Liquidation Map [Alpha Extract]A sophisticated liquidity distribution visualization system that identifies potential liquidation zones through pivot-based detection and renders them as an interactive histogram with cumulative distance-to-liquidation curves. Utilizing multi-exchange volume aggregation and ATR-scaled pocket detection, this indicator delivers institutional-grade liquidity mapping with real-time histogram display showing relative concentration of long and short liquidation levels across configurable price ranges. The system's box-based rendering architecture combined with cumulative distribution overlays provides comprehensive visual assessment of asymmetric liquidity positioning for strategic trade planning.
🔶 Advanced Multi-Exchange Aggregation Framework
Implements intelligent ticker detection and multi-source volume aggregation across major exchanges including Binance, Bybit, KuCoin, OKX, and MEXC for accurate liquidity weight calculations. The system automatically identifies base currency (BTC, ETH, SOL) from chart ticker, retrieves volume data from matching perpetual contracts across multiple venues, and aggregates into composite volume metric for enhanced pocket weighting accuracy.
🔶 Pivot-Based Liquidation Pocket Detection
Features sophisticated swing point identification using configurable pivot width with ATR-scaled vertical zone construction for volatility-adaptive pocket sizing. The system detects pivot highs for short liquidation zones (placed above swing) and pivot lows for long liquidation zones (placed below swing), applying 200-period ATR with percentage multipliers to determine pocket heights that adjust to market volatility conditions.
🔶 Interactive Histogram Visualization Engine
Provides real-time box-based histogram rendering in indicator pane with configurable bin counts (up to 400 columns) and adjustable height, displaying liquidity concentration across fixed percentage range above and below current price. The system calculates bin sizes from view range, accumulates pocket weights into price bins, and renders vertical bars with gradient color intensity reflecting relative liquidity concentration at each price level.
🔶 Cumulative Distance Overlay System
Implements innovative cumulative distribution curves showing aggregate liquidity distance from current price for both long (left) and short (right) positions. The system calculates running totals of pocket weights from current price outward in both directions, normalizes against maximum span, and overlays line segments showing how much total liquidity exists at various distances, enabling instant assessment of liquidation cascade potential.
🔶 Dynamic Price Range Adaptation
Features fixed percentage-based view window that maintains consistent price range visualization across all timeframes and instruments, automatically centering histogram on current price with configurable +/- percentage bounds. The system recalculates histogram bins and pocket distributions on each bar close, ensuring visualization adapts to price movement while maintaining interpretable scale regardless of volatility regime.
🔶 Touch Detection and Weight Adjustment
Provides intelligent pocket state tracking that identifies when price trades through liquidation zones and applies configurable weight multipliers to touched pockets for historical context. The system monitors price interaction with pocket midpoints, marks pockets as "hit" when violated, and optionally increases their visual weight (default 5x) to emphasize historical liquidation levels while distinguishing from untouched future zones.
🔶 Gradient Intensity Color System
Implements sophisticated color gradient engine that modulates bar opacity from transparent to opaque based on relative liquidity concentration within each bin. The system normalizes bin values against maximum liquidity, applies color interpolation from faded to vivid hues, and distinguishes long liquidation zones (cyan) from short liquidation zones (yellow/gold) with current price column highlighted in red for instant orientation.
🔶 Performance-Optimized Rendering Architecture
Utilizes efficient box and line object management with dynamic allocation based on histogram configuration, implementing intelligent cleanup and reuse to maintain smooth performance. The system includes adaptive line budget calculations that adjust segment density for cumulative curves based on available object limits, ensuring consistent operation even with maximum histogram resolution settings.
🔶 Asymmetric Distribution Analysis
Calculates separate cumulative distributions for long and short liquidation zones split at current price, enabling identification of imbalanced liquidity positioning. The system normalizes distributions against respective maximums and overlays both curves on single histogram, allowing traders to instantly assess whether more liquidation risk exists above (shorts vulnerable) or below (longs vulnerable) current price levels.
🔶 Configurable Label and Scale System
Provides price axis labeling with adjustable frequency to reduce clutter while maintaining reference points, displaying price values at regular column intervals with configurable offset positioning. The system includes current price label showing exact value and percentile position within view range, offering both absolute price reference and relative positioning context for distribution interpretation.
🔶 Historical Pocket Persistence Framework
Maintains rolling window of liquidation pockets up to 3000 bars with automatic expiration management and optional preservation of touched zones for historical analysis. The system tracks pocket creation time, monitors age against lookback limits, and manages array cleanup to prevent memory overflow while retaining relevant historical liquidation levels for pattern recognition and support/resistance validation.
This indicator delivers sophisticated liquidity distribution analysis through histogram visualization and cumulative distance curves that reveal asymmetric positioning of potential liquidation levels. Unlike simple liquidation heatmaps that show absolute levels, the Liquidation Map's cumulative distribution overlays instantly communicate how much total liquidity exists at various distances from current price, enabling assessment of cascade potential. The system's multi-exchange volume aggregation, touch-weighted historical zones, and fixed-range visualization make it essential for traders seeking strategic positioning around institutional liquidity clusters in cryptocurrency futures markets. The histogram format enables instant identification of price levels where concentrated liquidations may trigger significant volatility or reversal events, while the asymmetric distribution curves reveal whether market structure favors upside or downside cascades.
Cerca negli script per "histogram"
CryptoFlux Dynamo [JOAT]CryptoFlux Dynamo: Velocity Scalping Strategy
WHAT THIS STRATEGY IS
CryptoFlux Dynamo is an open-source Pine Script v6 strategy designed for momentum-based scalping on cryptocurrency perpetual futures. It combines multiple technical analysis methods into a unified system that adapts its behavior based on current market volatility conditions.
This script is published open-source so you can read, understand, and modify the complete logic. The description below explains everything the strategy does so that traders who cannot read Pine Script can fully understand how it works before using it.
HOW THIS STRATEGY IS ORIGINAL AND WHY THE INDICATORS ARE COMBINED
This strategy uses well-known indicators (MACD, EMA, RSI, MFI, Bollinger Bands, Keltner Channels, ATR). The originality is not in the individual indicators themselves, but in the specific way they are integrated into a regime-adaptive system. Here is the detailed justification for why these components are combined and how they work together:
The Problem Being Solved:
Standard indicator-based strategies use fixed thresholds. For example, a typical MACD strategy might enter when the histogram crosses above zero. However, in cryptocurrency markets, volatility changes dramatically throughout the day and week. A MACD crossover during a low-volatility consolidation period has very different implications than the same crossover during a high-volatility trending period. Using the same entry thresholds and stop distances in both conditions leads to either:
Too many false signals during consolidation (if thresholds are loose)
Missing valid opportunities during expansion (if thresholds are tight)
Stops that are too tight during volatility spikes (causing premature exits)
Stops that are too wide during compression (giving back profits)
The Solution Approach:
This strategy first classifies the current volatility regime using normalized ATR (ATR as a percentage of price), then dynamically adjusts ALL other parameters based on that classification. This creates a context-aware system rather than a static threshold comparison.
How Each Component Contributes to the System:
ATR-Based Regime Classification (The Foundation)
The strategy calculates ATR over 21 periods, smooths it with a 13-period EMA to reduce noise from wicks, then divides by price to get a normalized percentage. This ATR% is classified into three regimes:
- Compression (ATR% < 0.8%): Market is consolidating, breakouts are more likely but false signals are common
- Expansion (ATR% 0.8% - 1.6%): Normal trending conditions
- Velocity (ATR% > 1.6%): High volatility, larger moves but also larger adverse excursions
This regime classification then controls stop distances, profit targets, trailing stop offsets, and signal strength requirements. The regime acts as a "meta-parameter" that tunes the entire system.
EMA Ribbon (8/21/34) - Trend Structure Detection
The three EMAs establish trend direction and structure. When EMA 8 > EMA 21 > EMA 34, the trend structure is bullish. The slope of the middle EMA (21) is calculated over 8 bars and converted to degrees using arctangent. This slope measurement quantifies trend strength, not just direction.
Why these specific periods? The 8/21/34 sequence follows Fibonacci-like spacing and provides good separation on 5-minute cryptocurrency charts. The fast EMA (8) responds to immediate price action, the mid EMA (21) represents the short-term trend, and the slow EMA (34) acts as a trend filter.
The EMA ribbon works with the regime classification: during compression regimes, the strategy requires stronger ribbon alignment before entry because false breakouts are more common.
MACD (8/21/5) - Momentum Measurement
The MACD uses faster parameters (8/21/5) than the standard (12/26/9) because cryptocurrency markets move faster than traditional markets. The histogram is smoothed with a 5-period EMA to reduce noise.
The key innovation is the adaptive histogram baseline. Instead of using a fixed threshold, the strategy calculates a rolling baseline from the smoothed absolute histogram value, then multiplies by a sensitivity factor (1.15). This means the threshold for "significant momentum" automatically adjusts based on recent momentum levels.
The MACD works with the regime classification: during velocity regimes, the histogram baseline is effectively higher because recent momentum has been stronger, preventing entries on relatively weak momentum.
RSI (21 period) and MFI (21 period) - Independent Momentum Confirmation
RSI measures momentum using price changes only. MFI (Money Flow Index) measures momentum using price AND volume. By requiring both to confirm, the strategy filters out price moves that lack volume support.
The 21-period length is longer than typical (14) to reduce noise on 5-minute charts. The trigger threshold (55 for longs, 45 for shorts) is slightly offset from 50 to require momentum in the trade direction, not just neutral readings.
These indicators work together: a signal requires RSI > 55 AND MFI > 55 for longs. This dual confirmation reduces false signals from price manipulation or low-volume moves.
Bollinger Bands (1.5 mult) and Keltner Channels (1.8 mult) - Squeeze Detection
When Bollinger Bands contract inside Keltner Channels, volatility is compressing and a breakout is likely. This is the "squeeze" condition. When the bands expand back outside the channels, the squeeze "releases."
The strategy uses a 1.5 multiplier for Bollinger Bands (tighter than standard 2.0) and 1.8 for Keltner Channels. These values were chosen to identify meaningful squeezes on 5-minute cryptocurrency charts without triggering too frequently.
The squeeze detection works with the regime classification: squeeze releases during compression regimes receive additional signal strength points because breakouts from consolidation are more significant.
Volume Impulse Detection - Institutional Participation Filter
The strategy calculates a volume baseline (34-period SMA) and standard deviation. A "volume impulse" is detected when current volume exceeds the baseline by 1.15x OR when the volume z-score exceeds 0.5.
This filter ensures entries occur when there is meaningful market participation, not during low-volume periods where price moves are less reliable.
Volume impulse is required for all entries and adds points to the composite signal strength score.
Cycle Oscillator - Trend Alignment Filter
The strategy calculates a 55-period EMA as a cycle basis, then measures price deviation from this basis as a percentage. When price is more than 0.15% above the cycle basis, the cycle is bullish. When more than 0.15% below, the cycle is bearish.
This filter prevents counter-trend entries. Long signals require bullish cycle alignment; short signals require bearish cycle alignment.
BTC Dominance Filter (Optional) - Market Regime Filter
The strategy can optionally use BTC.D (Bitcoin Dominance) as a market regime filter. When BTC dominance is rising (slope > 0.12), the market is in "risk-off" mode and long entries on altcoins are filtered. When dominance is falling (slope < -0.12), short entries are filtered.
This filter is optional because the BTC.D data feed may lag during low-liquidity periods.
How The Components Work Together (The Mashup Justification):
The strategy uses a composite scoring system where each signal pathway contributes points:
Trend Break pathway (30 points): Requires EMA ribbon alignment + positive slope + price breaks above recent structure high
Momentum Surge pathway (30 points): Requires MACD histogram > adaptive baseline + MACD line > signal + RSI > 55 + MFI > 55 + volume impulse
Squeeze Release pathway (25 points): Requires BB inside KC (squeeze) then release + momentum bias + histogram confirmation
Micro Pullback pathway (15 points): Requires shallow retracement to fast EMA within established trend + histogram confirmation + volume impulse
Additional modifiers:
+5 points if volume impulse is present, -5 if absent
+5 points in velocity regime, -2 in compression regime
+5 points if cycle is aligned, -5 if counter-trend
A trade only executes when the composite score reaches the minimum threshold (default 55) AND all filters agree (session, cycle bias, BTC dominance if enabled).
This scoring system is the core innovation: instead of requiring ALL conditions to be true (which would generate very few signals) or ANY condition to be true (which would generate too many false signals), the strategy requires ENOUGH conditions to be true, with different conditions contributing different weights based on their reliability.
HOW THE STRATEGY CALCULATES ENTRIES AND EXITS
Entry Logic:
1. Calculate current volatility regime from ATR%
2. Calculate all indicator values (MACD, EMA, RSI, MFI, squeeze, volume)
3. Evaluate each signal pathway and sum points
4. Check all filters (session, cycle, dominance, kill switch)
5. If composite score >= 55 AND all filters pass, generate entry signal
6. Calculate position size based on risk per trade and regime-adjusted stop distance
7. Execute entry with regime name as comment
Position Sizing Formula:
RiskCapital = Equity * (0.65 / 100)
StopDistance = ATR * StopMultiplier(regime)
RawQuantity = RiskCapital / StopDistance
MaxQuantity = Equity * (12 / 100) / Price
Quantity = min(RawQuantity, MaxQuantity)
Quantity = round(Quantity / 0.001) * 0.001
This ensures each trade risks approximately 0.65% of equity regardless of volatility, while capping total exposure at 12% of equity.
Stop Loss Calculation:
Stop distance is ATR multiplied by a regime-specific multiplier:
Compression regime: 1.05x ATR (tighter stops because moves are smaller)
Expansion regime: 1.55x ATR (standard stops)
Velocity regime: 2.1x ATR (wider stops to avoid premature exits during volatility)
Take Profit Calculation:
Target distance is ATR multiplied by regime-specific multiplier and base risk/reward:
Compression regime: 1.6x ATR * 1.8 base R:R * 0.9 regime bonus = approximately 2.6x ATR
Expansion regime: 2.05x ATR * 1.8 base R:R * 1.0 regime bonus = approximately 3.7x ATR
Velocity regime: 2.8x ATR * 1.8 base R:R * 1.15 regime bonus = approximately 5.8x ATR
Trailing Stop Logic:
When adaptive trailing is enabled, the strategy calculates a trailing offset based on ATR and regime:
Compression regime: 1.1x base offset (looser trailing to avoid noise)
Expansion regime: 1.0x base offset (standard)
Velocity regime: 0.8x base offset (tighter trailing to lock in profits during fast moves)
The trailing stop only activates when it would be tighter than the initial stop.
Momentum Fail-Safe Exits:
The strategy closes positions early if momentum reverses:
Long positions close if MACD histogram turns negative OR EMA ribbon structure breaks (fast EMA crosses below mid EMA)
Short positions close if MACD histogram turns positive OR EMA ribbon structure breaks
This prevents holding through momentum reversals even if stop loss hasn't been hit.
Kill Switch:
If maximum drawdown exceeds 6.5%, the strategy disables new entries until manually reset. This prevents continued trading during adverse conditions.
HOW TO USE THIS STRATEGY
Step 1: Apply to Chart
Use a 5-minute chart of a high-liquidity cryptocurrency perpetual (BTC/USDT, ETH/USDT recommended)
Ensure at least 200 bars of history are loaded for indicator stabilization
Use standard candlestick charts only (not Heikin Ashi, Renko, or other non-standard types)
Step 2: Understand the Visual Elements
EMA Ribbon: Three lines (8/21/34 periods) showing trend structure. Bullish when stacked upward, bearish when stacked downward.
