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Viprasol KNN SuperTrend Engine## Overview The KNN SuperTrend Engine combines a K-Nearest Neighbors machine learning classifier with the SuperTrend indicator to generate high-confidence trend signals. Instead of relying solely on price crossing a band, this indicator uses 12 technical features across multiple time horizons, optional PCA dimensionality reduction, and Gaussian-weighted neighbor voting to predict market direction. Only when the KNN prediction aligns with a SuperTrend direction change does a confirmed signal fire. Built for traders who want data-driven, confidence-scored entries rather than simple crossover signals. ## How It Works **SuperTrend as Label Generator:** The SuperTrend indicator's direction changes serve as supervised learning labels. Each historical bar is labeled as bullish (+1), bearish (-1), or neutral (0) based on the SuperTrend state, with a 5-bar stability filter to avoid noisy labels near flip points. **12-Feature Engineering:** Four types of features, each calculated at three stride windows (short/medium/long), create a rich representation of current market conditions: 1. **RSI** — Momentum state at three temporal resolutions 2. **MA Deviation** — How far price deviates from its moving average (10 MA types supported) 3. **RSI-Signal Distance** — Momentum acceleration/deceleration 4. **Choppiness Index** — Whether the market is trending or ranging All 12 features are z-score normalized to ensure equal weighting. **PCA Compression (Optional):** When enabled, the 12 raw features are compressed into 4 principal components with decay weighting (1.0 / 0.9 / 0.8), reducing noise while preserving the most important variance. **KNN Classification:** The engine searches the training window for the K most similar historical patterns using Minkowski distance (configurable — Euclidean at p=2, Manhattan at p=1). Neighbors are weighted using a Gaussian kernel, so closer matches have exponentially more influence. The weighted vote produces bullish and bearish probability scores. **Signal Confirmation:** A signal fires only when: 1. The KNN probability crosses above the prediction threshold (default 90%) 2. The SuperTrend direction change confirms the same direction This dual-confirmation dramatically reduces false signals compared to either method alone. ## Key Features - K-Nearest Neighbors classifier with Gaussian-weighted Minkowski distance - 12 technical features across 3 stride windows (RSI, MA deviation, RSI-signal distance, Choppiness) - 10 moving average types for feature calculation (SMA, EMA, DEMA, TEMA, LSMA, WMA, HMA, ZLSMA, SMMA, THMA) - Optional PCA dimensionality reduction (12 features -> 4 components) - Configurable distance metric (Minkowski p-parameter) and kernel shape - Dual confirmation: KNN prediction + SuperTrend direction alignment - Confidence-based dynamic bar coloring (gradient from neutral to bull/bear) - Prediction probability labels at SuperTrend direction changes - ATR-based TP/SL levels on confirmed signals - Dashboard showing KNN probabilities, prediction, confidence, SuperTrend state, and feature values - SuperTrend gradient fill with toggle - 5 alert conditions with dynamic messages ## How to Use **Setup:** 1. Add to any chart (works on all markets and timeframes) 2. The indicator needs sufficient historical bars to train (at least Training Window + Stride bars) 3. Default settings work well on 15m-4H timeframes **Reading the Signals:** - **UP/DN labels** = KNN-confirmed signals (strongest, use for entries) - **Circles on SuperTrend line** = SuperTrend direction changes (colored by KNN agreement) - **Prediction % labels** = KNN confidence at each SuperTrend flip - **Bar colors** = Real-time confidence gradient (blue = bullish, pink = bearish, gray = neutral) **Dashboard:** Shows real-time KNN bull/bear probabilities, prediction state, confidence level, SuperTrend direction, RSI, Choppiness, and model settings. **Tuning Guide:** - More K-Neighbors = smoother predictions, slower to react - Higher Prediction Threshold = fewer but more confident signals - Larger Training Window = more historical data for pattern matching - PCA ON = cleaner signals in noisy markets - PCA OFF = more responsive in clean trending markets **Recommended Settings:** - Scalping (1m-5m): K=5, Threshold=0.85, Window=500, Stride=5 - Intraday (15m-1H): K=10, Threshold=0.90, Window=1000, Stride=10 - Swing (4H-1D): K=15, Threshold=0.90, Window=1000, Stride=15 ## Settings **SuperTrend:** ATR length and factor for the base SuperTrend calculation. **KNN Engine:** K-neighbors count, training window size, sampling stride, prediction threshold. **Feature Engineering:** MA type, RSI/MA/signal/choppiness periods, normalization window, distance exponent, kernel shape. **Dimensionality Reduction:** Toggle PCA compression on/off. **Risk Management:** TP/SL toggle, SL ATR multiplier, TP1/TP2 risk-reward ratios. **Visuals:** Bar coloring, prediction labels, SuperTrend fill, signal markers — all individually toggleable. **Dashboard:** Show/hide, position, font size. ## Alerts 1. **KNN Confirmed Long** — Fires when KNN + SuperTrend both confirm bullish 2. **KNN Confirmed Short** — Fires when KNN + SuperTrend both confirm bearish 3. **Any KNN Confirmed Signal** — Fires on either confirmed direction 4. **KNN Bullish Prediction** — Fires when KNN alone predicts bullish (before SuperTrend confirmation) 5. **KNN Bearish Prediction** — Fires when KNN alone predicts bearish (before SuperTrend confirmation) All alerts include {{ticker}}, {{close}}, and {{interval}} for dynamic notification messages. ## Limitations & Disclaimer - The KNN engine requires sufficient historical data to produce meaningful predictions — early bars and newly listed instruments may have unreliable output - Machine learning on financial data does not guarantee predictive accuracy; markets are non-stationary and patterns may not persist - The training window uses past data; the model cannot predict novel market regimes it hasn't seen - Computational load increases with larger training windows and more K-neighbors — very large settings may cause slow chart loading - PCA compression uses simplified linear combinations, not true eigenvector decomposition - TP/SL levels are visual guides — they do not execute trades automatically - This indicator is for educational and analytical purposes only — it is not financial advice. Always use proper risk management. --- *Based on a concept by SatohK. Upgraded to Pine Script v6 and significantly enhanced with TP/SL management, full dashboard, visual toggles, 5 alert conditions, tooltips on all inputs, and complete code restructuring by Viprasol.*
Indicador Pine Script®
por viprasol
1
AI-SuperTrend (KNN Machine Learning)AI-SuperTrend (KNN Machine Learning) ▶️Overview The AI-SuperTrend (KNN Machine Learning) is a trend-following indicator that integrates a K-Nearest Neighbors (KNN) classification engine into the classic SuperTrend algorithm. Rather than attempting to "predict" the future in the traditionally volatile and noise-heavy financial markets, this tool treats the market as a multi-dimensional state to be estimated. By continuously sampling historical data, the engine identifies clusters of past conditions that mirror the present. It then analyzes the trend of those neighbors to deduce the "True State" of the current market, using this statistical consensus to validate SuperTrend signals and filter out deceptive market noise. ▶️Why KNN for Financial Markets? In the noise-heavy environment of financial markets, complex parametric models like Support Vector Machines (SVM) or Deep Neural Networks often struggle with stability. These models frequently suffer from convergence issues during training, or they produce outputs that stagnate around the mean due to the low signal-to-noise ratio of financial data. Most critically, they are highly prone to overfitting, capturing random price fluctuations as if they were true alpha. KNN offers a distinct advantage through its Robustness and Adaptability: Non-parametric Nature: KNN makes no underlying assumptions about the distribution of data, allowing it to adapt to non-linear and evolving market regimes. Rolling Window Learning: The model utilizes a rolling "Learning Window" that naturally aligns with the bar-by-bar execution of Pine Script. This approach ensures that the engine is always synchronized with the most relevant, recent market structures while remaining computationally efficient within the platform's resource constraints. ▶️Core Methodology: KNN and State Estimation 1. The KNN Engine K-Nearest Neighbors is a non-parametric "Lazy Learning" algorithm. Instead of building a static model, it looks at the current market "Feature Vector" and searches the historical database for the K most similar instances. Distance Metric: Uses the Minkowski Distance. This is adjustable via the p-parameter, where p=1 represents Manhattan distance and p=2 represents Euclidean distance. Gaussian Weighting: Not all neighbors are equal. The script applies a Weighting kernel where neighbors closer to the current state carry significantly more weight in the final prediction than those further away. 2. State Estimation (Bayesian-like Approach) The state estimation logic implemented in this script follows the methodology used by myself in the "KNN Machine Learning Momentum Indicator." By applying this approach to the SuperTrend framework, the indicator achieves a higher level of precision in trend validation. Probability Calculation: The probability of a Bullish state is calculated as (Sum of Weights of Bullish Neighbors) divided by (Total Weights of all K Neighbors). Synergistic Robustness: By combining the volatility-based boundaries of SuperTrend with the KNN state estimation, the system significantly improves robustness against market noise. A SuperTrend flip is only considered a "Major" signal if the AI confirms that the underlying market state has truly shifted, based on historical probability. Confirmation: A signal is only triggered if the estimated probability exceeds the user-defined Prediction Threshold (e.g., 0.9 or 90%). 3. Sampling Stride (Efficiency and Diversity) To balance computational load and data diversity within Pine Script's limits, the engine utilizes a Stride mechanism: Computational Efficiency: Instead of checking every single bar in the lookback window, the script samples data at intervals defined by the Stride (e.g., every 15th bar). Pattern Diversity: By skipping adjacent, highly correlated bars, the "Learning Window" covers a broader range of market structures. This ensures the KNN engine sees various types of volatility and price action rather than redundant near-term data. ▶️Key Features Multi-Dimensional Feature Engineering The AI analyzes a "Feature Space" consisting of: RSI Momentum Clusters: Captures momentum across three different time horizons (Short, Medium, Long) to detect lead/lag convergence. MA Deviations: Measures the "stretch" or distance from the mean using various Moving Average types (ZLSMA, HMA, etc.). PCA Compression: An optional Dimensionality Reduction toggle that merges correlated features into 3 Principal Components. This reduces the "Curse of Dimensionality" and focuses the AI on the most impactful data trends. ▶️Parameter Guide 🔲SuperTrend Settings ATR Length: The lookback period for volatility calculation. Factor: The multiplier that determines the distance of the SuperTrend line from price. 🔲Machine Learning Engine K-Neighbors (K): The number of historical patterns to compare. A smaller K is more sensitive to recent changes, while a larger K is more robust but may lag. Learning Window Size: How far back in history the AI "remembers" or searches for neighbors. Stride: The sampling interval. A stride of 15 means the AI learns from every 15th bar, increasing the effective historical range without hitting script calculation limits. Prediction Threshold: The confidence level (0.1 to 1.0) required to trigger a signal. A value of 0.9 means the AI must be 90% certain based on historical weights. 🔲Feature Engineering Feature MA Type: Choose the baseline for deviation (e.g., ZLSMA for zero-lag, HMA for speed). Normalizing Window: The lookback for Z-Score normalization, ensuring all features are on the same scale (mean=0, std=1). Minkowski Parameter (p): Controls the distance logic. p=1 is Manhattan, p=2 is Euclidean. Shape Parameter: Controls the sensitivity of the Gaussian weighting. Higher values make the weights drop off more aggressively as distance increases. ▶️Visual Analytics Major Signals (▲/▼): High-confidence trend changes confirmed by the AI. These are plotted only when the SuperTrend direction aligns with the AI's predicted direction and its probability exceeds the defined threshold. Probability Labels: At every SuperTrend reversal point, the indicator displays a label showing the AI's estimated probability for that trend direction (e.g., "Pred 92%"). This allows for real-time visual assessment of the AI's confidence in the SuperTrend flip. Major Signals: High-confidence trend changes confirmed by the AI. ST Dots: Standard SuperTrend flips without full AI confirmation. Dynamic Bar Color: A gradient representing the real-time AI confidence score. 　Blue/Cyan: High Bullish Confidence. 　Red/Pink: High Bearish Confidence. 　Gray: Neutral or Indecisive state. Disclaimer Past performance does not guarantee future results. This indicator is a tool for statistical analysis and should be used in conjunction with a complete risk management strategy.
