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Buy Low Sell High Composite Upgraded V6 [kristian6ncqq]NOTICE: This script is an upgraded and enhanced version of the original "Buy Low Sell High Composite" indicator by (published in 2017). The original script provided a composite indicator combining multiple technical analysis metrics such as RSI, MACD, and MFI. Why I Republished This Script I found the original indicator to be exceptionally useful for identifying optimal accumulation zones for stocks or assets when prices are low (red area) and potential profit-taking zones when prices are high (green area). To ensure it remains accessible and functional for modern trading strategies, I have updated and enhanced the original version with additional features and flexibility. Intended Use This indicator is designed for traders and investors looking to: Accumulate stocks or assets when the price is in the low (red) zone. Take profits or reduce positions when the price is in the high (green) zone. The composite score provides a clear visualization of multiple technical indicators combined into a single actionable signal. Enhancements in This Version Updated to Pine Script v6 (from version 3). Added input parameters for key settings (e.g., RSI length, MACD parameters, smoothing). Introduced Chande Momentum Oscillator (CMO) and directional ADX for improved trend detection. Implemented slope-based trend coloring for outer edges to highlight significant changes in trend direction. Enhanced visualizations with customizable thresholds and smoothing for improved usability. Credits Original script: "Buy Low Sell High Composite" by , 2017. URL to the original script: Buy Low Sell High Composite. This script is designed to build upon the strengths of the original while adding flexibility and new features to meet the needs of modern traders.
อินดิเคเตอร์ Pine Script®
โดย kristian6ncqq
2
Volume Weighted TWAP (VW-TWAP)The Volume Weighted Time Weighted Average Price (VW-TWAP) is an indicator that combines the principles of price averaging with volume sensitivity. Unlike the traditional TWAP, which calculates a simple time-weighted average, VW-TWAP integrates volume into its computation, emphasizing price movements that occur during periods of higher trading activity. This makes it particularly effective for identifying realistic price levels influenced by significant market participation. It is computed by summing the volume-weighted prices over a specified period and dividing by the total volume, providing a more accurate reflection of the price participants value most. The key benefits of VW-TWAP lie in its ability to guide both traders and investors with a data-driven perspective. By accounting for both time and volume, it highlights fair value zones where significant accumulation or distribution might occur. This can improve trade entries and exits by aligning decisions with zones of substantial market consensus. Furthermore, its adaptability to different timeframes enhances its utility in multi-timeframe analysis, making it suitable for intraday scalpers and long-term swing traders alike. The VW-TWAP's focus on volume sensitivity also minimizes noise from low-volume, erratic price movements, offering a clearer view of market dynamics.
อินดิเคเตอร์ Pine Script®
โดย LuxmiAI
1
Coinbase Premium HeatmapCoinbase Premium Heatmap visualizes spot bitcoin premium (or discount) on Coinbase, relative to other spot markets, visualized as a heatmap overlay. OPTIMIZED FOR CLARITY Coinbase Premium can whipsaw quickly, with dramatic state changes over relatively brief periods, unnecessarily complicating its use (for our purposes). To mitigate whipsaws, the script (a) averages premium/discount on an hourly basis, and (b) introduces lightweight exponential smoothing, to further simplify/clarify state. WHY IT MATTERS Spot Coinbase premium is a strong proxy for bullish institutional sentiment and net inflows/accumulation by western financial institutions, ETF providers, and corporations (like MicroStrategy) adding bitcoin to their treasury. In aggregate, this holder cohort drives trend & sentiment more than any other, so it's important to know their directional bias. HOW IT'S CALCULATED Premium / discount calculates the spread between Coinbase spot BTC price, and spot price on Binance + Bybit. Calculation is averaged hourly, with light exponential smoothing. HOW WE USE THE SCRIPT When assessing optimal moments to hedge exposure (or sell spot assets) near a presumed impending cycle top, awareness of institutional sentiment is a crucial variable. This script: (a) Filters out unnecessarily early cycle exit signals (if Coinbase premium is still present) (b) Confirms other metrics that indicate an impending cycle top (if the neutral to bearish institutional sentiment we'd expect to see is in effect), and (c) Visualizes state changes (from bearish to bullish & vice versa), that often make for good swing entries & exits on lower timeframes.
อินดิเคเตอร์ Pine Script®
โดย AlchemicalIntel
Price Action Dynamics Oscillator (PADO)1 minute ago Price Action Dynamics Oscillator (PADO) Indicator Overview and Technical Deep Dive Concept and Philosophy The Price Action Dynamics Oscillator (PADO) is a sophisticated technical analysis tool designed to provide multi-dimensional insights into market behavior by decomposing price action into manipulation and distribution metrics. The indicator goes beyond traditional momentum or trend indicators by introducing a nuanced approach to understanding market microstructure. Key Architectural Components 1. Timeframe and Depth Selection Pivot Depth Options: Short Term (Length: 12 periods) Intermediate Term (Length: 20 periods) Long Term (Length: 100 periods) This flexible configuration allows traders to adapt the indicator's sensitivity to different market conditions and trading styles. 2. Core Calculation Methodology Manipulation Metrics Calculates manipulation differently for green (bullish) and red (bearish) candles Normalized against Average True Range (ATR) for consistent comparison across different volatility environments Green Candle Manipulation: (Open - Low) / ATR Red Candle Manipulation: (High - Open) / ATR Distribution Metrics Measures the directional strength and potential momentum shift Green Candle Distribution: (Close - Open) Red Candle Distribution: (Open - Close) 3. Normalization and Smoothing Uses Simple Moving Average (SMA) for smoothing Dynamic length calculation based on price range distance Ensures minimum SMA length of 2 to prevent calculation errors Unique Features Visualization Toggles Traders can selectively display: Manipulation data Distribution data Long-term reference lines Valuation metrics Strategy signals Valuation Comparative Analysis Compares current manipulation and distribution metrics to 1000-bar long-term averages Color-coded visualization for quick interpretation Blue: Manipulation above average Purple: Manipulation below average Orange: Distribution above average Yellow: Distribution below average Strategy Deployment Generates a composite strategy signal by comparing manipulation and distribution valuations Uses Exponential Moving Average (EMA) for smoother signal generation Incorporates volatility bands for context-aware signal interpretation Quadrant Analysis Classifies market state into four quadrants based on manipulation and distribution valuations: Q1: Low Manipulation, High Distribution Q2: High Manipulation, High Distribution Q3: Low Manipulation, Low Distribution Q4: High Manipulation, Low Distribution Each quadrant is color-coded to provide visual market state representation. Warning Signals Manipulation Warning: When strategy crosses below low volatility band Distribution Warning: When strategy crosses above high volatility band Visual Indicators Bar coloration based on strategy momentum Multiple color states representing different market dynamics Recommended Use Cases Intraday and swing trading Multi-timeframe market analysis Volatility and momentum assessment Trend reversal and continuation identification Potential Limitations Complexity might require significant trader education Performance can vary across different market conditions Requires careful parameter optimization Recommended Settings Best used on liquid markets with clear price action Ideal for: Forex Futures Large-cap stocks Cryptocurrency pairs Customization and Optimization Traders should: Backtest