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ATH Dip Levels - Crypto Edition with Reactive TPHarika bir fikir! Bu indikatörü toplulukla paylaşırken (TradingView Public Library veya GitHub gibi), insanların stratejinin mantığını ve gücünü anlamaları için etkileyici bir İngilizce açıklama hazırladım. İşte paylaşımın için kullanabileceğin başlık, özet ve özellikler listesi: 🚀 Indicator Title: ATH Dip Levels - Crypto Reactive Strategy Overview This indicator is a specialized "Buy the Dip" and "Reactive Take Profit" system designed specifically for the high volatility of the crypto market. Instead of following lagging indicators, it focuses on the most fundamental metric: Percentage drawdown from the rolling All-Time High (ATH). It identifies historical discount zones and automatically calculates a "Reactive Take Profit" target for each entry, allowing you to scale out during market bounces. Key Features 📉 1. Dynamic Buy Zones (DCA Levels) The script tracks a rolling 220-day ATH and plots 7 distinct discount levels: Minor Pullbacks: 10%, 20% Major Corrections: 30%, 40% Capitulation / Bear Market Bottoms: 55%, 70%, 85% (Highlighted in Neon for max opportunity). 💰 2. Reactive Take Profit (The "Half-Drop" Rule) This is the core of the strategy. For every buy level triggered, the script automatically sets a "RE-SELL" target based on the severity of the drop: Logic: The profit target is exactly half of the percentage drop. Example: If you buy at a 30% dip, the target is a +15% recovery from that entry. Example: If you buy at a 70% dip, the target is a +35% recovery from that entry. This captures the natural "Dead Cat Bounce" or "Mean Reversion" common in crypto. 🧠 3. Intelligent State Management Single Trigger per Cycle: Each level triggers only once per ATH cycle to avoid "choppy" market noise. Automatic Reset: All levels and status flags reset automatically when the price makes a New ATH, preparing you for the next market cycle. 📊 4. Live Status Dashboard A clean, real-time table on the top-right shows you: Current ATH price. Which buy levels have been Hit (✅). Which profit targets have been Sold (💰). How to Use Accumulate: When price hits a green "BUY" label, it's a historical discount zone. Scale Out: When price hits the purple "RE-SELL" label, take profits on that specific position to reclaim liquidity. HODL the Rest: Use this to lower your break-even price while keeping a "moon bag" for the next ATH. Author's Note Best used on 4H and 1D timeframes. This is a mathematical approach to volatility, removing emotions from your trading.
อินดิเคเตอร์ Pine Script®
โดย ardagzkr
ATH Dip Levels - Buy on Dips This indicator is a "Buy the Dip" guide designed for assets in long-term uptrends, such as Nasdaq (QQQ) or S&P 500 (SPY). It uses a mathematical discipline to identify accumulation zones based on the rolling 220-bar All-Time High (ATH). Key Features: Dynamic Levels: Automatically calculates entry points at 3%, 5%, 10%, 15%, 25%, 35%, and 50% retracements from the recent ATH. Smart Filter: Each level is triggered only once per ATH cycle. It prevents over-trading in sideways markets; levels only reset when a brand-new high is formed. Clean Visuals: Features precise "BUY" labels at exact price points and a handy status dashboard in the top-right corner. Unified Alerts: Simplify your workflow by setting a single alert for all 7 dip levels.
อินดิเคเตอร์ Pine Script®
โดย ardagzkr
Orion Time Matrix | ICT Macros [by AK]ORION TIME MATRIX | ICT MACRO SUITE The Orion Time Matrix is a precision timing instrument designed to decipher the algorithmic "Heartbeat" and the timing of institutional order flow in US Index Futures markets, specifically Nasdaq (NQ) and S&P 500 (ES). Inspired by the "Time & Price" teachings of Michael J. Huddleston (The Inner Circle Trader), this tool maps out the specific time windows where algorithms seek liquidity and price delivery is most efficient.