Indicador Pine Script®
por SatohK
3
Self-Playing Snake (For Fun) [UAlgo]Self Playing Snake is a lightweight mini game built entirely in Pine Script for entertainment and UI experimentation. It renders a 15 by 15 grid using TradingView tables, spawns a snake and a food item, then drives the snake automatically with a simple path selection algorithm. The game updates on the last bar, so it behaves like a small live widget on the chart while keeping the price chart itself visually clean. The script is intentionally simple and fun. It demonstrates how Pine can be used for stateful simulations, table based rendering, and basic decision making logic without requiring external inputs or manual controls. The snake continuously tries to reach food, grows when it eats, tracks score and high score, and resets automatically when it can no longer make a valid move. 🔹 Features 1) Table Rendered Game Board The game board is drawn using a table with fixed dimensions. Each cell represents a coordinate on the grid, and the script updates cell background color and emoji text to visualize the snake body, the snake head, and the food. This makes the game visible directly on the chart without using traditional plotting. 2) Persistent Game State with Custom Types The script uses two custom types: Point stores integer x and y coordinates. GameState stores the snake body as an array of Points, the food position, grid size, score, high score, and a game over flag. A single persistent GameState variable holds the full game memory across bars. 3) Automatic Food Spawning with Collision Avoidance Food spawns at a random grid coordinate. The script prevents food from spawning on the snake body by retrying up to 100 times. This keeps gameplay consistent as the snake grows and occupies more space. 4) Greedy AI Movement Logic Each tick, the snake evaluates four possible moves: up, down, left, right. It rejects moves that would collide with walls or the snake’s body, then chooses the valid move that minimizes Manhattan distance to the food. This creates a simple, deterministic style of behavior that looks intelligent in open space while still being vulnerable to self trapping as the snake becomes long. 5) Game Loop with Growth and Scoring On each update step: A new head point is added to the front of the snake. If the head reaches the food, score increases and the snake grows by keeping its tail. If the head does not reach the food, the tail is removed so snake length stays constant. High score is updated whenever a new best is reached. 6) Automatic Reset on Game Over If no valid move exists, the state is marked game over. On the next tick, the game resets automatically by restoring the initial snake shape, resetting score, and spawning new food. This keeps the widget running indefinitely without user interaction. 7) Minimal Chart Impact The script does not draw traditional overlays on the price chart. It includes a fully transparent plot call only to satisfy indicator output requirements while keeping the chart clean. 🔹 Calculations 1) Board Representation and Coordinates The grid uses integer coordinates from 0 to gridSize minus 1 in both x and y directions. Each Point represents one cell: type Point int x int y The board is rendered by looping through all cells and deciding what to draw in each position. 2) Snake Body Membership Check The script checks whether a coordinate is occupied by the snake by scanning the snake array: method isBody(GameState state, int x, int y) => bool found = false if not na(state.snake) and state.snake.size() > 0 for i = 0 to state.snake.size() - 1 p = state.snake.get(i) if p.x == x and p.y == y found := true break found This method is used both for collision checks and for rendering. 3) Manhattan Distance for AI Scoring The AI uses Manhattan distance to estimate how close a candidate move is to food: method dist(Point p1, Point p2) => math.abs(p1.x - p2.x) + math.abs(p1.y - p2.y) This favors direct horizontal or vertical progress toward the target. 4) Food Spawning Logic Food spawns randomly inside the grid, retrying if it lands on the snake: int newX = int(math.random(0, state.gridSize - 1)) int newY = int(math.random(0, state.gridSize - 1)) int attempts = 0 while state.isBody(newX, newY) and attempts < 100 newX := int(math.random(0, state.gridSize - 1)) newY := int(math.random(0, state.gridSize - 1)) attempts += 1 state.food := Point.new(newX, newY) 5) Reset Initialization The reset routine clears the snake and creates a small starting body, resets score, clears game over, and spawns food: state.snake.clear() state.snake.push(Point.new(7, 7)) state.snake.push(Point.new(7, 8)) state.score := 0 state.isGameOver := false state.spawnFood() 6) AI Next Move Selection The AI evaluates four directional moves. It rejects collisions with walls or body and chooses the valid move with the smallest distance: Point head = state.snake.first() array directions = array.from( Point.new(0, -1), Point.new(0, 1), Point.new(-1, 0), Point.new(1, 0) ) float minDistance = 1000.0 Point bestMove = na for i = 0 to directions.size() - 1 Point dir = directions.get(i) int nextX = head.x + dir.x int nextY = head.y + dir.y bool isWallHit = nextX < 0 or nextX >= state.gridSize or nextY < 0 or nextY >= state.gridSize bool isBodyHit = state.isBody(nextX, nextY) if not isWallHit and not isBodyHit Point potentialMove = Point.new(nextX, nextY) float d = potentialMove.dist(state.food) if d < minDistance minDistance := d bestMove := potentialMove If bestMove remains na, there is no safe move and the game ends. 7) Tick Update Rules The tick method controls the game loop: If game over, reset. Else decide next move. If no move, set game over. Else add head, then check food: If food eaten, increment score, update high score, and spawn new food. If not eaten, remove tail. 8) Rendering Logic with Tables On the last bar, the script renders each cell: Food cell draws an apple emoji and red tint. Snake body draws a green square emoji. Snake head draws a different emoji and brighter tint. It also renders a small UI table that displays the current score and best score. The script updates continuously on the last bar, making it behave like a live widget.
