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Trend Analizi

Deadband Hysteresis Supertrend [BackQuant]Deadband Hysteresis Supertrend A two-stage trend tool that first filters price with a deadband baseline, then runs a Supertrend around that baseline with optional flip hysteresis and ATR-based adverse exits. What this is A hybrid of two ideas: Deadband Hysteresis Baseline that only advances when price pulls far enough from the baseline to matter. This suppresses micro noise and gives you a stable centerline. Supertrend bands wrapped around that baseline instead of raw price. Flips are further gated by an extra margin so side changes are more deliberate. The goal is fewer whipsaws in chop and clearer regime identification during trends. How it works (high level) Deadband step — compute a per-bar “deadband” size from one of four modes: ATR, Percent of price, Ticks, or Points. If price deviates from the baseline by more than this amount, move the baseline forward by a fraction of the excess. If not, hold the line. Centered Supertrend — build upper and lower bands around the baseline using ATR and a user factor. Track the usual trailing logic that tightens a band while price moves in its favor. Flip hysteresis — require price to exceed the active band by an extra flip offset × ATR before switching sides. This adds stickiness at the boundary. Adverse exit — once a side is taken, trigger an exit if price moves against the entry by K × ATR . If you would like to check out the filter by itself: What it plots DBHF baseline (optional) as a smooth centerline. DBHF Supertrend as the active trailing band. Candle coloring by trend side for quick read. Signal markers 𝕃 and 𝕊 at flips plus ✖ on adverse exits. Inputs that matter Price Source — series being filtered. Close is typical. HL2 or HLC3 can be steadier. Deadband mode — ATR, Percent, Ticks, or Points. This defines the “it’s big enough to matter” zone. ATR Length / Mult (DBHF) — only used when mode = ATR. Larger values widen the do-nothing zone. Percent / Ticks / Points — alternatives to ATR; pick what fits your market’s convention. Enter Mult — scales the deadband you must clear before the baseline moves. Increase to filter more noise. Response — fraction of the excess applied to baseline movement. Higher responds faster; lower is smoother. Supertrend ATR Period & Factor — traditional band size controls; higher factor widens and flips less often. Flip Offset ATR — extra ATR buffer required to flip. Useful in choppy regimes. Adverse Stop K·ATR — per-trade danger brake that forces an exit if price moves K×ATR against entry. UI — toggle baseline, supertrend, signals, and bar painting; choose long and short colors. How to read it Green regime — candles painted long and the Supertrend running below price. Pullbacks toward the baseline that fail to breach the opposite band often resume higher. Red regime — candles painted short and the Supertrend running above price. Rallies that cannot reclaim the band may roll over. Frequent side swaps — reduce sensitivity by increasing Enter Mult, using ATR mode, raising the Supertrend factor, or adding Flip Offset ATR. Use cases Bias filter — allow entries only in the direction of the current side. Use your preferred triggers inside that bias. Trailing logic — treat the active band as a dynamic stop. If the side flips or an adverse K·ATR exit prints, reduce or close exposure. Regime map — on higher timeframes, the combination baseline + band produces a clean up vs down template for allocation decisions. Tuning guidance Fast markets — ATR deadband, modest Enter Mult (0.8–1.2), response 0.2–0.35, Supertrend factor 1.7–2.2, small Flip Offset (0.2–0.5 ATR). Choppy ranges — widen deadband or raise Enter Mult, lower response, and add more Flip Offset so flips require stronger evidence. Slow trends — longer ATR periods and higher Supertrend factor to keep you on side longer; use a conservative adverse K. Included alerts DBHF ST Long — side flips to long. DBHF ST Short — side flips to short. Adverse Exit Long / Short — K·ATR stop triggers against the current side. Strengths Deadbanded baseline reduces micro whipsaws before Supertrend logic even begins. Flip hysteresis adds a second layer of confirmation at the boundary. Optional adverse ATR stop provides a uniform risk cut across assets and regimes. Clear visuals and minimal parameters to adjust for symbol behavior. Putting it together Think of this tool as two decisions layered into one view. The deadband baseline answers “does this move even count,” then the Supertrend wrapped around that baseline answers “if it counts, which side should I be on and where do I flip.” When both parts agree you tend to stay on the correct side of a trend for longer, and when they disagree you get an early warning that conditions are changing. When the baseline bends and price cannot reclaim the opposite band , momentum is usually continuing. Pullbacks into the baseline that stall before the far band often resolve in trend. When the baseline flattens and the bands compress , expect indecision. Use the Flip Offset ATR to avoid reacting to the first feint. Wait for a clean band breach with follow through. When an adverse K·ATR exit prints while the side has not flipped , treat it as a risk event rather than a full regime change. Many users cut size, re-enter only if the side reasserts, and let the next flip confirm a new trend. Final thoughts Deadband Hysteresis Supertrend is best read as a regime lens. The baseline defines your tolerance for noise, the bands define your trailing structure, and the flip offset plus adverse ATR stop define how forgiving or strict you want to be at the boundary. On strong trends it helps you hold through shallow shakeouts. In choppy conditions it encourages patience until price does something meaningful. Start with settings that reflect the cadence of your market, observe how often flips occur, then nudge the deadband and flip offset until the tool spends most of its time describing the move you care about rather than the noise in between.
Pine Script® göstergesi
BackQuant tarafından
2
High Probability Order Blocks [AlgoAlpha]🟠 OVERVIEW This script detects and visualizes high-probability order blocks by combining a volatility-based z-score trigger with a statistical survival model inspired by Kaplan-Meier estimation. It builds and manages bullish and bearish order blocks dynamically on the chart, displays live survival probabilities per block, and plots optional rejection signals. What makes this tool unique is its use of historical mitigation behavior to estimate and plot how likely each zone is to persist, offering traders a probabilistic perspective on order block strength—something rarely seen in retail indicators. 🟠 CONCEPTS Order blocks are regions of strong institutional interest, often marked by large imbalances between buying and selling. This script identifies those areas using z-score thresholds on directional distance (up or down candles), detecting statistically significant moves that signal potential smart money footprints. A bullish block is drawn when a strong up-move (zUp > 4) follows a down candle, and vice versa for bearish blocks. Over time, each block is evaluated: if price “mitigates” it (i.e., closes cleanly past the opposite side and confirmed with a 1 bar delay), it’s considered resolved and logged. These resolved blocks then inform a Kaplan-Meier-like survival curve, estimating the likelihood that future blocks of a given age will remain unbroken. The indicator then draws a probability curve for each side (bull/bear), updating it in real time. 🟠 FEATURES Live label inside each block showing survival probability or “N.E.D.” if insufficient data. Kaplan-Meier survival curves drawn directly on the chart to show estimated strength decay. Rejection markers (▲ ▼) if price bounces cleanly off an active order block. Alerts for zone creation and rejection signals, supporting rule-based trading workflows. 🟠 USAGE Read the label inside each block for Age | Survival% (or N.E.D. if there aren’t enough samples yet); higher survival % suggests blocks of that age have historically lasted longer. Use the right-side survival curves to gauge how probability decays with age for bull vs bear blocks, and align entries with the side showing stronger survival at current age. Treat ▲ (bullish rejection) and ▼ (bearish rejection) as optional confluence when price tests a boundary and fails to break. Turn on alerts for “Bullish Zone Created,” “Bearish Zone Created,” and rejection signals so you don’t need to watch constantly. If your chart gets crowded, enable Prevent Overlap ; tune Max Box Age to your timeframe; and adjust KM Training Window / Minimum Samples to trade off responsiveness vs stability.