Background Color: Shows current volatility regime
- Indigo/dark blue = Compression (low volatility)
- Purple = Expansion (normal volatility)
- Magenta/pink = Velocity (high volatility)
Bar Colors: Reflect signal strength divergence. Brighter colors indicate stronger directional bias.
Triangle Markers: Entry signals. Up triangles below bars = long entry. Down triangles above bars = short entry.
Dashboard (top-right): Real-time display of regime, ATR%, signal strengths, position status, stops, targets, and risk metrics.
Step 3: Interpret the Dashboard
Regime: Current volatility classification (Compression/Expansion/Velocity)
ATR%: Normalized volatility as percentage of price
Long/Short Strength: Current composite signal scores (0-100)
Cycle Osc: Price deviation from 55-period EMA as percentage
Dominance: BTC.D slope and filter status
Position: Current position direction or "Flat"
Stop/Target: Current stop loss and take profit levels
Kill Switch: Status of drawdown protection
Volume Z: Current volume z-score
Impulse: Whether volume impulse condition is met
Step 4: Adjust Parameters for Your Needs
For more conservative trading: Increase "Minimum Composite Signal Strength" to 65 or higher
For more aggressive trading: Decrease to 50 (but expect more false signals)
For higher timeframes (15m+): Increase "Structure Break Window" to 12-15, increase "RSI Momentum Trigger" to 58
For lower liquidity pairs: Increase "Volume Impulse Multiplier" to 1.3, increase slippage in strategy properties
To disable short selling: Uncheck "Enable Short Structure"
To disable BTC dominance filter: Uncheck "BTC Dominance Confirmation"
STRATEGY PROPERTIES (BACKTEST SETTINGS)
These are the exact settings used in the strategy's Properties dialog box. You must use these same settings when evaluating the backtest results shown in the publication:
Initial Capital: $100,000
Justification: This amount is higher than typical retail accounts. I chose this value to demonstrate percentage-based returns that scale proportionally. The strategy uses percentage-based position sizing (0.65% risk per trade), so a $10,000 account would see the same percentage returns with 10x smaller position sizes. The absolute dollar amounts in the backtest should be interpreted as percentages of capital.
Commission: 0.04% (commission_value = 0.04)
Justification: This reflects typical perpetual futures exchange fees. Major exchanges charge between 0.02% (maker) and 0.075% (taker). The 0.04% value is a reasonable middle estimate. If your exchange charges different fees, adjust this value accordingly. Higher fees will reduce net profitability.
Slippage: 1 tick
Justification: This is conservative for liquid pairs like BTC/USDT on major exchanges during normal conditions. For less liquid altcoins or during high volatility, actual slippage may be higher. If you trade less liquid pairs, increase this value to 2-3 ticks for more realistic results.
Pyramiding: 1
Justification: No position stacking. The strategy holds only one position at a time. This simplifies risk management and prevents overexposure.
calc_on_every_tick: true
Justification: The strategy evaluates on every price update, not just bar close. This is necessary for scalping timeframes where waiting for bar close would miss opportunities. Note that this setting means backtest results may differ slightly from bar-close-only evaluation.
calc_on_order_fills: true
Justification: The strategy recalculates immediately after order fills for faster response to position changes.
RISK PER TRADE JUSTIFICATION
The default risk per trade is 0.65% of equity. This is well within the TradingView guideline that "risking more than 5-10% on a trade is not typically considered viable."
With the 12% maximum exposure cap, even if the strategy takes multiple consecutive losses, the total risk remains manageable. The kill switch at 6.5% drawdown provides additional protection by halting new entries during adverse conditions.
The position sizing formula ensures that stop distance (which varies by regime) is accounted for, so actual risk per trade remains approximately 0.65% regardless of volatility conditions.
SAMPLE SIZE CONSIDERATIONS
For statistically meaningful backtest results, you should select a dataset that generates at least 100 trades. On 5-minute BTC/USDT charts, this typically requires:
2-3 months of data during normal market conditions
1-2 months during high-volatility periods
3-4 months during low-volatility consolidation periods
The strategy's selectivity (requiring 55+ composite score plus all filters) means it generates fewer signals than less filtered approaches. If your backtest shows fewer than 100 trades, extend the date range or reduce the minimum signal strength threshold.
Fewer than 100 trades produces statistically unreliable results. Win rate, profit factor, and other metrics can vary significantly with small sample sizes.
STRATEGY DESIGN COMPROMISES AND LIMITATIONS
Every strategy involves trade-offs. Here are the compromises made in this design and the limitations you should understand:
Selectivity vs. Opportunity Trade-off
The 55-point minimum threshold filters many potential trades. This reduces false signals but also misses valid setups that don't meet all criteria. Lowering the threshold increases trade frequency but decreases win rate. There is no "correct" threshold; it depends on your preference for fewer higher-quality signals vs. more signals with lower individual quality.
Regime Classification Lag
The ATR-based regime detection uses historical data (21 periods + 13-period smoothing). It cannot predict sudden volatility spikes. During flash crashes or black swan events, the strategy may be classified in the wrong regime for several bars before the classification updates. This is an inherent limitation of any lagging indicator.
Indicator Parameter Sensitivity
The default parameters (MACD 8/21/5, EMA 8/21/34, RSI 21, etc.) are tuned for BTC/ETH perpetuals on 5-minute charts during 2024 market conditions. Different assets, timeframes, or market regimes may require different parameters. There is no guarantee that parameters optimized on historical data will perform similarly in the future.
BTC Dominance Filter Limitations
The CRYPTOCAP:BTC.D data feed may lag during low-liquidity periods or weekends. The dominance slope calculation uses a 5-bar SMA, adding additional delay. If you notice the filter behaving unexpectedly, consider disabling it.
Backtest vs. Live Execution Differences
TradingView backtesting does not replicate actual broker execution. Key differences:
Backtests assume perfect fills at calculated prices; real execution involves order book depth, latency, and partial fills
The calc_on_every_tick setting improves backtest realism but still cannot capture sub-bar price action or order book dynamics
Commission and slippage settings are estimates; actual costs vary by exchange, time of day, and market conditions
Funding rates on perpetual futures are not modeled in backtests and can significantly impact profitability over time
Exchange-specific limitations (position limits, liquidation mechanics, order types) are not modeled
Market Condition Dependencies
This strategy is designed for trending and breakout conditions. During extended sideways consolidation with no clear direction, the strategy may generate few signals or experience whipsaws. No strategy performs well in all market conditions.
Cryptocurrency-Specific Risks
Cryptocurrency markets operate 24/7 without session boundaries. This means:
No natural "overnight" risk reduction
Volatility can spike at any time
Liquidity varies significantly by time of day
Exchange outages or issues can occur at any time
WHAT THIS STRATEGY DOES NOT DO
To be straightforward about limitations:
This strategy does not guarantee profits. Past backtest performance does not indicate future results.
This strategy does not predict the future. It reacts to current conditions based on historical patterns.
This strategy does not account for funding rates, which can significantly impact perpetual futures profitability.
This strategy does not model exchange-specific execution issues (partial fills, requotes, outages).
This strategy does not adapt to fundamental news events or black swan scenarios.
This strategy is not optimized for all market conditions. It may underperform during extended consolidation.
IMPORTANT RISK WARNINGS
Past performance does not guarantee future results. The backtest results shown reflect specific historical market conditions and parameter settings. Markets change constantly, and strategies that performed well historically may underperform or lose money in the future. A single backtest run does not constitute proof of future profitability.
Trading involves substantial risk of loss. Cryptocurrency derivatives are highly volatile instruments. You can lose your entire investment. Only trade with capital you can afford to lose completely.
This is not financial advice. This strategy is provided for educational and informational purposes only. It does not constitute investment advice, trading recommendations, or any form of financial guidance. The author is not a licensed financial advisor.
You are responsible for your own decisions. Before using this strategy with real capital:
Thoroughly understand the code and logic by reading the open-source implementation
Forward test with paper trading or very small positions for an extended period
Verify that commission, slippage, and execution assumptions match your actual trading environment
Understand that live results will differ from backtest results
Consider consulting with a qualified financial advisor
No guarantees or warranties. This strategy is provided "as is" without any guarantees of profitability, accuracy, or suitability for any purpose. The author is not responsible for any losses incurred from using this strategy.
OPEN-SOURCE CODE STRUCTURE
The strategy code is organized into these sections for readability:
Configuration Architecture: Input parameters organized into logical groups (Core Controls, Optimization Constants, Regime Intelligence, Signal Pathways, Risk Architecture, Visualization)
Helper Functions: calcQty() for position sizing, clamp01() and normalize() for value normalization, calcMFI() for Money Flow Index calculation
Core Indicator Engine: EMA ribbon, ATR and regime classification, MACD with adaptive baseline, RSI, MFI, volume analytics, cycle oscillator, BTC dominance filter, squeeze detection
Signal Pathway Logic: Trend break, momentum surge, squeeze release, micro pullback pathways with composite scoring
Entry/Exit Orchestration: Signal filtering, position sizing, entry execution, stop/target calculation, trailing stop logic, momentum fail-safe exits
Visualization Layer: EMA plots, regime background, bar coloring, signal labels, dashboard table
You can read and modify any part of the code. Understanding the logic before deployment is strongly recommended.
- Made with passion by officialjackofalltrades
Regime [CHE] Regime — Minimal HTF MACD histogram regime marker with a simple rising versus falling state.
Summary
Regime is a lightweight overlay that turns a higher-timeframe-style MACD histogram condition into a simple regime marker on your chart. It queries an imported core module to determine whether the histogram is rising and then paints a consistent marker color based on that boolean state. The output is intentionally minimal: no lines, no panels, no extra smoothing visuals, just a repeated marker that reflects the current regime. This makes it useful as a quick context filter for other signals rather than a standalone system.
Motivation: Why this design?
A common problem in discretionary and systematic workflows is clutter and over-interpretation. Many regime tools draw multiple plots, which can distract from price structure. This script reduces the regime idea to one stable question: is the MACD histogram rising under a given preset and smoothing length. The core logic is delegated to a shared module to keep the indicator thin and consistent across scripts that rely on the same definition.
What’s different vs. standard approaches?
Reference baseline: A standard MACD histogram plotted in a separate pane with manual interpretation.
Architecture differences:
Uses a shared library call for the regime decision, rather than re-implementing MACD logic locally.
Uses a single boolean output to drive marker color, rather than plotting histogram bars.
Uses fixed marker placement at the bottom of the chart for consistent visibility.
Practical effect:
You get a persistent “context layer” on price without dedicating a separate pane or reading histogram amplitude. The chart shows state, not magnitude.
How it works (technical)
1. The script imports `chervolino/CoreMACDHTF/2` and calls `core.is_hist_rising()` on each bar.
2. Inputs provide the source series, a preset string for MACD-style parameters, and a smoothing length used by the library function.
3. The library returns a boolean `rising` that represents whether the histogram is rising according to the library’s internal definition.
4. The script maps that boolean to a color: yellow when rising, blue otherwise.
5. A circle marker is plotted on every bar at the bottom of the chart, colored by the current regime state. Only the most recent five hundred bars are displayed to limit visual load.
Notes:
The exact internal calculation details of `core.is_hist_rising()` are not shown in this code. Any higher timeframe mechanics, security usage, or confirmation behavior are determined by the imported library. (Unknown)
Parameter Guide
Source — Selects the price series used by the library call — Default: close — Tips: Use close for consistency; alternate sources may shift regime changes.
Preset — Chooses parameter preset for the library’s MACD-style configuration — Default: 3,10,16 — Trade-offs: Faster presets tend to flip more often; slower presets tend to react later.
Smoothing Length — Controls smoothing used inside the library regime decision — Default: 21 — Bounds: minimum one — Trade-offs: Higher values typically reduce noise but can delay transitions. (Library behavior: Unknown)
Reading & Interpretation
Yellow markers indicate the library considers the histogram to be rising at that bar.
Blue markers indicate the library considers it not rising, which may include falling or flat conditions depending on the library definition. (Unknown)
Because markers repeat on every bar, focus on transitions from one color to the other as regime changes.
This tool is best read as context: it does not express strength, only direction of change as defined by the library.
Practical Workflows & Combinations
Trend following:
Use yellow as a condition to allow long-side entries and blue as a condition to allow short-side entries, then trigger entries with your primary setup such as structure breaks or pullback patterns. (Optional)
Exits and stops:
Consider tightening management after a color transition against your position direction, but do not treat a single flip as an exit signal without price-based confirmation. (Optional)
Multi-asset and multi-timeframe:
Keep `Source` consistent across assets.
Use the slower preset when instruments are noisy, and the faster preset when you need earlier context shifts. The best transferability depends on the imported library’s behavior. (Unknown)
Behavior, Constraints & Performance
Repaint and confirmation:
This script itself uses no forward-looking indexing and no explicit closed-bar gating. It evaluates on every bar update.
Any repaint or confirmation behavior may come from the imported library. If the library uses higher timeframe data, intrabar updates can change the state until the higher timeframe bar closes. (Unknown)
security and HTF:
Not visible here. The library name suggests HTF behavior, but the implementation is not shown. Treat this as potentially higher-timeframe-driven unless you confirm the library source. (Unknown)
Resources:
No loops, no arrays, no heavy objects. The plotting is one marker series with a five hundred bar display window.
Known limits:
This indicator does not convey histogram magnitude, divergence, or volatility context.
A binary regime can flip in choppy phases depending on preset and smoothing.
Sensible Defaults & Quick Tuning
Starting point:
Source: close
Preset: 3,10,16
Smoothing Length: 21
Tuning recipes:
Too many flips: choose the slower preset and increase smoothing length.
Too sluggish: choose the faster preset and reduce smoothing length.
Regime changes feel misaligned with your entries: keep the preset, switch the source back to close, and tune smoothing length in small steps.
What this indicator is—and isn’t
This is a minimal regime visualization and a context filter. It is not a complete trading system, not a risk model, and not a prediction engine. Use it together with price structure, execution rules, and position management. The regime definition depends on the imported library, so validate it against your market and timeframe before relying on it.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
MACD HTF Hardcoded
Trend Continuation [OmegaTools]Trend Continuation is a trend-following and trend-continuation tool designed to highlight high-probability pullbacks within an existing directional bias. It helps discretionary and systematic traders visually isolate “continuation zones” where a retracement is more likely to resolve in favor of the prevailing trend rather than trigger a full reversal.
1. Concept and Objective
The indicator combines two key components:
1. A trend bias engine (based either on a Rolling VWAP regime or on swing market structure).
2. A pullback pressure model, which quantifies how deep and “aggressive” the recent retracement has been relative to the trend.
The goal is to identify moments where the market pulls back against the trend, builds enough “reversal pressure,” and then shows signs that the trend is likely to **continue** rather than flip. When specific conditions are met, the indicator highlights bars and plots reference levels that can be used as potential continuation zones, filters, or confluence areas in a broader trading plan.
2. Trend Bias Modes
The primary trend direction is defined through the `Trend Mode` input:
* **RVWAP Mode (default)**
The script computes two rolling volume-weighted average prices over different lengths:
* A **shorter-term rolling VWAP**
* A **longer-term rolling VWAP**
When the shorter RVWAP is above the longer one, the bias is set to **bullish (+1)**. When it is below, the bias is **bearish (-1)**.
This creates a smooth, volume-weighted trend definition that tends to adapt to shifting regimes and filters out minor noise.
* **Market Structure Mode**
In this mode, trend bias is derived from **pivot highs and lows**:
* When price breaks above a recent pivot high, the bias flips to **bullish (+1)**.
* When price breaks below a recent pivot low, the bias flips to **bearish (-1)**.
This approach is more structurally oriented and reacts to significant swing breaks rather than just moving-average style relationships.