across multiple assets Adjust timeframe settings Calibrate visualization toggles Use in conjunction with other technical indicators Licensing Mozilla Public License 2.0 Open-source and modification-friendly Conclusion The PADO represents an advanced approach to market analysis, blending traditional technical analysis with innovative metrics for deeper market understanding. PADO Quadrant Color Analysis: Deep Dive Quadrant Color Scheme Breakdown Quadrant 1: Lime Green Background (RGB: 0, 255, 21, 90) Condition: val_manip < 1 AND val_distr > 1 Market Interpretation: Low Manipulation Pressure High Distribution Activity Potential Scenario: Smart money might be gradually distributing positions Trading Implications: Caution for current trend followers Potential preparation for trend change Increased probability of consolidation or reversal Quadrant 2: Bright Blue Background (RGB: 0, 191, 255, 90) Condition: val_manip > 1 AND val_distr > 1 Market Interpretation: High Manipulation Pressure High Distribution Activity Potential Scenario: Strong institutional involvement Potential market transition phase Significant volume and momentum Trading Implications: High volatility expected Increased market uncertainty Potential for sharp price movements Requires careful risk management Quadrant 3: Light Gray Background (RGB: 252, 252, 252, 90) Condition: val_manip < 1 AND val_distr < 1 Market Interpretation: Low Manipulation Pressure Low Distribution Activity Potential Scenario: Market consolidation Reduced institutional activity Potential low-volatility period Trading Implications: Range-bound market Reduced trading opportunities Potential setup for future breakout Ideal for mean reversion strategies Quadrant 4: Light Yellow Background (Hex: #f6ff0019) Condition: val_manip > 1 AND val_distr < 1 Market Interpretation: High Manipulation Pressure Low Distribution Activity Potential Scenario: Accumulation of positions Trading Implications: Increased probability of directional move soon Color Psychology and Technical Significance Color Selection Rationale Lime Green (Q1): Represents potential growth and transition Bright Blue (Q2): Signifies high energy and institutional activity Light Gray (Q3): Indicates neutrality and consolidation Transparent Green (Q4): Suggests emerging trend potential Advanced Interpretation Guidelines Color Transition Analysis Observe how the quadrant colors change Rapid color shifts might indicate: Market regime changes Shifts in institutional sentiment Potential trend acceleration or reversal Technical Implementation Notes Calculation Snippet pinescriptCopyq1 = (val_manip < 1) and (val_distr > 1) q2 = (val_manip > 1) and (val_distr > 1) q3 = (val_manip < 1) and (val_distr < 1) q4 = (val_manip > 1) and (val_distr < 1) bgcolor(q1 ? color.rgb(0, 255, 21, 90): q2 ? color.rgb(0, 191, 255, 90): q3 ? color.rgb(252, 252, 252, 90): q4 ? #f6ff0019:na) Alpha Channel (Transparency) 90 and 0x19 values ensure background color doesn't overwhelm chart Allows underlying price action to remain visible Subtle visual cue without significant chart obstruction Practical Trading Recommendations Never Trade Solely on Quadrant Colors Use as a complementary analysis tool Combine with other technical and fundamental indicators Timeframe Considerations Validate quadrant signals across multiple timeframes Longer timeframes provide more reliable signals Risk Management Set appropriate stop-loss levels Use position sizing strategies Be prepared for false signals Recommended Workflow Identify current quadrant Assess overall market context Confirm with other indicators Execute with proper risk management
อินดิเคเตอร์ Pine Script®
โดย Kill_Streak
VAMA - Volume Adjusted Moving Average [jpkxyz]VAMA is a moving average that adapts to volume, giving more weight to price movements backed by higher relative volume. This VAMA (Volume Adjusted Moving Average) indicator implementation emphasizes visual clarity. It is based on the VAMA script by @allanster Dual VAMA lines (Fast/Slow) with dynamic coloring: Single-color scheme switches between green (bullish) and red (bearish) Color changes on crossovers rather than relative position Configurable line widths (set to 1 for clean appearance) Visual enhancements: Optional fill between VAMA lines (50% transparency) Crossover dots can be toggled Fills and dots match the current trend color Customization parameters: Independent source inputs for Fast/Slow lines Adjustable VI Factor (volume influence) Sample size control Strict/non-strict calculation toggle The code maintains efficient computation while prioritizing visual feedback for trend changes. It's designed for clear signal identification without visual clutter. Notable style choices: Consistent color theming throughout all visual elements Simplified color transitions (only at crossovers) Subtle transparency for fill areas Minimal dot size for crossover markers VAMA (Volume Adjusted Moving Average) Technical Analysis: Core Calculation: 1. Volume Influence (v2i): v2i = volume / ((total_volume/total_periods) * volume_factor) - total_volume: Sum of volume over sample period - total_periods: Either full history (nvb=0) or specified sample size - volume_factor: Controls sensitivity to volume deviation 2. Price Weighting: weighted_price = source_price * v2i 3. Accumulation Process: - Iterates through length*10 periods - Accumulates weighted prices and volume influence values - Continues until volume influence sum >= specified length or strict rule triggers 4. Final VAMA Value: vama = (weighted_sum - (volume_sum - length) * last_price) / length Parameters: - SampleN: Historical reference length (0=full history) - Length: Base period for calculation - VI Factor: Volume influence multiplier (>0.01) - Strict: Forces exact length period completion when true - Source: Input price data
อินดิเคเตอร์ Pine Script®
โดย GLITCHCAPITAL
DeepSignalFilterHelpersLibrary "DeepSignalFilterHelpers" filter_intraday_intensity(useIiiFilter)   Parameters:      useIiiFilter (bool) filter_vwma(src, length, useVwmaFilter)   Parameters:      src (float)      length (int)      useVwmaFilter (bool) filter_nvi(useNviFilter)   Parameters:      useNviFilter (bool) filter_emv(length, emvThreshold, useEmvFilter, useMovingAvg)   EMV filter for filtering signals based on Ease of Movement   Parameters:      length (int) : The length of the EMV calculation      emvThreshold (float) : The EMV threshold      useEmvFilter (bool) : Whether to apply the EMV filter      useMovingAvg (bool) : Whether to use moving average as threshold   Returns: Filtered result indicating whether the signal should be used filter_adi(length, threshold, useAdiFilter, useMovingAvg)   ADI filter for filtering signals based on Accumulation/Distribution Index   Parameters:      length (int) : The length of the ADI moving average calculation      threshold (float) : The ADI threshold      useAdiFilter (bool) : Whether to apply the ADI filter      useMovingAvg (bool) : Whether to use moving average as threshold   Returns: Filtered result indicating whether the signal should be used filter_mfi(length, mfiThreshold, useMfiFilter, useMovingAvg)   MFI filter for filtering signals based on Money Flow Index   Parameters:      length (int) : The length of the MFI calculation      mfiThreshold (float) : The MFI threshold      useMfiFilter (bool) : Whether to apply the MFI filter      useMovingAvg (bool) : Whether to use moving average as threshold   Returns: Filtered result indicating whether the signal should be used detect_obv_states(obvThresholdStrong, obvThresholdModerate, lookbackPeriod, obvMode)   detect_obv_states: Identify OBV states with three levels (Strong, Moderate, Weak) over a configurable period   Parameters:      obvThresholdStrong (float) : Threshold for strong OBV movements      obvThresholdModerate (float) : Threshold for moderate OBV movements      lookbackPeriod (int) : Number of periods to analyze OBV trends      obvMode (string) : OBV mode to filter ("Strong", "Moderate", "Weak")   Returns: OBV state ("Strong Up", "Moderate Up", "Weak Up", "Positive Divergence", "Negative Divergence", "Consolidation", "Weak Down", "Moderate Down", "Strong Down") filter_obv(src, length, obvMode, threshold, useObvFilter, useMovingAvg)   filter_obv: Filter signals based on OBV states   Parameters:      src (float) : The source series (default: close)      length (int) : The length of the OBV moving average calculation      obvMode (string) : OBV mode to filter ("Strong", "Moderate", "Weak")      threshold (float) : Optional threshold for additional filtering      useObvFilter (bool) : Whether to apply the OBV filter      useMovingAvg (bool) : Whether to use moving average as threshold   Returns: Filtered result indicating whether the signal should be used filter_cmf(length, cmfThreshold, useCmfFilter, useMovingAvg)   CMF filter for filtering signals based on Chaikin Money Flow   Parameters:      length (int) : The length of the CMF calculation      cmfThreshold (float) : The CMF threshold      useCmfFilter (bool) : Whether to apply the CMF filter      useMovingAvg (bool) : Whether to use moving average as threshold   Returns: Filtered result indicating whether the signal should be used filter_vwap(useVwapFilter)   VWAP filter for filtering signals based on Volume-Weighted Average Price   Parameters:      useVwapFilter (bool) : Whether to apply the VWAP filter   Returns: Filtered result indicating whether the signal should be used filter_pvt(length, pvtThreshold, usePvtFilter, useMovingAvg)   PVT filter for filtering signals based on Price Volume Trend   Parameters:      length (int) : The length of the PVT moving average calculation      pvtThreshold (float) : The PVT threshold      usePvtFilter (bool) : Whether to apply the PVT filter      useMovingAvg (bool) : Whether to use moving average as threshold   Returns: Filtered result indicating whether the signal should be used filter_vo(shortLength, longLength, voThreshold, useVoFilter, useMovingAvg)   VO filter for filtering signals based on Volume Oscillator   Parameters:      shortLength (int) : The length of the short-term volume moving average      longLength (int) : The length of the long-term volume moving average      voThreshold (float) : The Volume Oscillator threshold      useVoFilter (bool) : Whether to apply the VO filter      useMovingAvg (bool) : Whether to use moving average as threshold   Returns: Filtered result indicating whether the signal should be used filter_cho(shortLength, longLength, choThreshold, useChoFilter, useMovingAvg)   CHO filter for filtering signals based on Chaikin Oscillator   Parameters:      shortLength (int) : The length of the short-term ADI moving average      longLength (int) : The length of the long-term ADI moving average      choThreshold (float) : The Chaikin Oscillator threshold      useChoFilter (bool) : Whether to apply the CHO filter      useMovingAvg (bool) : Whether to use moving average as threshold   Returns: Filtered result indicating whether the signal should be used filter_fi(length, fiThreshold, useFiFilter, useMovingAvg)   FI filter for filtering signals based on Force Index   Parameters:      length (int) : The length of the FI calculation      fiThreshold (float) : The Force Index threshold      useFiFilter (bool) : Whether to apply the FI filter      useMovingAvg (bool) : Whether to use moving average as threshold   Returns: Filtered result indicating whether the signal should be used filter_garman_klass_volatility(length, useGkFilter)   Parameters:      length (int)      useGkFilter (bool) filter_frama(src, length, useFramaFilter)   Parameters:      src (float)      length (int)      useFramaFilter (bool) filter_bollinger_bands(src, length, stdDev, useBollingerFilter)   Parameters:      src (float)      length (int)      stdDev (float)      useBollingerFilter (bool) filter_keltner_channel(src, length, atrMult, useKeltnerFilter)   Parameters:      src (float)      length (simple int)      atrMult (float)      useKeltnerFilter (bool) regime_filter(src, threshold, useRegimeFilter)   Regime filter for filtering signals based on trend strength   Parameters:      src (float) : The source series      threshold (float) : The threshold for the filter      useRegimeFilter (bool) : Whether to apply the regime filter   Returns: Filtered result indicating whether the signal should be used regime_filter_v2(src, threshold, useRegimeFilter)   Regime filter for filtering signals based on trend strength   Parameters:      src (float) : The source series      threshold (float) : The threshold for the filter      useRegimeFilter (bool) : Whether to apply the regime filter   Returns: Filtered result indicating whether the signal should be used filter_adx(src, length, adxThreshold, useAdxFilter)   ADX filter for filtering signals based on ADX strength   Parameters:      src (float) : The source series      length (simple int) : The length of the ADX calculation      adxThreshold (int) : The ADX threshold      useAdxFilter (bool) : Whether to apply the ADX filter   Returns: Filtered result indicating whether the signal should be used filter_volatility(minLength, maxLength, useVolatilityFilter)   Volatility filter for filtering signals based on volatility   Parameters:      minLength (simple int) : The minimum length for ATR calculation      maxLength (simple int) : The maximum length for ATR calculation      useVolatilityFilter (bool) : Whether to apply the volatility filter   Returns: Filtered result indicating whether the signal should be used filter_ulcer(src, length, ulcerThreshold, useUlcerFilter)   Ulcer Index filter for filtering signals based on Ulcer Index   Parameters:      src (float) : The source series      length (int) : The length of the Ulcer Index calculation      ulcerThreshold (float) : The Ulcer Index threshold (default: average Ulcer Index)      useUlcerFilter (bool) : Whether to apply the Ulcer Index filter   Returns: Filtered result indicating whether the signal should be used filter_stddev(src, length, stdDevThreshold, useStdDevFilter)   Standard Deviation filter for filtering signals based on Standard Deviation   Parameters:      src (float) : The source series      length (int) : The length of the Standard Deviation calculation      stdDevThreshold (float) : The Standard Deviation threshold (default: average Standard Deviation)      useStdDevFilter (bool) : Whether to apply the Standard Deviation filter   Returns: Filtered result indicating whether the signal should be used filter_macdv(src, shortLength, longLength, signalSmoothing, macdVThreshold, useMacdVFilter)   MACD-V filter for filtering signals based on MACD-V   Parameters:      src (float) : The source series      shortLength (simple int) : The short length for MACD calculation      longLength (simple int) : The long length for MACD calculation      signalSmoothing (simple int) : The signal smoothing length for MACD      macdVThreshold (float) : The MACD-V threshold (default: average MACD-V)      useMacdVFilter (bool) : Whether to apply the MACD-V filter   Returns: Filtered result indicating whether the signal should be used filter_atr(length, atrThreshold, useAtrFilter)   ATR filter for filtering signals based on Average True Range (ATR)   Parameters:      length (simple int) : The length of the ATR calculation      atrThreshold (float) : The ATR threshold (default: average ATR)      useAtrFilter (bool) : Whether to apply the ATR filter   Returns: Filtered result indicating whether the signal should be used filter_candle_body_and_atr(length, bodyThreshold, atrThreshold, useFilter)   Candle Body and ATR filter for filtering signals   Parameters:      length (simple int) : The length of the ATR calculation      bodyThreshold (float) : The threshold for candle body size (relative to ATR)      atrThreshold (float) : The ATR threshold (default: average ATR)      useFilter (bool) : Whether to apply the candle body and ATR filter   Returns: Filtered result indicating whether the signal should be used filter_atrp(length, atrpThreshold, useAtrpFilter)   ATRP filter for filtering signals based on ATR Percentage (ATRP)   Parameters:      length (simple int) : The length of the ATR calculation      atrpThreshold (float) : The ATRP threshold (default: average ATRP)      useAtrpFilter (bool) : Whether to apply the ATRP filter   Returns: Filtered result indicating whether the signal should be used filter_jma(src, length, phase, useJmaFilter)   Parameters:      src (float)      length (simple int)      phase (float)      useJmaFilter (bool) filter_cidi(src, rsiLength, shortMaLength, longMaLength, useCidiFilter)   Parameters:      src (float)      rsiLength (simple int)      shortMaLength (int)      longMaLength (int)      useCidiFilter (bool) filter_rsi(src, length, rsiThreshold, useRsiFilter)   Parameters:      src (float)      length (simple int)      rsiThreshold (float)      