อินดิเคเตอร์ Pine Script®
โดย Ali_Kamberoglu
Web3Labs ICT SweepsWeb3Labs ICT Sweeps is an ICT - SMC style TradingView indicator that combines market structure, prior session liquidity, HTF levels and fair value gaps into one tool. It helps you see where liquidity sits, where sweeps occur and how intraday context is forming. ​ ​ Overview ICT Sweeps focuses on four core components - sessions - market structure - FVGs - HTF liquidity. The goal is to give a clean but information dense view of where price is likely to take liquidity and react. ​ ​ 1 - Sessions The Sessions block controls markup for the Asian - London - New York sessions relative to the instrument exchange timezone. ​ Sessions - toggles session boxes on or off - each session is highlighted with its own color so you instantly see when each market is active. Session text - adds labels with session name and total traded volume in notional terms - this makes it easy to spot where the main liquidity flow appeared during the day. Sessions High - Low lines - plots horizontal lines for each session high and low - these levels act as local liquidity pools and potential sweep - reaction zones. Separate box and text colors for Asia - London - NY let you tune visibility for dark - light themes and your personal chart style. ​ 2 - Market Structure The Market Structure block automatically marks Changes of Character - ChoCh - and Breaks of Structure - BOS - on the chart. ​ MS swing length - 3 to 10 - controls how sensitive the structure is 3 - more frequent - noisy swings for aggressive intraday analysis 10 - smoother - larger swings for higher timeframes. Separate colors for bullish - bearish ChoCh - BOS make trend reading intuitive - you clearly see where structure flipped bullish or bearish and where key structural breaks occurred. ​ 3 - Fair Value Gaps - FVG The Fair Value Gaps block highlights price imbalance zones following ICT logic. ​ Max active FVG limits the number of FVGs visible at the same time - from 1 to 50 - so the chart stays readable and focused on the most relevant zones. Separate settings for bull - bear FVG fill - border - text color let you visually distinguish bullish vs bearish imbalances and keep zones clean and easy to read. ​ 4 - HTF Liquidity Levels The HTF Liquidity Levels block plots key higher timeframe liquidity levels directly on the current timeframe. ​ Custom HTF lines ON enables Previous 4H High - Low Previous Day High - Low Previous Week High - Low Previous Month High - Low. These levels serve as targets - liquidity pools - reference points for sweeps and strong reactions. Individual line width controls for 4H - Day - Week - Month let you visually prioritize more important levels - for example making weekly - monthly thicker and 4H thinner. Text color and text size options keep labels like Previous Day High or Previous Week Low readable without cluttering the chart.
อินดิเคเตอร์ Pine Script®
โดย Web3_Labs
CBDR Standard Deviation V2CBDR Standard Deviation measures how far price statistically deviates from the central bank dealer range before institutional rebalancing occurs. CBDR defines fair value, while standard deviation highlights liquidity expansion zones. Moves into ±2 SD or beyond often signal stop-loss sweeps and inventory imbalance, where institutions favor mean reversion, not breakouts. CBDR SD Core Checklist □ Daily IPDA bias defined □ Clean CBDR formed (Asia / early London) □ CBDR high & low marked □ ±1 and ±2 SD levels plotted □ Liquidity sweep beyond CBDR □ No high-impact news in session CBDR SD Reversal Trade Checklist □ Price taps ±2 SD or ±2.5 SD □ Clear rejection (wick / displacement) □ Entry against the expansion, not on breakout □ Stop placed beyond liquidity extreme □ TP1: CBDR boundary □ TP2: CBDR midpoint (mean) □ TP3 (optional): Opposite CBDR extreme □ Invalidate if strong trend displacement continues This reversal model captures institutional fade trades after liquidity is harvested, keeping execution statistical, disciplined, and prop-firm resilient.
อินดิเคเตอร์ Pine Script®
โดย jasiremail
Advanced Concept V4 Change your trading time zone to New York . To maximize readiness for institutional trading setups based on the prescribed models, traders should set alarms for specific times in the New York Time Zone (EST/EDT), which is generally 10.5 hours behind IST. Asian Stop Hunt Model The Stop Hunt Model is a liquidity-based strategy designed to exploit market stop-loss sweeps by aligning with the IPDA daily bias. The core idea is to wait for price to sweep the engineered liquidity of the Asian Session High or Low (after 10:30 AM IST). Once the sweep occurs, the trader confirms the market's true direction via a Change of Character (CHoCH) on the lower timeframe. The entry is then taken only on a retest of the resulting price inefficiency, specifically a Balanced Price Range (BPR) or imbalance, which represents the institutional entry point. By targeting the next major liquidity pool with a minimum 1:3 risk-to-reward ratio, the model prioritizes discipline and quality over frequent trading. The New York Open Model The New York Open Model is an index-focused strategy (SPX500, NAS100, US30) that trades solely during the New York Session (9:30 AM – 12:30 PM NYT). It establishes a Range Zone high and low from midnight until the open, treating these boundaries as institutional liquidity targets. Execution is triggered by a mandatory liquidity sweep of one side of this range, followed by a confirming Change of Character (CHoCH) on the 1-minute chart. Entry is taken precisely on the retest of a resulting price inefficiency (like an FVG), aiming for the opposite side of the session range, prioritizing simplicity, timing, and controlled risk over external biases like IPDA. The ATM Strategy The ATM Strategy is a high-precision, New York-session trading model designed to capture institutional liquidity moves using the IPDA directional bias. The strategy operates by first defining a Range Zone (00:00 to 8:30 AM NY time) where high and low boundaries act as liquidity targets. Execution is restricted to the Trading Zone (8:30AM to 12:30 PM NY time) and is only triggered when price executes a mandatory liquidity sweep of one range boundary that aligns with the IPDA bias. This sweep must then be confirmed on the 1-minute chart by a Change of Character (CHoCH). Final entry is taken on the retest of a resulting price inefficiency (like an FVG or BPR), with targets set at session highs or lows, ensuring institutional-style execution with high clarity and discipline. The Central Bank Dealer Range (CBDR) The Central Bank Dealer Range (CBDR) model is a disciplined, institutional trading strategy used on the 15-minute chart, primarily focusing on London Session liquidity for major currency pairs. The core idea is to align with Interbank Price Delivery Algorithm (IPDA) bias, which dictates a mandatory liquidity sweep (a false breakout of the previous day's high or low) must occur first. Following this sweep, a visible price imbalance (Fair Value Gap) must form within the London Session. Entry is strictly taken only on the retest of this imbalance zone, confirming institutional order flow, with a fixed target at the opposite boundary of the previous day's range.