If no clear condition is met, the internal bias can temporarily be neutral, though the main design assumes working with clearly bullish or bearish environments.
3. Pullback and Reversal Pressure Logic
Once the trend bias is defined, the indicator measures **pullback intensity** against that trend:
* A **lookback window (“Pullback Length”)** scans recent highs and lows:
* In an uptrend, it tracks the **highest high** over the window and measures how far the current low pulls back from that high.
* In a downtrend, it tracks the **lowest low** and measures how far the current high bounces up from that low.
* This distance is converted into a **“reversal pressure” value**:
* In a bullish bias, deeper pullbacks (lower lows relative to the recent high) indicate stronger counter-trend pressure.
* In a bearish bias, stronger rallies (higher highs relative to the recent low) indicate stronger counter-trend pressure.
The raw reversal pressure is then smoothed with a long-term moving average to separate normal retracements from **statistically significant extremes**.
4. Thresholds and Histogram Coloring
To avoid reacting to every minor pullback, the indicator builds a **dynamic threshold** using a combination of:
* Long-term averages of reversal pressure.
* Standard deviation of reversal pressure.
* High-percentile values of reversal behavior over different sample sizes.
From this, a **threshold line** is derived, and the script then compares the current reversal pressure to this adaptive level:
* The **Reversal Histogram** (column plot) represents the excess reversal pressure above its own long-term average.
* When:
* There is a valid bullish or bearish bias, and
* The histogram is above the dynamic threshold,
the bars of the histogram are **colored**:
* Blue (or a similar “positive” color) in bullish bias.
* Red/pink (or a similar “negative” color) in bearish bias.
* When reversal pressure is below threshold or bias is not relevant, the histogram remains **neutral gray**.
These colored histogram segments represent **“high-tension” pullback states**, where counter-trend pressure has reached an extreme that, historically, often resolves with the original trend continuing rather than fully reversing.
5. Continuation Level and Bar Coloring on Price Chart
To connect the oscillator logic back to the chart:
* A **continuation reference level** is computed on the price series:
* In an uptrend, this is derived by subtracting the threshold from recent highs.
* In a downtrend, it is derived by adding the threshold to recent lows.
* This level is plotted as a **line on the price chart** (only when the trend bias is stable), acting as a visual guide for:
* Potential continuation zones,
* Possible stop-placement or invalidation areas,
* Or filters for entries/exits.
The bars are then **colored** when price crosses or interacts with these levels in the direction of the trend:
* In a bullish bias, bars closing below the continuation level can be highlighted as potential **deep pullback/continuation opportunities** or as warning signals, depending on the user’s playbook.
* In a bearish bias, bars closing above the continuation level are similarly highlighted.
This makes it easy to see where the oscillator’s “extreme pullback” conditions align with structural movements on the actual price bars.
6. Embedded Win-Rate Estimation (WR Table)
The script also includes an internal **win-rate style metric (WR%)** displayed in a small table on the chart:
* It tracks occurrences where:
* A valid bullish or bearish bias is present, and
* The Reversal Histogram is **above the threshold** (i.e., histogram is colored).
* It then approximates the **probability that the trend bias does not change** following such high-pressure pullback events.
* The WR value is shown as a percentage and represents, in essence, the **historical trend-continuation rate** under these specific conditions over the most recent sample of events.
This is not a formal statistical test and does not guarantee future performance, but it provides a quick visual indication of how often these continuation setups have led to **trend persistence** in the recent past.
7. How to Use in Practice
Typical applications include:
Trend-following entries on pullbacks
Identify the main trend using either RVWAP or Market Structure mode.
Wait for a colored histogram bar (reversal pressure above threshold).
Use the continuation reference line and bar coloring on the price chart to refine entry zones or invalidation levels.
Filtering signals from other systems
Run the indicator in the background to confirm trend continuation conditions before taking signals from another strategy (e.g., breakouts or momentum entries).
Only act on long signals when the bias is bullish and a high-pressure pullback has recently occurred; similarly for short signals in bearish conditions.
Risk management and trend monitoring
Monitor when reversal pressure is building against your current position.
Use shifts in bias combined with high reversal pressure to re-evaluate or scale out of trend-following trades.
Recommended steps:
1. Choose your Trend Mode:
- RVWAP for smoother, regime-style trend detection.
- Market Structure for swing-based structural changes.
2. Adjust Trend Length and Pullback Length to match your timeframe (shorter for intraday, longer for swing/position trading).
3. Observe where histogram colors appear and how price reacts around the continuation line and highlighted bars.
4. Integrate these signals into a pre-defined trading plan with clear entry, exit, and risk rules.
8. Limitations and Disclaimer
* This tool is a **technical analysis aid**, not a complete trading system.
* Past behavior of trend continuation or reversal pressure does **not** guarantee future results.
* The embedded WR metric is a **descriptive statistic** based on recent historical conditions only; it is not a promise of performance or a robust statistical forecast.
* All parameters (lengths, thresholds, modes) are user-configurable and should be **tested and validated** on your own data, instruments, and timeframes before any live use.
Disclaimer
This indicator is provided for informational and educational purposes only and does not constitute financial, investment, or trading advice. Trading and investing in financial markets involve substantial risk, including the possible loss of all capital. You are solely responsible for your own trading decisions and for evaluating all information provided by this tool. OmegaTools and the author of this script expressly disclaim any liability for any direct or indirect loss resulting from the use of this indicator. Always consult with a qualified financial professional before making any investment decisions.
Squeeze Go Momentum Pro [KingThies] █ OVERVIEW
The Squeeze Momentum Pro indicator identifies volatility compression phases and breakout opportunities by comparing Bollinger Bands to Keltner Channels. When price consolidates (squeeze), the bands contract inside the channels, signaling an imminent breakout. The momentum histogram shows directional bias, helping traders anticipate which way price will move when the squeeze releases.
This indicator displays in a separate panel below the price chart, providing clear visual signals without cluttering price action.
█ KEY FEATURES
Momentum Histogram
The histogram is the primary visual element, displaying momentum strength and direction with four distinct color states:
• Dark Green (#00C853) — Strong bullish momentum that is increasing. This signals strengthening upward pressure and potential continuation.
• Light Green (#26A69A) — Bullish momentum that is decreasing. Price remains in bullish territory but upward force is weakening.
• Dark Red (#D32F2F) — Strong bearish momentum that is increasing. This signals strengthening downward pressure and potential continuation.
• Light Red (#EF5350) — Bearish momentum that is decreasing. Price remains in bearish territory but downward force is weakening.
The color intensity provides immediate feedback on momentum strength and trend health.
Squeeze State Indicator
Colored dots on the zero line communicate the current volatility state:
• Orange Dots — Squeeze is ON. Bollinger Bands have contracted inside Keltner Channels, indicating consolidation and low volatility.
A breakout is building and traders should prepare for directional movement.
• Green Dots — Squeeze is OFF. Bollinger Bands have expanded outside Keltner Channels, indicating active momentum and higher volatility.
Price is moving with conviction in the current direction.
• Gray Dots — Neutral state. The bands are transitioning between squeeze states.
Release Triangles
Triangle shapes mark the exact bar when a squeeze releases, providing precise entry timing:
• Green Triangle Up — Bullish squeeze release. The squeeze has ended with positive momentum, suggesting a long setup opportunity.
• Red Triangle Down — Bearish squeeze release. The squeeze has ended with negative momentum, suggesting a short setup opportunity.
Information Panel
A compact dashboard in the top-right corner displays real-time trading intelligence:
• Squeeze Status — Current state: ON, OFF, or NEUTRAL with color coding
• Momentum Direction — Current bias: BULL or BEAR
• Momentum Value — Precise numerical reading of momentum strength
• Trading Signal — Actionable status: LONG SETUP, SHORT SETUP, WAIT, or MONITOR
Configurable Parameters
All calculation inputs are adjustable to match your trading style and timeframe:
• BB Length — Bollinger Bands period (default: 20)
• BB StdDev — Bollinger Bands standard deviation multiplier (default: 2.0)
• KC Length — Keltner Channels period (default: 20)
• KC ATR Multiplier — Keltner Channels range multiplier (default: 1.5)
• Momentum Length — Linear regression period for momentum calculation (default: 20)
Alert System
Four alert conditions notify you of critical trading opportunities:
• Bullish Squeeze Release — Squeeze has released with bullish momentum, indicating a potential long entry
• Bearish Squeeze Release — Squeeze has released with bearish momentum, indicating a potential short entry
• Squeeze Started — Volatility compression detected, prepare for upcoming breakout
• Squeeze Ended — Volatility expansion confirmed, breakout is active
█ TRADING METHODOLOGY
The indicator follows a clear four-step process for identifying and trading squeeze breakouts:
1 - Wait for Orange Dots . When orange dots appear on the zero line, a squeeze is building. This indicates price consolidation and declining volatility.
Do not enter trades during this phase. Instead, prepare by identifying key support and resistance levels and potential breakout directions.
2 - Watch for Release Triangle . When a triangle appears, the squeeze has released and a breakout is beginning. This is your entry signal.
The triangle color (green up or red down) combined with the histogram direction indicates the breakout direction.
3 - Confirm with Histogram Direction . Check the momentum histogram for directional confirmation:
• Green histogram + green triangle up = Go long. Bullish momentum supports upward breakout.
• Red histogram + red triangle down = Go short. Bearish momentum supports downward breakout.
4 - Monitor Momentum Intensity . Stay in the trade while histogram bars maintain their dark, intense color.
When colors lighten (dark green to light green, or dark red to light red), momentum is weakening and you should consider taking profits or tightening stops.
█ INTERPRETATION GUIDE
Squeeze Detection Logic
A squeeze occurs when Bollinger Bands contract inside Keltner Channels. This happens when:
• Standard deviation of price decreases (BB narrows)
• Price consolidates within a tight range
• Volatility compresses to unsustainable levels
The orange dots signal this condition, warning traders that explosive movement is imminent.
Squeeze Release Logic
A squeeze releases when Bollinger Bands expand outside Keltner Channels. This happens when:
• Price volatility increases sharply
• Price breaks out of consolidation
• Volume typically expands (check volume separately)
The green dots and release triangles signal this condition, indicating the direction and timing of the breakout.
Momentum Reading
The histogram uses linear regression to calculate momentum relative to the midpoint of the recent range:
• Above Zero : Price is trading above the range midpoint with bullish pressure
• Below Zero : Price is trading below the range midpoint with bearish pressure
• Increasing Bars : Momentum is strengthening in the current direction (darker color)
• Decreasing Bars : Momentum is weakening in the current direction (lighter color)
█ BEST PRACTICES
• Timeframe Selection — The indicator works on all timeframes but performs best on 15-minute to daily charts.
Lower timeframes may produce more false signals due to noise.
• Confluence Trading — Combine squeeze releases with support/resistance levels, trend lines, or other indicators for higher probability setups.
• Volume Confirmation — Check that squeeze releases occur with increasing volume. Low volume breakouts are more likely to fail.
• Multiple Timeframe Analysis — Check higher timeframes for overall trend direction. Trade squeeze releases that align with the larger trend.
• Parameter Adjustment — Increase BB and KC lengths for smoother signals on higher timeframes. Decrease for more sensitive signals on lower timeframes.
█ LIMITATIONS
• The indicator does not predict breakout direction before the squeeze releases. The momentum histogram provides bias but is not definitive until the breakout occurs.
• False breakouts can occur, particularly in choppy or low-volume market conditions. Always use proper risk management and stop losses.
• The indicator works best in trending markets. In deeply ranging markets with no clear direction, squeeze signals may be less reliable.
• Momentum calculations use linear regression which can lag during extremely fast price movements. Confirm signals with price action.
█ NOTES
This implementation uses linear regression for momentum calculation rather than simple moving averages, providing more responsive and accurate directional signals. The four-color histogram system gives traders nuanced feedback on momentum strength that binary color schemes cannot provide.
The indicator automatically adjusts to any symbol and timeframe without modification, making it suitable for stocks, forex, crypto, and futures markets.
█ CREDITS
Squeeze methodology inspired by John Carter's TTM Squeeze indicator. Momentum calculation and visual design optimized for modern trading workflows.
Mean Reversion Oscillator [Alpha Extract]An advanced composite oscillator system specifically designed to identify extreme market conditions and high-probability mean reversion opportunities, combining five proven oscillators into a single, powerful analytical framework.
By integrating multiple momentum and volume-based indicators with sophisticated extreme level detection, this oscillator provides precise entry signals for contrarian trading strategies while filtering out false reversals through momentum confirmation.
🔶 Multi-Oscillator Composite Framework
Utilizes a comprehensive approach that combines Bollinger %B, RSI, Stochastic, Money Flow Index, and Williams %R into a unified composite score. This multi-dimensional analysis ensures robust signal generation by capturing different aspects of market extremes and momentum shifts.
// Weighted composite (equal weights)
normalized_bb = bb_percent
normalized_rsi = rsi
normalized_stoch = stoch_d_val
normalized_mfi = mfi
normalized_williams = williams_r
composite_raw = (normalized_bb + normalized_rsi + normalized_stoch + normalized_mfi + normalized_williams) / 5
composite = ta.sma(composite_raw, composite_smooth)
🔶 Advanced Extreme Level Detection
Features a sophisticated dual-threshold system that distinguishes between moderate and extreme market conditions. This hierarchical approach allows traders to identify varying degrees of mean reversion potential, from moderate oversold/overbought conditions to extreme levels that demand immediate attention.
🔶 Momentum Confirmation System
Incorporates a specialized momentum histogram that confirms mean reversion signals by analyzing the rate of change in the composite oscillator. This prevents premature entries during strong trending conditions while highlighting genuine reversal opportunities.
// Oscillator momentum (rate of change)
osc_momentum = ta.mom(composite, 5)
histogram = osc_momentum
// Momentum confirmation
momentum_bullish = histogram > histogram
momentum_bearish = histogram < histogram
// Confirmed signals
confirmed_bullish = bullish_entry and momentum_bullish
confirmed_bearish = bearish_entry and momentum_bearish
🔶 Dynamic Visual Intelligence
The oscillator line adapts its color intensity based on proximity to extreme levels, providing instant visual feedback about market conditions. Background shading creates clear zones that highlight when markets enter moderate or extreme territories.
🔶 Intelligent Signal Generation
Generates precise entry signals only when the composite oscillator crosses extreme thresholds with momentum confirmation. This dual-confirmation approach significantly reduces false signals while maintaining sensitivity to genuine mean reversion opportunities.
How It Works
🔶 Composite Score Calculation
The indicator simultaneously tracks five different oscillators, each normalized to a 0-100 scale, then combines them into a smoothed composite score. This approach eliminates the noise inherent in single-oscillator analysis while capturing the consensus view of multiple momentum indicators.
// Mean reversion entry signals
bullish_entry = ta.crossover(composite, 100 - extreme_level) and composite < (100 - extreme_level)
bearish_entry = ta.crossunder(composite, extreme_level) and composite > extreme_level
// Bollinger %B calculation
bb_basis = ta.sma(src, bb_length)
bb_dev = bb_mult * ta.stdev(src, bb_length)
bb_percent = (src - bb_lower) / (bb_upper - bb_lower) * 100
🔶 Extreme Zone Identification
The system automatically identifies when markets reach statistically significant extreme levels, both moderate (65/35) and extreme (80/20). These zones represent areas where mean reversion has the highest probability of success based on historical market behavior.
🔶 Momentum Histogram Analysis
A specialized momentum histogram tracks the velocity of oscillator changes, helping traders distinguish between healthy corrections and potential trend reversals. The histogram's color-coded display makes momentum shifts immediately apparent.
