetic mean of the data. VWMA and RMA have both been excluded for varying reasons, both being unworthy of having explanation here. By allowing for adjustment refinements between these filter methods, traders may garner the flexibility to adapt their analysis to different market dynamics, optimizing their algorithms for improved decision-making and performance on demand. INDICATOR INTRODUCTION: ChartPrime's Composite Trend Oscillator operates as an oscillator based on the concept of a moving average ribbon. It utilizes up to 32 filters with progressively longer periods to assess trend direction and strength. Embedded within this indicator is an alternative view that utilizes the separation of the ribbon filaments to assess volatility. Both versions are excellent candidates for trend and momentum, both offering visualization of polarity, directional coloring, and filter crossings. Anyone who has former experience using RSI or stochastics may have ease of understanding applying this to their chart. COMPOSITE CLUSTER MODES EXPLAINED: In Trend Strength mode, the oscillator behavior signifies market direction and movement strength. When the oscillator is rising and above zero, the market is within a bullish phase, and visa versa. If the signal filter crosses the composite trend, this indicates a potential dynamic shift signaling a possible reversal. When the oscillator is teetering on its extremities, the market is more inclined to reverse later. With Volatility mode, the oscillator undergoes a transformation, displaying an unbounded oscillator driven by market volatility. While it still employs the same scoring mechanism, it is now scaled according to the strength of the market move. This can aid with identification of ranging scenarios. However, one side effect is that the oscillator no longer has minimum or maximum boundaries. This can still be advantageous when considering divergences. NOTEWORTHY SETTINGS FEATURES: The following input settings described offer comprehensive control over the indicator's behavior and visualization. Common Controls: Price Source Selection - The indicator offers flexibility in choosing the price source for analysis. Traders can select from multiple options. Composite Cluster Mode - Choose between "Trend Strength" and "Volatility" modes, providing insights into trend directionality or volatility weighting. Cluster Filter and Length - Selects a filter for the cluster composition. This includes a length parameter adjustment. Cluster Options: Cluster Dispersion - Users can adjust the separation between moving averages in the cluster, influencing the sensitivity of the analysis. Cluster Trimming - By modifying upper and lower trim parameters, traders can adjust the sensitivity of the moving averages within the cluster, enhancing its adaptability. PostSmooth Filter and Length - Choose a filter to refine the composite cluster's post-smoothing with a length parameter adjustment. Signal Filter and Length - Users can select a filter for the lagging signal plot, also having a length parameter adjustment. Transition Easing - Sensitivity adjustment to influence the transition between bullish and bearish colors. Enjoy
Indicatore Pine Script®
di ChartPrime
15
Long EMA Strategy with Advanced Exit OptionsThis strategy is designed for traders seeking a trend-following system with a focus on precision and adaptability. **Core Strategy Concept** The essence of this strategy lies in use of Exponential Moving Averages (EMAs) to identify potential long (buy) positions based on the relative positions of short-term, medium-term, and long-term EMAs. The use of EMAs is a classic yet powerful approach to trend detection, as these indicators smooth out price data over time, emphasizing the direction of recent price movements and potentially signaling the beginning of new trends. **Customizable Parameters** - **EMA Periods**: Users can define the periods for three EMAs - long-term, medium-term, and short-term - allowing for a tailored approach to capture trends based on individual trading styles and market conditions. - **Volatility Filter**: An optional Average True Range (ATR)-based volatility filter can be toggled on or off. When activated, it ensures that trades are only entered when market volatility exceeds a user-defined threshold, aiming to filter out entries during low-volatility periods which are often characterized by indecisive market movements. - **Trailing Stop Loss**: A trailing stop loss mechanism, expressed as a percentage of the highest price achieved since entry, provides a dynamic way to manage risk by allowing profits to run while cutting losses. - **EMA Exit Condition**: This advanced exit option enables closing positions when the short-term EMA crosses below the medium-term EMA, serving as a signal that the immediate trend may be reversing. - **Close Below EMA Exit**: An additional exit condition, which is disabled by default, allows positions to be closed if the price closes below a user-selected EMA. This provides an extra layer