อินดิเคเตอร์ Pine Script®
โดย jasiremail
London Session Counter-Trend Strategy 👉 Timeframe: 15 minutes 🕗 Phase 1 — Morning Market Reading Between 8:00 and 9:00, we observe the dominant market direction. This direction is considered structural for the rest of the trading day. If this movement continues until 10:00, it is also validated until a clear pullback occurs. ➡️ Therefore: 8:00–9:00 (and possibly until 10:00) = analysis zone 📐 Phase 2 — Trendline Construction We draw a dashed trendline based on: the lowest point if the 9:00 trend is bullish the highest point if the 9:00 trend is bearish This trendline acts as a key reference level. 🔄 Phase 3 — Trade Setup We do NOT trade in the direction of the 8:00 trend. Instead, we wait for: a price retracement back to the trendline Then: we enter a position in the opposite direction of the 8:00 trend 👉 This is a counter-trend strategy, but a structural and rule-based one — not emotional.
อินดิเคเตอร์ Pine Script®
โดย hsemmam
2
Adaptive Quant RSI [ML + MTF]This is an advanced momentum indicator that integrates Machine Learning (K-Means Clustering) with Multi-Timeframe (MTF) analysis. Unlike traditional RSI which uses fixed 70/30 levels, this script dynamically calculates support and resistance zones based on real-time historical data distribution. Key Features: 🤖 ML Dynamic Thresholds: Uses K-Means clustering to segment RSI data into clusters, automatically plotting dynamic long/short thresholds that adapt to market volatility. ⏳ MTF Trend Background: The background color changes based on a Higher Timeframe (e.g., 5-min) RSI trend, helping you align with the broader market direction. 📊 Extreme Statistics: Incorporates percentile analysis (95th/5th) and historical pivots to identify extreme overbought/oversold conditions with high reversal probability. 📈 Probability Analysis: Displays the statistical probability of the current RSI value being at the top or bottom of its historical range. Usage: Look for confluence between the dynamic ML thresholds and the MTF background color to identify high-probability reversal setups.
อินดิเคเตอร์ Pine Script®
โดย tkwatck
Institutional Flow Journey v2Institutional Flow Journey v2: A Comprehensive Analysis of Order Flow Edge for Retail Traders: 1. The Institutional Flow Journey v2 indicator represents a sophisticated attempt to democratize institutional-grade market analysis by bringing order flow concepts traditionally reserved for professional trading desks into the accessible realm of TradingView's Pine Script ecosystem. At its core, this indicator is built on the fundamental premise that markets move not on price action alone, but on the underlying battle between buying and selling pressure at specific price levels, and that by analyzing this pressure at granular timeframes, retail traders can gain insights into where institutional players are positioning themselves for significant moves. The script employs a multi-layered approach that combines volume delta analysis, absorption zone identification, exhaustion pattern recognition, and journey metrics to paint a comprehensive picture of market participant behavior during critical junctures in price discovery. 2. The technical foundation of this indicator rests on the concept of clustering lower timeframe data into meaningful analytical periods. Rather than analyzing every single second or five-second bar independently, the script aggregates this granular data into user-defined clusters, typically ranging from thirty seconds to five minutes depending on the chosen resolution and cluster settings. This clustering approach serves a dual purpose: it reduces noise inherent in tick-level data while preserving the essential information about volume distribution and directional bias within each period. By requesting lower timeframe data using TradingView's security function with the lower_tf parameter, the indicator gains access to intrabar information that would otherwise be invisible on standard chart timeframes. This is where the real power emerges, as each cluster becomes a window into the micro-structure of order flow, revealing whether buyers or sellers were more aggressive during specific price ranges. 3. The volume delta calculation methodology employed in this script is particularly noteworthy for its pragmatic approach to estimating buy versus sell volume in the absence of true tape reading data. Since most retail platforms, including TradingView, do not provide genuine bid-ask level execution data, the indicator uses a price-weighted approximation that assumes closes near the high of a bar represent buying pressure while closes near the low indicate selling pressure. The formula divides volume proportionally based on where the close occurred within the bar's range, creating synthetic buy and sell volume metrics. While this approximation has limitations compared to actual Level 2 data or time and sales information, it provides a remarkably consistent proxy for directional pressure when analyzed across multiple bars and clusters. The aggregation of these individual bar calculations into cluster-level metrics creates a smoothed representation of flow that filters out the random noise of individual transactions while highlighting persistent directional bias. 4. What makes this approach particularly valuable for retail traders is how it addresses the fundamental information asymmetry that exists in modern markets. Institutional traders have access to sophisticated order flow tools, dark pool data, and execution algorithms that provide real-time visibility into supply and demand imbalances. Retail traders, conversely, typically rely on delayed price charts and basic technical indicators that show only the outcome of these battles rather than the battle itself. The Institutional Flow Journey v2 bridges this gap by reconstructing order flow narratives from publicly available price and volume data. When the indicator identifies absorption at