Indicador Pine Script®
por UAlgo
Smart Money Concepts AI - AdaptiveSmart Money Concepts AI scores every Fair Value Gap and Order Block with a 5-factor quality engine so you can instantly see which zones are worth trading and which are noise. ◈ How It Works This indicator detects three core Smart Money / ICT concepts and layers an AI scoring system on top. Market Structure tracks swing highs and lows to identify Break of Structure (BOS) and Change of Character (CHoCH). BOS means the trend is continuing. CHoCH means it may be reversing. The indicator automatically classifies each break and draws labeled lines on your chart. CHoCH lines are solid and thicker since they're the more significant events. BOS lines are dashed. Both can be toggled independently. Fair Value Gaps (FVGs) are 3-candle imbalances where price moved so fast it left a gap. The indicator detects these automatically and draws scored boxes on the chart. Each FVG gets a quality score from 0 to 100. Higher-scored zones appear more vivid; lower-scored zones fade out. When price fills the gap (mitigation), the box turns dashed. Order Blocks (OBs) are the last opposite candle before a structural break. They represent institutional accumulation or distribution. When a bullish CHoCH/BOS fires, the indicator looks back for the last bearish candle near the swing low and marks it as a demand zone. Bearish OBs work in reverse. Each OB is scored by displacement strength, volume, and trend alignment. ◈ The AI Scoring Engine Every zone gets a 0-100 quality score based on 5 factors: For FVGs: Gap Size vs ATR: sweet spot is 0.3x to 1.5x ATR. Too small = noise, too big = likely fills fast Displacement Strength: body-to-range ratio of the middle candle. Full-body candles = institutional conviction Volume: displacement candle volume vs 20-period average Trend Alignment: does the FVG direction match the EMA trend? Structure Alignment: does it align with the current BOS/CHoCH direction? For Order Blocks: OB Size: tighter zones (0.3-1.0 ATR) score higher for precision Post-OB Displacement: how far price moved after leaving the OB. Bigger moves = stronger institutional interest Volume, Trend, and Structure: same alignment checks as FVGs The score directly controls visual opacity. High-scoring zones are vivid and prominent, low-scoring zones are subtle and transparent. You can filter to "High Only" to hide zones scoring below 50. ◈ Signals Signals fire when price enters a scored FVG zone with structural and trend alignment. If a scored Order Block overlaps the FVG, the score increases further. ★ Bright signals = high confluence (score ≥ 70 default). FVG + OB overlap + structure + trend all confirm. ○ Dim signals = moderate confluence (score ≥ 50). The setup exists but not all factors align perfectly. By default, only ★ bright signals are shown to keep the chart clean. You can enable dim signals in settings if you want to see every zone touch. A configurable cooldown (default 10 bars) prevents signal spam. All signals are non-repainting. They only appear on confirmed bar closes. ◈ How to Read the Dashboard SMC AI ◈: header Structure: current direction (▲ BULLISH / ▼ BEARISH / — RANGING) with bias label Trend(50): whether the EMA trend agrees with structure (✓ ALIGNED = go / ✗ COUNTER = caution) Best FVG: quality score of the highest-rated active FVG in the current direction, with visual bar Best OB: quality score of the highest-rated active Order Block, with visual bar Signal: last signal state (★ LONG / ○ SHORT / — WAITING) with the actual entry score Zones: count of active bull/bear FVGs and OBs on chart The Signal row shows the actual score from when the signal fired, so it always matches the label on the chart. ◈ Recommended Settings Forex (EUR/USD, GBP/JPY) 1H to 4H: Swing Length 5, ATR 14, Trend EMA 50, Signal Cooldown 10 Forex scalping 15min: Swing Length 3, ATR 10, Trend EMA 34, Signal Cooldown 5 Crypto (BTC, ETH) 1H to 4H: Swing Length 5, ATR 14, Trend EMA 50, Signal Cooldown 10 Gold / Commodities 4H to Daily: Swing Length 7, ATR 14, Trend EMA 50, Signal Cooldown 15 Indices (NAS100, SPX500) 15min to 1H: Swing Length 3 to 5, ATR 10, Trend EMA 34, Signal Cooldown 8 For aggressive setups: Lower Min Signal Score to 40, enable dim signals, show more FVGs (8 to 10) For conservative setups: Raise Min Signal Score to 70, filter FVGs to "High Only", increase cooldown ◈ Key Features ✓ Non-repainting: all signals confirmed on bar close ✓ AI zone scoring: 5-factor quality engine, 0-100 per zone ✓ Visual hierarchy: opacity reflects score, you instantly see what matters ✓ Mitigation tracking: filled FVGs and broken OBs fade automatically ✓ Rich tooltips: hover any signal for full breakdown ✓ 9 alert conditions: BOS, CHoCH, bull/bear signals, AI-confirmed signals ✓ Signal clutter control: cooldown + dim toggle keeps charts clean ✓ Fully customizable: colors, zone counts, thresholds, all adjustable ✓ 100% original code: not derived from any existing script ◈ What This Is NOT This is not a "paint arrows and win" indicator. SMC/ICT trading requires understanding context. Where is structure pointing? Which zones are institutionally significant? Is the trend aligned? This indicator helps you answer those questions faster by scoring every zone objectively. Always use proper risk management. Past performance does not guarantee future results. Happy trading.
Indicador Pine Script®
por DefinedEdge
3
Polynomial Regression Clustering [LuxAlgo]The Polynomial Regression Clustering indicator utilizes K-Means clustering to categorize historical price data into discrete levels and fits polynomial regression curves to each identified cluster. This tool allows traders to visualize non-linear trends within specific price regimes, providing a unique perspective on support, resistance, and price momentum. 🔶 USAGE The indicator identifies "K" number of clusters based on the vertical distribution of price over a user-defined lookback period. Each cluster represents a group of bars that share similar price levels, and a polynomial regression line is calculated to represent the localized trend for that specific group. 🔹 Cluster Identification The script groups price action into color-coded dots. By default, it uses the HL2 (Average price) to determine which cluster a bar belongs to. This is particularly useful for identifying historical value areas where price has spent a significant amount of time. 🔹 Polynomial Fitting Unlike standard linear regression, which produces a straight line, the polynomial regression curves can bend to fit the data more accurately. A Polynomial Degree of 1 will result in a standard linear regression (straight lines). A Polynomial Degree of 2 or higher allows for curves that capture parabolic moves or cyclical swings within each cluster. 🔹 Future Projections The current active cluster (the one containing the most recent price point) can be projected into the future. This allows you to see where the localized trend for the current price regime is heading based on the mathematical fit of historical data. 🔶 DETAILS 🔹 K-Means Algorithm The script uses an iterative K-Means algorithm to find the optimal centroids (center points) for the price levels. It calculates the distance of each price point to the nearest centroid and refines the centroid position until the clusters are stable or the maximum iterations are reached. 🔹 Regression Logic Once price points are assigned to a cluster, the script solves for the coefficients of a polynomial equation that minimizes the distance between the line and the cluster's data points. To ensure numerical stability with higher degrees, the horizontal (time) axis is normalized before performing matrix operations. 🔶 SETTINGS 🔹 K-Means Number of Clusters (K): Defines how many price levels the indicator should look for. Higher values create more granular levels. Lookback Period: The number of recent bars used to perform the clustering and regression calculation. Max Iterations: The maximum number of refinement steps for the K-Means algorithm. 🔹 Regression Polynomial Degree: Controls the "bend" of the regression lines. Higher degrees allow for more complex curves. Extend All Fits to Current Bar: When enabled, the regression lines for all historical clusters are extended to the rightmost edge of the chart. Project Current Cluster into Future: Extends the current regime's regression line into the future (empty space) using a dashed line. 🔹 Visual Style Show Regression Lines: Toggles the visibility of the polynomial curves. Show Cluster Dots: Toggles the visibility of the colored dots on each price bar. Dot Size: Adjusts the size of the cluster dots. Cluster Colors: Customizable colors for each of the identified clusters.
Indicador Pine Script®
por LuxAlgo
3
Big Tech AI vs AI Semi Market Cap Recently, Big Tech stocks have faced downward pressure due to growing concerns over whether they can sustain massive AI CapEx and ultimately achieve monetization. In contrast, AI-related semiconductor stocks—the direct recipients of these investment funds—are rebounding and gaining momentum. Some market participants compare this flow to the Dot-com bubble era. I created this script to track in real-time whether a true "Market Cap Flipping" (reversal) is occurring between the AI Service providers (Big Tech) and the AI Infrastructure providers (Semiconductors). This indicator aggregates and compares the total Market Capitalization (Price × Shares Outstanding) of two distinct groups: 🟦 Big Tech (AI Solutions & Services): The companies spending heavily on AI infrastructure. 🟧 AI Semiconductors (Hardware & Infra): The companies benefiting from Big Tech's CapEx. Real-time Comparison: Visualizes the aggregate value of both sectors on a single chart to spot divergence or convergence. Cap Flipping Watch: Easily identify if the "Hardware" sector's valuation overtakes the "Solution" sector. Percentage Ratio: Displays a label showing the Semiconductor sector's size relative to Big Tech (e.g., "Semi is 60% of Tech"). Customizable Tickers: You can toggle individual companies On/Off in the settings to adjust your basket. Big Tech: MSFT, GOOGL, AMZN, META, AAPL, TSLA, PLTR, ORCL, ADBE AI Semi: NVDA, TSM, AVGO, AMD, MU, ARM, ASML, ANET, MRVL If the Orange Line (Semi) rises while the Blue Line (Tech) falls/stagnates, it indicates the market is favoring "Infrastructure Builders" over "Service Providers." Use this to gauge the maturity of the AI investment cycle.