🔶 Divergence Detection Framework
Built-in divergence analysis identifies situations where price and oscillator movements diverge, often signaling impending reversals. Diamond-shaped markers highlight these critical divergence patterns for enhanced pattern recognition.
🔶 Real-Time Information Dashboard
An integrated information table provides instant access to current oscillator readings, market status, and individual component values. This dashboard eliminates the need to manually check multiple indicators while trading.
🔶 Individual Component Display
Optional display of individual oscillator components allows traders to understand which specific indicators are driving the composite signal. This transparency enables more informed decision-making and deeper market analysis.
🔶 Adaptive Background Coloring
Intelligent background shading automatically adjusts based on market conditions, creating visual zones that correspond to different levels of mean reversion potential. The subtle color gradations make pattern recognition effortless.
1D
3D
🔶 Comprehensive Alert System
Multi-tier alert system covers confirmed entry signals, divergence patterns, and extreme level breaches. Each alert type provides specific context about the detected condition, enabling traders to respond appropriately to different signal strengths.
🔶 Customizable Threshold Management
Fully adjustable extreme and moderate levels allow traders to fine-tune the indicator's sensitivity to match different market volatilities and trading timeframes. This flexibility ensures optimal performance across various market conditions.
🔶 Why Choose AE - Mean Reversion Oscillator?
This indicator provides the most comprehensive approach to mean reversion trading by combining multiple proven oscillators with advanced confirmation mechanisms. By offering clear visual hierarchies for different extreme levels and requiring momentum confirmation for signals, it empowers traders to identify high-probability contrarian opportunities while avoiding false reversals. The sophisticated composite methodology ensures that signals are both statistically significant and practically actionable, making it an essential tool for traders focused on mean reversion strategies across all market conditions.
BecakFloatingPanelsLibrary "BecakFloatingPanels"
Library for creating floating indicator panels with MACD, RSI, and Stochastic indicators
calculateMacd(source, fastLength, slowLength, signalLength)
Calculate MACD components
Parameters:
source (float) : Price source for calculation
fastLength (simple int) : Fast EMA period
slowLength (simple int) : Slow EMA period
signalLength (simple int) : Signal line period
Returns: MacdData MACD calculation results
calculateRsi(source, length)
Calculate RSI
Parameters:
source (float) : Price source for calculation
length (simple int) : RSI period
Returns: float RSI value
calculateStochastic(source, high, low, kLength, kSmoothing, dSmoothing)
Calculate Stochastic components
Parameters:
source (float) : Price source for calculation
high (float) : High prices
low (float) : Low prices
kLength (int) : %K period
kSmoothing (int) : %K smoothing period
dSmoothing (int) : %D smoothing period
Returns: StochData Stochastic calculation results
calculateStochSignals(stochK, stochD, overboughtLevel, oversoldLevel)
Calculate Stochastic signals
Parameters:
stochK (float) : Stochastic %K series
stochD (float) : Stochastic %D series
overboughtLevel (float) : Overbought threshold
oversoldLevel (float) : Oversold threshold
Returns: StochSignals Signal flags
calculateChartMetrics(high, low, lookbackLength)
Calculate chart range and positioning metrics
Parameters:
high (float) : High prices
low (float) : Low prices
lookbackLength (int) : Lookback period
Returns: ChartMetrics Chart positioning data
calculateMacdRange(macdLine, signalLine, histogram, safeLookback)
Calculate MACD range for normalization
Parameters:
macdLine (float) : MACD line series
signalLine (float) : Signal line series
histogram (float) : Histogram series
safeLookback (int) : Lookback period
Returns: MacdRange MACD range metrics
initVisualArrays()
Initialize visual arrays
Returns: VisualArrays Container with initialized arrays
clearVisuals(visuals)
Clear all visual elements
Parameters:
visuals (VisualArrays) : VisualArrays container
Returns: void
calculatePanelPositions(chartMetrics, oscPlacement, panelHeight, panelSpacing, centerOffset)
Calculate panel positions based on placement option
Parameters:
chartMetrics (ChartMetrics) : Chart metrics object
oscPlacement (string) : Panel placement option
panelHeight (float) : Panel height percentage
panelSpacing (float) : Panel spacing percentage
centerOffset (float) : Center offset percentage
Returns: PanelPositions Panel boundary coordinates
createPanelBackgrounds(visuals, positions, panelLeft, panelRight, showBackground, transparency)
Create panel backgrounds
Parameters:
visuals (VisualArrays) : VisualArrays container
positions (PanelPositions) : PanelPositions object
panelLeft (int) : Left boundary
panelRight (int) : Right boundary
showBackground (bool) : Show background flag
transparency (int) : Background transparency
Returns: void
drawReferenceLines(visuals, positions, chartMetrics, macdRange, dataLeft, dataRight, panelHeight, rsiOverbought, rsiOversold, stochOverbought, stochOversold)
Draw reference lines for all panels
Parameters:
visuals (VisualArrays) : VisualArrays container
positions (PanelPositions) : PanelPositions object
chartMetrics (ChartMetrics) : ChartMetrics object
macdRange (MacdRange) : MacdRange object
dataLeft (int) : Left data boundary
dataRight (int) : Right data boundary
panelHeight (float) : Panel height percentage
rsiOverbought (int) : RSI overbought level
rsiOversold (int) : RSI oversold level
stochOverbought (int) : Stochastic overbought level
stochOversold (int) : Stochastic oversold level
Returns: void
drawMacdIndicator(visuals, macdLine, signalLine, histogram, macdRange, positions, chartMetrics, barIndex, nextBarIndex, barIndexOffset, panelHeight)
Draw MACD indicator
Parameters:
visuals (VisualArrays) : VisualArrays container
macdLine (float) : MACD line series
signalLine (float) : Signal line series
histogram (float) : Histogram series
macdRange (MacdRange) : MacdRange object
positions (PanelPositions) : PanelPositions object
chartMetrics (ChartMetrics) : ChartMetrics object
barIndex (int) : Current bar index
nextBarIndex (int) : Next bar index
barIndexOffset (int) : Horizontal offset
panelHeight (float) : Panel height percentage
Returns: void
drawRsiIndicator(visuals, rsiValue, positions, chartMetrics, barIndex, nextBarIndex, barIndexOffset, panelHeight)
Draw RSI indicator
Parameters:
visuals (VisualArrays) : VisualArrays container
rsiValue (float) : RSI value
positions (PanelPositions) : PanelPositions object
chartMetrics (ChartMetrics) : ChartMetrics object
barIndex (int) : Current bar index
nextBarIndex (int) : Next bar index
barIndexOffset (int) : Horizontal offset
panelHeight (float) : Panel height percentage
Returns: void
drawStochasticIndicator(visuals, stochK, stochD, positions, chartMetrics, barIndex, nextBarIndex, barIndexOffset, panelHeight, stochOverbought, stochOversold)
Draw Stochastic indicator
Parameters:
visuals (VisualArrays) : VisualArrays container
stochK (float) : Stochastic %K series
stochD (float) : Stochastic %D series
positions (PanelPositions) : PanelPositions object
chartMetrics (ChartMetrics) : ChartMetrics object
barIndex (int) : Current bar index
nextBarIndex (int) : Next bar index
barIndexOffset (int) : Horizontal offset
panelHeight (float) : Panel height percentage
stochOverbought (int) : Overbought level
stochOversold (int) : Oversold level
Returns: void
addStochasticSignals(visuals, buySignal, sellSignal, positions, chartMetrics, currentBarIndex, barIndexOffset, panelHeight, signalIndex)
Add Stochastic buy/sell signals
Parameters:
visuals (VisualArrays) : VisualArrays container
buySignal (bool) : Buy signal series
sellSignal (bool) : Sell signal series
positions (PanelPositions) : PanelPositions object
chartMetrics (ChartMetrics) : ChartMetrics object
currentBarIndex (int) : Current bar index
barIndexOffset (int) : Horizontal offset
panelHeight (float) : Panel height percentage
signalIndex (int) : Signal index for lookback
Returns: void
setPanelLabels(macdLabel, rsiLabel, stochLabel, positions, chartMetrics, labelOffset, panelHeight, barIndexOffset)
Set panel title labels
Parameters:
macdLabel (label) : MACD label reference
rsiLabel (label) : RSI label reference
stochLabel (label) : Stochastic label reference
positions (PanelPositions) : PanelPositions object
chartMetrics (ChartMetrics) : ChartMetrics object
labelOffset (int) : Label horizontal offset
panelHeight (float) : Panel height percentage
barIndexOffset (int) : Horizontal offset
Returns: void
showDebugInfo(chartMetrics, debugMode)
Display debug information
Parameters:
chartMetrics (ChartMetrics) : ChartMetrics object
debugMode (bool) : Debug mode flag
Returns: void
ChartMetrics
Chart metrics container
Fields:
visibleHigh (series float) : Highest visible price
visibleLow (series float) : Lowest visible price
chartRange (series float) : Price range of chart
chartCenter (series float) : Center point of chart
MacdData
MACD calculation results
Fields:
macdLine (series float) : Main MACD line
signalLine (series float) : Signal line
histogram (series float) : MACD histogram
MacdRange
MACD range metrics for normalization
Fields:
highest (series float) : Highest MACD value
lowest (series float) : Lowest MACD value
BRange (series float) : Total range
StochData
Stochastic calculation results
Fields:
k_smooth (series float) : Smoothed %K line
d (series float) : %D line
StochSignals
Stochastic signals
Fields:
buySignal (series bool) : Buy signal flag
sellSignal (series bool) : Sell signal flag
PanelPositions
Panel positioning data
Fields:
macdTop (series float) : MACD panel top
macdBottom (series float) : MACD panel bottom
rsiTop (series float) : RSI panel top
rsiBottom (series float) : RSI panel bottom
stochTop (series float) : Stochastic panel top
stochBottom (series float) : Stochastic panel bottom
VisualArrays
Visual elements arrays container
Fields:
macdLines (array) : Array of MACD lines
macdHist (array) : Array of MACD histogram boxes
rsiLines (array) : Array of RSI lines
stochLines (array) : Array of Stochastic lines
stochAreas (array) : Array of Stochastic areas
stochSignals (array) : Array of Stochastic signals
panelBackgrounds (array) : Array of panel backgrounds
SMC Structures and FVGสวัสดีครับ! ผมจะอธิบายอินดิเคเตอร์ "SMC Structures and FVG + MACD" ที่คุณให้มาอย่างละเอียดในแต่ละส่วน เพื่อให้คุณเข้าใจการทำงานของมันอย่างถ่องแท้ครับ
อินดิเคเตอร์นี้เป็นการผสมผสานแนวคิดของ Smart Money Concept (SMC) ซึ่งเน้นการวิเคราะห์โครงสร้างตลาด (Market Structure) และ Fair Value Gap (FVG) เข้ากับอินดิเคเตอร์ MACD เพื่อใช้เป็นตัวกรองหรือตัวยืนยันสัญญาณ Choch/BoS (Change of Character / Break of Structure)
1. ภาพรวมอินดิเคเตอร์ (Overall Purpose)
อินดิเคเตอร์นี้มีจุดประสงค์หลักคือ:
ระบุโครงสร้างตลาด: ตีเส้นและป้ายกำกับ Choch (Change of Character) และ BoS (Break of Structure) บนกราฟโดยอัตโนมัติ
ผสานการยืนยันด้วย MACD: สัญญาณ Choch/BoS จะถูกพิจารณาก็ต่อเมื่อ MACD Histogram เกิดการตัดขึ้นหรือลง (Zero Cross) ในทิศทางที่สอดคล้องกัน
แสดง Fair Value Gap (FVG): หากเปิดใช้งาน จะมีการตีกล่อง FVG บนกราฟ
แสดงระดับ Fibonacci: คำนวณและแสดงระดับ Fibonacci ที่สำคัญตามโครงสร้างตลาดปัจจุบัน
ปรับตาม Timeframe: การคำนวณและการแสดงผลทั้งหมดจะปรับตาม Timeframe ที่คุณกำลังใช้งานอยู่โดยอัตโนมัติ
2. ส่วนประกอบหลักของโค้ด (Code Breakdown)
โค้ดนี้สามารถแบ่งออกเป็นส่วนหลัก ๆ ได้ดังนี้:
2.1 Inputs (การตั้งค่า)
ส่วนนี้คือตัวแปรที่คุณสามารถปรับแต่งได้ในหน้าต่างการตั้งค่าของอินดิเคเตอร์ (คลิกที่รูปฟันเฟืองข้างชื่ออินดิเคเตอร์บนกราฟ)
MACD Settings (ตั้งค่า MACD):
fast_len: ความยาวของ Fast EMA สำหรับ MACD (ค่าเริ่มต้น 12)
slow_len: ความยาวของ Slow EMA สำหรับ MACD (ค่าเริ่มต้น 26)
signal_len: ความยาวของ Signal Line สำหรับ MACD (ค่าเริ่มต้น 9)
= ta.macd(close, fast_len, slow_len, signal_len): คำนวณค่า MACD Line, Signal Line และ Histogram โดยใช้ราคาปิด (close) และค่าความยาวที่กำหนด
is_bullish_macd_cross: ตรวจสอบว่า MACD Histogram ตัดขึ้นเหนือเส้น 0 (จากค่าลบเป็นบวก)
is_bearish_macd_cross: ตรวจสอบว่า MACD Histogram ตัดลงใต้เส้น 0 (จากค่าบวกเป็นลบ)
Fear Value Gap (FVG) Settings:
isFvgToShow: (Boolean) เปิด/ปิดการแสดง FVG บนกราฟ
bullishFvgColor: สีสำหรับ Bullish FVG
bearishFvgColor: สีสำหรับ Bearish FVG
mitigatedFvgColor: สีสำหรับ FVG ที่ถูก Mitigate (ลดทอน) แล้ว
fvgHistoryNbr: จำนวน FVG ย้อนหลังที่จะแสดง
isMitigatedFvgToReduce: (Boolean) เปิด/ปิดการลดขนาด FVG เมื่อถูก Mitigate
Structures (โครงสร้างตลาด) Settings:
isStructBodyCandleBreak: (Boolean) หากเป็น true การ Break จะต้องเกิดขึ้นด้วย เนื้อเทียน ที่ปิดเหนือ/ใต้ Swing High/Low หากเป็น false แค่ไส้เทียนทะลุก็ถือว่า Break
isCurrentStructToShow: (Boolean) เปิด/ปิดการแสดงเส้นโครงสร้างตลาดปัจจุบัน (เส้นสีน้ำเงินในภาพตัวอย่าง)
pivot_len: ความยาวของแท่งเทียนที่ใช้ในการมองหาจุด Pivot (Swing High/Low) ยิ่งค่าน้อยยิ่งจับ Swing เล็กๆ ได้, ยิ่งค่ามากยิ่งจับ Swing ใหญ่ๆ ได้
bullishBosColor, bearishBosColor: สีสำหรับเส้นและป้าย BOS ขาขึ้น/ขาลง
bosLineStyleOption, bosLineWidth: สไตล์ (Solid, Dotted, Dashed) และความหนาของเส้น BOS
bullishChochColor, bearishChochColor: สีสำหรับเส้นและป้าย CHoCH ขาขึ้น/ขาลง
chochLineStyleOption, chochLineWidth: สไตล์ (Solid, Dotted, Dashed) และความหนาของเส้น CHoCH
currentStructColor, currentStructLineStyleOption, currentStructLineWidth: สี, สไตล์ และความหนาของเส้นโครงสร้างตลาดปัจจุบัน
structHistoryNbr: จำนวนการ Break (Choch/BoS) ย้อนหลังที่จะแสดง
Structure Fibonacci (จากโค้ดต้นฉบับ):
เป็นชุด Input สำหรับเปิด/ปิด, กำหนดค่า, สี, สไตล์ และความหนาของเส้น Fibonacci Levels ต่างๆ (0.786, 0.705, 0.618, 0.5, 0.382) ที่จะถูกคำนวณจากโครงสร้างตลาดปัจจุบัน
2.2 Helper Functions (ฟังก์ชันช่วยทำงาน)
getLineStyle(lineOption): ฟังก์ชันนี้ใช้แปลงค่า String ที่เลือกจาก Input (เช่น "─", "┈", "╌") ให้เป็นรูปแบบ line.style_ ที่ Pine Script เข้าใจ
get_structure_highest_bar(lookback): ฟังก์ชันนี้พยายามหา Bar Index ของแท่งเทียนที่ทำ Swing High ภายในช่วง lookback ที่กำหนด
get_structure_lowest_bar(lookback): ฟังก์ชันนี้พยายามหา Bar Index ของแท่งเทียนที่ทำ Swing Low ภายในช่วง lookback ที่กำหนด
is_structure_high_broken(...): ฟังก์ชันนี้ตรวจสอบว่าราคาปัจจุบันได้ Break เหนือ _structureHigh (Swing High) หรือไม่ โดยพิจารณาจาก _highStructBreakPrice (ราคาปิดหรือราคา High ขึ้นอยู่กับการตั้งค่า isStructBodyCandleBreak)
FVGDraw(...): ฟังก์ชันนี้รับ Arrays ของ FVG Boxes, Types, Mitigation Status และ Labels มาประมวลผล เพื่ออัปเดตสถานะของ FVG (เช่น ถูก Mitigate หรือไม่) และปรับขนาด/ตำแหน่งของ FVG Box และ Label บนกราฟ
2.3 Global Variables (ตัวแปรทั่วทั้งอินดิเคเตอร์)
เป็นตัวแปรที่ประกาศด้วย var ซึ่งหมายความว่าค่าของมันจะถูกเก็บไว้และอัปเดตในแต่ละแท่งเทียน (persists across bars)
structureLines, structureLabels: Arrays สำหรับเก็บอ็อบเจกต์ line และ label ของเส้น Choch/BoS ที่วาดบนกราฟ
fvgBoxes, fvgTypes, fvgLabels, isFvgMitigated: Arrays สำหรับเก็บข้อมูลของ FVG Boxes และสถานะต่างๆ