lows, it is detecting a pattern where significant volume traded at depressed price levels but prices failed to continue lower, suggesting that institutional buyers stepped in to absorb all available selling pressure. This is precisely the type of behavior that precedes significant reversals, as it indicates a shift in the balance of power from sellers to buyers at a specific price zone. 5. The concept of absorption zones represents one of the most sophisticated elements of this indicator's analytical framework. The script divides each session's price range into quartiles and focuses particular attention on the bottom twenty-five percent and top twenty-five percent of the range. These zones are not arbitrary; they represent areas where price tested extremes and where the market's response to those extremes reveals critical information about participant intentions. When price reaches the lower zone and significant volume trades there, the script analyzes the composition of that volume. If buy volume dominates or approaches parity with sell volume despite being at session lows, this indicates absorption—institutional players are willing to accumulate positions at depressed prices. The script quantifies this by calculating volume ratios relative to average cluster volume, delta percentages within the zone, and buy-to-sell ratios. The combination of these metrics creates a multi-dimensional view that goes far beyond simple support and resistance concepts. 6. Exhaustion analysis forms the second pillar of the indicator's edge-generating methodology. Unlike absorption, which focuses on volume composition at price extremes, exhaustion analysis examines the temporal pattern of buying or selling pressure as price approaches and leaves those extremes. The script calculates average sell volume in the clusters immediately preceding the session low and compares this to average sell volume in clusters immediately following the low. A significant decrease in selling pressure after the low indicates seller exhaustion—the participants who were aggressively pushing price lower have either completed their selling or lost conviction. This creates a vacuum that often leads to rapid reversals as even modest buying pressure encounters diminished resistance. The same logic applies inversely at session highs, where declining buy volume after the peak suggests buyer exhaustion. By quantifying these pressure changes as percentage deltas, the indicator provides objective measures of phenomena that discretionary traders might sense intuitively but struggle to quantify systematically. 7. The journey metrics component adds a dynamic dimension to what would otherwise be a static zone analysis. Once the script identifies session extremes and analyzes the absorption and exhaustion characteristics at those levels, it then tracks how price has developed since leaving those zones. The delta since low metric accumulates the net buying or selling pressure across all clusters that have formed since the session low was established. If this cumulative delta is strongly positive and growing, it confirms that the initial absorption and exhaustion signals were genuine precursors to a trend change rather than temporary imbalances. The script also counts directional clusters, tallying how many bullish versus bearish periods have formed since each extreme. A healthy recovery from a low should show predominantly bullish clusters with strong cumulative positive delta, while a distribution phase after a high should show the inverse. These journey metrics transform static level analysis into a narrative framework that tracks the market's evolving story bar by bar. 8. The scoring system that synthesizes all these analytical threads into actionable signals represents the indicator's practical edge for trading decisions. Rather than relying on a single metric or threshold, the script employs a weighted scoring approach that considers absorption presence, exhaustion confirmation, recovery or pullback degree, delta characteristics, cluster directional bias, and position relative to the session's volume-weighted average price. Bull scores accumulate points when absorption appears at lows, selling exhausts, price recovers significantly from lows, delta turns positive, winning clusters dominate the journey from the low, and price trades above VWAP. Bear scores accumulate through the inverse conditions. The relative magnitude of these scores, combined with specific combinations of high-value signals, determines the final classification ranging from strong accumulation to strong distribution, with various intermediate states of bullish lean, bearish lean, and neutral conditions. This multi-factor approach reduces false signals that plague single-metric systems while increasing confidence when multiple confirming factors align. 9. The emphasis on using one-second intrabar resolution deserves particular attention as it fundamentally impacts the quality and reliability of the analysis. When the script operates on one-second data, each cluster of sixty seconds contains sixty individual data points, each representing a discrete moment in price discovery. This granularity allows for highly accurate volume delta calculations because the approximation errors in any single second are minimized through the law of large numbers when aggregated across sixty readings. More importantly, one-second data captures institutional order flow with minimal latency. Large institutional orders, even when sliced into smaller pieces by execution algorithms, leave footprints across multiple seconds that become visible in the micro-structure. A five-hundred-lot accumulation order might execute over two minutes, appearing as consistent buying pressure across a hundred and twenty one-second bars clustered into two sixty-second periods. On a five-second or fifteen-second resolution, much of this granular information blurs together, reducing the indicator's ability to distinguish genuine persistent flow from random volatility. The computational cost of processing one-second data is negligible on modern systems, making this the optimal choice for traders seeking maximum analytical precision. 