useRsiFilter (bool) filter_ichimoku_oscillator(length, threshold, useFilter)   Ichimoku Oscillator filter for filtering signals based on Ichimoku Oscillator   Parameters:      length (int) : The length of the Ichimoku Oscillator calculation      threshold (float) : The threshold for the filter (default: average Ichimoku Oscillator)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_cmb_composite_index(src, shortLength, longLength, threshold, useFilter)   CMB Composite Index filter for filtering signals based on CMB Composite Index   Parameters:      src (float) : The source series      shortLength (simple int) : The short length for CMB calculation      longLength (simple int) : The long length for CMB calculation      threshold (float) : The threshold for the filter (default: average CMB Composite Index)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_connors_rsi(src, rsiLength, rocLength, streakLength, threshold, useFilter)   Connors RSI filter for filtering signals based on Connors RSI   Parameters:      src (float) : The source series      rsiLength (simple int) : The length for RSI calculation      rocLength (int) : The length for ROC calculation      streakLength (simple int) : The length for streak calculation      threshold (float) : The threshold for the filter (default: average Connors RSI)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_coppock_curve(src, roc1Length, roc2Length, wmaLength, threshold, useFilter)   Coppock Curve filter for filtering signals based on Coppock Curve   Parameters:      src (float) : The source series      roc1Length (int) : The length for the first ROC calculation      roc2Length (int) : The length for the second ROC calculation      wmaLength (int) : The length for the WMA calculation      threshold (float) : The threshold for the filter (default: average Coppock Curve)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_pmo(src, pmoLength, smoothingLength, threshold, useFilter)   DecisionPoint Price Momentum Oscillator filter for filtering signals based on PMO   Parameters:      src (float) : The source series      pmoLength (simple int) : The length for PMO calculation      smoothingLength (simple int) : The smoothing length for PMO      threshold (float) : The threshold for the filter (default: average PMO Oscillator)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_macd(src, shortLength, longLength, signalSmoothing, threshold, useFilter)   MACD filter for filtering signals based on MACD   Parameters:      src (float) : The source series      shortLength (simple int) : The short length for MACD calculation      longLength (simple int) : The long length for MACD calculation      signalSmoothing (simple int) : The signal smoothing length for MACD      threshold (float) : The threshold for the filter (default: average MACD)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_macd_histogram(src, shortLength, longLength, signalSmoothing, threshold, useFilter)   MACD-Histogram filter for filtering signals based on MACD-Histogram   Parameters:      src (float) : The source series      shortLength (simple int) : The short length for MACD calculation      longLength (simple int) : The long length for MACD calculation      signalSmoothing (simple int) : The signal smoothing length for MACD      threshold (float) : The threshold for the filter (default: average MACD-Histogram)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_kst(src, r1, r2, r3, r4, sm1, sm2, sm3, sm4, signalLength, threshold, useFilter)   Pring's Know Sure Thing filter for filtering signals based on KST   Parameters:      src (float) : The source series      r1 (int) : The first ROC length      r2 (int) : The second ROC length      r3 (int) : The third ROC length      r4 (int) : The fourth ROC length      sm1 (int) : The first smoothing length      sm2 (int) : The second smoothing length      sm3 (int) : The third smoothing length      sm4 (int) : The fourth smoothing length      signalLength (int) : The signal line smoothing length      threshold (float) : The threshold for the filter (default: average KST Oscillator)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_special_k(src, r1, r2, r3, r4, sm1, sm2, sm3, sm4, threshold, useFilter)   Pring's Special K filter for filtering signals based on Special K   Parameters:      src (float) : The source series      r1 (int) : The first ROC length      r2 (int) : The second ROC length      r3 (int) : The third ROC length      r4 (int) : The fourth ROC length      sm1 (int) : The first smoothing length      sm2 (int) : The second smoothing length      sm3 (int) : The third smoothing length      sm4 (int) : The fourth smoothing length      threshold (float) : The threshold for the filter (default: average Special K)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_roc_momentum(src, rocLength, momentumLength, threshold, useFilter)   ROC and Momentum filter for filtering signals based on ROC and Momentum   Parameters:      src (float) : The source series      rocLength (int) : The length for ROC calculation      momentumLength (int) : The length for Momentum calculation      threshold (float) : The threshold for the filter (default: average ROC and Momentum)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_rrg_relative_strength(src, length, threshold, useFilter)   RRG Relative Strength filter for filtering signals based on RRG Relative Strength   Parameters:      src (float) : The source series      length (int) : The length for RRG Relative Strength calculation      threshold (float) : The threshold for the filter (default: average RRG Relative Strength)      useFilter (bool) : Whether to apply the filter   Returns: Filtered result indicating whether the signal should be used filter_alligator(useFilter)   Parameters:      useFilter (bool) filter_wyckoff(useFilter)   Parameters:      useFilter (bool) filter_squeeze_momentum(bbLength, bbStdDev, kcLength, kcMult, useFilter)   Parameters:      bbLength (int)      bbStdDev (float)      kcLength (simple int)      kcMult (float)      useFilter (bool) filter_atr_compression(length, atrThreshold, useFilter)   Parameters:      length (simple int)      atrThreshold (float)      useFilter (bool) filter_low_volume(length, useFilter)   Parameters:      length (int)      useFilter (bool) filter_nvi_accumulation(useFilter)   Parameters:      useFilter (bool) filter_ma_slope(src, length, slopeThreshold, useFilter)   Parameters:      src (float)      length (int)      slopeThreshold (float)      useFilter (bool) filter_adx_low(len, lensig, adxThreshold, useFilter)   Parameters:      len (simple int)      lensig (simple int)      adxThreshold (int)      useFilter (bool) filter_choppiness_index(length, chopThreshold, useFilter)   Parameters:      length (int)      chopThreshold (float)      useFilter (bool) filter_range_detection(length, useFilter)   Parameters:      length (int)      useFilter (bool)
ไลบรารี Pine Script®
โดย crypto_pumpppp
Infinity Market Grid -AynetConcept Imagine viewing the market as a dynamic grid where price, time, and momentum intersect to reveal infinite possibilities. This indicator leverages: Grid-Based Market Flow: Visualizes price action as a grid with zones for: Accumulation Distribution Breakout Expansion Volatility Compression Predictive Dynamic Layers: Forecasts future price zones using historical volatility and momentum. Tracks event probabilities like breakout, fakeout, and trend reversals. Data Science Visuals: Uses heatmap-style layers, moving waveforms, and price trajectory paths. Interactive Alerts: Real-time alerts for high-probability market events. Marks critical zones for "buy," "sell," or "wait." Key Features Market Layers Grid: Creates dynamic "boxes" around price using fractals and ATR-based volatility. These boxes show potential future price zones and probabilities. Volatility and Momentum Waves: Overlay volatility oscillators and momentum bands for directional context. Dynamic Heatmap Zones: Colors the chart dynamically based on breakout probabilities and risk. Price Path Prediction: Tracks price trajectory as a moving "wave" across the grid. How It Works Grid Box Structure: Upper and lower price levels are based on ATR (volatility) and plotted dynamically. Dashed green/red lines show the grid for potential price expansion zones. Heatmap Zones: Colors the background based on probabilities: Green: High breakout probability. Blue: High consolidation probability. Price Path Prediction: Forecasts future price movements using momentum. Plots these as a dynamic "wave" on the chart. Momentum and Volatility Waves: Shows the relationship between momentum and volatility as oscillating waves. Helps identify when momentum exceeds volatility (potential breakouts). Buy/Sell Signals: Triggers when price approaches grid edges with strong momentum. Provides alerts and visual markers. Why Is It Revolutionary? Grid and Wave Synergy: Combines structural price zones (grid boxes) with real-time momentum and volatility waves. Predictive Analytics: Uses momentum-based forecasting to visualize what’s next, not just what’s happening. Dynamic Heatmap: Creates a living map of breakout/consolidation zones in real-time. Scalable for Any Market: Works seamlessly with forex, crypto, and stocks by adjusting the ATR multiplier and box length. This indicator is not just a tool but a framework for understanding market dynamics at a deeper level. Let me know if you'd like to take it even further — for example, adding machine learning-inspired probability models or multi-timeframe analysis! 🚀
