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Dynamic Support and Resistance with Trend LinesDynamic Support and Resistance with Trend Lines (DSRTL) 1. Introduction & Methodology The DSRTL indicator is designed to provide a multidimensional analysis of market structure. Unlike traditional tools that rely solely on price pivots, this script combines Static Volume-based Zones with Dynamic Trend Lines to evaluate the price's position relative to critical market components. The S/R Identification Technique Instead of standard pivot points, DSRTL utilizes Volume Analysis to highlight areas of significant trader participation: - Strategy A: Matrix Climax: Identifies candles within the lookback period that are near price extremes (Highs/Lows) and coincide with significant buying or selling volume. - Strategy B: Volume Extremes: Detects candles with the absolute highest buy/sell volumes within the selected lookback window, creating extreme volume-based S/R zones. - Result: This creates Support/Resistance (S/R) zones that are validated by actual market activity, not just price geometry. Dynamic Trend Lines To complement the static zones, the indicator employs two adaptive channel methods: - Pivot Span: Connects recent significant pivots for a fast, reactive trend corridor. - 5-Point Channel: Segments the lookback period into 5 parts to perform a linear regression analysis, creating a stable and statistically significant channel. 2. Volume Calculation Methodology Accurate S/R detection requires distinguishing Buy Volume from Sell Volume. DSRTL offers two calculation modes: - Geometry (Source File): Estimates buy/sell volume based on the Close price's position relative to the High/Low of the candle. Note: This is an approximation that works on all plan types as it does not require intrabar data. - Intrabar (Precise): Analyzes historical lower-timeframe data (e.g., 15S) to calculate intrabar-based volume deltas with higher precision compared to the geometric method. Note: This offers superior accuracy. It requires access to historical intrabar data (depending on your plan limits). For the best analytical results, use this mode if available. 3. The Smart Matrix Engine (3D Analysis) The core of DSRTL is its dashboard, powered by the "Smart Matrix Engine." This engine evaluates the current price in a multi-layer market structure context (Static Volume Zones + Dynamic Channels + Volume Metrics).: A. S-State (Static): Where is the price relative to the Volume S/R zones? B. D-State (Dynamic): Where is the price relative to the Trend Channels? How to read the Matrix Map: The dashboard displays a 5x5 grid representing 25 possible market scenarios. - Rows (S1-S5): Represent the Static State (S1=Breakout, S3=Mid-Range, S5=Breakdown). - Columns (D1-D5): Represent the Dynamic State (D1=Overextended Up, D3=Neutral, D5=Overextended Down). - Active Cell: Marked with a dot, indicating the specific intersection of price action and market structure. 4. Matrix Interpretations (The 25 Scenarios) Below is the detailed logic for every possible state displayed on the dashboard, explaining the Title, Bias, and actionable Signal. Section I: S1 - Static Breakout (Price > Static Resistance) The price has cleared the static volume resistance zone. - S1 / D1: HYPER EXTENSION Bias: Extreme Bullish Signal: Caution: Exhaustion Risk. Trail stops tight. - S1 / D2: RESISTANCE CLASH Bias: Bullish Signal: Breakout confirmed but facing immediate dynamic resistance. - S1 / D3: CHANNEL BREAKOUT Bias: Strong Bullish Signal: Ideal Trend Continuation. Look to buy dips. - S1 / D4: SMART PULLBACK Bias: Bullish (Pullback) Signal: A pullback occurring after a breakout. Strong buy opportunity. - S1 / D5: CONFLICT (DIV) Bias: Conflict/Reversal Signal: Major Divergence. Static breakout is failing against dynamic structure. High Risk. Section II: S2 - Inside Static Resistance The price is currently testing the overhead resistance zone. - S2 / D1: WEAK SPIKE Bias: Neutral/Bullish Signal: Testing resistance, but short-term overextended. - S2 / D2: IRON FORTRESS (R) Bias: Rejection Risk Signal: Double Resistance (Static + Dynamic). High probability of rejection. - S2 / D3: TESTING RES Bias: Neutral Signal: Consolidating at resistance. Wait for a clear break or rejection. - S2 / D4: COMPRESSION (UP) Bias: Conflict (Squeeze) Signal: Squeezed between Static Resistance and Dynamic Support. Volatility imminent. - S2 / D5: RES vs DOWN-TREND Bias: Bearish Signal: Strong downtrend meeting static resistance. Potential Short entry. Section III: S3 - Mid-Range The price is floating between significant Static Support and Resistance. - S3 / D1: OVERBOUGHT RANGE Bias: Rejection Risk (OB) Signal: Overextended within the range. Potential fade (short). - S3 / D2: RANGE HIGH LIMIT Bias: Neutral/Bearish Signal: At the top of the dynamic channel. Look for rejection signs. - S3 / D3: NEUTRAL / CHOPPY Bias: Neutral Signal: Dead Center. Low probability environment. Avoid trading. - S3 / D4: RANGE DIP BUY Bias: Neutral/Bullish Signal: At the bottom of the dynamic channel. Look for bounce signs. - S3 / D5: WEAK RANGE (OS) Bias: Bounce Risk (OS) Signal: Oversold within the range. Potential fade (long). Section IV: S4 - Inside Static Support The price is currently testing the floor support zone. - S4 / D1: SUP vs UP-TREND Bias: Bullish Signal: Strong uptrend