10. The practical application of this indicator in real trading scenarios involves understanding both its strengths and the market contexts where it provides maximum edge. The indicator performs best during range-bound sessions where price tests both highs and lows, creating the absorption zones and exhaustion patterns that form the basis of its analysis. In strong trending markets that never test significant support or resistance, the indicator may remain neutral or provide late signals as it requires price to establish extremes before analyzing behavior at those levels. This characteristic actually serves as a protective feature, keeping traders out of difficult-to-trade environments where reversal patterns are less reliable. When the indicator flashes its strongest signals—strong accumulation or strong distribution—the alignment of absorption, exhaustion, recovery, and positive delta creates a high-probability setup that justifies aggressive position sizing. The intermediate signals like "absorbing at lows" or "sellers exhausted" represent earlier-stage patterns that might be suitable for scaling into positions or heightened monitoring rather than full commitment. 11. Integration with other analytical frameworks significantly enhances the indicator's practical utility. While the Institutional Flow Journey v2 provides exceptional insight into micro-structure and order flow, combining it with higher timeframe structural analysis creates a powerful multi-timeframe edge. For example, if the daily chart shows price approaching a key support level and the intraday indicator begins showing absorption at lows with seller exhaustion on one-second clusters, the confluence of structural and flow-based signals multiplies the probability of a significant reversal. Similarly, pairing the indicator with order block analysis or liquidity pool mapping helps identify where institutional players might be targeting for their accumulation or distribution activities. The indicator's VWAP integration already provides one layer of this context, as institutional traders commonly use VWAP as both a benchmark and a tactical reference point, making its inclusion in the scoring system particularly relevant. 12. The visual presentation of the analysis through both the dashboard and on-chart elements demonstrates thoughtful design that balances information density with usability. The dashboard provides a comprehensive view of all analytical components, allowing traders to understand not just the final signal but the underlying evidence supporting that conclusion. This transparency is crucial for building trust in the indicator's logic and for educational purposes as traders learn to recognize these patterns independently. The zone boxes overlaid on the chart provide immediate visual context about where absorption or distribution occurred relative to current price, while the VWAP line offers a real-time reference for bias. The color-coding throughout the interface uses conventional green for bullish and red for bearish elements, reducing cognitive load and allowing quick interpretation during fast-moving markets. The signal strength visualization helps traders distinguish between high-conviction setups worthy of larger risk and lower-conviction ideas suitable for reduced size or paper trading. 13. From a risk management perspective, the indicator's graduated signal framework aligns well with position sizing principles. Strong accumulation signals with scores of six or higher and confirmed absorption plus exhaustion might justify position sizes at the upper end of a trader's risk tolerance, perhaps two to three percent of capital on the trade. Lean bullish signals with scores in the three to four range might warrant more conservative sizing at one percent or less. The neutral classification serves as an explicit instruction to preserve capital and avoid forcing trades when edges are absent. This built-in risk guidance helps traders avoid the common pitfall of treating all signals as equally valid, a mistake that often leads to giving back profits from high-quality setups on mediocre trades. The alert conditions for the strongest patterns enable traders to monitor multiple instruments without constant chart observation, expanding opportunity capture across a broader universe of potential trades. 14. The indicator's contribution to the Pine Script ecosystem extends beyond its immediate trading utility into the realm of education and free to use innovations. The code structure demonstrates advanced Pine Script techniques including multi-dimensional array management, complex conditional logic, dynamic table rendering, and sophisticated lower timeframe data handling. For developers, studying this script provides a masterclass in organizing complex analytical workflows within Pine Script's unique programming constraints. The separation of concerns into distinct phases—data collection, cluster building, zone analysis, exhaustion detection, journey tracking, scoring, and visualization—creates maintainable code that other developers can adapt for their own analytical needs. The comprehensive commenting throughout the script further enhances its educational value, explaining not just what the code does but why specific approaches were chosen. 