Indicador Pine Script®
por KBG86
Clusters Volume Profile [LuxAlgo]The Clusters Volume Profile indicator utilizes K-Means clustering to categorize historical price action into distinct groups and generates individual volume profiles for each detected cluster. This tool provides a unique perspective on volume distribution by isolating price behaviors based on proximity rather than strictly chronological order. 🔶 USAGE The indicator identifies "clusters" of price activity within a user-defined lookback period. Each cluster is assigned a unique color and its own horizontal volume profile, allowing traders to see where liquidity is most concentrated within specific price regimes. 🔹 Identifying Institutional Zones Traders can use the Point of Control (POC) of high-volume clusters to identify significant institutional interest. Because the K-Means algorithm groups price action by density rather than time, a cluster's POC often represents a "fair value" level where significant exchange occurred. These dashed POC lines frequently act as robust support or resistance levels when price returns to them in the future. 🔹 Market Regime Detection By observing the vertical distribution and overlap of clusters, traders can identify market phases. Overlapping clusters with high volume often indicate accumulation or distribution phases (sideways markets), whereas distinct, vertically separated clusters with lower volume gaps between them suggest a trending environment. A shift from multiple overlapping clusters to a new, isolated cluster can signal a breakout or the start of a new trend. 🔹 Precision Entry & Exits Cluster boundaries and POC lines provide concrete levels for trade management. An entry can be sought when price retests a high-volume cluster POC, while stops can be placed outside the total price range of that specific cluster (the area covered by its volume profile). Conversely, targets can be set at the POC of the next major cluster above or below current price action. 🔹 Volume Conviction The tool provides specific volume metrics that allow traders to gauge conviction. By comparing the "Total" volume label of one cluster against another, a trader can determine which price regime had more participation. A breakout into a price zone with a high-volume cluster suggests stronger conviction and a higher probability of the level holding compared to a zone with low total volume. 🔶 DETAILS The script employs a K-Means clustering algorithm. This process involves: Initializing "centroids" across the price range of the lookback period. Iteratively assigning each price bar to the nearest centroid based on the HLC2 (median) price. Recalculating centroids based on the volume-weighted average price of the assigned bars. Finalizing assignments after the specified number of iterations to ensure stable clusters. By separating price action into these clusters, the tool helps identify high-interest zones that might be obscured by a single, traditional Volume Profile. 🔶 SETTINGS 🔹 Clustering Settings Lookback Period: Determines the number of recent bars used for the clustering analysis. Number of Clusters: Sets how many distinct price groups the algorithm should attempt to find (2 to 10). K-Means Iterations: Controls the number of times the algorithm refines the cluster centers. Higher values can lead to more stable results. 🔹 Volume Profile Settings Rows per Cluster VP: Defines the vertical resolution (number of bins) for each individual cluster's profile. Max VP Width (Bars): Sets the maximum horizontal length of the volume profile histograms. VP Offset: Adjusts the horizontal spacing between the current bar and the start of the volume profiles. Highlight Price Dots: Toggles the visibility of the colored dots on the price action to identify cluster assignments. Dot Size: Adjusts the size of the cluster assignment dots on the chart, ranging from tiny to huge.
Indicador Pine Script®
por LuxAlgo
23
Adaptive AI SuperTrend [AlgoPoint]🚀 Adaptive AI SuperTrend Adaptive AI SuperTrend is a high-performance trading terminal that redefines trend-following by integrating Machine Learning (ML) principles with advanced market regime detection. Unlike static indicators, this system dynamically recalibrates its internal parameters to match the ever-changing volatility of the financial markets. Equipped with a custom "Wizard Engine," it filters out market noise during consolidation and identifies high-probability trend continuation points, making it an essential tool for scalpers, day traders, and swing traders alike. 🧠 What Makes it "AI"? While traditional indicators use fixed rules, Adaptive AI SuperTrend utilizes Algorithmic Intelligence to make real-time decisions: KNN-Inspired Adaptation: The engine analyzes the last 150 bars of volatility and trend strength to automatically adjust its sensitivity. Market Regime Intelligence: It distinguishes between "Trending" and "Ranging" states using a sophisticated Squeeze Momentum module, preventing "whipsaws" during low-volume periods. Self-Backtesting Logic: The indicator continuously calculates its own historical Win-Rate. If the probability of success falls below a certain threshold, it suppresses lower-quality signals. 🛠 Key Features Dynamic Consolidation Boxes: Automatically identifies and wraps "choppy" price action in professional gray boxes. It waits for 3+ bars of consolidation before marking the zone, helping you spot breakout opportunities early. Multi-Strategy Aggression: - Conservative: Filtered signals for long-term trend following. - Balanced: Optimized for daily volatility. - Aggressive: High-frequency signals for capturing micro-trends. Dual-Exit Risk Management: - ATR TP-SL Mode: Sets mathematical targets based on market volatility with persistent on-screen lines. - Smart Trailing Mode: Rides the trend to its exhaustion point. Includes intelligent labeling (🎯 TP or 🛑 SL) based on the trade's net profitability. - RSI Pullback Confirmation: Beyond simple trend flips, it detects "buy the dip" or "sell the rip" opportunities within an existing trend using RSI 50-level crossovers. 📊 Real-Time Analytics Dashboard The integrated AlgoPoint Dashboard provides a surgical view of the market: - Market State: Instant "Trending" vs. "Ranging" (Consolidation) detection. - Trend Strength: ADX-based momentum tracking. - Strategy Status: Real-time feedback on your active aggression and exit modes. 🎨 Clean Charting & Customization Built for professional clarity, you have total control over the UI: Toggle Consolidation Boxes on/off. Toggle ATR Target Lines and Exit Labels. Customize background filters and dashboard visibility.
Indicador Pine Script®
por Iamalala
1
Mission Control Dashboard (AI, Crypto, Liquidity) FASTCONCEPT Price is a lagging indicator. Liquidity is a leading indicator. "Mission Control Dashboard (AI, Crypto, Liquidity) FAST" is a sophisticated macroeconomic dashboard designed to audit the "plumbing" of the financial system in real-time. Unlike standard indicators that rely solely on price action, this tool pulls data from the Federal Reserve (FRED), Treasury Statements, Corporate Financials (10-K/10-Q), and On-Chain Stablecoin metrics to visualize the structural flows driving the market. THE "UNIFIED FIELD" SOLVER One of the hardest challenges in cross-asset scripting is "Time Dilation"—synchronizing 24/7 Crypto markets (Bitcoin) with Mon-Fri Traditional markets (Stocks/Bonds). Standard scripts fail on weekends, showing mismatched data. This engine uses a Weekly Anchor system. It calculates all momentum and liquidity metrics based on "Week-to-Date" or "Month-Ago" anchors. This ensures that a "Liquidity Drain" looks identical whether you are viewing a Bitcoin chart on Saturday or an Apple chart on Monday. THE CHRONOS LOGIC The dashboard is sorted by Time Sensitivity (Speed of impact), from fast-twitch tactical signals to slow-moving structural fundamentals. 1. TACTICAL (Reacts in 24–48h) Stablecoin Flight: Measures the immediate flow of capital from Volatile Assets to Stablecoins (USDT/USDC). A spike (>0.5%) indicates fear/sidelining. Liquidity Alpha: Calculates the efficiency of capital. It subtracts "Friction" (Dollar Strength + Yields) from "Flow" (Liquidity Beta). High Alpha means money is flowing easily into risk assets. Alt Euphoria: Tracks the overheating of the Altcoin market (TOTAL3). Green indicates sustainable growth; Red (>45%) warns of a "blow-off top." Retail FOMO: A sentiment gauge comparing Coinbase Stock ( NASDAQ:COIN ) performance vs. Bitcoin ( CRYPTOCAP:BTC ). When Retail outperforms the Asset, local tops often follow. 2. LIQUIDITY & MACRO (Reacts in 1–4 Weeks) Debt Wall (10Y): The Rate-of-Change of the US 10-Year Treasury Yield. Spiking yields act as gravity on risk assets. Liquidity Beta: The raw "Quantity of Money." Tracks the 4-week change in Net Liquidity (Fed Balance Sheet - TGA + Stablecoins). TGA Balance: The Critical Monitor. Tracks the Treasury General Account. When the TGA rises (Red), the government is draining liquidity from the banking system. When it falls (Green), it releases cash. Note: This script includes an auto-scaler to handle TGA data in both Billions and Millions. 3. STRUCTURAL (Reacts in 3–12 Months) AI Capex (YoY & QoQ): The "Floor" of the 2025/2026 cycle. Tracks the Capital Expenditure of the Hyperscalers (MSFT, GOOGL, AMZN, META). As long as this remains high (>30%), the infrastructure boom supports the tech narrative. PMI Manufacturing: Tracks the ISM Manufacturing cycle. Contraction (<50) often forces Fed intervention. Micron Inventory: A lead indicator for the hardware cycle. HOW TO USE Status Colors: The traffic light system helps you assess risk at a glance. 🟢 GREEN (Healthy): Flow is positive, friction is low, fundamentals are strong. 🔴 RED (Danger): Liquidity is draining (TGA spike), yields are shock-rising, or FOMO is excessive. Zero Configuration: The script auto-detects asset classes and scales units (Billions/Trillions) automatically. DATA SOURCES Federal Reserve Economic Data (FRED) Daily Treasury Statement (DTS) CryptoCap (TradingView) Nasdaq/Corporate Financials Disclaimer: This tool is for informational purposes only and does not constitute financial advice. Macro data feeds are subject to reporting delays.
Indicador Pine Script®
por SebK85
2
Mission Control Dashboard (AI, Crypto, Liquidity)Description: Mission Control Dashboard (AI, Liquidity) is a comprehensive macro-liquidity and cycle-analysis dashboard designed to track the "Flow of Funds" across traditional and crypto markets. Instead of looking at price action alone, this script monitors the fundamental "plumbing" of the global economy. Key Metrics Tracked: The Debt Wall: Monitors the US 10Y Yield and TLT price. It signals a "Critical" state if yields spike above 5% or TLT drops below $80, indicating high stress in the bond market. Global Liquidity (MTF Stable): A proprietary calculation summing the balance sheets of the FED, ECB, BoJ, and PBoC, plus Stablecoin market cap. It calculates the Rate of Change (ROC) to see if the world is "printing" or "draining" money. TGA Hidden Fuel: Tracks the Treasury General Account. A falling TGA is often bullish for risk assets as it injects liquidity into the banking system. Universal Alt Season: Monitors TOTAL3 (Crypto market cap excluding BTC & ETH) for parabolic moves (>30% ROC). AI Infra Capex: Real-time tracking of Capital Expenditures from MSFT, GOOG, AMZN, and META to gauge the health of the AI cycle. How to use: Green Status across the board: High probability for "Risk-On" environments (Alt season, Tech rallies). Strategic Beta vs. Tactical Alpha: If Beta is draining but Alpha is accelerating, it suggests a "False Breakout" or a divergence in liquidity. Uranium Trend: Used as a proxy for the energy transition and long-term industrial cycle strength.