Pine Script® göstergesi
AlgoAlpha tarafından
10
Pattern ScannerUltimate Pattern Scanner — multi-timeframe candlestick discovery tool (educational use only). Purpose: This script scans user-selected timeframes for classical candlestick patterns (for example: engulfing, morning/evening stars, hammers, dojis, tasuki gaps, three soldiers/crows, tweezers, marubozu, and others) and reports pattern name, detection price, directional signal (Bull / Bear / Neutral), and a simple volume participation metric. It is intended as an idea-generation and training tool to help traders learn pattern mechanics, not as an automated trading system. Main modules and rationale: 1) Pattern engine — applies classical candle structure rules to detect formations; 2) SMA trend filter (configurable length) — provides a directional bias to favor trade-with-trend setups; 3) Volume heuristic — approximates participation by separating candles into buy-like and sell-like volume and comparing total volume to a moving average; 4) Multi-timeframe aggregator — collects and presents pattern results from multiple timeframes; 5) Alerts — optional alerts list detected patterns and TFs. Combining these modules is intentional: patterns provide structure, SMA provides context, and volume supplies participation confirmation. Together they improve the educational value and practical relevance of each detected pattern. How to use: Choose timeframes and SMA length that match your trading horizon. Use the scanner to locate pattern candidates, then confirm with higher-timeframe agreement and volume ratio before considering trade entry. Use structural stops (recent swing highs/lows or ATR-based stops) and define risk:reward rules. For learning, replay alerted bars and record outcomes over fixed horizons to build empirical statistics. Limitations: Volume classification (close>open) is a heuristic and not a true bid/ask tape. SMA is a lagging trend proxy. Multi-timeframe agreement reduces but does not eliminate false signals, especially around news or in low-liquidity instruments. Use demo accounts and backtesting before live trading. Inputs you can adjust: timeframe list, SMA length, volume MA length, which patterns to enable/disable, display options. Compliance notes: This description explains why modules are combined and what the script does without exposing source code logic; it is non-promotional and contains no contact links. Remove any trademark symbols unless registration details are provided. Risk Disclaimer: This tool is provided for education and analysis only. It is not financial advice and does not guarantee returns. Users assume all risk for trades made based on this script. Backtest thoroughly and use proper risk management.
Pine Script® göstergesi
aiTrendview-Charting-System tarafından
3
Hurst Momentum Oscillator | AlphaNattHurst Momentum Oscillator | AlphaNatt An adaptive oscillator that combines the Hurst Exponent - which identifies whether markets are trending or mean-reverting - with momentum analysis to create signals that automatically adjust to market regime. "The Hurst Exponent reveals a hidden truth: markets aren't always trending. This oscillator knows when to ride momentum and when to fade it." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📐 THE MATHEMATICS Hurst Exponent (H): Measures the long-term memory of time series: H > 0.5: Trending (persistent) behavior H = 0.5: Random walk H < 0.5: Mean-reverting behavior Originally developed for analyzing Nile river flooding patterns, now used in: Fractal market analysis Network traffic prediction Climate modeling Financial markets The Innovation: This oscillator multiplies momentum by the Hurst coefficient: When trending (H > 0.5): Momentum is amplified When mean-reverting (H < 0.5): Momentum is reduced Result: Adaptive signals based on market regime ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💎 KEY ADVANTAGES Regime Adaptive: Automatically adjusts to trending vs ranging markets False Signal Reduction: Reduces momentum signals in mean-reverting markets Trend Amplification: Stronger signals when trends are persistent Mathematical Edge: Based on fractal dimension analysis No Repainting: All calculations on historical data ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 TRADING SIGNALS Visual Interpretation: Cyan zones: Bullish momentum in trending market Magenta zones: Bearish momentum or mean reversion Background tint: Blue = trending, Pink = mean-reverting Gradient intensity: Signal strength Trading Strategies: 1. Trend Following: Trade momentum signals when background is blue (trending) 2. Mean Reversion: Fade extreme readings when background is pink 3. Regime Transition: Watch for background color changes as early warning ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 OPTIMAL USAGE Best Conditions: Strong trending markets (crypto bull runs) Clear ranging markets (forex sessions) Regime transitions Multi-timeframe analysis Market Applications: Crypto: Excellent for identifying trend persistence Forex: Detects when pairs are ranging Stocks: Identifies momentum stocks Commodities: Catches persistent trends ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Developed by AlphaNatt | Fractal Market Analysis Version: 1.0 Classification: Adaptive Regime Oscillator Not financial advice. Always DYOR.
Pine Script® göstergesi
AlphaNatt tarafından
CF Cycle Low Projection V2Overview This indicator helps traders analyze repeating market cycles by detecting significant pivot lows and projecting when the next cycle low may occur. It provides timing context to support decision-making but does not generate direct buy/sell signals. How it works Pivot detection : Confirms swing lows using left/right bars. Filters (minimum % move and optional ATR separation) ensure only meaningful lows are counted. Cycle averaging : Calculates the average interval (and standard deviation) between recent pivot lows. Projection : Adds the average interval to the last pivot low to forecast the next potential cycle low. If that point lies in the past, the script rolls forward until the projection is in the future. Timing window : A shaded area around the ETA is drawn, based on either standard deviation or a percentage of the average, showing when a low is statistically more likely to occur. Visualization: • Vertical line = projected cycle low • Shaded box = timing window • Label = countdown in weeks/days/hours • HUD = status, ETA, intervals used How to use Select your preferred timeframe (works on intraday and higher). Allow pivots to accumulate; once the HUD shows Status: OK, projections will appear. Use the ETA line and timing window together with structure, liquidity levels, and support/resistance zones. Combine with your own strategy and risk management rules. Notes Works on any market supported by TradingView (crypto, stocks, forex, indices). Filters can be adjusted to reduce noise (e.g., increase % move or ATR multiplier). This tool is designed for cycle timing analysis only. It does not predict exact prices or guarantee outcomes. Some traders refer to this approach as “camel cycle trading,” but here it is implemented as a pivot-based cycle projection tool.