15. The philosophical approach underlying this indicator challenges the conventional wisdom that retail traders should stick to simple moving averages and basic momentum indicators. While simplicity has its place, especially for beginners, the reality of modern markets is that edges are increasingly difficult to find and maintain. The proliferation of algorithmic trading and the professionalization of retail trading through education and better tools means that obvious patterns get arbitraged away quickly. Indicators like the Institutional Flow Journey v2 represent the next evolution in retail technical analysis, bringing institutional-grade concepts within reach of individual traders willing to invest time in understanding more sophisticated market mechanics. The learning curve is steeper than basic indicators, but the potential edge justifies the investment for serious traders committed to long-term profitability. 16. The script's handling of data limitations and approximations reveals mature design thinking about the real-world constraints of retail trading infrastructure. Rather than claiming to provide perfect order flow data that would require exchange-level access, the indicator makes reasonable approximations and combines multiple imperfect metrics to arrive at robust conclusions. This ensemble approach, where multiple independent measures must align to generate the strongest signals, creates a system that is greater than the sum of its parts. Even if the buy-sell volume approximation is only seventy percent accurate and the exhaustion detection occasionally produces false positives, the requirement that both confirm alongside other factors dramatically reduces the likelihood that all metrics would simultaneously generate false positives. This principle of convergent validation mirrors how institutional research teams operate, requiring multiple analysts using different methodologies to reach similar conclusions before committing significant capital. 18. The temporal aspects of the indicator's design also merit consideration, particularly regarding the cluster duration parameter. The default sixty-second clusters represent a sweet spot for intraday trading, providing enough aggregation to smooth random noise while maintaining sufficient granularity to catch institutional flow patterns. Traders on different timeframes might experiment with this parameter—scalpers might prefer thirty-second clusters for faster signal generation, while position traders working off fifteen-minute or hourly charts might extend clusters to two or three minutes. The key is maintaining the relationship between the underlying data resolution and the cluster period. Thirty-second clusters work best with one-second data, which provides thirty data points per cluster. Using thirty-second clusters with fifteen-second data would yield only two data points per cluster, insufficient for meaningful statistical analysis. This relationship underscores again why one-second resolution provides optimal results across the widest range of cluster settings. 19. Looking at the indicator through the lens of market microstructure theory, it effectively operationalizes concepts from academic research on order flow and price discovery. The absorption detection mirrors studies on iceberg orders and hidden liquidity, where large institutional players place orders beyond what is visible in the order book, absorbing all incoming flow at specific price levels without revealing their full intentions. The exhaustion patterns relate to theories of inventory management by market makers and the depletion of available liquidity at price extremes. The journey metrics connect to momentum and trend continuation research, examining how price develops after significant events. By synthesizing these academic concepts into a practical trading tool, the indicator bridges the gap between theoretical market understanding and actionable trading methodology. This academic grounding provides confidence that the patterns being detected have sound theoretical foundations rather than being mere curve-fitted historical artifacts. 20. In the broader context of trading system development, this indicator serves as an excellent foundation for a complete trading methodology. A trader could build an entire approach around waiting for the strong accumulation or strong distribution signals, using them as entry triggers with clearly defined stops and targets. The position of price relative to the session range provides natural stop placement—accumulation signals near session lows might use a stop just below the absorption zone, while distribution signals near session highs would place stops above the distribution zone. Profit targets might reference the opposite extreme of the session range or previous day's value areas. The indicator's frequent updates as new clusters form provide dynamic information for trade management, allowing traders to exit if absorption fails to hold or if exhaustion reverses into renewed pressure in the original direction. This complete framework from signal generation through risk management to trade management represents a significant advantage over indicators that only provide entries without guidance on the complete trade lifecycle. 21. The indicator ultimately delivers substantial value to the Pine Script universe and the retail trading community by making sophisticated order flow analysis accessible without requiring expensive third-party platforms or institutional-level data feeds. It demonstrates that with creative thinking and sound methodology, retail traders can construct powerful analytical tools using only the data available through standard charting platforms. The open-source nature of Pine Script means that traders can examine every line of code, understand exactly how signals are generated, and modify the logic to suit their specific trading styles and market preferences. This transparency and customizability represent a profound democratization of trading technology, shifting power from proprietary black-box systems toward open, verifiable, community-driven innovation. For traders willing to invest the time to understand its mechanics and apply it thoughtfully within a comprehensive trading plan, the Institutional Flow Journey v2 offers a genuine edge in the ongoing challenge of extracting consistent profits from financial markets.