structureHigh, structureLow: เก็บราคาของ Swing High/Low ที่สำคัญของโครงสร้างตลาดปัจจุบัน
structureHighStartIndex, structureLowStartIndex: เก็บ Bar Index ของจุดเริ่มต้นของ Swing High/Low ที่สำคัญ
structureDirection: เก็บสถานะของทิศทางโครงสร้างตลาด (1 = Bullish, 2 = Bearish, 0 = Undefined)
fiboXPrice, fiboXStartIndex, fiboXLine, fiboXLabel: ตัวแปรสำหรับเก็บข้อมูลและอ็อบเจกต์ของเส้น Fibonacci Levels
isBOSAlert, isCHOCHAlert: (Boolean) ใช้สำหรับส่งสัญญาณ Alert (หากมีการตั้งค่า Alert ไว้)
2.4 FVG Processing (การประมวลผล FVG)
ส่วนนี้จะตรวจสอบเงื่อนไขการเกิด FVG (Bullish FVG: high < low , Bearish FVG: low > high )
หากเกิด FVG และ isFvgToShow เป็น true จะมีการสร้าง box และ label ใหม่เพื่อแสดง FVG บนกราฟ
มีการจัดการ fvgBoxes และ fvgLabels เพื่อจำกัดจำนวน FVG ที่แสดงตาม fvgHistoryNbr และลบ FVG เก่าออก
ฟังก์ชัน FVGDraw จะถูกเรียกเพื่ออัปเดตสถานะของ FVG (เช่น การถูก Mitigate) และปรับการแสดงผล
2.5 Structures Processing (การประมวลผลโครงสร้างตลาด)
Initialization: ที่ bar_index == 0 (แท่งเทียนแรกของกราฟ) จะมีการกำหนดค่าเริ่มต้นให้กับ structureHigh, structureLow, structureHighStartIndex, structureLowStartIndex
Finding Current High/Low: highest, highestBar, lowest, lowestBar ถูกใช้เพื่อหา High/Low ที่สุดและ Bar Index ของมันใน 10 แท่งล่าสุด (หรือทั้งหมดหากกราฟสั้นกว่า 10 แท่ง)
Calculating Structure Max/Min Bar: structureMaxBar และ structureMinBar ใช้ฟังก์ชัน get_structure_highest_bar และ get_structure_lowest_bar เพื่อหา Bar Index ของ Swing High/Low ที่แท้จริง (ไม่ใช่แค่ High/Low ที่สุดใน lookback แต่เป็นจุด Pivot ที่สมบูรณ์)
Break Price: lowStructBreakPrice และ highStructBreakPrice จะเป็นราคาปิด (close) หรือราคา Low/High ขึ้นอยู่กับ isStructBodyCandleBreak
isStuctureLowBroken / isStructureHighBroken: เงื่อนไขเหล่านี้ตรวจสอบว่าราคาได้ทำลาย structureLow หรือ structureHigh หรือไม่ โดยพิจารณาจากราคา Break, ราคาแท่งก่อนหน้า และ Bar Index ของจุดเริ่มต้นโครงสร้าง
Choch/BoS Logic (ส่วนสำคัญที่ถูกผสานกับ MACD):
if(isStuctureLowBroken and is_bearish_macd_cross): นี่คือจุดที่ MACD เข้ามามีบทบาท หากราคาทำลาย structureLow (สัญญาณขาลง) และ MACD Histogram เกิด Bearish Zero Cross (is_bearish_macd_cross เป็น true) อินดิเคเตอร์จะพิจารณาว่าเป็น Choch หรือ BoS
หาก structureDirection == 1 (เดิมเป็นขาขึ้น) หรือ 0 (ยังไม่กำหนด) จะตีเป็น "CHoCH" (เปลี่ยนทิศทางโครงสร้างเป็นขาลง)
หาก structureDirection == 2 (เดิมเป็นขาลง) จะตีเป็น "BOS" (ยืนยันโครงสร้างขาลง)
มีการสร้าง line.new และ label.new เพื่อวาดเส้นและป้ายกำกับ
structureDirection จะถูกอัปเดตเป็น 1 (Bullish)
structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow จะถูกอัปเดตเพื่อกำหนดโครงสร้างใหม่
else if(isStructureHighBroken and is_bullish_macd_cross): เช่นกันสำหรับขาขึ้น หากราคาทำลาย structureHigh (สัญญาณขาขึ้น) และ MACD Histogram เกิด Bullish Zero Cross (is_bullish_macd_cross เป็น true) อินดิเคเตอร์จะพิจารณาว่าเป็น Choch หรือ BoS
หาก structureDirection == 2 (เดิมเป็นขาลง) หรือ 0 (ยังไม่กำหนด) จะตีเป็น "CHoCH" (เปลี่ยนทิศทางโครงสร้างเป็นขาขึ้น)
หาก structureDirection == 1 (เดิมเป็นขาขึ้น) จะตีเป็น "BOS" (ยืนยันโครงสร้างขาขึ้น)
มีการสร้าง line.new และ label.new เพื่อวาดเส้นและป้ายกำกับ
structureDirection จะถูกอัปเดตเป็น 2 (Bearish)
structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow จะถูกอัปเดตเพื่อกำหนดโครงสร้างใหม่
การลบเส้นเก่า: d.delete_line (หากไลบรารีทำงาน) จะถูกเรียกเพื่อลบเส้นและป้ายกำกับเก่าออกเมื่อจำนวนเกิน structHistoryNbr
Updating Structure High/Low (else block): หากไม่มีการ Break เกิดขึ้น แต่ราคาปัจจุบันสูงกว่า structureHigh หรือต่ำกว่า structureLow ในทิศทางที่สอดคล้องกัน (เช่น ยังคงเป็นขาขึ้นและทำ High ใหม่) structureHigh หรือ structureLow จะถูกอัปเดตเพื่อติดตาม High/Low ที่สุดของโครงสร้างปัจจุบัน
Current Structure Display:
หาก isCurrentStructToShow เป็น true อินดิเคเตอร์จะวาดเส้น structureHighLine และ structureLowLine เพื่อแสดงขอบเขตของโครงสร้างตลาดปัจจุบัน
Fibonacci Display:
หาก isFiboXToShow เป็น true อินดิเคเตอร์จะคำนวณและวาดเส้น Fibonacci Levels ต่างๆ (0.786, 0.705, 0.618, 0.5, 0.382) โดยอิงจาก structureHigh และ structureLow ของโครงสร้างตลาดปัจจุบัน
Alerts:
alertcondition: ใช้สำหรับตั้งค่า Alert ใน TradingView เมื่อเกิดสัญญาณ BOS หรือ CHOCH
plot(na):
plot(na) เป็นคำสั่งที่สำคัญในอินดิเคเตอร์ที่ไม่ได้ต้องการพล็อต Series ของข้อมูลบนกราฟ (เช่น ไม่ได้พล็อตเส้น EMA หรือ RSI) แต่ใช้วาดอ็อบเจกต์ (Line, Label, Box) โดยตรง
การมี plot(na) ช่วยให้ Pine Script รู้ว่าอินดิเคเตอร์นี้มีเอาต์พุตที่แสดงผลบนกราฟ แม้ว่าจะไม่ได้เป็น Series ที่พล็อตตามปกติก็ตาม
3. วิธีใช้งาน
คัดลอกโค้ดทั้งหมด ที่อยู่ในบล็อก immersive ด้านบน
ไปที่ TradingView และเปิดกราฟที่คุณต้องการ
คลิกที่เมนู "Pine Editor" ที่อยู่ด้านล่างของหน้าจอ
ลบโค้ดเดิมที่มีอยู่ และ วางโค้ดที่คัดลอกมา ลงไปแทน
คลิกที่ปุ่ม "Add to Chart"
อินดิเคเตอร์จะถูกเพิ่มลงในกราฟของคุณโดยอัตโนมัติ คุณสามารถคลิกที่รูปฟันเฟืองข้างชื่ออินดิเคเตอร์บนกราฟเพื่อเข้าถึงหน้าต่างการตั้งค่าและปรับแต่งตามความต้องการของคุณได้
Hello! I will explain the "SMC Structures and FVG + MACD" indicator you provided in detail, section by section, so you can fully understand how it works.This indicator combines the concepts of Smart Money Concept (SMC), which focuses on analyzing Market Structure and Fair Value Gaps (FVG), with the MACD indicator to serve as a filter or confirmation for Choch (Change of Character) and BoS (Break of Structure) signals.1. Overall PurposeThe main purposes of this indicator are:Identify Market Structure: Automatically draw lines and label Choch (Change of Character) and BoS (Break of Structure) on the chart.Integrate MACD Confirmation: Choch/BoS signals will only be considered when the MACD Histogram performs a cross (Zero Cross) in the corresponding direction.Display Fair Value Gap (FVG): If enabled, FVG boxes will be drawn on the chart.Display Fibonacci Levels: Calculate and display important Fibonacci levels based on the current market structure.Adapt to Timeframe: All calculations and displays will automatically adjust to the timeframe you are currently using.2. Code BreakdownThis code can be divided into the following main sections:2.1 Inputs (Settings)This section contains variables that you can adjust in the indicator's settings window (click the gear icon next to the indicator's name on the chart).MACD Settings:fast_len: Length of the Fast EMA for MACD (default 12)slow_len: Length of the Slow EMA for MACD (default 26)signal_len: Length of the Signal Line for MACD (default 9) = ta.macd(close, fast_len, slow_len, signal_len): Calculates the MACD Line, Signal Line, and Histogram using the closing price (close) and the specified lengths.is_bullish_macd_cross: Checks if the MACD Histogram crosses above the 0 line (from negative to positive).is_bearish_macd_cross: Checks if the MACD Histogram crosses below the 0 line (from positive to negative).Fear Value Gap (FVG) Settings:isFvgToShow: (Boolean) Enables/disables the display of FVG on the chart.bullishFvgColor: Color for Bullish FVG.bearishFvgColor: Color for Bearish FVG.mitigatedFvgColor: Color for FVG that has been mitigated.fvgHistoryNbr: Number of historical FVG to display.isMitigatedFvgToReduce: (Boolean) Enables/disables reducing the size of FVG when mitigated.Structures (โครงสร้างตลาด) Settings:isStructBodyCandleBreak: (Boolean) If true, the break must occur with the candle body closing above/below the Swing High/Low. If false, a wick break is sufficient.isCurrentStructToShow: (Boolean) Enables/disables the display of the current market structure lines (blue lines in the example image).pivot_len: Lookback length for identifying Pivot points (Swing High/Low). A smaller value captures smaller, more frequent swings; a larger value captures larger, more significant swings.bullishBosColor, bearishBosColor: Colors for bullish/bearish BOS lines and labels.bosLineStyleOption, bosLineWidth: Style (Solid, Dotted, Dashed) and width of BOS lines.bullishChochColor, bearishChochColor: Colors for bullish/bearish CHoCH lines and labels.chochLineStyleOption, chochLineWidth: Style (Solid, Dotted, Dashed) and width of CHoCH lines.currentStructColor, currentStructLineStyleOption, currentStructLineWidth: Color, style, and width of the current market structure lines.structHistoryNbr: Number of historical breaks (Choch/BoS) to display.Structure Fibonacci (from original code):A set of inputs to enable/disable, define values, colors, styles, and widths for various Fibonacci Levels (0.786, 0.705, 0.618, 0.5, 0.382) that will be calculated from the current market structure.2.2 Helper FunctionsgetLineStyle(lineOption): This function converts the selected string input (e.g., "─", "┈", "╌") into a line.style_ format understood by Pine Script.get_structure_highest_bar(lookback): This function attempts to find the Bar Index of the Swing High within the specified lookback period.get_structure_lowest_bar(lookback): This function attempts to find the Bar Index of the Swing Low within the specified lookback period.is_structure_high_broken(...): This function checks if the current price has broken above _structureHigh (Swing High), considering _highStructBreakPrice (closing price or high price depending on isStructBodyCandleBreak setting).FVGDraw(...): This function takes arrays of FVG Boxes, Types, Mitigation Status, and Labels to process and update the status of FVG (e.g., whether it's mitigated) and adjust the size/position of FVG Boxes and Labels on the chart.2.3 Global VariablesThese are variables declared with var, meaning their values are stored and updated on each bar (persists across bars).structureLines, structureLabels: Arrays to store line and label objects for Choch/BoS lines drawn on the chart.fvgBoxes, fvgTypes, fvgLabels, isFvgMitigated: Arrays to store FVG box data and their respective statuses.structureHigh, structureLow: Stores the price of the significant Swing High/Low of the current market structure.structureHighStartIndex, structureLowStartIndex: Stores the Bar Index of the start point of the significant Swing High/Low.structureDirection: Stores the status of the market structure direction (1 = Bullish, 2 = Bearish, 0 = Undefined).fiboXPrice, fiboXStartIndex, fiboXLine, fiboXLabel: Variables to store data and objects for Fibonacci Levels.isBOSAlert, isCHOCHAlert: (Boolean) Used to trigger alerts in TradingView (if alerts are configured).2.4 FVG ProcessingThis section checks the conditions for FVG formation (Bullish FVG: high < low , Bearish FVG: low > high ).If FVG occurs and isFvgToShow is true, a new box and label are created to display the FVG on the chart.fvgBoxes and fvgLabels are managed to limit the number of FVG displayed according to fvgHistoryNbr and remove older FVG.The FVGDraw function is called to update the FVG status (e.g., whether it's mitigated) and adjust its display.2.5 Structures ProcessingInitialization: At bar_index == 0 (the first bar of the chart), structureHigh, structureLow, structureHighStartIndex, and structureLowStartIndex are initialized.Finding Current High/Low: highest, highestBar, lowest, lowestBar are used to find the highest/lowest price and its Bar Index of it in the last 10 bars (or all bars if the chart is shorter than 10 bars).Calculating Structure Max/Min Bar: structureMaxBar and structureMinBar use get_structure_highest_bar and get_structure_lowest_bar functions to find the Bar Index of the true Swing High/Low (not just the highest/lowest in the lookback but a complete Pivot point).Break Price: lowStructBreakPrice and highStructBreakPrice will be the closing price (close) or the Low/High price, depending on the isStructBodyCandleBreak setting.isStuctureLowBroken / isStructureHighBroken: These conditions check if the price has broken structureLow or structureHigh, considering the break price, previous bar prices, and the Bar Index of the structure's starting point.Choch/BoS Logic (Key Integration with MACD):if(isStuctureLowBroken and is_bearish_macd_cross): This is where MACD plays a role. If the price breaks structureLow (bearish signal) AND the MACD Histogram performs a Bearish Zero Cross (is_bearish_macd_cross is true), the indicator will consider it a Choch or BoS.If structureDirection == 1 (previously bullish) or 0 (undefined), it will be labeled "CHoCH" (changing structure direction to bearish).If structureDirection == 2 (already bearish), it will be labeled "BOS" (confirming bearish structure).line.new and label.new are used to draw the line and label.structureDirection will be updated to 1 (Bullish).structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow will be updated to define the new structure.else if(isStructureHighBroken and is_bullish_macd_cross): Similarly for bullish breaks. If the price breaks structureHigh (bullish signal) AND the MACD Histogram performs a Bullish Zero Cross (is_bullish_macd_cross is true), the indicator will consider it a Choch or BoS.If structureDirection == 2 (previously bearish) or 0 (undefined), it will be labeled "CHoCH" (changing structure direction to bullish).If structureDirection == 1 (already bullish), it will be labeled "BOS" (confirming bullish structure).line.new and label.new are used to draw the line and label.structureDirection will be updated to 2 (Bearish).structureHighStartIndex, structureLowStartIndex, structureHigh, structureLow will be updated to define the new structure.Deleting Old Lines: d.delete_line (if the library works) will be called to delete old lines and labels when their number exceeds structHistoryNbr.Updating Structure High/Low (else block): If no break occurs, but the current price is higher than structureHigh or lower than structureLow in the corresponding direction (e.g., still bullish and making a new high), structureHigh or structureLow will be updated to track the highest/lowest point of the current structure.Current Structure Display:If isCurrentStructToShow is true, the indicator draws structureHighLine and structureLowLine to show the boundaries of the current market structure.Fibonacci Display:If isFiboXToShow is true, the indicator calculates and draws various Fibonacci Levels (0.786, 0.705, 0.618, 0.5, 0.382) based on the structureHigh and structureLow of the current market structure.Alerts:alertcondition: Used to set up alerts in TradingView when BOS or CHOCH signals occur.plot(na):plot(na) is an important statement in indicators that do not plot data series directly on the chart (e.g., not plotting EMA or RSI lines) but instead draw objects (Line, Label, Box).Having plot(na) helps Pine Script recognize that this indicator has an output displayed on the chart, even if it's not a regularly plotted series.3. How to UseCopy all the code in the immersive block above.Go to TradingView and open your desired chart.Click on the "Pine Editor" menu at the bottom of the screen.Delete any existing code and paste the copied code in its place.Click the "Add to Chart" button.The indicator will be added to your chart automatically. You can click the gear icon next to the indicator's name on the chart to access the settings window and customize it to your needs.I hope this explanation helps you understand this indicator in detail. If anything is unclear, or you need further adjustments, please let me know.