Pine Script® göstergesi
Hugors00 tarafından
4
Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading. "Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🤖 THE MACHINE LEARNING Gaussian Mixture Models (GMM): Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering : Models data as coming from multiple Gaussian distributions Each market regime is a different Gaussian component Provides probability of belonging to each regime More sophisticated than simple clustering Expectation-Maximization Algorithm: The indicator continuously learns and adapts using the E-M algorithm: E-step: Calculate probability of current market belonging to each regime M-step: Update regime parameters based on new data Continuous learning without repainting Adapts to changing market conditions ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 THREE MARKET REGIMES The GMM identifies three distinct market states: Regime 1 - Low Volatility: Quiet, ranging markets Uses RSI-based momentum calculation Reduces false signals in choppy conditions Background: Pink tint Regime 2 - Normal Market: Standard trending conditions Uses Rate of Change momentum Balanced sensitivity Background: Gray tint Regime 3 - High Volatility: Strong trends or volatility events Uses Z-score based momentum Captures extreme moves Background: Cyan tint ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 KEY INNOVATIONS 1. Probabilistic Regime Detection: Instead of binary regime assignment, provides probabilities: 30% Regime 1, 60% Regime 2, 10% Regime 3 Smooth transitions between regimes No sudden indicator jumps 2. Weighted Momentum Calculation: Combines three different momentum formulas Weights based on regime probabilities Automatically adapts to market conditions 3. Confidence Indicator: Shows how certain the model is (white line) High confidence = strong regime identification Low confidence = transitional market state Line transparency changes with confidence ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ PARAMETER OPTIMIZATION Training Period (50-500): 50-100: Quick adaptation to recent conditions 100: Balanced (default) 200-500: Stable regime identification Number of Components (2-5): 2: Simple bull/bear regimes 3: Low/Normal/High volatility (default) 4-5: More granular regime detection Learning Rate (0.1-1.0): 0.1-0.3: Slow, stable learning 0.3: Balanced (default) 0.5-1.0: Fast adaptation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 TRADING STRATEGIES Visual Signals: Cyan gradient: Bullish momentum Magenta gradient: Bearish momentum Background color: Current regime Confidence line: Model certainty 1. Regime-Based Trading: Regime 1 (pink): Expect mean reversion Regime 2 (gray): Standard trend following Regime 3 (cyan): Strong momentum trades 2. Confidence-Filtered Signals: Only trade when confidence > 70% High confidence = clearer market state Avoid transitions (low confidence) 3. Adaptive Position Sizing: Regime 1: Smaller positions (choppy) Regime 2: Normal positions Regime 3: Larger positions (trending) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 ADVANTAGES OVER OTHER ML INDICATORS vs K-Means Clustering: Soft clustering (probabilities) vs hard boundaries Captures uncertainty and transitions More mathematically robust vs KNN (K-Nearest Neighbors): Unsupervised learning (no historical labels needed) Continuous adaptation Lower computational complexity vs Neural Networks: Interpretable (know what each regime means) No overfitting issues Works with limited data ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📈 PERFORMANCE CHARACTERISTICS Best Market Conditions: Markets with clear regime shifts Volatile to trending transitions Multi-timeframe analysis Cryptocurrency markets (high regime variation) Key Strengths: Automatically adapts to market changes No manual parameter adjustment needed Smooth transitions between regimes Probabilistic confidence measure ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔬 TECHNICAL BACKGROUND Gaussian Mixture Models are used extensively in: Speech recognition (Google Assistant) Computer vision (facial recognition) Astronomy (galaxy classification) Genomics (gene expression analysis) Finance (risk modeling at investment banks) The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 PRO TIPS Watch regime transitions: Best opportunities often occur when regimes change Combine with volume: High volume + regime change = strong signal Use confidence filter: Avoid low confidence periods Multi-timeframe: Compare regimes across timeframes Adjust position size: Scale based on identified regime ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ IMPORTANT NOTES Machine learning adapts but doesn't predict the future Best used with other confirmation indicators Allow time for model to learn (100+ bars) Not financial advice - educational purposes Backtest thoroughly on your instruments ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏆 CONCLUSION The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides: Automatic adaptation to market conditions Clear regime identification with confidence levels Smooth, professional signal generation True unsupervised machine learning This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in: Google's speech recognition Tesla's computer vision NASA's data analysis Wall Street risk models "Let the machine learn the market regimes. Trade with statistical confidence." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Developed by AlphaNatt | Machine Learning Trading Systems Version: 1.0 Algorithm: Gaussian Mixture Model with E-M Classification: Unsupervised Learning Oscillator Not financial advice. Always DYOR.
Pine Script® göstergesi
AlphaNatt tarafından
Trend FriendTrend Friend — What it is and how to use it I built Trend Friend to stop redrawing the same trendlines all day. It automatically connects confirmed swing points (fractals) and keeps the most relevant lines in front of you. The goal: give you clean, actionable structure without the guesswork. What it does (in plain English) Finds swing highs/lows using a Fractal Period you choose. Draws auto-trendlines between the two most recent confirmed highs and the two most recent confirmed lows. Colours by intent: Lines drawn from highs (potential resistance / bearish) = Red Lines drawn from lows (potential support / bullish) = Green Keeps the chart tidy: The newest lines are styled as “recent,” older lines are dimmed as “historical,” and it prunes anything beyond your chosen limit. Optional crosses & alerts: You can highlight when price closes across the most recent line and set alerts for new lines formed and upper/lower line crosses. Structure labels: It tags HH, LH, HL, LL at the swing points, so you can quickly read trend/rotation. How it works (under the hood) A “fractal” here is a confirmed pivot: the highest high (or lowest low) with n bars on each side. That means pivots only confirm after n bars, so signals are cleaner and less noisy. When a new pivot prints, the script connects it to the prior pivot of the same type (high→high, low→low). That gives you one “bearish” line from highs and one “bullish” line from lows. The newest line is marked as recent (brighter), and the previous recent line becomes historical (dimmed). You can keep as many pairs as you want, but I usually keep it tight. Inputs you’ll actually use Fractal Period (n): this is the big one. It controls how swingy/strict the pivots are. Lower n → more swings, more lines (faster, noisier) Higher n → fewer swings, cleaner lines (slower, swing-trade friendly) Max pair of lines: how many pairs (up+down) to keep on the chart. 1–3 is a sweet spot. Extend: extend lines Right (my default) or Both ways if you like the context. Line widths & colours: recent vs. historical are separate so you can make the active lines pop. Show crosses: toggle the X markers when price crosses a line. I turn this on when I’m actively hunting breakouts/retests. Reading the chart Red lines (from highs): I treat these as potential resistance. A clean break + hold above a red line often flips me from “fade” to “follow.” Green lines (from lows): Potential support. Same idea in reverse: break + hold below and I stop buying dips until I see structure reclaim. HH / LH / HL / LL dots: quick read on structure. HH/HL bias = uptrend continuation potential LH/LL bias = downtrend continuation potential Mixed prints = rotation/chop—tighten risk or wait for clarity. My H1 guidance (fine-tuning Fractal Period) If you’re mainly on H1 (my use case), tune like this: Fast / aggressive: n = 6–8 (lots of signals, good for momentum days; more chop risk) Balanced (recommended): n = 9–12 (keeps lines meaningful but responsive) Slow / swing focus: n = 13–21 (filters noise; better for trend days and higher-TF confluence) Rule of thumb: if you’re getting too many touches and whipsaws, increase n. If you’re late to obvious breaks, decrease n. How I trade it (example workflow) Pick your n for the session (H1: start at 9–12). Mark the recent red & green lines. That’s your immediate structure. Look for interaction: Rejections from a line = fade potential back into the range. Break + close across a line = watch the retest for continuation. Confirm with context: session bias, HTF structure, and your own tools (VWAP, RSI, volume, FVG/OB, etc.). Plan the trade: enter on retest or reclaim, stop beyond the line/last swing, target the opposite side or next structure. Alerts (set and forget) “New trendline formed” — fires when a new high/low pivot confirms and a fresh line is drawn. “Upper/lower trendline crossed” — fires when price crosses the most recent red/green line. Use these to track structure shifts without staring at the screen. Good to know (honest limitations) Confirmation lag: pivots need n bars on both sides, so signals arrive after the swing confirms. That’s by design—less noise, fewer fake lines. Lines update as structure evolves: when a new pivot forms, the previous “recent” line becomes “historical,” and older ones can be removed based on your max setting. Not an auto trendline crystal ball: it won’t predict which line holds or breaks—it just keeps the most relevant structure clean and up to date. Final notes Works on any timeframe; I built it with H1 in mind and scale to H4/D1 by increasing n. Pairs nicely with session tools and VWAP for intraday, or with supply/demand / FVGs for swing planning. Risk first: lines are structure, not guarantees. Manage position size and stops as usual. Not financial advice. Trade your plan. Stay nimble.