Indicador Pine Script®
por SebK85
AI-Weighted RSI (Zeiierman)█ Overview AI-Weighted RSI (Zeiierman) is an adaptive oscillator that enhances classic RSI by applying a correlation-weighted prediction layer. Instead of looking only at RSI values directly, this indicator continuously evaluates how other price- and volume-based features (returns, volatility, volume shifts) correlate with RSI, and then weights them accordingly to project the next RSI state. The result is a smoother, forward-looking RSI framework that adapts to market conditions in real time. By leveraging feature correlation instead of static formulas, AI-Weighted RSI behaves like a lightweight learning model, adjusting its emphasis depending on which features are most aligned with RSI behavior during the current regime. █ How It Works ⚪ Feature Extraction Each bar, the script computes features: log returns, RSI itself, ATR% (volatility), volume, and volume log-change. ⚪ Correlation Screening Over a rolling learning window, it measures the correlation of each feature against RSI. The strongest relationships are ranked and selected. ⚪ Adaptive Weighting Features are standardized (z-scored), then combined using their signed correlations as weights, building a rolling, adaptive prediction of RSI. ⚪ Prediction to RSI Weight The predicted RSI is mapped back into a “weight” scale (±2 by default). Above 0 = bullish bias, below 0 = bearish bias, with color-graded fills to visualize overbought/oversold pressure. ⚪ Signal Line A smoothing option (signal length) overlays a moving average of the AI-Weighted RSI for clearer trend confirmation. █ Why AI-Weighted RSI ⚪ Adaptive to Market Regime Because the model re-evaluates correlations continuously, it naturally shifts which features dominate, sometimes volatility explains RSI best, sometimes volume, sometimes returns. ⚪ Forward-Looking Bias Instead of simply reflecting RSI, the model provides a projection, helping anticipate shifts in momentum before RSI itself flips. █ How to Use ⚪ Directional Bias Read the RSI relative to 0. Above = bullish momentum bias, below = bearish. ⚪ Overbought / Oversold Zones Shaded fills beyond +0.5 or -0.5 highlight extremes where RSI pressure often exhausts. ⚪ Divergences When price makes new highs/lows but AI-Weighted RSI fails to confirm, it often signals weakening momentum. █ Settings RSI Length: Lookback for the core RSI calculation. Signal Length: Smoothing applied to the AI-Weighted RSI output. Learning Window: Bars used for correlation learning and z-scoring. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Indicador Pine Script®
por Zeiierman
3
Machine Learning BBPct [BackQuant]Machine Learning BBPct What this is (in one line) A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system. Background: %B in plain terms Bollinger %B measures where price sits inside its dynamic envelope: 0 at the lower band, 1 at the upper band, ~ 0.5 near the basis (the moving average). Readings toward 1 indicate pressure near the envelope’s upper edge (often strength or stretch), while readings toward 0 indicate pressure near the lower edge (often weakness or stretch). Because bands adapt to volatility, %B is naturally comparable across regimes. Why add (simplified) KNN? Classic %B is reactive and can be whippy in fast regimes. The simplified KNN layer builds a “nearest-neighbor memory” of recent market states and asks: “When the market looked like this before, where did %B tend to be next bar?” It then blends that estimate with the current %B. Key ideas: • Feature vector . Each bar is summarized by up to five normalized features:   – %B itself (normalized)   – Band width (volatility proxy)   – Price momentum (ROC)   – Volume momentum (ROC of volume)   – Price position within the bands • Distance metric . Euclidean distance ranks the most similar recent bars. • Prediction . Average the neighbors’ prior %B (lagged to avoid lookahead), inverse-weighted by distance. • Blend . Linearly combine raw %B and KNN-predicted %B with a configurable weight; optional filtering then adapts to confidence. This remains “simplified” KNN: no training/validation split, no KD-trees, no scaling beyond windowed min-max, and no probabilistic calibration. How the script is organized (by input groups) 1) BBPct Settings • Price Source – Which price to evaluate (%B is computed from this). • Calculation Period – Lookback for SMA basis and standard deviation. • Multiplier – Standard deviation width (e.g., 2.0). • Apply Smoothing / Type / Length – Optional smoothing of the %B stream before ML (EMA, RMA, DEMA, TEMA, LINREG, HMA, etc.). Turning this off gives you the raw %B. 2) Thresholds • Overbought/Oversold – Default 0.8 / 0.2 (inside ). • Extreme OB/OS – Stricter zones (e.g., 0.95 / 0.05) to flag stretch conditions. 3) KNN Machine Learning • Enable KNN – Switch between pure %B and hybrid. • K (neighbors) – How many historical analogs to blend (default 8). • Historical Period – Size of the search window for neighbors. • ML Weight – Blend between raw %B and KNN estimate. • Number of Features – Use 2–5 features; higher counts add context but raise the risk of overfitting in short windows. 4) Filtering • Method – None, Adaptive, Kalman-style (first-order), or Hull smoothing. • Strength – How aggressively to smooth. “Adaptive” uses model confidence to modulate its alpha: higher confidence → stronger reliance on the ML estimate. 5) Performance Tracking • Win-rate Period – Simple running score of past signal outcomes based on target/stop/time-out logic (informational, not a robust backtest). • Early Entry Lookback – Horizon for forecasting a potential threshold cross. • Profit Target / Stop Loss – Used only by the internal win-rate heuristic. 6) Self-Optimization • Enable Self-Optimization – Lightweight, rolling comparison of a few canned settings (K = 8/14/21 via simple rules on %B extremes). • Optimization Window & Stability Threshold – Governs how quickly preferred K changes and how sensitive the overfitting alarm is. • Adaptive Thresholds – Adjust the OB/OS lines with volatility regime (ATR ratio), widening in calm markets and tightening in turbulent ones (bounded 0.7–0.9 and 0.1–0.3). 7) UI Settings • Show Table / Zones / ML Prediction / Early Signals – Toggle informational overlays. • Signal Line Width, Candle Painting, Colors – Visual preferences. Step-by-step logic A) Compute %B Basis = SMA(source, len); dev = stdev(source, len) × multiplier; Upper/Lower = Basis ± dev. %B = (price − Lower) / (Upper − Lower). Optional smoothing yields standardBB . B) Build the feature vector All features are min-max normalized over the KNN window so distances are in comparable units. Features include normalized %B, normalized band width, normalized price ROC, normalized volume ROC, and normalized position within bands. You can limit to the first N features (2–5). C) Find nearest neighbors For each bar inside the lookback window, compute the Euclidean distance between current features and that bar’s features. Sort by distance, keep the top K . D) Predict and blend Use inverse-distance weights (with a strong cap for near-zero distances) to average neighbors’ prior %B (lagged by one bar). This becomes the KNN estimate. Blend it with raw %B via the ML weight. A variance of neighbor %B around the prediction becomes an uncertainty proxy ; combined with a stability score (how long parameters remain unchanged), it forms mlConfidence ∈ . The Adaptive filter optionally transforms that confidence into a smoothing coefficient. E) Adaptive thresholds Volatility regime (ATR(14) divided by its 50-bar SMA) nudges OB/OS thresholds wider or narrower within fixed bounds. The aim: comparable extremeness across regimes. F) Early entry heuristic A tiny two-step slope/acceleration probe extrapolates finalBB forward a few bars. If it is on track to cross OB/OS soon (and slope/acceleration agree), it flags an EARLY_BUY/SELL candidate with an internal confidence score. This is explicitly a heuristic—use as an attention cue, not a signal by itself. G) Informational win-rate The script keeps a rolling array of trade outcomes derived from signal transitions + rudimentary exits (target/stop/time). The percentage shown is a rough diagnostic , not a validated backtest. Outputs and visual language • ML Bollinger %B (finalBB) – The main line after KNN blending and optional filtering. • Gradient fill – Greenish tones above 0.5, reddish below, with intensity following distance from the midline. • Adaptive zones – Overbought/oversold and extreme bands; shaded backgrounds appear at extremes. • ML Prediction (dots) – The KNN estimate plotted as faint circles; becomes bright white when confidence > 0.7. • Early arrows – Optional small triangles for approaching OB/OS. • Candle painting – Light green above the midline, light red below (optional). • Info panel – Current value, signal classification, ML confidence, optimized K, stability, volatility regime, adaptive thresholds, overfitting flag, early-entry status, and total signals processed. Signal classification (informational) The indicator does not fire trade commands; it labels state: • STRONG_BUY / STRONG_SELL – finalBB beyond extreme OS/OB thresholds. • BUY / SELL – finalBB beyond adaptive OS/OB. • EARLY_BUY / EARLY_SELL – forecast suggests a near-term cross with decent internal confidence. • NEUTRAL – between adaptive bands. Alerts (what you can automate) • Entering adaptive OB/OS and extreme OB/OS. • Midline cross (0.5). • Overfitting detected (frequent parameter flipping). • Early signals when early confidence > 0.7. These are purely descriptive triggers around the indicator’s state. Practical interpretation • Mean-reversion context – In range markets, adaptive OS/OB with ML smoothing can reduce whipsaws relative to raw %B. • Trend context – In persistent trends, the KNN blend can keep finalBB nearer the mid/upper region during healthy pullbacks if history supports similar contexts. • Regime awareness – Watch the volatility regime and adaptive thresholds. If thresholds compress (high vol), “OB/OS” comes sooner; if thresholds widen (calm), it takes more stretch to flag. • Confidence as a weight – High mlConfidence implies neighbors agree; you may rely more on the ML curve. Low confidence argues for de-emphasizing ML and leaning on raw %B or other tools. • Stability score – Rising stability indicates consistent parameter selection and fewer flips; dropping stability hints at a shifting backdrop. Methodological notes • Normalization uses rolling min-max over the KNN window. This is simple and scale-agnostic but sensitive to outliers; the distance metric will reflect that. • Distance is unweighted Euclidean. If you raise featureCount, you increase dimensionality; consider keeping K larger and lookback ample to avoid sparse-neighbor artifacts. • Lag handling intentionally uses neighbors’ previous %B for prediction to avoid lookahead bias. • Self-optimization is deliberately modest: it only compares a few canned K/threshold choices using simple “did an extreme anticipate movement?” scoring, then enforces a stability regime and an overfitting guard. It is not a grid search or GA. • Kalman option is a first-order recursive filter (fixed gain), not a full state-space estimator. • Hull option derives a dynamic length from 1/strength; it is a convenience smoothing alternative. Limitations and cautions • Non-stationarity – Nearest neighbors from the recent window may not represent the future under structural breaks (policy shifts, liquidity shocks). • Curse of dimensionality – Adding features without sufficient lookback can make genuine neighbors rare. • Overfitting risk – The script includes a crude overfitting detector (frequent parameter flips) and will fall back to defaults when triggered, but this is only a guardrail. • Win-rate display – The internal score is illustrative; it does not constitute a tradable backtest. • Latency vs. smoothness – Smoothing and ML blending reduce noise but add lag; tune to your timeframe and objectives. Tuning guide • Short-term scalping – Lower len (10–14), slightly lower multiplier (1.8–2.0), small K (5–8), featureCount 3–4, Adaptive filter ON, moderate strength. • Swing trading – len (20–30), multiplier ~2.0, K (8–14), featureCount 4–5, Adaptive thresholds ON, filter modest. • Strong trends – Consider higher adaptive_upper/lower bounds (or let volatility regime do it), keep ML weight moderate so raw %B still reflects surges. • Chop – Higher ML weight and stronger Adaptive filtering; accept lag in exchange for fewer false extremes. How to use it responsibly Treat this as a state descriptor and context filter. Pair it with your execution signals (structure breaks, volume footprints, higher-timeframe bias) and risk management. If mlConfidence is low or stability is falling, lean less on the ML line and more on raw %B or external confirmation. Summary Machine Learning BBPct augments a familiar oscillator with a transparent, simplified KNN memory of recent conditions. By blending neighbors’ behavior into %B and adapting thresholds to volatility regime—while exposing confidence, stability, and a plain early-entry heuristic—it provides an informational, probability-minded view of stretch and reversion that you can interpret alongside your own process.