อินดิเคเตอร์ Pine Script®
โดย Aynet-London
Enhanced Volume Flow Analysis Pro ♾️ IFEnhanced Volume Flow Analysis Pro (EVFA Pro) A Comprehensive Guide to Understanding and Using Volume Flow Analysis Introduction The Enhanced Volume Flow Analysis Pro (EVFA Pro) represents a sophisticated approach to understanding market dynamics through the lens of volume analysis. This advanced technical indicator has been designed to peel back the layers of market activity, revealing the intricate dance between institutional and retail traders. By combining volume analysis, participant behavior patterns, and market condition recognition, EVFA Pro provides traders with a deeper understanding of market movements and potential opportunities. Understanding the Core Framework At its heart, EVFA Pro works by analyzing and categorizing trading volume based on several key characteristics. The indicator examines not just the raw volume, but also the context in which that volume occurs. It considers factors such as price movement, historical patterns, and market conditions to classify trading activity as either institutional or retail in nature. The framework adapts dynamically to different market environments. Whether you're trading stocks, ETFs, cryptocurrencies, or commodities, the indicator automatically adjusts its parameters to match the typical behavior patterns of each asset class. This adaptability extends to different trading styles as well, with optimizations for everything from quick-paced scalping to longer-term position trading. Market Participant Analysis One of the most powerful aspects of EVFA Pro is its ability to distinguish between institutional and retail trading activity. The indicator accomplishes this through a sophisticated analysis of volume patterns, order flow, and price action. Institutional trading typically leaves distinct footprints in the market - large, well-organized volume patterns that often occur at strategic price levels. EVFA Pro identifies these patterns and separates them from the more scattered, emotion-driven patterns typical of retail trading. The indicator maintains a constant watch on participation rates from both groups. When institutional participation rises above normal levels, it could signal the beginning of a significant move. Similarly, spikes in retail activity, especially when combined with certain price patterns, might indicate potential market turning points. Reading Market Conditions Market conditions are not static, and EVFA Pro recognizes this fundamental truth. The indicator continuously evaluates market conditions, classifying them into four main categories: normal, volatile, ranging, and trending. This classification isn't merely descriptive - it directly influences how the indicator interprets various patterns and signals. In volatile markets, the indicator becomes more conservative in its pattern recognition, requiring stronger confirmation before signaling potential opportunities. During ranging periods, it adjusts to look for shorter-term movements and potential breakout scenarios. In trending markets, the focus shifts to finding continuation patterns and potential exhaustion points. Pattern Recognition and Signal Generation Pattern recognition in EVFA Pro goes beyond simple technical patterns. The indicator looks for complex interactions between volume, price, and participant behavior. It identifies accumulation patterns - periods where institutional buyers are actively building positions, often while keeping price movements relatively subtle to avoid drawing attention. Similarly, it recognizes distribution patterns, where larger players are gradually reducing positions. Signal generation involves a sophisticated weighing of multiple factors. Volume strength, institutional participation, trend alignment, and price momentum all play roles in determining signal strength. This multi-factor approach helps reduce false signals and provides a more reliable indication of potential market moves. Visual Analysis Tools The visual components of EVFA Pro have been carefully designed to present complex information in an intuitive format. The main chart overlay uses color-coded volume bars to show the relative participation of institutional and retail traders. The intensity of these colors varies with volume significance, helping traders quickly identify potentially important market activity. The information table provides a real-time summary of market conditions, participant activity, and detected patterns. This dashboard-style display allows traders to quickly assess market conditions and potential opportunities without needing to analyze multiple indicators. Practical Application in Trading To use EVFA Pro effectively, traders should integrate it into a comprehensive trading strategy. The indicator works best when its signals are considered alongside other forms of analysis and risk management tools. Strong signals from EVFA Pro might suggest potential opportunities, but traders should always consider the broader market context, their own risk tolerance, and their overall trading plan. The indicator's alerts system can help traders stay informed of potentially significant market developments. However, these alerts should be viewed as starting points for analysis rather than automatic trading signals. Each alert provides specific information about the type of pattern or condition detected, allowing traders to quickly assess whether further investigation is warranted. Advanced Features and Customization EVFA Pro offers extensive customization options to suit different trading styles and preferences. Traders can adjust sensitivity levels, color schemes, and display options to match their needs. The indicator also includes special considerations for different trading sessions, allowing for more accurate analysis during pre-market, regular trading hours, and after-hours periods. Market Application and Interpretation Success with EVFA Pro comes from understanding not just what it shows, but why it shows what it does. The indicator's patterns and signals reflect real market dynamics - the actions and reactions of different types of traders. By understanding these underlying dynamics, traders can make more informed decisions about market opportunities and risks. Disclaimer This indicator and documentation are provided for educational and informational purposes only. Trading in financial markets involves substantial risk of loss and is not suitable for every investor. The analysis provided by the Enhanced Volume Flow Analysis Pro indicator should not be considered as financial advice or a recommendation to make any specific trade or investment. Users of this indicator should understand that: 1. Past performance is not indicative of future results 2. All trading decisions and their outcomes are the responsibility of the individual trader 3. This tool should be used as part of a comprehensive trading strategy that includes proper risk management and due diligence 4. Markets can be highly unpredictable, and no technical analysis tool can guarantee success Users should carefully consider their investment objectives, level of experience, and risk appetite before using this indicator. It is strongly recommended to consult with a qualified financial advisor before making any investment decisions.