DAMA OSC - Directional Adaptive MA OscillatorOverview:
The DAMA OSC (Directional Adaptive MA Oscillator) is a highly customizable and versatile oscillator that analyzes the delta between two moving averages of your choice. It detects trend progression, regressions, rebound signals, MA cross and critical zone crossovers to provide highly contextual trading information.
Designed for trend-following, reversal timing, and volatility filtering, DAMA OSC adapts to market conditions and highlights actionable signals in real-time.
Features:
Support for 11 custom moving average types (EMA, DEMA, TEMA, ALMA, KAMA, etc.)
Customizable fast & slow MA periods and types
Histogram based on percentage delta between fast and slow MA
Trend direction coloring with “Green”, “Blue”, and “Red” zones
Rebound detection using close or shadow logic
Configurable thresholds: Overbought, Oversold, Underbought, Undersold
Optional filters: rebound validation by candle color or flat-zone filter
Full visual overlay: MA lines, crossover markers, rebound icons
Complete alert system with 16 preconfigured conditions
How It Works:
Histogram Logic:
The histogram measures the percentage difference between the fast and slow MA:
hist_value = ((FastMA - SlowMA) / SlowMA) * 100
Trend State Logic (Green / Blue / Red):
Green_Up = Bullish acceleration
Blue_Up (or Red_Up, depending the display settings) = Bullish deceleration
Blue_Down (or Green_Down, depending the display settings) = Bearish deceleration
Red_Down = Bearish acceleration
Rebound Logic:
A rebound is detected when price:
Crosses back over a selected MA (fast or slow)
After being away for X candles (rebound_backstep)
Optional: filtered by histogram zones or candle color
Inputs:
Display Options:
Show/hide MA lines
Show/hide MA crosses
Show/hide price rebounds
Enable/disable blue deceleration zones
DAMA Settings:
Fast/Slow MA type and length
Source input (close by default)
Overbought/Oversold levels
Underbought/Undersold levels
Rebound Settings:
Use Close and/or Shadow
Rebound MA (Fast/Slow)
Candle color validation
Flat zone filter rebounds (between UnderSold and UnderBought)
Available MA type:
SMA (Simple MA)
EMA (Exponential MA)
DEMA (Double EMA)
TEMA (Triple EMA)
WMA (Weighted MA)
HMA (Hull MA)
VWMA (Volume Weighted MA)
Kijun (Ichimoku Baseline)
ALMA (Arnaud Legoux MA)
KAMA (Kaufman Adaptive MA)
HULLMOD (Modified Hull MA, Same as HMA, tweaked for Pine v6 constraints)
Notes:
**DEMA/TEMA** reduce lag compared to EMA, useful for faster reaction in trending markets.
**KAMA/ALMA** are better suited to noisy or volatile environments (e.g., BTC).
**VWMA** reacts strongly to volume spikes.
**HMA/HULLMOD** are great for visual clarity in fast moves.
Alerts Included (Fully Configurable):
Golden Cross:
Fast MA crosses above Slow MA
Death Cross:
Fast MA crosses below Slow MA
Bullish Rebound:
Rebound from below MA in uptrend
Bearish Rebound:
Rebound from above MA in downtrend
Bull Progression:
Transition into Green_Up with positive delta
Bear Progression:
Transition into Red_Down with negative delta
Bull Regression:
Exit from Red_Down into Blue/Green with negative delta
Bear Regression:
Exit from Green_Up into Blue/Red with positive delta
Crossover Overbought:
Histogram crosses above Overbought
Crossunder Overbought:
Histogram crosses below Overbought
Crossover Oversold:
Histogram crosses above Oversold
Crossunder Oversold:
Histogram crosses below Oversold
Crossover Underbought:
Histogram crosses above Underbought
Crossunder Underbought:
Histogram crosses below Underbought
Crossover Undersold:
Histogram crosses above Undersold
Crossunder Undersold:
Histogram crosses below Undersold
Credits:
Created by Eff_Hash. This code is shared with the TradingView community and full free. do not hesitate to share your best settings and usage.
MACD with DPO Strategy by NGExplanation of the MACD with DPO Strategy:
MACD (Moving Average Convergence Divergence):
The MACD is a trend-following indicator that shows the relationship between two moving averages of a price.
In this script:
We calculate the MACD line by subtracting the slow moving average (typically 26-period EMA) from the fast moving average (typically 12-period EMA).
The Signal line is calculated as a 9-period EMA of the MACD line.
The Histogram is the difference between the MACD line and the Signal line, indicating the momentum of the price trend.
Buy Condition: The script generates a buy signal when the MACD histogram crosses from negative to positive (indicating a bullish momentum) and DPO is also positive.
Sell Condition: The script generates a sell signal when the MACD histogram crosses from positive to negative (indicating a bearish momentum) and DPO is also negative.
DPO (Detrended Price Oscillator):
The DPO removes long-term trends from prices, making it easier to identify shorter-term cycles or oscillations.
In this script:
We calculate the DPO by subtracting a shifted simple moving average (SMA) from the close price. The shifting period depends on half the specified period.
We also calculate the DPO SMA as a 30-period EMA of the DPO values.
DPO Color: The DPO line is colored green when the DPO is above zero (indicating upward momentum) and red when it is below zero (indicating downward momentum). The histogram is also colored based on whether the DPO is positive or negative.
Plotting and Alerts:
The script plots the MACD, Signal, and Histogram on the chart.
Additionally, it plots the DPO and its SMA with different colors depending on whether the DPO is above or below zero.
Buy Signal: A green arrow labeled "BUY" is plotted below the bar when both MACD and DPO indicate a bullish condition.
Sell Signal: A red arrow labeled "SELL" is plotted above the bar when both MACD and DPO indicate a bearish condition.
Background colors are used to highlight the chart whenever a buy or sell condition occurs.
The script also includes alerts for both buy and sell signals, allowing users to set notifications when conditions are met.
How to Use:
Identify Buy and Sell Signals:
The script generates a Buy signal when:
The MACD histogram crosses from negative to positive (bullish momentum), and
The DPO is above zero (indicating upward momentum).
The script generates a Sell signal when:
The MACD histogram crosses from positive to negative (bearish momentum), and
The DPO is below zero (indicating downward momentum).
Chart Visualization:
The MACD histogram and Signal line help visualize the momentum and potential trend reversal.
The DPO and DPO SMA help visualize the shorter-term price cycles.
The signals (Buy and Sell) will be plotted on the chart with arrows indicating entry points.
Customization:
You can adjust the MACD and DPO parameters (such as fast_length, slow_length, period_) to fit your trading style or market conditions.
The script can be used in any timeframe depending on your strategy (e.g., intraday trading or longer-term trading).
Example Scenario:
If you're looking for potential buy opportunities, wait for the script to generate a buy signal (green arrow) where the MACD histogram has shifted to positive, and DPO is also in the green (above zero). This signals that both momentum and cycle direction are aligned for a potential upward movement.
Conversely, for sell opportunities, wait for the red arrow where MACD momentum is turning negative and DPO is also negative (below zero), indicating a bearish condition.
This combination of MACD and DPO allows traders to identify stronger and more reliable entry/exit points by confirming the trend with the MACD and detecting shorter-term price cycles with the DPO.
Trend Flow Profile [AlgoAlpha]Description:
The "Trend Flow Profile" indicator is a powerful tool designed to analyze and interpret the underlying trends and reversals in a financial market. It combines the concepts of Order Flow and Rate of Change (ROC) to provide valuable insights into market dynamics, momentum, and potential trade opportunities. By integrating these two components, the indicator offers a comprehensive view of market sentiment and price movements, facilitating informed trading decisions.
Rationale:
The combination of Order Flow and ROC in the "Trend Flow Profile" indicator stems from the recognition that both factors play critical roles in understanding market behavior. Order Flow represents the net buying or selling pressure in the market, while ROC measures the rate at which prices change. By merging these elements, the indicator captures the interplay between market participants' actions and the momentum of price movements, enabling traders to identify trends, spot reversals, and gauge the strength of price acceleration or deceleration.
Calculation:
The Order Flow component is computed by summing the volume when prices move up and subtracting the volume when prices move down. This cumulative measure reflects the overall order imbalance in the market, providing insights into the dominant buying or selling pressure.
The ROC component calculates the percentage change in price over a given period. It compares the current price to a previous price and expresses the change as a percentage. This measurement indicates the velocity and direction of price movement, allowing traders to assess the market's momentum.
How to Use It?
The "Trend Flow Profile" indicator offers valuable information to traders for making informed trading decisions. It enables the identification of underlying trends and potential reversals, providing a comprehensive view of market sentiment and momentum. Here are some key ways to utilize the indicator:
Spotting Trends: The indicator helps identify the prevailing market trend, whether bullish or bearish. A consistent positive (green) histogram indicates a strong uptrend, while a consistent negative (red) histogram suggests a robust downtrend.
Reversal Signals: Reversal patterns can be identified when the histogram changes color, transitioning from positive to negative (or vice versa). These reversals can signify potential turning points in the market, highlighting opportunities for counter-trend trades.
Momentum Assessment: By observing the width and intensity of the histogram, traders can assess the acceleration or deceleration of price momentum. A wider histogram suggests strong momentum, while a narrower histogram indicates a potential slowdown.
Utility:
The "Trend Flow Profile" indicator serves as a valuable tool for traders, providing several benefits. Traders can easily identify the prevailing market trend, enabling them to align their trading strategies with the dominant direction of the market. The indicator also helps spot potential reversals, allowing traders to anticipate market turning points and capture counter-trend opportunities. Additionally, the green and red histogram colors provide visual cues to determine the optimal duration of a long or short position. Following the green histogram signals when in a long position and the red histogram signals when in a short position can assist traders in managing their trades effectively. Moreover, the width and intensity of the histogram offer insights into the acceleration or deceleration of momentum. Traders can gauge the strength of price movements and adjust their trading strategies accordingly. By leveraging the "Trend Flow Profile" indicator, traders gain a comprehensive understanding of market dynamics, which enhances their decision-making and improves their overall trading outcomes.
Price Deviation Indicator (PDI)Management
The Price Deviation Indicator (PDI) was developed by "DimArt". This indicator allows you to determine the percentage deviation of the price from its average value over a certain period of time. The larger the deviation, the higher the histogram on the indicator chart. The PDI indicator can be useful for identifying a trend reversal in combination with other technical indicators, such as RSI, MACD, and others. For example, if the RSI and MACD indicators show the beginning of a possible trend reversal, using the PDI indicator can confirm this signal by showing the deviation of the current price from the average price. This can help the trader make more accurate trading decisions based on a strong signal.
Description
To calculate the values of the "Price Deviation Indicator" (PDI), we use the following steps:
• Determine the "Period" variable, which specifies the number of bars used to calculate the average price. (Default value is 20)
• Calculate the average price over the specified period using the "sma()" (simple moving average) function.
• Calculate the percentage difference between the current price and the average price using the formula: ((close - avg_price) / avg_price) * 100 .
• Set levels to change the color of the histogram based on price deviation from the average value. "Histogram Color" is a parameter to customize the color of the histogram based on deviation levels. By default, if the deviation is more than 5%, the histogram will be red; if it is less than -5%, it will be green, and for all other deviations, it will be blue. However, this parameter can be changed to other values.
• Draw a histogram of price change relative to the average value. The "Style" parameter allows you to choose the style of the indicator (histogram). By default, the "Histogram" style is set, but you can also select "Line on Close" or "Line on Open".
Application of the Indicator
The PDI indicator is based on the assumption that the price of any asset always tends to its mean value. Using PDI on higher timeframes allows you to determine the overall market trend, whereas on smaller timeframes, situations can be found when the price is in negative territory, and the histogram starts to smoothly transition from negative to positive value. This can be a signal to buy, as the price is likely in an oversold condition and ready to change its trend. On the other hand, if the strength of the price slows down or begins to approach 0, this may indicate that the asset is overbought and starting to turn towards oversold, which is a signal to sell. A beautiful feature of the PDI indicator is its simplicity and conciseness, which allows you to quickly and easily identify a trend change and make trading decisions based on a strong signal.