of flexibility and risk management, catering to traders who prefer to exit positions based on specific EMA thresholds. **Operational Mechanics** Upon activation, the strategy evaluates the current price in relation to the set EMAs. A long position is considered when the current price is above the long-term EMA, and the short-term EMA is above the medium-term EMA. This setup aims to identify moments where the price momentum is strong and likely to continue. The strategy's versatility is further enhanced by its optional settings: - The **Volatility Filter** adjusts the sensitivity of the strategy to market movements, potentially improving the quality of the entries during volatile market conditions. The Average True Range (ATR) is a key component of this filter, providing a measure of market volatility by calculating the average range between the high and low prices over a specified number of periods. Here's how you can adjust the volatility filter settings for various market conditions, focusing on filtering out low-volatility markets: Setting Examples for Volatility Filter 1. High Volatility Markets (e.g., Cryptocurrencies, Certain Forex Pairs): ATR Periods: 14 (default) ATR Multiplier: Setting the multiplier to a lower value, such as 1.0 or 1.2, can be beneficial in high-volatility markets. This sensitivity allows the strategy to react to volatility changes more quickly, ensuring that you're entering trades during periods of significant movement. 2. Medium Volatility Markets (e.g., Major Equity Indices, Medium-Volatility Forex Pairs): ATR Periods: 14 (default) ATR Multiplier: A multiplier of 1.5 (default) is often suitable for medium volatility markets. It provides a balanced approach, ensuring that the strategy filters out low-volatility conditions without being overly restrictive. 3. Low Volatility Markets (e.g., Some Commodities, Low-Volatility Forex Pairs): ATR Periods: Increasing the ATR period to 20 or 25 can smooth out the volatility measure, making it less sensitive to short-term fluctuations. This adjustment helps in focusing on more significant trends in inherently stable markets. ATR Multiplier: Raising the multiplier to 2.0 or even 2.5 increases the threshold for volatility, effectively filtering out low-volatility conditions. This setting ensures that the strategy only triggers trades during periods of relatively higher volatility, which are more likely to result in significant price movements. How to Use the Volatility Filter for Low-Volatility Markets For traders specifically interested in filtering out low-volatility markets, the key is to adjust the ATR Multiplier to a higher level. This adjustment increases the threshold required for the market to be considered sufficiently volatile for trade entries. Here's a step-by-step guide: Adjust the ATR Multiplier: Increase the ATR Multiplier to create a higher volatility threshold. A multiplier of 2.0 to 2.5 is a good starting point for very low-volatility markets. Fine-Tune the ATR Periods: Consider lengthening the ATR calculation period if you find that the strategy is still entering trades in undesirable low-volatility conditions. A longer period provides a more averaged-out measure of volatility, which might better suit your needs. Monitor and Adjust: Volatility is not static, and market conditions can change. Regularly review the performance of your strategy in the context of current market volatility and adjust the settings as necessary. Backtest in Different Conditions: Before applying the strategy live, backtest it across different market conditions with your adjusted settings. This process helps ensure that your approach to filtering low-volatility conditions aligns with your trading objectives and risk tolerance. By fine-tuning the volatility filter settings according to the specific characteristics of the market you're trading in, you can enhance the performance of this strategy - The **Trailing Stop Loss** and **EMA Exit Conditions** provide two layers of exit strategies, focusing on capital preservation and profit maximization. **Visualizations** For clarity and ease of use, the strategy plots the three EMAs and, if enabled, the ATR threshold on the chart. These visual cues not only aid in decision-making but also help in understanding the market's current trend and volatility state. **How to Use** Traders can customize the EMA periods to fit their trading horizon, be it short, medium, or long-term trading. The volatility filter and exit options allow for further customization, making the strategy adaptable to different market conditions and personal risk tolerance levels. By offering a blend of trend-following principles with advanced risk management features, this strategy aims to cater to a wide range of trading styles, from cautious to aggressive. Its strength lies in its flexibility, allowing traders to fine-tune settings to their specific needs, making it a potentially valuable tool in the arsenal of any trader looking for a disciplined approach to navigating the markets.