อินดิเคเตอร์ Pine Script®
โดย jaydesaigu
1
Asian Stop Hunt ModelSTOP HUNT MODEL – STRATEGY DESCRIPTION The Stop Hunt Model is designed to capture high-probability trades by targeting stop-loss liquidity from retail traders at buy-side and sell-side liquidity zones. The strategy focuses on identifying where liquidity is taken during the Asian session, waiting for a Change of Character (CHoCH), and then entering from unfilled orders (Balanced Price Range / Imbalance) in the direction of the dominant IPDA bias. The objective is to trade from engineered liquidity sweeps toward the next logical liquidity pool, while maintaining strict risk control. The model operates primarily on the 5-minute chart, with early confirmation on the 3-minute chart. The Asian Killzone is used to define the initial range, plotting its high and low. Higher-timeframe liquidity from Daily, 4H, and 1H charts is marked in advance to provide directional context. IPDA direction is determined using macro alignment such as global interest rate bias and long-term trend behavior. Once the Asian session concludes, price is expected to sweep either the high or low of the Asian range or the previous day’s high/low. After the liquidity sweep, the market must show a valid CHoCH, confirming a shift in internal structure. Entries are taken only after the formation and retest of a Balanced Price Range (BPR) created by overlapping imbalances. Trades are executed from these imbalance zones, targeting the next liquidity area, with stop loss placed at the most recent swing high or low. This model prioritizes precision over frequency, aiming for fewer trades with higher reward-to-risk ratios, typically 1:3 or better, and a strict daily risk cap. CHECKLIST – STOP HUNT MODEL 1.Mark Asian Killzone High and Low 2.Identify IPDA directional bias for the pair 3.Mark Buy-side and Sell-side liquidity from Daily, 4H, and 1H 4.Wait for a liquidity sweep (Asian High/Low or Previous Day High/Low) 5.Confirm a valid CHoCH 6.Identify a valid BPR (overlapping imbalance) 7.Enter trade from the BPR zone 8.Target the next liquidity pool 9.Place stop loss at the last swing high or low RULES – STOP HUNT MODEL STRATEGY > Always pre-mark Buy-side and Sell-side liquidity on 1D, 4H, and 1H > Asian Killzone must complete by 10:30 AM IST > After Asian close, mark 15-minute timeframe liquidity > Trade only after the market sweeps the Asian session high or low > Align trades with IPDA direction: > Bullish IPDA → Prefer sweep of Asian Low > Bearish IPDA → Prefer sweep of Asian High > CHoCH confirmation is mandatory: > Green CHoCH for bullish setups > Red CHoCH for bearish setups Setup conditions: 1. Bullish: CHoCH above price + BPR below price 2. Bearish: CHoCH below price + BPR above price 3.BPR must be formed by overlapping imbalances: 4.Red → Green for bullish 5.Green → Red for bearish 6.Look for V-shaped (bullish) or A-shaped (bearish) candle behavior 7.Entry only on imbalance retest — no chase entries 8.Targets must be killzone extremes or next liquidity zone 9.Stop loss must always be at the last swing high or low 10.No manual exits if aiming for 1:3 RR 11.If price sweeps both sides or no clean sweep occurs → No Trade 12.Trade less, execute cleaner setups 13.Daily target: 1% maximum
อินดิเคเตอร์ Pine Script®
โดย jasiremail
3CRGANG - DIVERSIFIED TREND INDICATOROverview The "3CRGANG - DIVERSIFIED TREND INDICATOR" (DTI) is an advanced macro regime tool rooted in Victor Sperandeo’s timeless diversified trend approach, but fully evolved for modern global markets. It evaluates trend breadth and conviction by splitting the financial world into two critical layers: Drivers (Rates, Commodities, FX): Leading macro forces that reflect liquidity, inflation expectations, and dollar dynamics. Participation (US sector equities, Crypto, Emerging Markets): Risk assets that either confirm the macro signal through broad involvement or reveal dangerous divergences. The indicator delivers normalized scores (-1 to +1) for each layer and offers three modes: Drivers only, Participation only, or Blended overlay. This framework helps traders instantly identify high-conviction regimes, leadership shifts, late-cycle warnings, early recovery signals or cautionary divergences—providing institutional-grade context in a single pane. How It's Built: Core Concepts and Calculations Methodology Trend Determination: Each month, the indicator evaluates more than 30 key continuous futures contracts. It calculates the cumulative percentage price change over recent months and compares it to an exponential moving average (EMA) of the previous monthly returns. The EMA places greater emphasis on more recent data, with weights decreasing steadily for older periods (summing to 100%). An