Pine Script® göstergesi
OakleyJM tarafından
Auto Trend Channel with Fibonacci‼️ PLEASE USE WITH LOG CHART 🟠 Overview This indicator introduces a novel approach to trend channel construction by implementing a touch-based validation system that ensures channels actually function as dynamic support and resistance levels. Unlike traditional linear regression channels that simply fit a mathematical line through price data, this indicator validates channel effectiveness by measuring how frequently price interacts with the boundaries, creating channels that traders can reliably use for entry and exit decisions. 🟠 Core Idea: Touch-Based Channel Validation The fundamental problem with standard regression channels is that they often create mathematically correct but practically useless boundaries that price rarely respects. This indicator solves this by introducing a dual-scoring optimization system that evaluates each potential channel based on two critical factors: Trend Correlation (70% weight): Measures how well prices follow the overall trend direction using Pearson correlation coefficient Boundary Touch Frequency (30% weight): Counts actual instances where price highs touch the upper channel and lows touch the lower channel This combination ensures the selected channel not only follows the trend but actively serves as support and resistance. 🟠 Trading Applications Trend Following Strong Uptrend: Price consistently bounces off lower channel and Fibonacci levels Strong Downtrend: Price repeatedly fails at upper channel and Fibonacci resistance Trend Weakening: Price fails to reach channel extremes or breaks through Entry Strategies Channel Bounce Entries: Enter long when price touches lower channel with confirmation; short at upper channel touches Fibonacci Retracement Entries: Use 38.2% or 61.8% levels for pullback entries in trending markets Breakout Entries: Trade breakouts when price closes beyond channels with increased volume 🟠 Customization Parameters Automatic/Manual Period: Choose between intelligent auto-detection or fixed lookback period Touch Sensitivity (0.1%-10%): Defines how close price must be to count as a boundary touch Minimum Touches (1-10): Filter threshold for channel validation Adaptive Deviation: Toggle between calculated or manual deviation multipliers
Pine Script® göstergesi
osprey0 tarafından
Interval Highlighter with High/Low AlertsInterval Highlighter with High/Low Alerts Overview: This Pine Script indicator enhances chart analysis by highlighting specific time intervals and marking the highest and lowest prices within those periods. It supports three customizable modes: Date Range: Highlight a user-defined period with background shading and plot the highest and lowest prices. Days of the Week: Highlight specific weekdays with background colors and plot the highest and lowest prices for each day. Intraday Interval: Highlight a specific intraday time range (e.g., 12:30 PM to 4:30 PM) with background shading and plot the highest and lowest prices within that interval. Alerts are triggered when the price touches any of the highlighted high or low levels, providing real-time notifications for potential trading opportunities. High and low lines extend to the right and remain visible after the interval ends. This ensures they act as actionable reference points for alerts between intervals, allowing users to monitor critical levels until a new interval of the same category forms. Features: Customizable Time Intervals: Define specific date ranges, weekdays, or intraday intervals to highlight on the chart. High/Low Tracking: Automatically plots the highest and lowest prices within the defined intervals. Real-Time Alerts: Set up alerts to notify when the price touches any of the highlighted high or low levels. Actionable Lines: High/low lines remain visible after interval completion to serve as reference points for alerts. Visual Enhancements: Customize background colors and line styles for each interval type. Usage: Apply the indicator to your chart. Configure the desired modes (Date Range, Days of the Week, Intraday Interval) in the settings. Customize the appearance settings to match your preferences. Set up alerts based on the highlighted high/low levels. Disclaimer: This indicator is designed to assist in identifying potential areas of interest based on historical high and low levels within specified intervals. It is not intended as a standalone trading signal. Users should employ additional technical analysis tools and conduct thorough research before making trading decisions.
Pine Script® göstergesi
BaidehnXBT tarafından
Crypto OI AgregatedCrypto OI Aggregated — Open Interest Aggregator for Crypto Exchanges General Description The indicator is designed for comprehensive analysis of Open Interest (OI) across major cryptocurrency exchanges. It consolidates data from multiple platforms, visualizes it as candlestick charts or deltas, and builds tables with breakdowns by exchange and contract type. This allows traders to quickly understand where market interest is concentrated and how the market structure is shifting. Unlike standard tools that only show data from a single exchange, this indicator provides a full market overview and makes it easy to compare dynamics across different platforms. ⸻ Key Features • Aggregation of OI data from exchanges: Binance, Bybit, OKX, Bitget, Kraken, HTX, Deribit (feel free to leave a comment if you’d like me to add other exchanges that provide open interest data) • Support for contract types: USDT.P, USD.P, USDC.P, USD.PM • Automatic normalization of various OI data formats from different providers • Display modes: • OI candlestick chart (total aggregated OI) • OI Delta (change in OI per bar) • Full table with detailed data by exchange and contract type • Short summary table with totals in USD and base assets • Support for USD or COIN denomination • Convenient formatting for large numbers • Customizable colors ⸻ How to Use the Indicator 1. Select Exchanges In the settings, enable or disable specific exchanges. It is recommended to activate only the ones you need for analysis — this will make the indicator faster. 2. Choose Data Type • OI — aggregated open interest from selected exchanges. • OI delta — delta (change in OI compared to the previous bar). 3. Denomination • USD — values are converted into USD equivalents. • COIN — values are shown in the base asset (BTC, ETH, etc.). 4. Reading the Chart • OI candlesticks show the overall OI dynamics. • Delta histogram highlights how much OI has grown or decreased per bar. • Colors are fully customizable. 5. Tables • Enabled via the Show table option. • Full Table → Rows = exchanges, Columns = contract types. Cells contain OI values in either USD or the base asset, depending on settings. Quickly shows where the main interest is concentrated. • Short Table → Displays only the total OI values in USD and the base asset. ⸻ Important Notes • For better readability of large values, two custom formatting functions were implemented. They work similarly to format.volume, but with improved digit grouping and adjustable decimal precision. In the tables, the top row is formatted using format.volume, while the bottom row uses the improved formatting functions for clearer representation. str(d, n, s) => str.substring(d, 0, str.length(d) - n) + '.' + str.substring(d, str.length(d) - n, str.length(d) - (n - 2)) + s format(_r) => d = str.tostring(math.round(_r)) str.length(d) > 9 ? str(d, 9, " B") : str.length(d) > 6 ? str(d, 6, " M") : str.length(d) > 3 ? str(d, 3, " K") : d ⸻ Conclusion: Crypto OI Aggregated is a convenient and powerful tool for cryptocurrency derivatives traders. It enables tracking of OI dynamics across multiple exchanges simultaneously, detecting imbalances between contracts, and identifying signals that are not visible when analyzing a single exchange.