Strategia Pine Script®
di Giuseppe-76
The Flash-Strategy with Minervini Stage Analysis QualifierThe Flash-Strategy (Momentum-RSI, EMA-crossover, ATR) with Minervini Stage Analysis Qualifier Introduction Welcome to a comprehensive guide on a cutting-edge trading strategy I've developed, designed for the modern trader seeking an edge in today's dynamic markets. This strategy, which I've honed through my years of experience in the trading arena, stands out for its unique blend of technical analysis and market intuition, tailored specifically for use on the TradingView platform. As a trader with a deep passion for the financial markets, my journey began several years ago, driven by a relentless pursuit of a trading methodology that is both effective and adaptable. My background in trading spans various market conditions and asset classes, providing me with a rich tapestry of experiences from which to draw. This strategy is the culmination of that journey, embodying the lessons learned and insights gained along the way. The cornerstone of this strategy lies in its ability to generate precise long signals in a Stage 2 uptrend and equally accurate short signals in a Stage 4 downtrend. This approach is rooted in the principles of trend following and momentum trading, harnessing the power of key indicators such as the Momentum-RSI, EMA Crossover, and Average True Range (ATR). What sets this strategy apart is its meticulous design, which allows it to adapt to the ever-changing market conditions, providing traders with a robust tool for navigating both bullish and bearish scenarios. This strategy was born out of a desire to create a trading system that is not only highly effective in identifying potential trade setups but also straightforward enough to be implemented by traders of varying skill levels. It's a reflection of my belief that successful trading hinges on clarity, precision, and disciplined execution. Whether you are a seasoned trader or just beginning your journey, this guide aims to provide you with a comprehensive understanding of how to harness the full potential of this strategy in your trading endeavors. In the following sections, we will delve deeper into the mechanics of the strategy, its implementation, and how to make the most out of its features. Join me as we explore the nuances of a strategy that is designed to elevate your trading to the next level. Stage-Specific Signal Generation A distinctive feature of this trading strategy is its focus on generating long signals exclusively during Stage 2 uptrends and short signals during Stage 4 downtrends. This approach is based on the widely recognized market cycle theory, which divides the market into four stages: Stage 1 (accumulation), Stage 2 (uptrend), Stage 3 (distribution), and Stage 4 (downtrend). By aligning the signal generation with these specific stages, the strategy aims to capitalize on the most dynamic and clear-cut market movements, thereby enhancing the potential for profitable trades. 1. Long Signals in Stage 2 Uptrends • Characteristics of Stage 2: Stage 2 is characterized by a strong uptrend, where prices are consistently rising. This stage typically follows a period of accumulation (Stage 1) and is marked by increased investor interest and bullish sentiment in the market. • Criteria for Long Signal Generation: Long signals are generated during this stage when the technical indicators align with the characteristics of a Stage 2 uptrend. • Rationale for Stage-Specific Signals: By focusing on Stage 2 for long trades, the strategy seeks to enter positions during the phase of strong upward momentum, thus riding the wave of rising prices and investor optimism. This stage-specific approach minimizes exposure to less predictable market phases, like the consolidation in Stage 1 or the indecision in Stage 3. 2. Short Signals in Stage 4 Downtrends • Characteristics of Stage 4: Stage 4 is identified by a pronounced downtrend, with declining prices indicating prevailing bearish sentiment. This stage typically follows the distribution phase (Stage 3) and is characterized by increasing selling pressure. • Criteria for Short Signal Generation: Short signals are generated in this stage when the indicators reflect a strong bearish trend. • Rationale for Stage-Specific Signals: Targeting Stage 4 for shorting capitalizes on the market's downward momentum. This tactic aligns with the natural market cycle, allowing traders to exploit the downward price movements effectively. By doing so, the strategy avoids the potential pitfalls of shorting during the early or late stages of the market cycle, where trends are less defined and more susceptible to reversals. In conclusion, the strategy’s emphasis on stage-specific signal generation is a testament to its sophisticated understanding of market dynamics. By tailoring the long and short signals to Stages 2 and 4, respectively, it leverages the most compelling phases of the market cycle, offering traders a clear and structured approach to aligning their trades with dominant market trends. Strategy Overview At the heart of this trading strategy is a philosophy centered around capturing market momentum and trend efficiency. The core objective is to identify and capitalize on clear uptrends and downtrends, thereby allowing traders to position themselves in sync with the market's prevailing direction. This approach is grounded in the belief that aligning trades with these dominant market forces can lead to more consistent and profitable outcomes. The strategy is built on three foundational components, each playing a critical role in the decision-making process: 1. Momentum-RSI (Relative Strength Index): The Momentum-RSI is a pivotal element of this strategy. It's an enhanced version of the traditional RSI, fine-tuned to better capture the strength and velocity of market trends. By measuring the speed and change of price movements, the Momentum-RSI provides invaluable insights into whether a market is potentially overbought or oversold, suggesting possible entry and exit points. This indicator is especially effective in filtering out noise and focusing on substantial market moves. 