Indicador Pine Script®
por BackQuant
2
Lorentzian Key Support and Resistance Level Detector [mishy]🧮 Lorentzian Key S/R Levels Detector Advanced Support & Resistance Detection Using Mathematical Clustering The Problem Traditional S/R indicators fail because they're either subjective (manual lines), rigid (fixed pivots), or break when price spikes occur. Most importantly, they don't tell you where prices actually spend time, just where they touched briefly. The Solution: Lorentzian Distance Clustering This indicator introduces a novel approach by using Lorentzian distance instead of traditional Euclidean distance for clustering. This is groundbreaking for financial data analysis. Data Points Clustering: 🔬 Why Euclidean Distance Fails in Trading Traditional K-means uses Euclidean distance: • Formula: distance = (price_A - price_B)² • Problem: Squaring amplifies differences exponentially • Real impact: One 5% price spike has 25x more influence than a 1% move • Result: Clusters get pulled toward outliers, missing real support/resistance zones Example scenario: Prices: ← flash spike Euclidean: Centroid gets dragged toward 150 Actual S/R zone: Around 100 (where prices actually trade) ⚡ Lorentzian Distance: The Game Changer Our approach uses Lorentzian distance: • Formula: distance = log(1 + (price_difference)² / σ²) • Breakthrough: Logarithmic compression keeps outliers in check • Real impact: Large moves still matter, but don't dominate • Result: Clusters focus on where prices actually spend time Same example with Lorentzian: Prices: ← flash spike Lorentzian: Centroid stays near 100 (real trading zone) Outlier (150): Acknowledged but not dominant 🧠 Adaptive Intelligence The σ parameter isn't fixed,it's calculated from market disturbance/entropy: • High volatility: σ increases, making algorithm more tolerant of large moves • Low volatility: σ decreases, making algorithm more sensitive to small changes • Self-calibrating: Adapts to any instrument or market condition automatically Why this matters: Traditional methods treat a 2% move the same whether it's in a calm or volatile market. Lorentzian adapts the sensitivity based on current market behavior. 🎯 Automatic K-Selection (Elbow Method) Instead of guessing how many S/R levels to draw, the indicator: • Tests 2-6 clusters and calculates WCSS (tightness measure) • Finds the "elbow" - where adding more clusters stops helping much • Uses sharpness calculation to pick the optimal number automatically Result: Perfect balance between detail and clarity. How It Works 1. Collect recent closing prices 2. Calculate entropy to adapt to current market volatility 3. Cluster prices using Lorentzian K-means algorithm 4. Auto-select optimal cluster count via statistical analysis 5. Draw levels at cluster centers with deviation bands 📊 Manual K-Selection Guide (Using WCSS & Sharpness Analysis) When you disable auto-selection, use both WCSS and Sharpness metrics from the analysis table to choose manually: What WCSS tells you: • Lower WCSS = tighter clusters = better S/R levels • Higher WCSS = scattered clusters = weaker levels What Sharpness tells you: • Higher positive values = optimal elbow point = best K choice • Lower/negative values = poor elbow definition = avoid this K • Measures the "sharpness" of the WCSS curve drop-off Decision strategy using both metrics: K=2: WCSS = 150.42 | Sharpness = - | Selected = K=3: WCSS = 89.15 | Sharpness = 22.04 | Selected = ✓ ← Best choice K=4: WCSS = 76.23 | Sharpness = 1.89 | Selected = K=5: WCSS = 73.91 | Sharpness = 1.43 | Selected = Quick decision rules: • Pick K with highest positive Sharpness (indicates optimal elbow) • Confirm with significant WCSS drop (30%+ reduction is good) • Avoid K values with negative or very low Sharpness (<1.0) • K=3 above shows: Big WCSS drop (41%) + High Sharpness (22.04) = Perfect choice Why this works: The algorithm finds the "elbow" where adding more clusters stops being useful. High Sharpness pinpoints this elbow mathematically, while WCSS confirms the clustering quality. Elbow Method Visualization: Traditional clustering problems: ❌ Price spikes distort results ❌ Fixed parameters don't adapt ❌ Manual tuning is subjective ❌ No way to validate choices Lorentzian solution: ☑️ Outlier-resistant distance metric ☑️ Entropy-based adaptation to volatility ☑️ Automatic optimal K selection ☑️ Statistical validation via WCSS & Sharpness Features Visual: • Color-coded levels (red=highest resistance, green=lowest support) • Optional deviation bands showing cluster spread • Strength scores on labels: Each cluster shows a reliability score. • Higher scores (0.8+) = very strong S/R levels with tight price clustering • Lower scores (0.6-0.7) = weaker levels, use with caution • Based on cluster tightness and data point density • Clean line extensions and labels Analytics: • WCSS analysis table showing why K was chosen • Cluster metrics and statistics • Real-time entropy monitoring Control: • Auto/manual K selection toggle • Customizable sample size (20-500 bars) • Show/hide bands and metrics tables The Result You get mathematically validated S/R levels that focus on where prices actually cluster, not where they randomly spiked. The algorithm adapts to market conditions and removes guesswork from level selection. Best for: Traders who want objective, data-driven S/R levels without manual chart analysis. Credits: This script is for educational purposes and is inspired by the work of @ThinkLogicAI and an amazing mentor @DskyzInvestments . It demonstrates how Lorentzian geometrical concepts can be applied not only in ML classification but also quite elegantly in clustering.