meeting static support. Potential Long entry. - S4 / D2: COMPRESSION (DN) Bias: Conflict (Squeeze) Signal: Squeezed between Static Support and Dynamic Resistance. Volatility imminent. - S4 / D3: TESTING SUPPORT Bias: Neutral Signal: Consolidating at support. Wait for a bounce or breakdown. - S4 / D4: IRON FLOOR (S) Bias: Bounce Risk Signal: Double Support (Static + Dynamic). High probability of a bounce. - S4 / D5: WEAK DIP Bias: Neutral/Bearish Signal: Testing support, but short-term oversold. Section V: S5 - Static Breakdown (Price < Static Support) The price has dropped below the static volume support zone. - S5 / D1: CONFLICT (DIV) Bias: Conflict/Reversal Signal: Major Divergence. Static breakdown is failing. High Risk. - S5 / D2: BEAR PULLBACK Bias: Bearish (Pullback) Signal: A pullback occurring after a breakdown. Strong selling opportunity. - S5 / D3: CHANNEL BREAKDOWN Bias: Strong Bearish Signal: Ideal Trend Continuation (Down). Sell rallies. - S5 / D4: SUPPORT CLASH Bias: Bearish Signal: Breakdown confirmed but facing immediate dynamic support. - S5 / D5: HYPER DROP (VOID) Bias: Extreme Bearish Signal: Caution: Climax risk. Trail stops for shorts. DISCLAIMER & EDUCATIONAL PURPOSE This indicator is strictly an educational tool designed to visualize complex market structure concepts. Its primary purpose is to help traders "bridge the gap" between academic theory and real-time market behavior by providing a visual representation of support, resistance, and volume dynamics. Please Note: 1. Not a Trading Strategy: This script is an analytical assistant, not a standalone "Black Box" trading system. It does not generate buy or sell signals that should be followed blindly. 2. No Financial Advice: The data provided by this tool is for informational purposes only. It is not a recommendation to buy or sell any asset. 3. Risk Warning: Trading involves significant risk. Always use your own judgment, perform your own technical analysis, and use proper risk management. Do not use this tool as the sole basis for your trading decisions. 4. Data Precision & Platform Limits: The "Intrabar (Precise)" calculation mode relies on high-resolution historical data to provide exact results. Access to this specific data depth depends entirely on your platform's subscription capabilities. If your plan does not support this level of historical intrabar data, the Precise mode may have limited coverage. In that case, you should switch to "Geometry" mode for a fully populated view.
Indicator
by ata_sabanci
38
Smart Money Dynamics Blocks - Pearson MatrixSmart Money Dynamics Blocks — Pearson Matrix A structural fusion of Prime Number Theory, Pearson Correlation, and Cumulative Delta Geometry. 1. Mathematical Foundation This indicator is built on the intersection of Prime Number Theory and the Pearson correlation coefficient, creating a structural framework that quantifies how price and time evolve together. Prime numbers — unique, indivisible, and irregular — are used here as nonlinear time intervals. Each prime length (2, 3, 5, 7, 11…97) represents a regression horizon where correlation is measured between price and time. The result is a multi-scale correlation lattice — a geometric matrix that captures hidden directional strength and temporal bias beyond traditional moving averages. 2. The Pearson Matrix Logic For every prime interval p, the indicator calculates the linear correlation: r_p = corr(price, bar_index, p) Each r_p reflects how closely price and time move together across a prime-defined window. All r_p values are then averaged to create avgR, a single adaptive coefficient summarizing overall structural coherence. - When avgR > 0.8 → strong positive correlation (labeled R+). - When avgR < -0.8 → strong negative correlation (labeled R−). This approach gives a mathematically grounded definition of trend — one that isn’t based on pattern recognition, but on measurable correlation strength. 3. Sequential Prime Slope and Median Pivot Using the ordered sequence of 25 prime intervals, the model computes sequential slopes between adjacent primes. These slopes represent the rate of change of structure between two prime scales. A robust median aggregator smooths the slopes, producing a clean, stable directional vector. The system anchors this slope to the 41-bar pivot — the median of the first 25 primes — serving as the geometric midpoint of the prime lattice. The resulting yellow line on the chart is not an ordinary regression line; it’s a dynamic prime-slope function, adapting continuously with correlation feedback. 4. Regression-Style Parallel Bands Around this prime-slope line, the indicator constructs parallel bands using standard deviation envelopes — conceptually similar to a regression channel but recalculated through the prime–Pearson matrix. These bands adjust dynamically to: - Volatility, via standard deviation of residuals. - Correlation strength, via avgR sign weighting. Together, they visualize statistical deviation geometry, making it easier to observe symmetry, expansion, and contraction phases of price structure. 