อินดิเคเตอร์ Pine Script®
โดย Infinitefutures
Chaikin DivergenceOverview The Chaikin Divergence is a powerful technical indicator designed to enhance the traditional Chaikin Oscillator by incorporating divergence detection between the oscillator and price action. This advanced tool not only plots the Chaikin Oscillator but also identifies and highlights bullish and bearish divergences, providing traders with valuable insights into potential trend reversals and momentum shifts. Key Features Chaikin Oscillator Plotting: Visual representation of the Chaikin Oscillator, aiding in the analysis of market momentum based on volume and price. Divergence Detection: Bullish Divergence: Indicates potential upward reversals when price forms lower lows while the oscillator forms higher lows. Bearish Divergence: Signals possible downward reversals when price creates higher highs while the oscillator forms lower highs. Customizable Settings: Fast Length & Slow Length: Adjust the periods for the Exponential Moving Averages (EMA) used in the oscillator calculation. Pivot Detection Parameters: Define the sensitivity of pivot high and pivot low detection with adjustable left and right bars. Bars Lookback for Divergence: Set the number of bars to look back for identifying divergences. Color Customization: Choose distinct colors for bullish and bearish divergence labels to match your trading preferences. Visual Indicators: Divergence Labels: Clear and distinct labels (arrows or dots) on the chart indicating the type and location of divergences. Zero Line: A dashed zero line to reference the oscillator’s crossing points. Chaikin Oscillator Calculation: The indicator calculates the Chaikin Oscillator by subtracting the slow EMA of the Accumulation/Distribution Line (ta.accdist) from the fast EMA. This oscillates around the zero line, indicating buying and selling pressure. Pivot Detection: Utilizes ta.pivothigh and ta.pivotlow functions to identify significant pivot points in price action. These pivot points serve as reference points for divergence analysis. Divergence Identification: Bullish Divergence: Detected when a recent pivot low in price is lower than the previous pivot low, while the corresponding oscillator value is higher than the previous oscillator pivot. Bearish Divergence: Identified when a recent pivot high in price is higher than the previous pivot high, but the oscillator value is lower than the previous oscillator pivot. Label Plotting: When a divergence is detected, the indicator plots a label (arrow or dot) on the chart at the pivot point, signaling the type of divergence. Adding the Indicator: Open TradingView and navigate to the chart where you want to apply the indicator. Open the Pine Editor, paste the Chaikin Oscillator with Divergences script, and add it to your chart. Configuring Settings: Fast Length & Slow Length: Adjust these to modify the sensitivity of the Chaikin Oscillator. Shorter periods make the oscillator more responsive to price changes. Left Bars for Pivots & Right Bars for Pivots: Define how many bars to the left and right are considered when identifying pivot points. Increasing these values makes pivot detection less sensitive. Bars Lookback for Divergence: Set how far back the indicator should search for previous pivot points when identifying divergences. A higher value allows detection over a longer timeframe. Bullish/Bearish Divergence Colors: Choose colors that stand out against your chart background for easy identification of divergence signals. Interpreting Signals: Bullish Divergence Labels: Appear when there's a potential upward reversal, signaling a possible buying opportunity. Bearish Divergence Labels: Show up when a downward reversal might be imminent, indicating a possible selling opportunity. Oscillator Crosses Zero: Pay attention to when the oscillator crosses the zero line, as this can also signal changes in momentum. Combining with Other Indicators: For enhanced trading strategies, combine divergence signals with other technical indicators or chart patterns to confirm potential trade setups.
อินดิเคเตอร์ Pine Script®
โดย zaqwasick
Pulse DPO: Major Cycle Tops and Bottoms█  OVERVIEW Pulse DPO is an oscillator designed to highlight Major Cycle Tops and Bottoms . It works on any market driven by cycles. It operates by removing the short-term noise from the price action and focuses on the market's cyclical nature. This indicator uses a Normalized version of the Detrended Price Oscillator (DPO) on a 0-100 scale, making it easier to identify major tops and bottoms. Credit: The DPO was first developed by William Blau in 1991. █  HOW TO READ IT Pulse DPO oscillates in the range between 0 and 100. A value in the upper section signals an OverBought (OB) condition, while a value in the lower section signals an OverSold (OS) condition. Generally, the triggering of OB and OS conditions don't necessarily translate into swing tops and bottoms, but rather suggest caution on approaching a market that might be overextended. Nevertheless, this indicator has been customized to trigger the signal only during remarkable top and bottom events. I suggest using it on the Daily Time Frame , but you're free to experiment with this indicator on other time frames. The indicator has Built-in Alerts to signal the crossing of the Thresholds. Please don't act on an isolated signal, but rather integrate it to work in conjunction with the indicators present in your Trading Plan. █  OB SIGNAL ON: ENTERING OVERBOUGHT CONDITION When Pulse DPO crosses Above the Top Threshold it Triggers ON the OB signal. At this point the oscillator line shifts to OB color. When Pulse DPO enters the OB Zone, please beware! In this Area the Major Players usually become Active Sellers to the Public. While the OB signal is On, it might be wise to Consider Selling a portion or the whole Long Position. Please note that even though this indicator aims to focus on major tops and bottoms, a strong trending market might trigger the OB signal and stay with it for a long time. That's especially true on young markets and on bubble-mode markets. █  OB SIGNAL OFF: EXITING OVERBOUGHT CONDITION When Pulse DPO crosses Below the Top Threshold it Triggers OFF the OB signal. At this point the oscillator line shifts to its normal color. When Pulse DPO exits the OB Zone, please beware because a Major Top might just have occurred. In this Area the Major Players usually become Aggressive Sellers. They might wind up any remaining Long Positions and Open new Short Positions. This might be a good area to Open Shorts or to Close/Reverse any remaining Long Position. Whatever you choose to do, it's usually best to act quickly because the market is prone to enter into panic mode. █  OS SIGNAL ON: ENTERING OVERSOLD CONDITION When Pulse DPO crosses Below the Bottom Threshold it Triggers ON the OS signal. At this point the oscillator line shifts to OS color. When Pulse DPO enters the OS Zone, please beware because in this Area the Major Players usually become Active Buyers accumulating Long Positions from the desperate Public. While the OS signal is On, it might be wise to Consider becoming a Buyer or to implement a Dollar-Cost Averaging (DCA) Strategy to build a Long Position towards the next Cycle. In contrast to the tops, the OS state usually takes longer to resolve a major bottom. █  OS SIGNAL OFF: EXITING OVERSOLD CONDITION When Pulse DPO crosses Above the Bottom Threshold it Triggers OFF the OS signal. At this point the oscillator line shifts to its normal color. When Pulse DPO exits the OS Zone, please beware because a Major Bottom might already be in place. In this Area the Major Players become Aggresive Buyers. They might wind up any remaining Short Positions and Open new Long Positions. This might be a good area to Open Longs or to Close/Reverse any remaining Short Positions. █  WHY WOULD YOU BE INTERESTED IN THIS INDICATOR? This indicator is built over a solid foundation capable of signaling Major Cycle Tops and Bottoms across many markets. Let's see some examples: Early Bitcoin Years: From 0 to 1242 This chart is in logarithmic mode in order to properly display various exponential cycles. Pulse DPO is properly signaling the major early highs from 9-Jun-2011 at 31.50, to the next one on 9-Apr-2013 at 240 and the epic top from 29-Nov-2013 at 1242. Due to the massive price movements, the OB condition stays pinned during most of the exponential price action. But as you can see, the OB condition quickly vanishes once the Cycle Top has been reached. As the market matures, the OB condition becomes more exceptional and triggers much closer from the Cycle Top. With regards to Cycle Bottoms, the early bottom of 2 after having peaked at 31.50 doesn’t get captured by the indicator. That is the only cycle bottom that escapes the Pulse DPO when the bottom threshold is set at a value of 5. In that event, the oscillator low reached 