2. EMA (Exponential Moving Average) Crossover: The EMA Crossover is a crucial component for trend identification. This strategy employs two EMAs with different timeframes to determine the market trend. When the shorter-term EMA crosses above the longer-term EMA, it signals an emerging uptrend, suggesting a potential long entry. Conversely, a crossover below indicates a possible downtrend, hinting at a short entry opportunity. This simple yet powerful tool is key in confirming trend directions and timing market entries. 3. ATR (Average True Range): The ATR is instrumental in assessing market volatility. This indicator helps in understanding the average range of price movements over a given period, thus providing a sense of how much a market might move on a typical day. In this strategy, the ATR is used to adjust stop-loss levels and to gauge the potential risk and reward of trades. It allows for more informed decisions by aligning trade management techniques with the current volatility conditions. The synergy of these three components – the Momentum-RSI, EMA Crossover, and ATR – creates a robust framework for this trading strategy. By combining momentum analysis, trend identification, and volatility assessment, the strategy offers a comprehensive approach to navigating the markets. Whether it's capturing a strong trend in its early stages or identifying a potential reversal, this strategy aims to provide traders with the tools and insights needed to make well-informed, strategically sound trading decisions. Detailed Component Analysis The efficacy of this trading strategy hinges on the synergistic functioning of its three key components: the Momentum-RSI, EMA Crossover, and Average True Range (ATR). Each component brings a unique perspective to the strategy, contributing to a well-rounded approach to market analysis. 1. Momentum-RSI (Relative Strength Index) • Definition and Function: The Momentum-RSI is a modified version of the classic Relative Strength Index. While the traditional RSI measures the velocity and magnitude of directional price movements, the Momentum-RSI amplifies aspects that reflect trend strength and momentum. • Significance in Identifying Trend Strength: This indicator excels in identifying the strength behind a market's move. A high Momentum-RSI value typically indicates strong bullish momentum, suggesting the potential continuation of an uptrend. Conversely, a low Momentum-RSI value signals strong bearish momentum, possibly indicative of an ongoing downtrend. • Application in Strategy: In this strategy, the Momentum-RSI is used to gauge the underlying strength of market trends. It helps in filtering out minor fluctuations and focusing on significant movements, providing a clearer picture of the market's true momentum. 2. EMA (Exponential Moving Average) Crossover • Definition and Function: The EMA Crossover component utilizes two exponential moving averages of different timeframes. Unlike simple moving averages, EMAs give more weight to recent prices, making them more responsive to new information. • Contribution to Market Direction: The interaction between the short-term and long-term EMAs is key to determining market direction. A crossover of the shorter EMA above the longer EMA is an indicator of an emerging uptrend, while a crossover below signals a developing downtrend. • Application in Strategy: The EMA Crossover serves as a trend confirmation tool. It provides a clear, visual representation of the market's direction, aiding in the decision-making process for entering long or short positions. This component ensures that trades are aligned with the prevailing market trend, a crucial factor for the success of the strategy. 3. ATR (Average True Range) • Definition and Function: The ATR is an indicator that measures market volatility by calculating the average range between the high and low prices over a specified period. • Role in Assessing Market Volatility: The ATR provides insights into the typical market movement within a given timeframe, offering a measure of the market's volatility. Higher ATR values indicate increased volatility, while lower values suggest a calmer market environment. • Application in Strategy: Within this strategy, the ATR is instrumental in tailoring risk management techniques, particularly in setting stop-loss levels. By accounting for the market's volatility, the ATR ensures that stop-loss orders are placed at levels that are neither too tight (risking premature exits) nor too loose (exposing to excessive risk). In summary, the combination of Momentum-RSI, EMA Crossover, and ATR in this trading strategy provides a comprehensive toolkit for market analysis. The Momentum-RSI identifies the strength of market trends, the EMA Crossover confirms the market direction, and the ATR guides in risk management by assessing volatility. Together, these components form the backbone of a strategy designed to navigate the complexities of the financial markets effectively. 1. Signal Generation Process • Combining Indicators: The strategy operates by synthesizing signals from the Momentum-RSI, EMA Crossover, and ATR indicators. Each indicator serves a specific purpose: the Momentum-RSI gauges trend momentum, the EMA Crossover identifies the trend direction, and the ATR assesses the market’s volatility. • Criteria for Signal Validation: For a signal to be considered valid, it must meet specific criteria set by each of the three indicators. This multi-layered approach ensures that signals are not only based on one aspect of market behavior but are a result of a comprehensive analysis. 