5. Volume and Cumulative Delta Peaks Below the geometric layer, the indicator incorporates a custom lower-timeframe volume feed — by default using 15-second data (custom_tf_input_volume = “15S”). This allows precise delta computation between up-volume and down-volume even on higher timeframe charts. From this feed, the indicator accumulates delta over a configurable period (default: 100 bars). When cumulative delta reaches a local maximum or minimum, peak and trough markers appear, showing the precise bar where buying or selling pressure statistically peaked. This combination of geometry and order flow reveals the intersection of market structure and energy — where liquidity pressure expresses itself through mathematical form. 6. Chart Interpretation The primary chart view represents the live execution of the indicator. It displays the relationship between structural correlation and volume behavior in real time. Orange “R+” and blue “R−” labels indicate regions of strong positive or negative Pearson correlation across the prime matrix. The yellow median prime-slope line serves as the structural backbone of the indicator, while green and red parallel bands act as dynamic regression boundaries derived from the underlying correlation strength. Peaks and troughs in cumulative delta — displayed as numerical annotations — mark statistically significant shifts in buying and selling pressure. The secondary visualization (Prime Regression Concept) expands on this by illustrating how regression behavior evolves across prime intervals. Each colored regression fan corresponds to a prime number window (2, 3, 5, 7, …, 97), demonstrating how multiple regression lines would appear if drawn independently. The indicator integrates these into one unified geometric model — eliminating the need to plot tens of regression lines manually. It’s a conceptual tool to help visualize the internal logic: the synthesis of many small-scale regressions into a single coherent structure. 7. Interpretive Insight This model is not a prediction tool; it’s an instrument of mathematical observation. By translating price dynamics into a prime-structured correlation space, it reveals how coherence unfolds through time — not as a forecast, but as a measurable evolution of structure. It unifies three analytical domains: - Prime distribution — defines a nonlinear temporal architecture. - Pearson correlation — quantifies statistical cohesion. - Cumulative delta — expresses behavioral imbalance in order flow. The synthesis creates a geometric analysis of liquidity and time — where structure meets energy, and where the invisible rhythm of market flow becomes measurable. 8. Contribution & Feedback Share your observations in the comments: - The time gap and alternation between R+ and R− clusters. - How different timeframes change delta sensitivity or reveal compression/expansion. - Prime intervals/clusters that tend to sit near turning points or liquidity shifts. - How avgR behaves across assets or regimes (trending, ranging, high-vol). - Notable interactions with the parallel bands (touches, breaks, mean-revert). Your field notes help others read the model more effectively and compare contexts. Summary - Primes define the structure. - Pearson quantifies coherence. - Slope median stabilizes geometry. - Regression bands visualize deviation. - Cumulative delta locates imbalance. Together, they construct a framework where mathematics meets market behavior.
Indicator
by ata_sabanci
26
Machine Learning: Trend Pulse⚠️❗ Important Limitations: Due to the way this script is designed, it operates specifically under certain conditions: Stocks & Forex : Only compatible with timeframes of 8 hours and above ⏰ Crypto : Only works with timeframes starting from 4 hours and higher ⏰ ❗Please note that the script will not work on lower timeframes.❗ Feature Extraction : It begins by identifying a window of past price changes. Think of this as capturing the "mood" of the market over a certain period. Distance Calculation : For each historical data point, it computes a distance to the current window. This distance measures how similar past and present market conditions are. The smaller the distance, the more similar they are. Neighbor Selection : From these, it selects 'k' closest neighbors. The variable 'k' is a user-defined parameter indicating how many of the closest historical points to consider. Price Estimation : It then takes the average price of these 'k' neighbors to generate a forecast for the next stock price. Z-Score Scaling: Lastly, this forecast is normalized using the Z-score to make it more robust and comparable over time. Inputs: histCap (Historical Cap) : histCap limits the number of past bars the script will consider. Think of it as setting the "memory" of model—how far back in time it should look. sampleSpeed (Sampling Rate) : sampleSpeed is like a time-saving shortcut, allowing the script to skip bars and only sample data points at certain intervals. This makes the process faster but could potentially miss some nuances in the data. winSpan (Window Size) : This is the size of the "snapshot" of market data the script will look at each time. The window size sets how many bars the algorithm will include when it's measuring how "similar" the current market conditions are to past conditions. All these variables help to simplify and streamline the k-NN model, making it workable within limitations. You could see them as tuning knobs, letting you balance between computational efficiency and predictive accuracy.
Indicator
by Uldisbebris
3
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