Pine Script® göstergesi
NoveltyTrade tarafından
Kyoshiro - FVG + Order Blocks📌 Kyoshiro – FVG + Order Blocks This indicator combines Order Block (OB) detection with an intelligent auto-management system and a clean visual display on the chart. It is designed to help traders better identify institutional zones where price frequently reacts. ⚙️ Key Features: ✅ Real-time detection of bullish and bearish Order Blocks. ✅ Automatic cleanup: invalidated OBs are removed to keep the chart clean. ✅ Customizable display: Maximum number of visible OBs (bullish / bearish). Zone colors, outlines, and midlines. Line styles (solid, dashed, dotted) and adjustable width. ✅ Choice of mitigation method: Wick Close ✅ Built-in alerts: Formation of bullish or bearish OB. Mitigation of an existing OB. 🔔 Available Alerts: Bullish OB Formed → A bullish order block is detected. Bearish OB Formed → A bearish order block is detected. Bullish OB Mitigated → A bullish OB has been invalidated. Bearish OB Mitigated → A bearish OB has been invalidated. 🎯 Use Cases: Quickly identify key liquidity zones. Track institutional activity in the market. Improve entry and exit precision.
Pine Script® göstergesi
KyoshiroG tarafından
MatrixScalper Tablo + 3 Bant Osilatör MatrixScalper “Table + 3-Band Oscillator” is a lightweight, multi-timeframe trend-momentum filter that stacks three histograms (TF1/TF2/TF3—default 5m/15m/1h) and a compact table showing EMA trend, Supertrend, RSI and MACD direction for each timeframe. Green bars/✓ mean bullish alignment, red bars/✗ bearish; mixed or gray implies neutrality. Use it to trade with the higher-timeframe bias (e.g., look for longs when 15m & 60m are bullish and the 5m band flips back to green after a pullback). It’s a filter—not a standalone signal—so combine with price action/S&R/volume; optional alerts can be added for “all-bull” or “all-bear” alignment.
Pine Script® göstergesi
NizamettinGOZEL tarafından
BUY & SELL Probability (M5..D1) - MTFMTF Probability Indicator (M5 to D1) Indicator — Dual Histogram with Buy/Sell Labels This indicator is designed to provide a probabilistic bias for bullish or bearish conditions by combining three different analytical components across multiple timeframes. The goal is to reduce noise from single-indicator signals and instead highlight confluence where trend, momentum, and strength agree. Why this combination is useful - EMA(200) Trend Filter: Identifies whether price is trading above or below a widely used long-term moving average. - MACD Momentum: Detects short-term directional momentum through line crossovers. - ADX Strength: Measures how strong the trend is, preventing signals in weak or flat markets. By combining these, the indicator avoids situations where one tool signals a trade but others do not, helping to filter out low-probability setups. How it works - Each timeframe (M5, M15, H1, H4, D1) generates its own trend, momentum, and strength score. - Scores are weighted according to user-defined importance and then aggregated into a single probability. - Proximity to recent support and resistance levels can adjust the final score, accounting for nearby barriers. - The final probability is displayed as: - Histogram (subwindow): Green bars for bullish probability >50%, red bars for bearish <50%. - On-chart labels: Showing exact buy/sell percentages on the last bar for quick reference. Inputs - EMA length (default 200), MACD settings, ADX period. - Weights for each timeframe and component (trend, momentum, strength). - Optional boost for the chart’s current timeframe. - Smoothing length for probability values. - Lookback period for support/resistance adjustment. How to use it - A green histogram above zero indicates bullish probability >50%. - A red histogram below zero indicates bearish probability >50%. - Neutral readings near 50% show low confluence and may be best avoided. - Users can adjust weights to emphasize higher or lower timeframes, depending on their trading style. Notes - This script does not guarantee profitable trades. - Best used together with price action, volume, or additional confirmation tools. - Signals are calculated only on closed bars to avoid repainting. - For testing and learning purposes — not financial advice.
Pine Script® göstergesi
DenizTheTrader tarafından
[quantish] ORB - Opening Range Breakout SignalsA streamlined opening range breakout indicator focused purely on identifying and signaling potential entry points. This simplified version removes complex profit-taking and risk management features to provide clear, actionable breakout signals. Key Features Multiple ORB Timeframes - 15 minutes to 4 hours opening range periods Clean Breakout Detection - Simple close-based signals above/below opening range Trade Window Control - Optional time limit for valid entries after ORB period Visual Clarity - Shaded opening range zones with optional trade windows Entry Signals - Clear "Bullish" and "Bearish" labels with dotted entry lines Customizable Display - Toggle opening range, trade window, and entry signal visibility Entry Alerts - Real-time notifications when breakout conditions are met Custom Sessions - Define your own market opening times if needed Best Used For Intraday trading on sub-30 minute timeframes. Ideal for traders who prefer to manage their own exits and risk management while getting clean entry signals based on opening range breakouts. Important Notes This indicator provides entry signals only - no exit or risk management guidance Works on all markets with defined opening sessions Always use proper position sizing and risk management Test thoroughly before live trading Simplified from the original FluxCharts ORB indicator with enhanced visuals and focused functionality.
Pine Script® göstergesi
kasinath tarafından
NQ Bias — Fixed Session (6PM & Midnight Opens)📰 Script Summary — NQ Bias (Fixed Session) 📌 Purpose: This Pine Script helps traders track bias shifts on Nasdaq futures (NQ) using ICT-style reference points like the 6PM open, Midnight open, and 4PM close. ⚙️ How It Works: Plots session opens (6PM, Midnight) as bias reference lines. Highlights when price is trading above or below the 4PM New York Close → tells you if the market is leaning bullish or bearish. Uses background coloring (green/red) to visually show bias. Lets you filter by specific trading dates for focused backtesting. Designed with ICT principles → focus on daily opens, session shifts, and premium/discount logic. 📈 Trader Benefit: Quickly see if NQ is in bullish or bearish context relative to key opens. Simplifies daily bias framework into an easy chart overlay. Helps align trades with the higher-probability side of the market.