2. Conditions for Long Positions • Uptrend Confirmation: A long position signal is generated when the shorter-term EMA crosses above the longer-term EMA, indicating an uptrend. • Momentum-RSI Alignment: Alongside the EMA crossover, the Momentum-RSI should indicate strong bullish momentum. This is typically represented by the Momentum-RSI being at a high level, confirming the strength of the uptrend. • ATR Consideration: The ATR is used to fine-tune the entry point and set an appropriate stop-loss level. In a low volatility scenario, as indicated by the ATR, the stop-loss can be set tighter, closer to the entry point. 3. Conditions for Short Positions • Downtrend Confirmation: Conversely, a short position signal is indicated when the shorter-term EMA crosses below the longer-term EMA, signaling a downtrend. • Momentum-RSI Confirmation: The Momentum-RSI should reflect strong bearish momentum, usually seen when the Momentum-RSI is at a low level. This confirms the bearish strength of the market. • ATR Application: The ATR again plays a role in determining the stop-loss level for the short position. Higher volatility, as indicated by a higher ATR, would warrant a wider stop-loss to accommodate larger market swings. By adhering to these mechanics, the strategy aims to ensure that each trade is entered with a high probability of success, aligning with the market’s current momentum and trend. The integration of these indicators allows for a holistic market analysis, providing traders with clear and actionable signals for both entering and exiting trades. Customizable Parameters in the Strategy Flexibility and adaptability are key features of this trading strategy, achieved through a range of customizable parameters. These parameters allow traders to tailor the strategy to their individual trading style, risk tolerance, and specific market conditions. By adjusting these parameters, users can fine-tune the strategy to optimize its performance and align it with their unique trading objectives. Below are the primary parameters that can be customized within the strategy: 1. Momentum-RSI Settings • Period: The lookback period for the Momentum-RSI can be adjusted. A shorter period makes the indicator more sensitive to recent price changes, while a longer period smoothens the RSI line, offering a broader view of the momentum. • Overbought/Oversold Thresholds: Users can set their own overbought and oversold levels, which can help in identifying extreme market conditions more precisely according to their trading approach. 2. EMA Crossover Settings • Timeframes for EMAs: The strategy uses two EMAs with different timeframes. Traders can modify these timeframes, choosing shorter periods for a more responsive approach or longer periods for a more conservative one. • Source Data: The choice of price data (close, open, high, low) used in calculating the EMAs can be varied depending on the trader’s preference. 3. ATR Settings • Lookback Period: Adjusting the lookback period for the ATR impacts how the indicator measures volatility. A longer period may provide a more stable but less responsive measure, while a shorter period offers quicker but potentially more erratic readings. • Multiplier for Stop-Loss Calculation: This parameter allows traders to set how aggressively or conservatively they want their stop-loss to be in relation to the ATR value. Here are the standard settings:
Strategia Pine Script®
di JS_TechTrading
1
EMA Envelope - Signal with Stoploss and Takeprofit LevelsDescription: This Pine Script indicator implements the EMA Envelope strategy, which utilizes Exponential Moving Averages (EMA) to create an envelope around the price chart. The strategy generates buy and sell signals based on the crossing of the price above and below the upper and lower EMA envelopes, respectively. It also incorporates additional features such as stop-loss and take-profit levels for risk management. Indicator Settings: EMA Length: Specifies the period for the short-term Exponential Moving Average. Long Term EMA Length: Defines the period for the long-term Exponential Moving Average used for signal filtering. Take Profit Ratio: Determines the ratio for calculating the take-profit levels based on the stop-loss. Filter Signal on Long Term EMA: Enables or disables the filtering of buy/sell signals using the long-term EMA. Show only recent signal: When enabled, shows only the most recent buy/sell signals. Buy and Sell Signals: The indicator generates buy signals when the price crosses above the upper EMA envelope and the previous low was below the upper EMA envelope. Additionally, you can choose to filter buy signals based on whether the closing price is above the long-term EMA. Conversely, sell signals are generated when the price crosses below the lower EMA envelope, and the previous high was above the lower EMA envelope. Similar to buy signals, sell signals can also be filtered using the long-term EMA. Note: Signal works well on Higher Timeframes like Daily/8hrs/4hrs/1hr. Stop-Loss and Take-Profit Levels: For buy signals, the stop-loss is set at the lower EMA level, while the take-profit level is calculated by adding a specified ratio of the difference between the low and the stop-loss level to the low price. For sell signals, the stop-loss is set at the upper EMA level, and the take-profit level is calculated by subtracting a specified ratio of the difference between the stop-loss level and the high price from the high price. Disclaimer: This indicator is provided for educational and informational purposes only. Trading involves significant risk, and past performance does not guarantee future results. Users are solely responsible for their trading decisions and should conduct their own research and risk management. The author shall not be held liable for any losses or damages arising from the use of this indicator. Note: Always test the indicator thoroughly on historical data and consider paper trading before applying it to live trading environments.