Indicador Pine Script®
por mishy_Champ
8
AI Breakout Bands (Zeiierman)█ Overview AI Breakout Bands (Zeiierman) is an adaptive trend and breakout detection system that combines Kalman filtering with advanced K-Nearest Neighbor (KNN) smoothing. The result is a smart, self-adjusting band structure that adapts to dynamic market behavior, identifying breakout conditions with precision and visual clarity. At its core, this indicator estimates price behavior using a two-dimensional Kalman filter (position + velocity), then enhances the smoothing process with a nonlinear, similarity-based KNN filter. This unique blend enables it to handle noisy markets and directional shifts with both speed and stability — providing breakout traders and trend followers a reliable framework to act on. Whether you're identifying volatility expansions, capturing trend continuations, or spotting early breakout conditions, AI Breakout Bands gives you a mathematically grounded, visually adaptive roadmap of real-time market structure. █ How It Works ⚪ Kalman Filter Engine The Kalman filter models price movement as a state system with two components: Position (price) Velocity (trend direction) It recursively updates predictions using real-time price as a noisy observation, balancing responsiveness with smoothness. Process Noise (Position) controls sensitivity to sudden moves. Process Noise (Velocity) controls smoothing of directional flow. Measurement Noise (R) defines how much the filter "trusts" live price data. This component alone creates a responsive yet stable estimate of the market’s center of gravity. ⚪ Advanced K-Neighbor Smoothing After the Kalman estimate is computed, the script applies a custom K-Nearest Neighbor (KNN) smoother. Rather than averaging raw values, this method: Finds K most similar past Kalman values Weighs them by similarity (inverse of absolute distance) Produces a smoother that emphasizes structural similarity This nonlinear approach gives the indicator an AI feature — reacting fast when needed, yet staying calm in consolidation. █ How to Use ⚪ Trend Recognition The line color shifts dynamically based on slope direction and breakout confirmation. Bullish conditions: price above the mid band with positive slope Bearish conditions: price below the mid band with negative slope ⚪ Breakout Signals Price breaking above or below the bands may signal momentum acceleration. Combine with your own volume or momentum confirmation for stronger entries. Bands adapt to market noise, helping filter out low-quality whipsaws. █ Settings Process Noise (Position): Controls Kalman filter’s sensitivity to price changes. Process Noise (Velocity): Controls smoothing of directional component. Measurement Noise (R): Defines how much trust is placed in price data. K-Neighbor Length: Number of historical Kalman values considered for smoothing. Slope Calculation Window: Number of bars used to compute trend slope of the smoothed Kalman. Band Lookback (MAE): Rolling period for average absolute error. Band Multiplier: Multiplies MAE to determine band width. ----------------- Disclaimer The content provided in my scripts, indicators, ideas, algorithms, and systems is for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information. All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
Indicador Pine Script®
por Zeiierman
8
ThinkTech AI SignalsThink Tech AI Strategy The Think Tech AI Strategy provides a structured approach to trading by integrating liquidity-based entries, ATR volatility thresholds, and dynamic risk management. This strategy generates buy and sell signals while automatically calculating take profit and stop loss levels, boasting a 64% win rate based on historical data. Usage The strategy can be used to identify key breakout and retest opportunities. Liquidity-based zones act as potential accumulation and distribution areas and may serve as future support or resistance levels. Buy and sell zones are identified using liquidity zones and ATR-based filters. Risk management is built-in, automatically calculating take profit and stop loss levels using ATR multipliers. Volume and trend filtering options help confirm directional bias using a 50 EMA and RSI filter. The strategy also allows for session-based trading, limiting trades to key market hours for higher probability setups. Settings The risk/reward ratio can be adjusted to define the desired stop loss and take profit calculations. The ATR length and threshold determine ATR-based breakout conditions for dynamic entries. Liquidity period settings allow for customized analysis of price structure for support and resistance zones. Additional trend and RSI filters can be enabled to refine trade signals based on moving averages and momentum conditions. A session filter is included to restrict trade signals to specific market hours. Style The strategy includes options to display liquidity lines, showing key support and resistance areas. The first 15-minute candle breakout zones can also be visualized to highlight critical market structure points. A win/loss statistics table is included to track trade performance directly on the chart. This strategy is intended for descriptive analysis and should be used alongside other confluence factors. Optimize your trading process with Think Tech AI today!
Estratégia Pine Script®
por Theonealgo
Machine Learning Trendlines Cluster [LuxAlgo]The ML Trendlines Cluster indicator allows traders to automatically identify trendlines using a machine learning algorithm based on k-means clustering and linear regression, highlighting trendlines from clustered prices. For trader's convenience, trendlines can be filtered based on their slope, allowing them to filter out trendlines that are too horizontal, or instead keep them depending on the user-selected settings. 🔶 USAGE Traders only need to set the number of trendlines (clusters) they want the tool to detect and the algorithm will do the rest. By default the tool is set to detect 4 clusters over the last 500 bars, in the image above it is set to detect 10 clusters over the same period. This approach only focuses on drawing trendlines from prices that share a common trading range, offering a unique perspective to traditional trendlines. Trendlines with a significant slope can highlight higher dispersion within its cluster. 🔹 Trendline Slope Filtering Traders can filter trendlines by their slope to display only steep or flat trendlines relative to a user-defined threshold. The image above shows the three different configurations of this feature: Filtering disabled Filter slopes above threshold Filter slopes below threshold 🔶 DETAILS K-means clustering is a popular machine-learning algorithm that finds observations in a data set that are similar to each other and places them in a group. The process starts by randomly assigning each data point to an initial group and calculating the centroid for each. A centroid is the center of the group. K-means clustering forms the groups in such a way that the variances between the data points and the centroid of the cluster are minimized. The trendlines are displayed according to the linear regression function calculated for each cluster. 🔶 SETTINGS Window Size: Maximum number of bars to get data from Clusters: Maximum number of clusters (trendlines) to detect 🔹 Optimization Maximum Iteration Steps: Maximum loop iterations for cluster computation 🔹 Slope Filter Threshold Multiplier: Multiplier applied to a volatility measure, higher multiplier equals higher threshold Filter Slopes: Enable/Disable Trendline Slope Filtering, select to filter trendlines with slopes ABOVE or BELOW the threshold 🔹 Style Upper Zone: Color to display in the top zone Lower Zone: Color to display in the bottom zone Lines: Style for the lines Size: Line size
Indicador Pine Script®
por LuxAlgo
14
AI Adaptive Oscillator [PhenLabs]📊 Algorithmic Adaptive Oscillator Version: PineScript™ v6 📌 Description The AI Adaptive Oscillator is a sophisticated technical indicator that employs ensemble learning and adaptive weighting techniques to analyze market conditions. This innovative oscillator combines multiple traditional technical indicators through an AI-driven approach that continuously evaluates and adjusts component weights based on historical performance. By integrating statistical modeling with machine learning principles, the indicator adapts to changing market dynamics, providing traders with a responsive and reliable tool for market analysis. 🚀 Points of Innovation: Ensemble learning framework with adaptive component weighting Performance-based scoring system using directional accuracy Dynamic volatility-adjusted smoothing mechanism Intelligent signal filtering with cooldown and magnitude requirements Signal confidence levels based on multi-factor analysis 🔧 Core Components Ensemble Framework : Combines up to five technical indicators with performance-weighted integration Adaptive Weighting : Continuous performance evaluation with automated weight adjustment Volatility-Based Smoothing : Adapts sensitivity based on current market volatility Pattern Recognition : Identifies potential reversal patterns with signal qualification criteria Dynamic Visualization : Professional color schemes with gradient intensity representation Signal Confidence : Three-tiered confidence assessment for trading signals 🔥 Key Features The indicator provides comprehensive market analysis through: Multi-Component Ensemble : Integrates RSI, CCI, Stochastic, MACD, and Volume-weighted momentum Performance Scoring : Evaluates each component based on directional prediction accuracy Adaptive Smoothing : Automatically adjusts based on market volatility Pattern Detection : Identifies potential reversal patterns in overbought/oversold conditions Signal Filtering : Prevents excessive signals through cooldown periods and minimum change requirements Confidence Assessment : Displays signal strength through intuitive confidence indicators (average, above average, excellent) 🎨 Visualization Gradient-Filled Oscillator : Color intensity reflects strength of market movement Clear Signal Markers : Distinct bullish and bearish pattern signals with confidence indicators Range Visualization : Clean representation of oscillator values from -6 to 6 Zero Line : Clear demarcation between bullish and bearish territory Customizable Colors : Color schemes that can be adjusted to match your chart style Confidence Symbols : Intuitive display of signal confidence (no symbol, +, or ++) alongside direction markers 📖 Usage Guidelines ⚙️ Settings Guide Color Settings Bullish Color Default: #2b62fa (Blue) This setting controls the color representation for bullish movements in the oscillator. The color appears when the oscillator value is positive (above zero), with intensity indicating the strength of the bullish momentum. A brighter shade indicates stronger bullish pressure. Bearish Color Default: #ce9851 (Amber) This setting determines the color representation for bearish movements in the oscillator. The color appears when the oscillator value is negative (below zero), with intensity reflecting the strength of the bearish momentum. A more saturated shade indicates stronger bearish pressure. Signal Settings Signal Cooldown (bars) Default: 10 Range: 1-50 This parameter sets the minimum number of bars that must pass before a new signal of the same type can be generated. Higher values reduce signal frequency and help prevent overtrading during choppy market conditions. Lower values increase signal sensitivity but may generate more false positives. Min Change For New Signal Default: 1.5 Range: 0.5-3.0 This setting defines the minimum required change in oscillator value between consecutive signals of the same type. It ensures that new signals represent meaningful changes in market conditions rather than minor fluctuations. Higher values produce fewer but potentially higher-quality signals, while lower values increase signal frequency. AI Core Settings Base Length Default: 14 Minimum: 2 This fundamental setting determines the primary calculation period for all technical components in the ensemble (RSI, CCI, Stochastic, etc.). It represents the lookback window for each component’s base calculation. Shorter periods create a more responsive but potentially noisier oscillator, while longer periods produce smoother signals with potential lag. Adaptive Speed Default: 0.1 Range: 0.01-0.3 Controls how quickly the oscillator adapts to new market conditions through its volatility-adjusted smoothing mechanism. Higher values make the oscillator more responsive to recent price action but potentially more erratic. Lower values create smoother transitions but may lag during rapid market changes. This parameter directly influences the indicator’s adaptiveness to market volatility. Learning Lookback Period Default: 150 Minimum: 10 Determines the historical data range used to evaluate each