Pine Script® göstergesi
ictsniperkills tarafından
Tzotchev Trend Measure [EdgeTools]Are you still measuring trend strength with moving averages? Here is a better variant at scientific level: Tzotchev Trend Measure: A Statistical Approach to Trend Following The Tzotchev Trend Measure represents a sophisticated advancement in quantitative trend analysis, moving beyond traditional moving average-based indicators toward a statistically rigorous framework for measuring trend strength. This indicator implements the methodology developed by Tzotchev et al. (2015) in their seminal J.P. Morgan research paper "Designing robust trend-following system: Behind the scenes of trend-following," which introduced a probabilistic approach to trend measurement that has since become a cornerstone of institutional trading strategies. Mathematical Foundation and Statistical Theory The core innovation of the Tzotchev Trend Measure lies in its transformation of price momentum into a probability-based metric through the application of statistical hypothesis testing principles. The indicator employs the fundamental formula ST = 2 × Φ(√T × r̄T / σ̂T) - 1, where ST represents the trend strength score bounded between -1 and +1, Φ(x) denotes the normal cumulative distribution function, T represents the lookback period in trading days, r̄T is the average logarithmic return over the specified period, and σ̂T represents the estimated daily return volatility. This formulation transforms what is essentially a t-statistic into a probabilistic trend measure, testing the null hypothesis that the mean return equals zero against the alternative hypothesis of non-zero mean return. The use of logarithmic returns rather than simple returns provides several statistical advantages, including symmetry properties where log(P₁/P₀) = -log(P₀/P₁), additivity characteristics that allow for proper compounding analysis, and improved validity of normal distribution assumptions that underpin the statistical framework. The implementation utilizes the Abramowitz and Stegun (1964) approximation for the normal cumulative distribution function, achieving accuracy within ±1.5 × 10⁻⁷ for all input values. This approximation employs Horner's method for polynomial evaluation to ensure numerical stability, particularly important when processing large datasets or extreme market conditions. Comparative Analysis with Traditional Trend Measurement Methods The Tzotchev Trend Measure demonstrates significant theoretical and empirical advantages over conventional trend analysis techniques. Traditional moving average-based systems, including simple moving averages (SMA), exponential moving averages (EMA), and their derivatives such as MACD, suffer from several fundamental limitations that the Tzotchev methodology addresses systematically. Moving average systems exhibit inherent lag bias, as documented by Kaufman (2013) in "Trading Systems and Methods," where he demonstrates that moving averages inevitably lag price movements by approximately half their period length. This lag creates delayed signal generation that reduces profitability in trending markets and increases false signal frequency during consolidation periods. In contrast, the Tzotchev measure eliminates lag bias by directly analyzing the statistical properties of return distributions rather than smoothing price levels. The volatility normalization inherent in the Tzotchev formula addresses a critical weakness in traditional momentum indicators. As shown by Bollinger (2001) in "Bollinger on Bollinger Bands," momentum oscillators like RSI and Stochastic fail to account for changing volatility regimes, leading to inconsistent signal interpretation across different market conditions. The Tzotchev measure's incorporation of return volatility in the denominator ensures that trend strength assessments remain consistent regardless of the underlying volatility environment. Empirical studies by Hurst, Ooi, and Pedersen (2013) in "Demystifying Managed Futures" demonstrate that traditional trend-following indicators suffer from significant drawdowns during whipsaw markets, with Sharpe ratios frequently below 0.5 during challenging periods. The authors attribute these poor performance characteristics to the binary nature of most trend signals and their inability to quantify signal confidence. The Tzotchev measure addresses this limitation by providing continuous probability-based outputs that allow for more sophisticated risk management and position sizing strategies. The statistical foundation of the Tzotchev approach provides superior robustness compared to technical indicators that lack theoretical grounding. Fama and French (1988) in "Permanent and Temporary Components of Stock Prices" established that price movements contain both permanent and temporary components, with traditional moving averages unable to distinguish between these elements effectively. The Tzotchev methodology's hypothesis testing framework specifically tests for the presence of permanent trend components while filtering out temporary noise, providing a more theoretically sound approach to trend identification. Research by Moskowitz, Ooi, and Pedersen (2012) in "Time Series Momentum in the Cross Section of Asset Returns" found that traditional momentum indicators exhibit significant variation in effectiveness across asset classes and time periods. Their study of multiple asset classes over decades revealed that simple price-based momentum measures often fail to capture persistent trends in fixed income and commodity markets. The Tzotchev measure's normalization by volatility and its probabilistic interpretation provide consistent performance across diverse asset classes, as demonstrated in the original J.P. Morgan research. Comparative performance studies conducted by AQR Capital Management (Asness, Moskowitz, and Pedersen, 2013) in "Value and Momentum Everywhere" show that volatility-adjusted momentum measures significantly outperform traditional price momentum across international equity, bond, commodity, and currency markets. The study documents Sharpe ratio improvements of 0.2 to 0.4 when incorporating volatility normalization, consistent with the theoretical advantages of the Tzotchev approach. The regime detection capabilities of the Tzotchev measure provide additional advantages over binary trend classification systems. Research by Ang and Bekaert (2002) in "Regime Switches in Interest Rates" demonstrates that financial markets exhibit distinct regime characteristics that traditional indicators fail to capture adequately. The Tzotchev measure's five-tier classification system (Strong Bull, Weak Bull, Neutral, Weak Bear, Strong Bear) provides more nuanced market state identification than simple trend/no-trend binary systems. Statistical testing by Jegadeesh and Titman (2001) in "Profitability of Momentum Strategies" revealed that traditional momentum indicators suffer from significant parameter instability, with optimal lookback periods varying substantially across market conditions and asset classes. The Tzotchev measure's statistical framework provides more stable parameter selection through its grounding in hypothesis testing theory, reducing the need for frequent parameter optimization that can lead to overfitting. Advanced Noise Filtering and Market Regime Detection A significant enhancement over the original Tzotchev methodology is the incorporation of a multi-factor noise filtering system designed to reduce false signals during sideways market conditions. The filtering mechanism employs four distinct approaches: adaptive thresholding based on current market regime strength, volatility-based filtering utilizing ATR percentile analysis, trend strength confirmation through momentum alignment, and a comprehensive multi-factor approach that combines all methodologies. The adaptive filtering system analyzes market microstructure through price change relative to average true range, calculates volatility percentiles over rolling windows, and assesses trend alignment across multiple timeframes using exponential moving averages of varying periods. This approach addresses one of the primary limitations identified in traditional trend-following systems, namely their tendency to generate excessive false signals during periods of low volatility or sideways price action. The regime detection component classifies market conditions into five distinct categories: Strong Bull (ST > 0.3), Weak Bull (0.1 < ST ≤ 0.3), Neutral (-0.1 ≤ ST ≤ 0.1), Weak Bear (-0.3 ≤ ST < -0.1), and Strong Bear (ST < -0.3). This classification system provides traders with clear, quantitative definitions of market regimes that can inform position sizing, risk management, and strategy selection decisions. Professional Implementation and Trading Applications The indicator incorporates three distinct trading profiles designed to accommodate different investment approaches and risk tolerances. The Conservative profile employs longer lookback periods (63 days), higher signal thresholds (0.2), and reduced filter sensitivity (0.5) to minimize false signals and focus on major trend changes. The Balanced profile utilizes standard academic parameters with moderate settings across all dimensions. The Aggressive profile implements shorter lookback periods (14 days), lower signal thresholds (-0.1), and increased filter sensitivity (1.5) to capture shorter-term trend movements. Signal generation occurs through threshold crossover analysis, where long signals are generated when the trend measure crosses above the specified threshold and short signals when it crosses below. The implementation includes sophisticated signal confirmation mechanisms that consider trend alignment