Indicatore Pine Script®
di hellomuthu23
2
Regularized-Moving-Average Oscillator SuiteThe Regularized-MA Oscillator Suite is a versatile indicator that transforms any moving average into an oscillator. It comprises up to 13 different moving average types, including KAMA, T3, and ALMA. This indicator serves as a valuable tool for both trend following and mean reversion strategies, providing traders and investors with enhanced insights into market dynamics. Methodology: The Regularized MA Oscillator Suite calculates the moving average (MA) based on user-defined parameters such as length, moving average type, and custom smoothing factors. It then derives the mean and standard deviation of the MA using a normalized period. Finally, it computes the Z-Score by subtracting the mean from the MA and dividing it by the standard deviation. KAMA (Kaufman's Adaptive Moving Average): KAMA is a unique moving average type that dynamically adjusts its smoothing period based on market volatility. It adapts to changing market conditions, providing a smoother response during periods of low volatility and a quicker response during periods of high volatility. This allows traders to capture trends effectively while reducing noise. T3 (Tillson's Exponential Moving Average): T3 is an exponential moving average that incorporates additional smoothing techniques to reduce lag and provide a more responsive indicator. It aims to maintain a balance between responsiveness and smoothness, allowing traders to identify trend reversals with greater accuracy. ALMA (Arnaud Legoux Moving Average): ALMA is a moving average type that utilizes a combination of linear regression and exponential moving average techniques. It offers a unique way of calculating the moving average by providing a smoother and more accurate representation of price trends. ALMA reduces lag and noise, enabling traders to identify trend changes and potential entry or exit points more effectively. Z-Score: The Z-Score calculation in the Regularized-MA Oscillator Suite standardizes the values of the moving average. It measures the deviation of each data point from the mean in terms of standard deviations. By normalizing the moving average through the Z-Score, the indicator enables traders to assess the relative position of price in relation to its mean and volatility. This information can be valuable for identifying overbought and oversold conditions, as well as potential trend reversals. Utility: The Regularized-MA Oscillator Suite with its unique moving average types and Z-Score calculation offers traders and investors powerful analytical tools. It can be used for trend following strategies by analyzing the oscillator's position relative to the midline. Traders can also employ it as a mean reversion tool by identifying peak values above user-defined deviations. These features assist in identifying potential entry and exit points, enhancing trading decisions and market analysis. Key Features: Variety of 13 MA types. Potential reversal point bubbles. Bar coloring methods - Trend (Midline cross), Extremities, Reversions, Slope Example Charts:
Indicatore Pine Script®
di EliCobra
13
Mad_MATHLibrary "MAD_MATH" This is a mathematical library where I store useful kernels, filters and selectors for the different types of computations. This library also contains opensource code from other scripters. Future extensions are very likely, there are some functions I would like to add, but I have to wait for approvals so i can include them. Ehlers_EMA(_src, _length)   Calculates the Ehlers Exponential Moving Average (Ehlers_EMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers EMA   Returns: The Ehlers EMA value Ehlers_Gaussian(_src, _length)   Calculates the Ehlers Gaussian Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Gaussian Filter   Returns: The Ehlers Gaussian Filter value Ehlers_supersmoother(_src, _length)   Calculates the Ehlers Supersmoother   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Supersmoother   Returns: The Ehlers Supersmoother value Ehlers_SMA_fast(_src, _length)   Calculates the Ehlers Simple Moving Average (SMA) Fast   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers SMA Fast   Returns: The Ehlers SMA Fast value Ehlers_EMA_fast(_src, _length)   Calculates the Ehlers Exponential Moving Average (EMA) Fast   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers EMA Fast   Returns: The Ehlers EMA Fast value Ehlers_RSI_fast(_src, _length)   Calculates the Ehlers Relative Strength Index (RSI) Fast   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers RSI Fast   Returns: The Ehlers RSI Fast value Ehlers_Band_Pass_Filter(_src, _length)   