asset is considered: In uptrend when the current cumulative change is at or above the EMA In downtrend when below the EMA Flat (neutral) for energy commodities (Uranium, Oil, Natural Gas) instead of downtrend—to avoid false bearish readings during supply-driven ranging periods. Group scores are combined using balanced weighting: Drivers integrate Rates, a GDP-weighted FX basket, and Commodities (with adaptive handling when energy is neutral). Participation uses inverse-volatility weighting across equities, crypto, and emerging markets to reduce the influence of overly noisy assets. Final DTI values range from -1 (strong bearish breadth) to +1 (strong bullish breadth), with added context based on magnitude, speed of change, and prior direction. Why It's Useful Single-market trends often mislead in interconnected environments. DTI delivers immediate macro clarity: Are rising yields pressuring risk assets? → divergence = caution Is dollar strength suppressing commodities while equities surge? → potential regime shift Is participation narrowing in a mature bull? → late-cycle distribution Traders use it to confirm higher-timeframe bias, detect leadership changes (e.g., commodities leading = inflation), and avoid fighting strong macro drivers without risk-asset confirmation. How to Use It Apply in a separate pane. Select DTI Mode : DRIVERS → classic macro leadership view PARTICIPATION → risk-on/risk-off scope BLENDED → spot alignment vs divergence Choose Output Mode : TABLE → detailed dashboard with icons, weights, contributions, and score cell tooltips explaining current regime (e.g., "RAPID TIGHTENING", "STRONG USD DOMINANCE") HISTOGRAMS → visual comparison with intelligent nesting (weaker bar nests inside stronger when aligned) PLOTS → individual group lines with clustered labels Adjust table position to fit your layout. Interpretation: Scores near ±1 indicate high-conviction regimes; divergences between layers often precede turns. Why It's Unique and Worth Invite-Only Access Many breadth and intermarket tools are available, but few combine classic macro leadership with modern risk-asset participation in one clean system: Sperandeo-inspired macro leadership fused with modern risk-asset participation Custom recency-focused EMA weighting optimized across 30+ diverse contracts GDP-weighted FX basket + inverse-vol participation scaling Energy-specific neutral logic + adaptive commodity redistribution Smart histograms and clear regime tooltips. The result is reliable, low-noise macro context developed to deliver genuine institutional insight. Protecting the exact methodology ensures the edge remains exclusive to dedicated traders who value precision and originality.
อินดิเคเตอร์ Pine Script®
โดย RotemB
Dynamic Band (UA)Dynamic Band — Multi-TF RSI Context Indicator Dynamic Band is a market-structure and context indicator that combines ATR-based dynamic price bands with multi-timeframe RSI signals plotted directly on the chart. Instead of using static levels, the indicator builds adaptive bands around EMA, reflecting real volatility and liquidity behavior. These bands act as dynamic reaction zones, not fixed support or resistance. 🔹 Dynamic Band Structure The indicator forms three adaptive zones: Outer Band – extreme volatility / liquidity exhaustion Mid Band – transition / decision zone Inner Band – mean-reversion and reaction area Each band expands and contracts automatically with ATR, keeping relevance across different market regimes. 🔹 Multi-Timeframe Support Dynamic Bands can be displayed from: the current timeframe up to two higher timeframes (MTF overlays) This allows traders to see HTF structure directly on LTF charts, without switching timeframes. 🔹 RSI Signals on Price RSI is used as a trigger, not a standalone oscillator: Overbought / oversold events are plotted on price, not in a sub-window Signals can be shown for: current timeframe multiple higher timeframes simultaneously Each marker includes its origin timeframe, enabling instant confluence reading 🔹 Signal Anchoring & Clarity RSI markers can be anchored to: candle high / low specific Dynamic Band levels (Inner / Mid / Outer) Markers automatically stack with adaptive spacing, keeping the chart readable even with multiple timeframe signals. 🔹 Designed Use-Cases Dynamic Band is built for: identifying reaction zones instead of exact entries aligning LTF execution with HTF context spotting liquidity extremes and RSI exhaustion avoiding indicator noise and repainting traps 🔹 Key Philosophy Price reacts to zones, not lines. RSI confirms context, not direction. Dynamic Band provides a clean structural framework for discretionary, systematic, and hybrid trading approaches.
อินดิเคเตอร์ Pine Script®
โดย yulche02