across multiple timeframes and momentum strength percentiles to reduce the likelihood of false breakouts. The alert system provides real-time notifications for trend threshold crossovers, strong regime changes, and signal generation events, with configurable frequency controls to prevent notification spam. Alert messages are standardized to ensure consistency across different market conditions and timeframes. Performance Optimization and Computational Efficiency The implementation incorporates several performance optimization features designed to handle large datasets efficiently. The maximum bars back parameter allows users to control historical calculation depth, with default settings optimized for most trading applications while providing flexibility for extended historical analysis. The system includes automatic performance monitoring that generates warnings when computational limits are approached. Error handling mechanisms protect against division by zero conditions, infinite values, and other numerical instabilities that can occur during extreme market conditions. The finite value checking system ensures data integrity throughout the calculation process, with fallback mechanisms that maintain indicator functionality even when encountering corrupted or missing price data. Timeframe validation provides warnings when the indicator is applied to unsuitable timeframes, as the Tzotchev methodology was specifically designed for daily and higher timeframe analysis. This validation helps prevent misapplication of the indicator in contexts where its statistical assumptions may not hold. Visual Design and User Interface The indicator features eight professional color schemes designed for different trading environments and user preferences. The EdgeTools theme provides an institutional blue and steel color palette suitable for professional trading environments. The Gold theme offers warm colors optimized for commodities trading. The Behavioral theme incorporates psychology-based color contrasts that align with behavioral finance principles. The Quant theme provides neutral colors suitable for analytical applications. Additional specialized themes include Ocean, Fire, Matrix, and Arctic variations, each optimized for specific visual preferences and trading contexts. All color schemes include automatic dark and light mode optimization to ensure optimal readability across different chart backgrounds and trading platforms. The information table provides real-time display of key metrics including current trend measure value, market regime classification, signal strength, Z-score, average returns, volatility measures, filter threshold levels, and filter effectiveness percentages. This comprehensive dashboard allows traders to monitor all relevant indicator components simultaneously. Theoretical Implications and Research Context The Tzotchev Trend Measure addresses several theoretical limitations inherent in traditional technical analysis approaches. Unlike moving average-based systems that rely on price level comparisons, this methodology grounds trend analysis in statistical hypothesis testing, providing a more robust theoretical foundation for trading decisions. The probabilistic interpretation of trend strength offers significant advantages over binary trend classification systems. Rather than simply indicating whether a trend exists, the measure quantifies the statistical confidence level associated with the trend assessment, allowing for more nuanced risk management and position sizing decisions. The incorporation of volatility normalization addresses the well-documented problem of volatility clustering in financial time series, ensuring that trend strength assessments remain consistent across different market volatility regimes. This normalization is particularly important for portfolio management applications where consistent risk metrics across different assets and time periods are essential. Practical Applications and Trading Strategy Integration The Tzotchev Trend Measure can be effectively integrated into various trading strategies and portfolio management frameworks. For trend-following strategies, the indicator provides clear entry and exit signals with quantified confidence levels. For mean reversion strategies, extreme readings can signal potential turning points. For portfolio allocation, the regime classification system can inform dynamic asset allocation decisions. The indicator's statistical foundation makes it particularly suitable for quantitative trading strategies where systematic, rules-based approaches are preferred over discretionary decision-making. The standardized output range facilitates easy integration with position sizing algorithms and risk management systems. Risk management applications benefit from the indicator's ability to quantify trend strength and provide early warning signals of potential trend changes. The multi-timeframe analysis capability allows for the construction of robust risk management frameworks that consider both short-term tactical and long-term strategic market conditions. Implementation Guide and Parameter Configuration The practical application of the Tzotchev Trend Measure requires careful parameter configuration to optimize performance for specific trading objectives and market conditions. This section provides comprehensive guidance for parameter selection and indicator customization. Core Calculation Parameters The Lookback Period parameter controls the statistical window used for trend calculation and represents the most critical setting for the indicator. Default values range from 14 to 63 trading days, with shorter periods (14-21 days) providing more sensitive trend detection suitable for short-term trading strategies, while longer periods (42-63 days) offer more stable trend identification appropriate for position trading and long-term investment strategies. The parameter directly influences the statistical significance of trend measurements, with longer periods requiring stronger underlying trends to generate significant signals but providing greater reliability in trend identification. The Price Source parameter determines which price series is used for return calculations. The default close price provides standard trend analysis, while alternative selections such as high-low midpoint ((high + low) / 2) can reduce noise in volatile markets, and volume-weighted average price (VWAP) offers superior trend identification in institutional trading environments where volume concentration matters significantly. The Signal Threshold parameter establishes the minimum trend strength required for signal generation, with values ranging from -0.5 to 0.5. Conservative threshold settings (0.2 to 0.3) reduce false signals but may miss early trend opportunities, while aggressive settings (-0.1 to 0.1) provide earlier signal generation at the cost of increased false positive rates. The optimal threshold depends on the trader's risk tolerance and the volatility characteristics of the traded instrument. Trading Profile Configuration The Trading Profile system provides pre-configured parameter sets optimized for different trading approaches. The Conservative profile employs a 63-day lookback period with a 0.2 signal threshold and 0.5 noise sensitivity, designed for long-term position traders seeking high-probability trend signals with minimal false positives. The Balanced profile uses a 21-day lookback with 0.05 signal threshold and 1.0 noise sensitivity, suitable for swing traders requiring moderate signal frequency with acceptable noise levels. The Aggressive profile implements a 14-day lookback with -0.1 signal threshold and 1.5 noise sensitivity, optimized for day traders and scalpers requiring frequent signal generation despite higher noise levels. Advanced Noise Filtering System The noise filtering mechanism addresses the challenge of false signals during sideways market conditions through four distinct methodologies. The Adaptive filter adjusts thresholds based on current trend strength, increasing sensitivity during strong trending periods while raising thresholds during consolidation phases. The Volatility-based filter utilizes Average True Range (ATR) percentile analysis to suppress signals during abnormally volatile conditions that typically generate false trend indications. The Trend Strength filter requires alignment between multiple momentum indicators before confirming signals, reducing the probability of false breakouts from consolidation patterns. The Multi-factor approach combines all filtering methodologies using weighted scoring to provide the most robust noise reduction while maintaining signal responsiveness during genuine trend initiations. The Noise Sensitivity parameter controls the aggressiveness of the filtering system, with lower values (0.5-1.0) providing conservative filtering suitable for volatile instruments, while higher values (1.5-2.0) allow more signals through but may increase false positive rates during choppy market conditions. Visual Customization and Display Options The Color Scheme parameter offers eight professional visualization options designed for different analytical preferences and market conditions. The EdgeTools scheme provides high contrast visualization optimized for trend strength differentiation, while the Gold scheme offers warm tones suitable for commodity analysis. The Behavioral scheme uses psychological color associations to enhance decision-making speed, and the Quant scheme provides neutral colors appropriate for quantitative analysis environments. The Ocean, Fire, Matrix, and Arctic schemes offer additional aesthetic options while maintaining analytical functionality. Each scheme includes