Conclusion
The "Price Deviation Indicator" (PDI) can be useful in analyzing price movements in the market. It allows you to calculate the relative difference between the current price and the average price, allowing you to identify market saturation and change in trend. The indicator can be used in technical analysis to make decisions about buying or selling assets on the exchange. It can also be useful for traders of different levels of experience, as its settings can be adapted depending on the user's needs and requirements. Overall, this indicator is one of the tools that can help in analyzing price and volumes to determine possible investment prospects in assets.
RSI, SRSI, MACD and DMI cross - Open source codeHello,
I'm a passionate trader who has spent years studying technical analysis and exploring different trading strategies. Through my research, I've come to realize that certain indicators are essential tools for conducting accurate market analysis and identifying profitable trading opportunities. In particular, I've found that the RSI, SRSI, MACD cross, and Di cross indicators are crucial for my trading success.
Detailed explanation:
The RSI is a momentum indicator that measures the strength of price movements. It is calculated by comparing the average of gains and losses over a certain period of time. In this indicator, the RSI is calculated based on the close price with a length of 14 periods.
The Stochastic RSI is a combination of the Stochastic Oscillator and the RSI. It is used to identify overbought and oversold conditions of the market. In this indicator, the Stochastic RSI is calculated based on the RSI with a length of 14 periods.
The MACD is a trend-following momentum indicator that shows the relationship between two moving averages of prices. It consists of two lines, the MACD line and the signal line, which are used to generate buy and sell signals. In this indicator, the MACD is calculated based on the close price with fast and slow lengths of 12 and 26 periods, respectively, and a signal length of 9 periods.
The DMI is a trend-following indicator that measures the strength of directional movement in the market. It consists of three lines, the Positive Directional Indicator (+DI), the Negative Directional Indicator (-DI), and the Average Directional Index (ADX), which are used to generate buy and sell signals. In this indicator, the DMI is calculated with a length of 14 periods and an ADX smoothing of 14 periods.
The indicator generates buy signals when certain conditions are met for each of these indicators.
1) For the RSI, a buy signal is generated when the RSI is below or equal to 35 and the Stochastic RSI %K is below or equal to 15, or when the RSI is below or equal to 28 the Stochastic RSI %K is below or equal to 15 or when the RSI is below or equal to 25 and the Stochastic RSI %K is below or equal to 10 or when the RSI is below or equal to 28.
2) For the MACD, a buy signal is generated when the MACD line is below 0, there is a change in the histogram from negative to positive, the MACD line and histogram are negative in the previous period, and the current histogram value is greater than 0.
3) For the DMI, a buy signal is generated when the Positive Directional Indicator (+DI) crosses above the Negative Directional Indicator (-DI), and the -DI is less than the +DI.
The indicator generates sell signals when certain conditions are met for each of these indicators:
1) For the RSI, a sell signal is generated when the RSI is above or equal to 75 and the Stochastic RSI %K is above or equal to 85, or when the RSI is above or equal to 80 and the Stochastic RSI %K is above or equal to 85, or when the RSI is above or equal to 85 and the Stochastic RSI %K is above or equal to 90 or when the RSI is above or equal to 82.
2)For the MACD, a sell signal is generated when the MACD line is above 0, there is a change in the histogram from positive to negative, the MACD line and histogram are positive in the previous period, and the current histogram value is less than the previous histogram value. On the other hand, a buy signal is generated when the MACD line is below 0, there is a change in the histogram from negative to positive, the MACD line and histogram are negative in the previous period, and the current histogram value is greater than the previous histogram value.
3)For the DMI a bearish signal is generated when plusDI crosses above minusDI, indicating that bulls are losing strength and bears are taking control.
The indicator uses a combination of these four indicators to generate potential buy and sell signals. The buy signals are generated when RSI and SRSI values are in oversold conditions, while sell signals are generated when RSI and SRSI values are in overbought conditions. The indicator also uses MACD crossovers and DMI crossovers to generate additional buy and sell signals.
When a signal is strong?
The use of multiple signals within a specific timeframe can increase the accuracy and reliability of the signals generated by this indicator. It is recommended to look for at least two signals within a range of 5-8 candles in order to increase the probability of a successful trade.
Why it's original?
1) There is no indicator in the library that combine all of these indicators and give you a 360 view
2)The combination of the RSI, Stochastic RSI, MACD, and DMI indicators in a single script it's unique and not available in the libray.
3)The specific parameters and conditions used to calculate the signals may be unique and not found in other scripts or libraries.
4)The use of plotshape() to plot the signals as shapes on the chart may be unique compared to other scripts that simply plot lines or bars to indicate signals.
5)The use of alertcondition() to trigger alerts based on the signals may be unique compared to other scripts that do not have custom alert functionality.
Keep attention!
It is important to note that no trading indicator or strategy is foolproof, and there is always a risk of losses in trading. While this indicator may provide useful information for making conclusions, it should not be used as the sole basis for making trading decisions. Traders should always use proper risk management techniques and consider multiple factors when making trading decisions.
Support me:)
If you find this new indicator helpful in your trading analysis, I would greatly appreciate your support! Please consider giving it a like, leaving feedback, or sharing it with your trading network. Your engagement will not only help me improve this tool but will also help other traders discover it and benefit from its features. Thank you for your support!
Stochastic Momentum Index (SMI) Refurbished▮Introduction
Stochastic Momentum Index (SMI) Indicator is a technical indicator used in technical analysis of stocks and other financial instruments.
It was developed by William Blau in 1993 and is considered to be a momentum indicator that can help identify trend reversal points.
Basically, it's a combination of the True Strength Index with a signal line to help identify turning points in the market.
SMI uses the stochastic formula to compare the current closing price of an asset with the maximum and minimum price range over a specific period.
He then compares this ratio to a short-term moving average to create an indicator that oscillates between -100 and +100.
When the SMI is above 0, it is considered positive, indicating that the current price is above the short-term moving average.
When it is below 0, it is considered negative, indicating that the current price is below the short-term moving average.
Traders use the SMI to identify potential trend reversal points.
When the indicator reaches an extreme level above +40 or below -40, a trend reversal is possible.
Furthermore, traders also watch for divergences between the SMI and the asset price to identify potential trading opportunities.
It is important to remember that the SMI is a technical indicator and as such should be used in conjunction with other technical analysis tools to get a complete picture of the market situation.
▮ Improvements
The following features were added:
1. 7 color themes, for TSI, Signal and Histogram.
2. Possibility to customize moving average type for TSI/Signal.
3. Dynamic Zones.
4. Crossing Alerts.
5. Alert points on specific ranges.
5. Coloring of bars according to TSI/Signal/Histogram.
▮ Themes
Examples:
▮ About Dynamic Zones
'Most indicators use a fixed zone for buy and sell signals.
Here's a concept based on zones that are responsive to the past levels of the indicator.'
The concept of Dynamic Zones was described by Leo Zamansky ( Ph .D.) and David Stendahl, in the magazine of Stocks & Commodities V15:7 (306-310).
Basically, a statistical calculation is made to define the extreme levels, delimiting a possible overbought/oversold region.
Given user-defined probabilities, the percentile is calculated using the method of Nearest Rank.
It is calculated by taking the difference between the data point and the number of data points below it, then dividing by the total number of data points in the set.
The result is expressed as a percentage.
This provides a measure of how a particular value compares to other values in a data set, identifying outliers or values that are significantly higher or lower than the rest of the data.
▮ What to look for
1. Divergences/weakening of a trend/reversal:
2. Supports, resistances, pullbacks:
3. Overbought/Oversold Points:
▮ Thanks and Credits
- TradingView and PineCoders: for SMI and Moving Averages
- allanster: for Dynamic Zones
Braid Filter+OVERVIEW
The Braid Filter indicator was initially made by Robert Hill and published in the Stocks and Commodities Magazine in 2006. This version of the Braid Filter expands upon Hill's original one by adding much more customization and tweaking abilities. Instead of using a simple moving average to calculate the Braid Filter, this version allows you to choose between 43 different moving average calculation types to suit your needs. The original also just used the close price for calculating its moving averages, however, this version allows you to specify different source prices, including the close, median (hl2), typical (hlc3), mean (ohlc4), and weighted (hlcc4) prices. This version also allows you to edit the lookback period for the average true range calculation. It also renamed some arbitrarily named input fields to make them more readable and understandable. Finally, it includes multi-timeframe support and the ability to color bars based on signals.
The Braid Filter calculates 3 average prices:
A short-term average close price
A medium-term average open price
A long-term average close price
It then finds the minimum and maximum of these three average prices. Then it calculates the difference between the highest and lowest average price. This difference is what the histogram shows. Then the filter line is calculated based on the ATR.
CONCEPTS
This indicator can be used to determine the start of trends. It can also be used to determine when the market is consolidating.
When the bar turns green, the average close price is greater than the average open price, indicating bullish momentum. In addition, if the histogram is green, the difference between the highest average price and the lowest average price is high enough to surpass the filter line. This means that not only is there bullish momentum, but there is stronger than average bullish momentum. Therefore, it is safe to assume that the market will trend higher. When the histogram turns red, this situation plays out except in reverse, indicating that the market will trend lower.
If the histogram color is gray, the difference between the highest average price and the lowest average price used to calculate the Braid Filter is meager. Since the highest and lowest average is close together, the price is unlikely to travel far in one direction. Therefore, it is safe to assume that the market is consolidating when this happens.
HOW DO I READ THIS INDICATOR
The signals between the histogram and filter are calculated as follows:
If the histogram is above the filter line and the fast average close price is greater than the average open price, the histogram is colored green, indicating bullish conditions.
If the histogram is above the filter line and the fast average close price is less than the average open price, the histogram is colored red, indicating bearish conditions.
If the histogram is below the filter line, the histogram is colored gray, indicating neutral conditions.
Williams Vix Fix OHLC candles plot indicator (Tartigradia)OHLC candles plot of the Williams VixFix indicator, which allows to draw trend lines.
Williams VixFix is a realized volatility indicator developed by Larry Williams, and can help in finding market bottoms.
Indeed, as Williams describe in his paper, markets tend to find the lowest prices during times of highest volatility, which usually accompany times of highest fear. The VixFix is calculated as how much the current low price statistically deviates from the maximum within a given look-back period.
The Williams VixFix indicator is usually presented as a curve or histogram. The novelty of this indicator is to present the data as a OHLC candles plot: whereas the original Williams VixFix calculation only involves the close value, we here use the open, high and low values as well. This led to some mathematical challenges because some of these calculations led to absurd values, so workarounds had to be found, but in the end I think the result was worth it, it reproduces the VIX chart quite well.
A great additional value of the OHLC chart is that it shows not just the close value, but all the values during the session: open, high and low in addition to close. This allows to draw trend lines and can provide additional information on momentum and sentiment. In addition, other indicators can be used on it, as if it was a price chart, such as RSI indicators (see RSI+ (alt) indicator for example).
For more information on the Vix Fix, which is a strategy published under public domain:
The VIX Fix, Larry Williams, Active Trader magazine, December 2007, web.archive.org
Fixing the VIX: An Indicator to Beat Fear, Amber Hestla-Barnhart, Journal of Technical Analysis, March 13, 2015, ssrn.com
Replicating the CBOE VIX using a synthetic volatility index trading algorithm, Dayne Cary and Gary van Vuuren, Cogent Economics & Finance, Volume 7, 2019, Issue 1, doi.org
This indicator includes only the Williams VixFix as an OHLC candles or bars plot, and price / vixfix candles plot, as well as the typical vixfix histogram. Indeed, it is much more practical for unbounded range indicators to be plotted in their own separate panel, hence why this indicator is released separately, so that it can work and be scaled adequately out of the box.
Note that the there are however no bottom buy signals. For a more complete indicator, which also includes the OHLC candles plots present here, but also bottom signals and Inverse VixFix (top signals), see:
Set Index symbol to SPX, and index_current = false, and timeframe Weekly, to reproduce the original VIX as close as possible by the VIXFIX (use the Add Symbol option, because you want to plot CBOE:VIX on the same timeframe as the current chart, which may include extended session / weekends). With the Weekly timeframe, off days / extended session days should not change much, but with lower timeframes this is important, because nights and weekends can change how the graph appears and seemingly make them different because of timing misalignment when in reality they are not when properly aligned.
MACD-X Overlay, More Than MACD by DGTMoving Average Convergence Divergence – MACD
The most popular indicator used in technical analysis , the moving average convergence divergence ( MACD ), created by Gerald Appel. MACD is a trend-following momentum indicator , designed to reveal changes in the strength, direction, momentum, and duration of a trend in a financial instrument’s price
Historical evolution of MACD ,
- Gerald Appel created the MACD line,
- Thomas Aspray added the histogram feature to MACD
- Giorgos E. Siligardos created a leader of MACD
MACD employs two Moving Averages of varying lengths (which are lagging indicators) to identify trend direction and duration. Then, MACD takes the difference in values between those two Moving Averages (MACD Line) and an EMA of those Moving Averages (Signal Line) and plots that difference between the two lines as a histogram which oscillates above and below a center Zero Line. The histogram is used as a good indication of a security's momentum.
The MACD indicator is typically good for identifying three types of basic signals;
Signal Line Crossovers
A Signal Line Crossover is the most common signal produced by the MACD . On the occasions where the MACD Line crosses above or below the Signal Line, that can signify a potentially strong move. The standard interpretation of such an event is a recommendation to buy if the MACD line crosses up through the Signal Line (a "bullish" crossover), or to sell if it crosses down through the Signal Line (a "bearish" crossover). These events are taken as indications that the trend in the financial instrument is about to accelerate in the direction of the crossover.
Zero Line Crossovers
Zero Line Crossovers occur when the MACD Line crossed the Zero Line and either becomes positive (above 0) or negative (below 0). A change from positive to negative MACD is interpreted as "bearish", and from negative to positive as "bullish". Zero crossovers provide evidence of a change in the direction of a trend but less confirmation of its momentum than a signal line crossover
Divergence
Divergence is another signal created by the MACD . Simply, divergence occurs when the MACD and actual price are not in agreement. A "positive divergence" or "bullish divergence" occurs when the price makes a new low but the MACD does not confirm with a new low of its own. A "negative divergence" or "bearish divergence" occurs when the price makes a new high but the MACD does not confirm with a new high of its own. A divergence with respect to price may occur on the MACD line and/or the MACD Histogram
Moving Average Crossovers , another hidden signal that MACD Indicator identifies
Many traders will watch for a short-term moving average to cross above a longer-term moving average and use this to signal increasing upward momentum. This bullish crossover suggests that the price has recently been rising at a faster rate than it has in the past, so it is a common technical buy sign. Conversely, a short-term moving average crossing below a longer-term average is used to illustrate that the asset's price has been moving downward at a faster rate and that it may be a good time to sell.
Moving Average Crossovers in reality is Zero Line Crossovers, the value of the MACD indicator is equal to zero each time the two moving averages cross over each other. For easy interpretation by trades, Zero Line Crossovers are simply described as positive or negative MACD
False signals
Like any forecasting algorithm, the MACD can generate false signals. A false positive, for example, would be a bullish crossover followed by a sudden decline in a financial instrument. A false negative would be a situation where there is bearish crossover, yet the financial instrument accelerated suddenly upwards
What is “MACD-X” and Why it is “More Than MACD”
In its simples form, MACD-X implements variety of different calculation techniques applied to obtain MACD Line. Different calculation techniques lead to different values for MACD Line, as will further discuss below, and as a consequence the signal line and the histogram values will differentiate accordingly.