Calculates the Ehlers BandPass Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers BandPass Filter   Returns: The Ehlers BandPass Filter value Ehlers_Butterworth(_src, _length)   Calculates the Ehlers Butterworth Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Butterworth Filter   Returns: The Ehlers Butterworth Filter value Ehlers_Two_Pole_Gaussian_Filter(_src, _length)   Calculates the Ehlers Two-Pole Gaussian Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Two-Pole Gaussian Filter   Returns: The Ehlers Two-Pole Gaussian Filter value Ehlers_Two_Pole_Butterworth_Filter(_src, _length)   Calculates the Ehlers Two-Pole Butterworth Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Two-Pole Butterworth Filter   Returns: The Ehlers Two-Pole Butterworth Filter value Ehlers_Band_Stop_Filter(_src, _length)   Calculates the Ehlers Band Stop Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Band Stop Filter   Returns: The Ehlers Band Stop Filter value Ehlers_Smoother(_src)   Calculates the Ehlers Smoother   Parameters:      _src (float) : The source series for calculation   Returns: The Ehlers Smoother value Ehlers_High_Pass_Filter(_src, _length)   Calculates the Ehlers High Pass Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers High Pass Filter   Returns: The Ehlers High Pass Filter value Ehlers_2_Pole_High_Pass_Filter(_src, _length)   Calculates the Ehlers Two-Pole High Pass Filter   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the Ehlers Two-Pole High Pass Filter   Returns: The Ehlers Two-Pole High Pass Filter value pr(_src, _length)   pr Calculates the percentage rank (PR) of a value within a range.   Parameters:      _src (float) : The source value for which the percentage rank is calculated. It represents the value to be ranked within the range.      _length (simple int) : The _length of the range over which the percentage rank is calculated. It determines the number of bars considered for the calculation.   Returns: The percentage rank (PR) of the source value within the range, adjusted by adding 50 to the result. smma(_src, _length)   Calculates the SMMA (Smoothed Moving Average)   Parameters:      _src (float) : The source series for calculation      _length (simple int)   Returns: The SMMA value hullma(_src, _length)   Calculates the Hull Moving Average (HullMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the HullMA   Returns: The HullMA value tma(_src, _length)   Calculates the Triple Moving Average (TMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the TMA   Returns: The TMA value dema(_src, _length)   Calculates the Double Exponential Moving Average (DEMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the DEMA   Returns: The DEMA value tema(_src, _length)   Calculates the Triple Exponential Moving Average (TEMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the TEMA   Returns: The TEMA value w2ma(_src, _length)   Calculates the Normalized Double Moving Average (N2MA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the N2MA   Returns: The N2MA value wma(_src, _length)   Calculates the Normalized Moving Average (NMA)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The _length of the NMA   Returns: The NMA value nma(_open, _close, _length)   Calculates the Normalized Moving Average (NMA)   Parameters:      _open (float) : The open price series      _close (float) : The close price series      _length (simple int) : The _length for finding the highest and lowest values   Returns: The NMA value lma(_src, _length)   Parameters:      _src (float)      _length (simple int) zero_lag(_src, _length, gamma1, zl)   Calculates the Zero Lag Moving Average (ZeroLag)   Parameters:      _src (float) : The source series for calculation      _length (simple int) : The length for the moving average      gamma1 (simple int) : The coefficient for calculating 'd'      zl (simple bool) : Boolean flag for applying Zero Lag   Returns: An array containing the ZeroLag Moving Average and a boolean flag indicating if it's flat copyright HPotter, thanks for that great function chebyshevI(src, len, ripple)   Calculates the Chebyshev Type I Filter   Parameters:      src (float) : The source series for calculation      len (int) : The length of the filter      ripple (float) : The ripple factor for the filter   Returns: The output of the Chebyshev Type I Filter math from Pafnuti Lwowitsch Tschebyschow (1821–1894) Thanks peacefulLizard50262 for the find and translation chebyshevII(src, len, ripple)   Calculates the Chebyshev Type II Filter   Parameters:      src (float) : The source series for calculation      len (int) : The length of the filter      