6.95. Bitcoin Adoption Spreading: From 257 to 73k This chart is in logarithmic mode in order to properly display various exponential cycles. Pulse DPO is properly signaling all the major highs from 17-Dec-2017 at 19k, to the next one on 14-Apr-2021 at 64k and the most recent top from 9-Nov-2021 at 68k. During the massive run of 2017, the OB condition still stayed triggered for a few weeks on each swing top. But on the next cycles it started to signal only for a few days before each swing top actually happened. The OB condition during the last cycle top triggered only for 3 days. Therefore the signal grows in focus as the market matures. At the time of publishing this indicator, Bitcoin printed a new All Time High (ATH) on 13-Mar-2024 at 73k. That run didn’t trigger the OB condition. Therefore, if the indicator is correct the Bitcoin market still has some way to grow during the next months. With regards to Cycle Bottoms, the bottom of 3k after having peaked at19k got captured within the wide OS zone. The bottom of 15k after having peaked at 68k got captured too within the OS accumulation area. Gold Pulse DPO behaves surprisingly well on a long standing market such as Gold. Moving back to the 197x years it’s been signaling most Cycle Tops and Bottoms with precision. During the last cycle, it shows topping at 2k and bottoming at 1.6k. The current price action is signaling OB condition in the range of 2.5k to 2.7k. Looking at past cycles, it tends to trigger on and off at multiple swing tops until reaching the final cycle top. Therefore this might indicate the first wave within a potential gold run. Oil On the Oil market, we can see that most of the cycle tops and bottoms since the 80s got signaled. The only exception being the low from 2020 which didn’t trigger. EURUSD On Forex markets the Pulse DPO also behaves as expected. Looking back at EURUSD we can see the marketing triggering OB and OS conditions during major cycle tops and bottoms from recent times until the 80s. S&P 500 On the S&P 500 the Pulse DPO catched the lows from 2016 and 2020. Looking at present price action, the recent ATH didn’t trigger the OB condition. Therefore, the indicator is allowing room for another leg up during the next months. Amazon On the Amazon chart the Pulse DPO is mirroring pretty accurately the major swings. Scrolling back to the early 2000s, this chart resembles early exponential swings in the crypto space. Tesla Moving onto a younger tech stock, Pulse DPO captures pretty accurately the major tops and bottoms. The chart is shown in logarithmic scale to better display the magnitude of the moves. █  SETTINGS This indicator is ideal for identifying major market turning points while filtering out short-term noise. You are free to adjust the parameters to align with your preferred trading style. Parameters : This section allows you to customize any of the Parameters that shape the Oscillator. Oscillator Length: Defines the period for calculating the Oscillator. Offset: Shifts the oscillator calculation by a certain number of periods, which is typically half the Oscillator Length. Lookback Period: Specifies how many bars to look back to find tops and bottoms for normalization. Smoothing Length: Determines the length of the moving average used to smooth the oscillator. Thresholds : This section allows you to customize the Thresholds that trigger the OB and OS conditions. Top: Defines the value of the Top Threshold. Bottom: Defines the value of the Bottom Threshold.
อินดิเคเตอร์ Pine Script®
โดย gliderfund
5
CRT AMD indicatorThis indicator is based on the Power of three (Accumulation Manipulation Distribution) Cycle, by marking the candle that Sweep the low or high of the previous candle and then closed back inside the range of the previous candle, indicating a possibility of a Manipulation or Reversal. Combining the indicator with HTF Array and LTF Setup Entry will significantly improve the accuracy.
อินดิเคเตอร์ Pine Script®
โดย thesyndicate_forex
1
Leonid's Bitcoin Full Cycle Simple SMA IndicatorThis is a straight-forward and customizable indicator to track Bitcoin cycles, specifically used for helping investors understand where to buy and sell. This is done by using a two year SMA period as the base calculation. With that calculation you create lower and upper bounds for bull market peaks and bear market bottoms. The novel idea here is that you can customize the SMA "strength" for both the upper and lower bounds as alpha decays over time and price get's less volatile with adoption increasing. The multiples are customizable for both the upper and lower bounds along with a mid-line that will adjust based on the settings input. Indicators don't always have to rely on crazy math or outlandish ideas to be useful, sometimes even the simplest of inputs can give investors (especially those that are new) a great base case for their strategy. Something being simple does not diminish the idea or strength behind the data. How to use this indicator: This script must be used on INDEX:BTCUSD (Bitcoin All-Time History Index) with the y-axis being set to Logarithmic scale. Details & how to interpret: The price is colored green when Bitcoin enters a "value zone" meaning it is heavily oversold and likely near a bottom for the bear market cycle. The price is colored red when Bitcoin enters an "overbought zone" meaning it is heavily overbought and is likely near a top for the bull market cycle. Along with the upper and lower bound I have plotted a mid-line (in orange) to establish a neutral zone which helps depict what phase of the cycle we're in (under mid-line = bearish/accumulation phase, over mid-line = bullish/distribution phase). The inputs for the upper and lower bound are customizable and will need to be adjusted over time as alpha decay will occur as time goes on. Currently the numbers are as follows: 0.2 for the lower bound 4.675 for the upper bound Both inputs can be modified depending on your risk tolerance. Mathematically it is safe to assume these numbers will decrease as time goes on and volatility during cycle peaks & troughs is reduced. I've also plotted an upper bound "heat zone" which is shaded in green, this area is great for signaling when you should be preparing to begin taking profits. It takes the upper bound and subtracts the lower bound to derive the band. All the colors are customizable and this indicator is best used on a line chart but can be customized to use on a bar chart/candlestick as well. Simple Moving Averages are a very basic indicator but are often extremely powerful because the majority of traders/investors are looking at such levels which creates a psychological/herd effect. Another good example is the law of round numbers. Regardless this script can be adapted with EMAs or additional standard deviations if necessary. If you have any questions or concerns please don't hesitate to message me.
อินดิเคเตอร์ Pine Script®
โดย DurdensBitcoinLedger
CRT candles Multi-Timeframe Intrabar(open Source ) # CRT candles Multi-Timeframe Intrabar Indicator( open source ) This advanced indicator visualizes Candle Range Theory (CRT) across multiple timeframes, providing traders with a comprehensive view of market structure and potential high-probability setups. ## Key Features: - Supports 7 timeframes: 30 minutes, 1 hour, 2 hours, 4 hours, daily, weekly, and monthly - Customizable color schemes for each timeframe - Options to display mid-level (50%) lines for each range - Bullish and bearish touch detection with customizable label display - End-of-line labels for easy identification of CRT levels - Flexible alert system for touch detections on each timeframe - Adjustable minimum and maximum bar count for range validity - Options for wick touch and body touch detection ## How It Works: The indicator plots CRT ranges for each selected timeframe, identifying potential accumulation, manipulation, and distribution phases. It detects when price touches these levels, providing visual cues and optional alerts for potential trade setups. snapshot ## Customization: Users can fine-tune the indicator's appearance and functionality through various input options, including: - Toggling timeframes on/off snapshot - Adjusting colors for range lines and mid-levels - Controlling label display and count - Setting alert preferences - Adjusting line widths and label offsets ## Usage: This indicator is designed for traders familiar with Candle Range Theory and multi-timeframe analysis. It can be used to identify potential entry and exit points, confirm trends, and spot potential reversals across different timeframes. ## Note: This indicator is for educational and informational purposes only. Always combine with other forms of analysis and proper risk management when making trading decisions. ## Credits: Inspired by Romeo's Candle Range Theory and developed to provide a comprehensive multi-timeframe analysis tool.