Main features of MACD-X ;
1- Plotting of the Oscillator presented on top of the price chart (main chart) and applicable on both log and linear scale. Maximum plotting length is limited to 250 bars
2- Introduces different proven techniques applied on MACD calculation, such as MACD-AS (Histogram), MACD-Leader and MACD-Source, besides the traditional MACD (MACD-TRADITIONAL)
• MACD-Traditional, by Gerald Appel
It is the MACD that we know, stated as traditional just to avoid confusion with other techniques used with this study
• MACD-Histogram, by Thomas Aspray
The MACD-Histogram measures the distance between MACD and its signal line (the 9-day EMA of MACD ). Aspray developed the MACD-Histogram to anticipate signal line crossovers in MACD . Because MACD uses moving averages and moving averages lag price, signal line crossovers can come late and affect the reward-to-risk ratio of a trade. Bullish or bearish divergences in the MACD-Histogram can alert chartists to an imminent signal line crossover in MACD
Aspray's contribution served as a way to anticipate (and therefore cut down on lag) possible MACD crossovers which are a fundamental part of the indicator.
• MACD-Leader, by Giorgos E. Siligardos, PhD
MACD Leader has the ability to lead MACD at critical situations. Almost all smoothing methods encounter in technical analysis are based on a relative-weighted sum of past prices, and the Leader is no exception. The concealed weights of MACD Leader are such that more relative weight is used in the more recent prices than the respective weights used by the components of MACD . In effect, the Leader expresses more changes in average price dynamics for the recent price movement than MACD , thus eventually leading MACD , especially when significant trend changes are about to take place.
• MACD-Source, a custom experimental interpretation of mine,
MACD Source, presents an application of MACD that evaluates Source/MA Ratio, relatively with less lag, as a basis for MACD Line, also can be expressed as source convergence/divergence to its moving average. Among the various techniques for removing the lag between price and moving average (MA) of the price, one in particular stands out: the addition to the moving average of a portion of the difference between the price and MA. MACD Source, is based on signal length mean of the difference between Source and average value of shot length and long length moving average of the source (Source/MA Ratio), where the source is actual value and hence no lag and relatively less lag with the average value of moving average of the source .
MACD Source provides relatively early crossovers comparing to MACD and better momentum direction indications, assuming the lengths are set to same values
3- Alerts presented for MACD and Signal Line Crosses both for Early Warning and Confirmed Crossovers
For more, You are kindly invited to have a look to other MACD or similar studies presented on separate pane
MACD-X, More Than MACD by DGT , P-MACD by DGT and Price Distance to its MA by DGT
Disclaimer : Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
TEMA/HMA/VWMACD - Short Strategy 4HAs we can discover by studying the history of BTCUSD, the fall is always swift. Confirmation of this - today's collapse. In this strategy, an attempt is made to catch such drop by using quick entry and quick exit.
Let's describe what this strategy consists of:
• TEMA (you can find this strategy separately on this page or on platform)
• VWMACD
• HMA
• Take-profit and Stop-losses
Logic:
Firstly we VWMACD (the difference between VWMACD and simple MACD is only in the way of calculating moving average) and plot it as a histogram.
Then HMA is adding as a trend filter. For easy understanding let's plot it now on chart separately.
Next step is to create and add TEMA. After it is needed to subtract slow TEMA from fast TEMA and plot this value around 0 on histogram. This is the main decision for the implementation of the short trade.
ENTRY the trade:
When VMACD is below 0 and price (src = close) is below the HMA and TEMA below 0.
CLOSE the trade:
When VWMACD is upper than 0 or price is upper than HMA or TEMA is upper than 0
You can find more strategies on tradingammo.pro.
CT Reverse MACD CrossIntroducing the Reverse MACD Cross
MACD.... short for moving average convergence/divergence, is a trading indicator used in technical analysis of stock prices, created by Gerald Appel in the late 1970s.
It is designed to reveal changes in the strength, direction, momentum, and duration of a trend in a stock's price.
Prior work by Johny Dough showed how we can compute the price level required to make the MACD stay at its current level,
and also how to compute the price level required for the MACD to cross the zero line.
I have brought that idea to it logical conclusion for the MACD by creating a new function which also computes the price level required to cross the MACD with its signal line.
This allows the user to quickly see all of the most relevant information from the MACD and the actual price levels where the indicator will change its posture.
The MACD indicator (or "oscillator") is a collection of three time series calculated from historical price data, most often the closing price.
These three series are:
the MACD series proper shown here in blue
the "Signal Line" or "average" series shown here in red
the "Divergence" series which is the difference between the two shown here as a histogram.
There is also usually a baseline set at zero.
The MACD series is the difference between a "fast" (short period) exponential moving average (EMA), and a "slow" (longer period) EMA of the price series.
The average series (signal line) is an EMA of the MACD series itself.
The MACD indicator thus depends on three parameters, namely the time periods of the three EMAs.
The notation "MACD ( a, b, c )" usually denotes the standard indicator where the MACD series is the difference of EMAs with characteristic times a and b, and the average series is an EMA of the MACD series with characteristic time c.
There is an infobox which displays...
Whether the MACD is falling or rising
the price level which will make the MACD to change from rising to falling or vice versa
the price level which will cause the MACD to cross the signal line
the price level which will cause the MACD to cross the zero line
The most commonly used values are 12 for the fast, 26 for the slow, and 9 for the signal line, that is, MACD ( 12, 26, 9 ) .
The MACD and average series are customarily displayed as continuous lines in a plot whose horizontal axis is time oscillating above and below a zero line, whereas the divergence is commonly shown as a bar graph / histogram.
A fast EMA responds more quickly than a slow EMA to recent changes in a stock's price.
By comparing EMAs of different periods, the MACD series can indicate changes in the trend of a stock.
It is claimed that the divergence series can reveal subtle shifts in the stock's trend.
Since the MACD is based on moving averages, it is a lagging indicator. As a future metric of price trends, the MACD is less useful for stocks that are not trending (trading in a range) or are trading with unpredictable price action.
Volume Scale by Price (VSP)Volume Scale by Price (VSP) show volume of bar in y-axis (price-axis) with options below:
1. Number of histogram
* Number of histogram = 0 => do not plot volume histogram
* Number of histogram = 1 => plot volume of 21 lasted bar (bar to bar )
* Number of histogram = 2 => plot volume of 21 bar next histogram 1 (bar to bar )
2. Line width
* Columns width of histogram
3. Volume scale ratio
* Scaling histogram with justifiable ratio depend on time frame and symbol
4. Histogram color
* Color of histogram
Linear Quadratic Convergence Divergence OscillatorIntroduction
I inspired myself from the MACD to present a different oscillator aiming to show more reactive/predictive information. The MACD originally show the relationship between two moving averages by subtracting one of fast period and another one of slow period. In my indicator i will use a similar concept, i will subtract a quadratic least squares moving average with a linear least squares moving average of same period, since the quadratic least squares moving average is faster than the linear one and both methods have low-lag this will result in a reactive oscillator.
LQCD In Details
A quadratic least squares moving average try to fit a quadratic function (parabola) to the price by using the method of least squares, the linear least squares moving average try to fit a line. Non-linear fit tend to minimize the sum of squares in non-linear data, this is why a quadratic method is more reactive. The difference of both filters give us an oscillator, then we apply a simple moving average to this oscillator to provide the signal line, subtracting the oscillator and its signal line give us the histogram, those two last steps are the same used in the MACD.
Length control the period of the quadratic/linear moving average. While the MACD use a signal line for plotting the histogram i also added the option to plot the momentum of the quadratic moving average instead, the result is smoother and reduce irregularities, in order to do so just check the differential option in the parameter box.
The period of the signal line and the momentum are both controlled by the signal parameter.
A predictive approach can be made by subtracting the histogram with the signal line, this process make the histogram way more predictive, in order to do so just check the predictive histogram option in the parameter box.
Predictive histogram with simple histogram option. The differential mode can also be used with the predictive parameter, this result in a smoother but less reactive prediction.
Information Interpretation
The amount of information the MACD can give us is high. We can use the histogram as signal generator, or the if the oscillator is over/under 0, combine the oscillator/signal line with histogram, combinations can provide various systems. Some traders use the histogram as signal generator and use the cross between the histogram and the signal line as a stop signal, this method can avoid some whipsaw trades. The study of divergences with the price is also another method.
Conclusion
This oscillator aim to show the same amount of information as the MACD with a similar calculation method but using different kind of filters as well as eliminating the need to use two separates periods for the moving averages calculation, its still possible to use different periods for the quadratic/linear moving average but the results can be less accurate. This indicator can be used like the MACD.
Zenith MACD Evolution [JOAT]
Zenith MACD Evolution - Volatility-Normalized Momentum Oscillator
Introduction and Purpose
Zenith MACD Evolution is an open-source oscillator indicator that takes the classic MACD and normalizes it by ATR (Average True Range) to create consistent overbought/oversold levels across different market conditions. The core problem this indicator solves is that traditional MACD values are incomparable across different volatility regimes. A MACD reading of 50 might be extreme in a quiet market but normal in a volatile one.
This indicator addresses that by dividing MACD by ATR and scaling to a consistent range, allowing traders to use fixed overbought/oversold levels that work across all market conditions.
Why ATR Normalization Works
Traditional MACD problems:
- Values vary wildly based on price and volatility
- No consistent overbought/oversold levels
- Hard to compare across different instruments
- Extreme readings in one period may be normal in another
ATR-normalized MACD (Zenith) solves these:
- Values scaled to consistent range
- Fixed overbought/oversold levels work across all conditions
- Comparable across different instruments
- Extreme readings are truly extreme regardless of volatility
How the Normalization Works
// Classic MACD
= ta.macd(close, fastLength, slowLength, signalLength)
// ATR for normalization
float atrValue = ta.atr(atrNormLength)
// Volatility-Normalized MACD
float zenithMACD = atrValue != 0 ? (histLine / atrValue) * 100 : 0
float zenithSignal = ta.ema(zenithMACD, signalLength)
The result is a MACD that typically ranges from -200 to +200, with consistent levels:
- Above +150 = Overbought
- Below -150 = Oversold
- Above +200 = Extreme overbought
- Below -200 = Extreme oversold
Signal Types
Zero Cross Up/Down - Zenith crosses zero line (trend change)
Overbought/Oversold Entry - Zenith enters extreme zones
Overbought/Oversold Exit - Zenith leaves extreme zones (potential reversal)
Momentum Shift - Histogram direction changes (early warning)
Divergence - Price makes new high/low but Zenith does not
Histogram Coloring
The histogram uses four colors to show momentum state:
- Strong Bull (Teal) - Positive and rising
- Weak Bull (Light Teal) - Positive but falling
- Strong Bear (Red) - Negative and falling
- Weak Bear (Light Red) - Negative but rising
This helps identify momentum shifts before crossovers occur.
Dashboard Information
Zenith - Current normalized MACD value with signal line
Zone - Current zone (EXTREME OB/OVERBOUGHT/NORMAL/OVERSOLD/EXTREME OS)
Momentum - Direction (RISING/FALLING/FLAT)
Histogram - Current histogram value
ATR Norm - Current ATR value used for normalization
Classic - Traditional MACD value for reference
How to Use This Indicator
For Mean-Reversion:
1. Wait for Zenith to reach extreme zones (+200/-200)
2. Look for momentum shift (histogram color change)
3. Enter counter-trend when exiting extreme zone
For Trend Following:
1. Enter long on zero cross up
2. Enter short on zero cross down
3. Use histogram color to gauge momentum strength
For Divergence Trading:
1. Watch for DIV labels (price vs Zenith divergence)
2. Bullish divergence at support = potential long
3. Bearish divergence at resistance = potential short
Input Parameters
Fast/Slow/Signal Length (12/26/9) - Standard MACD parameters
ATR Normalization Period (26) - Period for ATR calculation
Overbought/Oversold Zone (150/-150) - Zone thresholds
Extreme Level (200) - Extreme threshold
Show Classic MACD Lines (false) - Toggle traditional lines
Show Divergence Detection (true) - Toggle divergence signals
Divergence Lookback (14) - Bars to scan for divergence
Timeframe Recommendations
All timeframes work due to normalization
Higher timeframes provide smoother signals
Normalization makes cross-timeframe comparison meaningful
Limitations
ATR normalization adds slight lag
Divergence detection is simplified
Extreme zones can persist in strong trends
Works best when combined with price action analysis
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes.
This indicator does not constitute financial advice. Momentum analysis does not guarantee profitable trades. Always use proper risk management.
- Made with passion by officialjackofalltrades
WaveTrend MACD Fusion Oscillator [MatrixQuantLabs]WaveTrend MACD Fusion Oscillator is a composite momentum oscillator that combines WaveTrend trend structure with MACD momentum analysis in a unified panel.
It is designed to help traders better align trend strength, wave timing, and signal confirmation through normalization, filtering, and divergence analysis.
Core Concept
Instead of using the original WaveTrend histogram structure, this indicator adopts the standard MACD histogram as its momentum foundation.
This design choice allows for clearer and more reliable differentiation of trend strength, while maintaining consistency across different symbols and timeframes through normalization.
WaveTrend is then applied as a wave timing and signal layer, making the overall structure easier to interpret and more robust in varying market conditions.
Key Features
Normalized MACD for Cross-Market Consistency
• The indicator uses standard MACD parameters (12/26/9) and applies normalization to scale values into a consistent range.
• This helps avoid chart distortion across different symbols and timeframes.
• An optional “Hide Weekly+” mode is provided to prevent higher-timeframe MACD values from compressing the panel scale.
MACD Histogram with Optional MACD Lines
• The MACD histogram visually represents bullish and bearish momentum, with dynamic coloring to reflect momentum expansion or contraction.
• MACD and Signal lines can be optionally displayed when deeper analysis is required.
Divergence Detection (Regular & Hidden)
Built-in divergence detection supports both MACD lines and histogram-based divergences, with selectable modes:
• Regular Divergence for potential reversals
• Hidden Divergence for trend continuation
Pivot-based confirmation and range filters help reduce noise and false signals.
WaveTrend Oscillator with Cross Strength Classification
• The WaveTrend module consists of a main trend line and a signal line, highlighting wave structure and momentum shifts.
• Golden crosses and death crosses are classified into multiple strength levels, allowing users to distinguish minor signals from more significant ones.
• The closer a counter-directional cross occurs to the overbought or oversold zones, the stronger the potential reversal signal.
• Optional Laguerre smoothing can be enabled to reduce noise and improve signal stability.
MACD-Based Signal Filtering
WaveTrend cross signals can be filtered using MACD conditions:
• Histogram Filter: Bullish crosses only when the MACD histogram is bullish, bearish crosses only when bearish
• Zero-Line Filter: Bullish crosses only above the MACD zero line, bearish crosses only below
These filters help align wave signals with the broader momentum context and reduce counter-trend noise.
Parameter Notes
Normalization Period
• Adjust this value if the indicator appears compressed or if different symbols show inconsistent scaling.
Pivot Right
• Higher values provide stronger confirmation with more delay; lower values are more sensitive and respond faster.
Laguerre Smoothing (Gamma)
• Lower values offer stronger smoothing and stability; higher values provide faster but noisier responses.
Usage Notes
• Use WaveTrend crosses to identify wave timing and momentum shifts.
• Use MACD (histogram and zero line) to assess trend direction and momentum strength.
• Divergence signals should be treated as early warnings, not standalone trade triggers.
• Best results are achieved when combined with trend analysis, price structure, or higher-timeframe context.
Disclaimer
This indicator is intended for educational and analytical purposes only and does not constitute financial advice. Always apply proper risk management and confirm signals with additional analysis.