optimized colors for both light and dark chart backgrounds, ensuring visibility across different trading platform configurations. The Show Glow Effects parameter enhances plot visibility through multiple layered lines with progressive transparency, particularly useful when analyzing multiple timeframes simultaneously or when working with dense price data that might obscure trend signals. Performance Optimization Settings The Maximum Bars Back parameter controls the historical data depth available for calculations, with values ranging from 5,000 to 50,000 bars. Higher values enable analysis of longer-term trend patterns but may impact indicator loading speed on slower systems or when applied to multiple instruments simultaneously. The optimal setting depends on the intended analysis timeframe and available computational resources. The Calculate on Every Tick parameter determines whether the indicator updates with every price change or only at bar close. Real-time calculation provides immediate signal updates suitable for scalping and day trading strategies, while bar-close calculation reduces computational overhead and eliminates signal flickering during bar formation, preferred for swing trading and position management applications. Alert System Configuration The Alert Frequency parameter controls notification generation, with options for all signals, bar close only, or once per bar. High-frequency trading strategies benefit from all signals mode, while position traders typically prefer bar close alerts to avoid premature position entries based on intrabar fluctuations. The alert system generates four distinct notification types: Long Signal alerts when the trend measure crosses above the positive signal threshold, Short Signal alerts for negative threshold crossings, Bull Regime alerts when entering strong bullish conditions, and Bear Regime alerts for strong bearish regime identification. Table Display and Information Management The information table provides real-time statistical metrics including current trend value, regime classification, signal status, and filter effectiveness measurements. The table position can be customized for optimal screen real estate utilization, and individual metrics can be toggled based on analytical requirements. The Language parameter supports both English and German display options for international users, while maintaining consistent calculation methodology regardless of display language selection. Risk Management Integration Effective risk management integration requires coordination between the trend measure signals and position sizing algorithms. Strong trend readings (above 0.5 or below -0.5) support larger position sizes due to higher probability of trend continuation, while neutral readings (between -0.2 and 0.2) suggest reduced position sizes or range-trading strategies. The regime classification system provides additional risk management context, with Strong Bull and Strong Bear regimes supporting trend-following strategies, while Neutral regimes indicate potential for mean reversion approaches. The filter effectiveness metric helps traders assess current market conditions and adjust strategy parameters accordingly. Timeframe Considerations and Multi-Timeframe Analysis The indicator's effectiveness varies across different timeframes, with higher timeframes (daily, weekly) providing more reliable trend identification but slower signal generation, while lower timeframes (hourly, 15-minute) offer faster signals with increased noise levels. Multi-timeframe analysis combining trend alignment across multiple periods significantly improves signal quality and reduces false positive rates. For optimal results, traders should consider trend alignment between the primary trading timeframe and at least one higher timeframe before entering positions. Divergences between timeframes often signal potential trend reversals or consolidation periods requiring strategy adjustment. Conclusion The Tzotchev Trend Measure represents a significant advancement in technical analysis methodology, combining rigorous statistical foundations with practical trading applications. Its implementation of the J.P. Morgan research methodology provides institutional-quality trend analysis capabilities previously available only to sophisticated quantitative trading firms. The comprehensive parameter configuration options enable customization for diverse trading styles and market conditions, while the advanced noise filtering and regime detection capabilities provide superior signal quality compared to traditional trend-following indicators. Proper parameter selection and understanding of the indicator's statistical foundation are essential for achieving optimal trading results and effective risk management. References Abramowitz, M. and Stegun, I.A. (1964). Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Washington: National Bureau of Standards. Ang, A. and Bekaert, G. (2002). Regime Switches in Interest Rates. Journal of Business and Economic Statistics, 20(2), 163-182. Asness, C.S., Moskowitz, T.J., and Pedersen, L.H. (2013). Value and Momentum Everywhere. Journal of Finance, 68(3), 929-985. Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill. Fama, E.F. and French, K.R. (1988). Permanent and Temporary Components of Stock Prices. Journal of Political Economy, 96(2), 246-273. Hurst, B., Ooi, Y.H., and Pedersen, L.H. (2013). Demystifying Managed Futures. Journal of Investment Management, 11(3), 42-58. Jegadeesh, N. and Titman, S. (2001). Profitability of Momentum Strategies: An Evaluation of Alternative Explanations. Journal of Finance, 56(2), 699-720. Kaufman, P.J. (2013). Trading Systems and Methods. 5th Edition. Hoboken: John Wiley & Sons. Moskowitz, T.J., Ooi, Y.H., and Pedersen, L.H. (2012). Time Series Momentum. Journal of Financial Economics, 104(2), 228-250. Tzotchev, D., Lo, A.W., and Hasanhodzic, J. (2015). Designing robust trend-following system: Behind the scenes of trend-following. J.P. Morgan Quantitative Research, Asset Management Division.
Pine Script® göstergesi
EdgeTools tarafından
Pro Trend: Double BB + Chandelier + ZigZag by KidevThis indicator combines multiple powerful tools into a single overlay: Bollinger Bands (0.5σ & 2σ): Tracks short-term and wider volatility ranges. SMA 75: Smooth trend filter to identify medium-term direction. Centered Chandelier Exit: Dynamic stop/trend tool based on ATR; midline highlights trend bias. Double ZigZag with HH/LL Labels: Two independent ZigZags (configurable periods) mark pivots and identify Higher Highs / Higher Lows / Lower Highs / Lower Lows. Quickly visualize volatility channels and trend direction. Identify breakout vs. mean-reversion conditions. Spot pivot structure (HH/HL vs. LH/LL) for market structure analysis. Combine ATR-based stop levels with SMA filter for trade entries/exit
Pine Script® göstergesi
kidevswamynathan tarafından
NN Crypto Scalping ULTIMATE v6 - MTF mapercivNeural Network Crypto Trading System v6.1 Complete Technical Documentation Author : Neural Network Ensemble Trading System Version : 6.1 - MTF Corrected & Bias Fixed Date : January 2025 Platform : TradingView PineScript v6 Executive Summary The Neural Network Crypto Trading System v6.1 is an advanced algorithmic trading system that combines three specialized neural networks into an intelligent ensemble to generate cryptocurrency trading signals. The system integrates multi-timeframe analysis, crypto-specific optimizations, dynamic risk management, and continuous learning to maximize performance in highly volatile markets. Key Features: Ensemble of 3 specialized Neural Networks (Primary, Momentum, Volatility) Multi-Timeframe Analysis with 5 timeframes (5m, 15m, 1h, 4h, 1D) 22 Advanced Features for each model Anti-repainting guaranteed with confirmed data 8 Market Regime automatic detections 6 Signal Levels (Strong/Moderate/Weak Buy/Sell) Professional dashboard with 15+ real-time metrics Intelligent alert system with webhook integration
Pine Script® göstergesi
maperciv tarafından
Pivot Point TrendOverview A trend-following trailing line built from confirmed pivot highs/lows and ATR bands. The line turns green in uptrends and red in downtrends. A flip happens only when price closes on the other side of the opposite trail, helping filter noise. How it works: Finds confirmed swing points (pivots) and builds a smoothed center from them. From that center, creates ATR-based bands. The active trail “locks” in the trend: in uptrends it never moves down; in downtrends it never moves up. Close above the prior upper trail → bullish; close below the prior lower trail → bearish. Inputs Pivot Point Period (prd) – strictness of pivot confirmation (delay = prd bars). ATR Period (pd) and ATR Factor (factor) – band width; higher values = fewer flips. Calculation timeframe (calcTF) – leave empty to use chart TF, or set a hard TF like 1D, 4H. Show Center Line – optional central guide. Line Width – trail thickness. Alerts Bullish Flip – trend turns bullish. Bearish Flip – trend turns bearish. Trend Changed – any flip event. Usage tips Typical crypto intraday starters: prd 2–5, pd 10–14, factor 2.5–3.5. For smoother signals, compute on a higher TF (e.g., calcTF = 1D) and time entries on your lower TF. Prefer actions on bar close of the calculation TF to avoid intrabar whipsaw. Notes on repainting The script uses request.security(..., lookahead_off). Pivots confirm after prd bars by design; once confirmed, the center and trails do not use future data. Evaluate flips on bar close for consistency, especially when calcTF > chart TF. Disclaimer Educational use only. Not financial advice. Trading involves risk.
Pine Script® göstergesi
trading_mura tarafından