อินดิเคเตอร์ Pine Script®
โดย GoldForexDoc
9
Support, Resistance & Liquidity Pool ZonesSupport, Resistance & Liquidity Pool Zones This indicator automatically detects and plots support and resistance levels based on pivot points and highlights liquidity pool zones, areas where the trading volume exceeds the average over a set number of bars. It is designed to help traders identify key price levels and liquidity traps that can trigger significant market reactions. Key Features: Support & Resistance Levels: The indicator identifies pivot highs and pivot lows as potential resistance and support levels, respectively. You can customize the number of levels shown on the chart, making it easier to focus on the most recent and relevant price levels. Liquidity Pool Zones: The script detects liquidity pool zones, which are areas with above-average trading volume. These zones often act as regions of interest where price accumulation or distribution occurs, potentially leading to significant price moves. Liquidity zones are shaded to help traders visually identify areas of high interest in the market. Customizable Settings: You can adjust the pivot period to fine-tune how the indicator calculates support and resistance. Control the number of support/resistance levels displayed on the chart and the period used to detect liquidity pools. Customize the colors for support, resistance, and liquidity zones to match your charting preferences. Alerts: The script includes built-in alerts for when the price breaks above resistance or falls below support, helping traders catch key breakout opportunities. How It Works: The script calculates support and resistance levels using pivot highs and lows based on the user-defined pivot period. It monitors liquidity pool zones by comparing the current trading volume with the average volume over a customizable period. When the volume exceeds the set threshold, a liquidity pool zone is highlighted, providing insight into where the market may accumulate or distribute. Alerts are triggered when the price breaks above the first resistance level or falls below the first support level, giving traders immediate notification of key market events. How to Use: Tune the Pivot Period: Adjust the pivot period to your preferred time horizon (default: 10 bars). Set Liquidity Pool Parameters: Customize the number of bars considered for liquidity pool detection and the volume multiplier to detect high-volume zones. Monitor Breakouts: Use the built-in alerts to catch potential breakout or breakdown opportunities near support and resistance levels. This script is ideal for traders looking for an easy-to-use tool to visualize support and resistance levels and liquidity pools, aiding in decision-making and trade management.
อินดิเคเตอร์ Pine Script®
โดย teja50516
Bitcoin Cycle Master [InvestorUnknown]The "Bitcoin Cycle Master" indicator is designed for in-depth, long-term analysis of Bitcoin's price cycles, using several key metrics to track market behavior and forecast potential price tops and bottoms. The indicator integrates multiple moving averages and on-chain metrics, offering a comprehensive view of Bitcoin’s historical and projected performance. Each of its components plays a crucial role in identifying critical cycle points: Top Cap: This is a multiple of the Average Cap, which is calculated as the cumulative sum of Bitcoin’s price (price has a longer history than Market Cap) divided by its age in days. Top Cap serves as an upper boundary for speculative price peaks, multiplied by a factor of 35. Time_dif() => date = ta.valuewhen(bar_index == 0, time, 0) sec_r = math.floor(date / 1000) min_r = math.floor(sec_r / 60) h_r = math.floor(min_r / 60) d_r = math.floor(h_r / 24) // Launch of BTC start = timestamp(2009, 1, 3, 00, 00) sec_rb = math.floor(start / 1000) min_rb = math.floor(sec_rb / 60) h_rb = math.floor(min_rb / 60) d_rb = math.floor(h_rb / 24) difference = d_r - d_rb AverageCap() => ta.cum(btc_price) / (Time_dif() + btc_age) TopCap() => // To calculate Top Cap, it is first necessary to calculate Average Cap, which is the cumulative sum of Market Cap divided by the age of the market in days. // This creates a constant time-based moving average of market cap. // Once Average cap is calculated, those values are multiplied by 35. The result is Top Cap. // For AverageCap the BTC price was used instead of the MC because it has more history // (the result should have minimal if any deviation since MC would have to be divided by Supply) AverageCap() * 35 Delta Top: Defined as the difference between the Realized Cap and the Average Cap, this metric is further multiplied by a factor of 7. Delta Top provides a historically reliable signal for Bitcoin market cycle tops. DeltaTop() => // Delta Cap = Realized Cap - Average Cap // Average Cap is explained in the Top Cap section above. // Once Delta Cap is calculated, its values over time are then multiplied by 7. The result is Delta Top. (RealizedPrice() - AverageCap()) * 7 Terminal Price: Derived from Coin Days Destroyed, Terminal Price normalizes Bitcoin’s historical price behavior by its finite supply (21 million bitcoins), offering an adjusted price forecast as all bitcoins approach being mined. The original formula for Terminal Price didn’t produce expected results, hence the calculation was adjusted slightly. CVDD() => // CVDD stands for Cumulative Value Coin Days Destroyed. // Coin Days Destroyed is a term used for bitcoin to identify a value of sorts to UTXO’s (unspent transaction outputs). They can be thought of as coins moving between wallets. (MCR - TV) / 21000000 TerminalPrice() => // Theory: // Before Terminal price is calculated, it is first necessary to calculate Transferred Price. // Transferred price takes the sum of > Coin Days Destroyed and divides it by the existing supply of bitcoin and the time it has been in circulation. // The value of Transferred Price is then multiplied by 21. Remember that there can only ever be 21 million bitcoin mined. // This creates a 'terminal' value as the supply is all mined, a kind of reverse supply adjustment. // Instead of heavily weighting later behavior, it normalizes historical behavior to today. By normalizing by 21, a terminal value is created // Unfortunately the theoretical calculation didn't produce results it should, in pinescript. // Therefore the calculation was slightly adjusted/improvised TransferredPrice = CVDD() / (Supply * math.log(btc_age)) tp = TransferredPrice * 210000000 * 3 Realized Price: Calculated as the Market Cap Realized divided by the current supply of Bitcoin, this metric shows the average value of Bitcoin based on the price at which coins last moved, giving a market consensus price for long-term holders. CVDD (Cumulative Value Coin Days Destroyed): This on-chain metric analyzes Bitcoin’s UTXOs (unspent transaction outputs) and the velocity of coins moving between wallets. It highlights key market dynamics during prolonged accumulation or distribution phases. Balanced Price: The Balanced Price is the difference between the Realized Price and the Terminal Price, adjusted by Bitcoin's supply constraints. This metric provides a useful signal for identifying oversold market conditions during bear markets. BalancedPrice() => // It is calculated by subtracting Transferred Price from Realized Price RealizedPrice() - (TerminalPrice() / (21 * 3)) Each component can be toggled individually, allowing users to focus on specific aspects of Bitcoin’s price cycle and derive meaningful insights from its long-term behavior. The combination of these models provides a well-rounded view of both speculative peaks and long-term value trends. Important consideration: Top Cap did historically provide reliable signals for cycle peaks, however it may not be a relevant indication of peaks in the future.
อินดิเคเตอร์ Pine Script®
โดย InvestorUnknown
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