ripple (float) : The ripple factor for the filter   Returns: The output of the Chebyshev Type II Filter math from Pafnuti Lwowitsch Tschebyschow (1821–1894) Thanks peacefulLizard50262 for the find wavetrend(_src, _n1, _n2)   Calculates the WaveTrend indicator   Parameters:      _src (float) : The source series for calculation      _n1 (simple int) : The period for the first EMA calculation      _n2 (simple int) : The period for the second EMA calculation   Returns: The WaveTrend value f_getma(_type, _src, _length, ripple)   Calculates various types of moving averages   Parameters:      _type (simple string) : The type of indicator to calculate      _src (float) : The source series for calculation      _length (simple int) : The length for the moving average or indicator      ripple (simple float)   Returns: The calculated moving average or indicator value f_getfilter(_type, _src, _length)   Calculates various types of filters   Parameters:      _type (simple string) : The type of indicator to calculate      _src (float) : The source series for calculation      _length (simple int) : The length for the moving average or indicator   Returns: The filtered value f_getoszillator(_type, _src, _length)   Calculates various types of Deviations and other indicators   Parameters:      _type (simple string) : The type of indicator to calculate      _src (float) : The source series for calculation      _length (simple int) : The length for the moving average or indicator   Returns: The calculated moving average or indicator value
Libreria Pine Script®
di djmad
5
TRIX with Momentum----------- ENGLISH -------------- This indicator is called "TRIX with Momentum" and is used to analyze the momentum of an asset's price and predict potential trend reversals. The logic of operation is based on the combination of two indicators: the Triple Exponential Moving Average (TRIX) and the momentum oscillator. The TRIX is calculated using three exponential moving averages (EMA) of the asset's closing price, with a user-defined length (set to 14 by default). The TRIX is then normalized and centered around 0 to facilitate analysis of its relationship with the momentum oscillator. The momentum oscillator is calculated using the EMA of the normalized TRIX with a user-defined length (set to 14 by default). The indicator plots the normalized TRIX and the momentum oscillator on a chart, using different colors to indicate whether the TRIX is above or below 0. Additionally, the color of the y-axis label changes based on the position of the oscillator, while the color of the x-axis label remains gray. The indicator uses a weighted average between the normalized TRIX and the momentum oscillator to create a colored background of the chart, which changes based on the weighted average. If the weighted average is positive, the chart's background is green, otherwise it is red. Finally, a horizontal line is drawn at point 0 to facilitate visual analysis of the chart. ------------ ITALIANO ------------- Questo indicatore è chiamato "TRIX with Momentum" ed è utilizzato per analizzare il momentum del prezzo di un asset e prevedere eventuali inversioni di trend. La logica di funzionamento è basata sulla combinazione di due indicatori: il TRIX (Indicatori di media mobile Tripla Esponenziale) e l'oscillatore momentum. L'indicatore consente all'utente di impostare la lunghezza del TRIX e dell'oscillatore momentum come input personalizzato. Il TRIX viene calcolato utilizzando tre medie mobili esponenziali (EMA) della chiusura dei prezzi dell'asset, mentre l'oscillatore momentum viene calcolato utilizzando l'EMA del TRIX normalizzato. Il TRIX normalizzato viene centrato intorno allo 0 per facilitare l'analisi della sua relazione con l'oscillatore momentum. L'indicatore plotta il TRIX normalizzato e l'oscillatore momentum su un grafico, utilizzando diversi colori per indicare se il TRIX è sopra o sotto lo 0. L'indicatore utilizza una media pesata tra il TRIX normalizzato e l'oscillatore momentum per creare uno sfondo colorato del grafico, che cambia in base alla media pesata. L'utente può impostare il peso da dare al TRIX e all'oscillatore momentum come input personalizzato, e il peso dell'oscillatore momentum verrà automaticamente impostato come complementare al peso del TRIX. Se la media pesata è positiva, lo sfondo del grafico è verde, altrimenti è rosso. Viene tracciata anche una linea orizzontale al punto 0 per facilitare l'analisi visiva del grafico. Infine, il colore dell'etichetta dell'asse y cambia in base alla posizione dell'oscillatore, mentre il colore dell'etichetta dell'asse x rimane sempre grigio.
Indicatore Pine Script®
di c00l75