ensemble component’s performance and calculate adaptive weights. This setting controls how far back the AI “learns” from past performance to optimize current signals. Longer periods provide more stable weight distribution but may be slower to adapt to regime changes. Shorter periods adapt more quickly but may overreact to recent anomalies. Ensemble Size Default: 5 Range: 2-5 Specifies how many technical components to include in the ensemble calculation. Understanding The Interaction Between Settings Base Length and Learning Lookback : The base length determines the reactivity of individual components, while the lookback period determines how their weights are adjusted. These should be balanced according to your timeframe - shorter timeframes benefit from shorter base lengths, while the lookback should generally be 10-15 times the base length for optimal learning. Adaptive Speed and Signal Cooldown : These settings control sensitivity from different angles. Increasing adaptive speed makes the oscillator more responsive, while reducing signal cooldown increases signal frequency. For conservative trading, keep adaptive speed low and cooldown high; for aggressive trading, do the opposite. Ensemble Size and Min Change : Larger ensembles provide more stable signals, allowing for a lower minimum change threshold. Smaller ensembles might benefit from a higher threshold to filter out noise. Understanding Signal Confidence Levels The indicator provides three distinct confidence levels for both bullish and bearish signals: Average Confidence (▲ or ▼) : Basic signal that meets the minimum pattern and filtering criteria. These signals indicate potential reversals but with moderate confidence in the prediction. Consider using these as initial alerts that may require additional confirmation. Above Average Confidence (▲+ or ▼+) : Higher reliability signal with stronger underlying metrics. These signals demonstrate greater consensus among the ensemble components and/or stronger historical performance. They offer increased probability of successful reversals and can be traded with less additional confirmation. Excellent Confidence (▲++ or ▼++) : Highest quality signals with exceptional underlying metrics. These signals show strong agreement across oscillator components, excellent historical performance, and optimal signal strength. These represent the indicator’s highest conviction trade opportunities and can be prioritized in your trading decisions. Confidence assessment is calculated through a multi-factor analysis including: Historical performance of ensemble components Degree of agreement between different oscillator components Relative strength of the signal compared to historical thresholds ✅ Best Use Cases: Identify potential market reversals through oscillator extremes Filter trade signals based on AI-evaluated component weights Monitor changing market conditions through oscillator direction and intensity Confirm trade signals from other indicators with adaptive ensemble validation Detect early momentum shifts through pattern recognition Prioritize trading opportunities based on signal confidence levels Adjust position sizing according to signal confidence (larger for ++ signals, smaller for standard signals) ⚠️ Limitations Requires sufficient historical data for accurate performance scoring Ensemble weights may lag during dramatic market condition changes Higher ensemble sizes require more computational resources Performance evaluation quality depends on the learning lookback period length Even high confidence signals should be considered within broader market context 💡 What Makes This Unique Adaptive Intelligence : Continuously adjusts component weights based on actual performance Ensemble Methodology : Combines strength of multiple indicators while minimizing individual weaknesses Volatility-Adjusted Smoothing : Provides appropriate sensitivity across different market conditions Performance-Based Learning : Utilizes historical accuracy to improve future predictions Intelligent Signal Filtering : Reduces noise and false signals through sophisticated filtering criteria Multi-Level Confidence Assessment : Delivers nuanced signal quality information for optimized trading decisions 🔬 How It Works The indicator processes market data through five main components: Ensemble Component Calculation : Normalizes traditional indicators to consistent scale Includes RSI, CCI, Stochastic, MACD, and volume components Adapts based on the selected ensemble size Performance Evaluation : Analyzes directional accuracy of each component Calculates continuous performance scores Determines adaptive component weights Oscillator Integration : Combines weighted components into unified oscillator Applies volatility-based adaptive smoothing Scales final values to -6 to 6 range Signal Generation : Detects potential reversal patterns Applies cooldown and magnitude filters Generates clear visual markers for qualified signals Confidence Assessment : Evaluates component agreement, historical accuracy, and signal strength Classifies signals into three confidence tiers (average, above average, excellent) Displays intuitive confidence indicators (no symbol, +, ++) alongside direction markers 💡 Note: The AI Adaptive Oscillator performs optimally when used with appropriate timeframe selection and complementary indicators. Its adaptive nature makes it particularly valuable during changing market conditions, where traditional fixed-weight indicators often lose effectiveness. The ensemble approach provides a more robust analysis by leveraging the collective intelligence of multiple technical methodologies. Pay special attention to the signal confidence indicators to optimize your trading decisions - excellent (++) signals often represent the most reliable trade opportunities.
Indicador Pine Script®
por PhenLabs
1
Smart Adaptive Signal SystemSmart Adaptive Signal System Description: The Smart Adaptive Signal System is a sophisticated indicator that generates intelligent buy/sell signals by dynamically adapting to market conditions. It predicts target prices based on momentum and volatility, providing more accurate and reliable trading opportunities. How It Works: Dynamic Signal Generation: The system predicts target prices by considering factors such as volatility and momentum. This allows it to react instantly to trend changes and market fluctuations. Adaptive Thresholds: Buy and sell signals are triggered with adaptive thresholds, adjusting according to market volatility. This ensures flexibility in the face of sudden market changes. Trend-Based Reset: Users can choose to reset threshold values based on a time interval or trend change. This feature helps the system re-adapt to current market conditions for greater accuracy. Target Price Prediction: Target prices are calculated using momentum and volatility, helping the system predict future price movements. How to Use: Buy/Sell Signals: The indicator generates buy and sell signals based on market conditions. Look for a "down arrow" for a buy signal and an "up arrow" for a sell signal on the chart. Target Price Lines: Along with buy and sell signals, the system draws target price lines. This helps you visualize potential future price levels. Flexible Settings: Users can customize analysis periods, minimum change percentages, and other parameters to fit their needs. Features: Dynamic buy and sell signals Target price predictions Volatility and momentum-based analysis User-friendly and flexible settings Trend-based adaptive resetting Alerts: The Smart Adaptive Signal System responds quickly to sudden market changes, but always use it in conjunction with other indicators like support and resistance levels. Signal accuracy may vary depending on market conditions.
Indicador Pine Script®
por alwaysfree2021
10
AI Volume Breakout for scalpingPurpose of the Indicator This script is designed for trading, specifically for scalping, which involves making numerous trades within a very short time frame to take advantage of small price movements. The indicator looks for volume breakouts, which are moments when trading volume significantly increases, potentially signaling the start of a new price movement. Key Components: Parameters: Volume Threshold (volumeThreshold): Determines how much volume must increase from one bar to the next for it to be considered significant. Set at 4.0, meaning volume must quadruplicate for a breakout signal. Price Change Threshold (priceChangeThreshold): Defines the minimum price change required for a breakout signal. Here, it's 1.5% of the bar's opening price. SMA Length (smaLength): The period for the Simple Moving Average, which helps confirm the trend direction. Here, it's set to 20. Cooldown Period (cooldownPeriod): Prevents signals from being too close together, set to 10 bars. ATR Period (atrPeriod): The period for calculating Average True Range (ATR), used to measure market volatility. Volatility Threshold (volatilityThreshold): If ATR divided by the close price exceeds this, the market is considered too volatile for trading according to this strategy. Calculations: SMA (Simple Moving Average): Used for trend confirmation. A bullish signal is more likely if the price is above this average. ATR (Average True Range): Measures market volatility. Lower volatility (below the threshold) is preferred for this strategy. Signal Generation: The indicator checks if: Volume has increased significantly (volumeDelta > 0 and volume / volume >= volumeThreshold). There's enough price change (math.abs(priceDelta / open) >= priceChangeThreshold). The market isn't too volatile (lowVolatility). The trend supports the direction of the price change (trendUp for bullish, trendDown for bearish). If all these conditions are met, it predicts: 1 (Bullish) if conditions suggest buying. 0 (Bearish) if conditions suggest selling. Cooldown Mechanism: After a signal, the script waits for a number of bars (cooldownPeriod) before considering another signal to avoid over-trading. Visual Feedback: Labels are placed on the chart: Green label for bullish breakouts below the low price. Red label for bearish breakouts above the high price. How to Use: Entry Points: Look for the labels on your chart to decide when to enter trades. Risk Management: Since this is for scalping, ensure each trade has tight stop-losses to manage risk due to the quick, small movements. Market Conditions: This strategy might work best in markets with consistent volume and price changes but not extreme volatility. Caveats: This isn't real AI; it's a heuristic based on volume and price. Actual AI would involve machine learning algorithms trained on historical data. Always backtest any strategy, and consider how it behaves in different market conditions, not just the ones it was designed for.
Indicador Pine Script®
por Trading_Indicator_Pro
20
Machine Learning Moving Average [LuxAlgo]The Machine Learning Moving Average (MLMA) is a responsive moving average making use of the weighting function obtained Gaussian Process Regression method. Characteristic such as responsiveness and smoothness can be adjusted by the user from the settings. The moving average also includes bands, used to highlight possible reversals. 🔶 USAGE The Machine Learning Moving Average smooths out noisy variations from the price, directly estimating the underlying trend in the price. A higher "Window" setting will return a longer-term moving average while increasing the "Forecast" setting will affect the responsiveness and smoothness of the moving average, with higher positive values returning a more responsive moving average and negative values returning a smoother but less responsive moving average. Do note that an excessively high "Forecast" setting will result in overshoots, with the moving average having a poor fit with the price. The moving average color is determined according to the estimated trend direction based on the bands described below, shifting to blue (default) in an uptrend and fushia (default) in downtrends. The upper and lower extremities represent the range within which price movements likely fluctuate. Signals are generated when the price crosses above or below the band extremities, with turning points being highlighted by colored circles on the chart. 🔶 SETTINGS Window: Calculation period of the moving average. Higher values yield a smoother average, emphasizing long-term trends and filtering out short-term fluctuations. Forecast: Sets the projection horizon for Gaussian Process Regression. Higher values create a more responsive moving average but will result in more overshoots, potentially worsening the fit with the price. Negative values will result in a smoother moving average. Sigma: Controls the standard deviation of the Gaussian kernel, influencing weight distribution. Higher Sigma values return a longer-term moving average. Multiplicative Factor: Adjusts the upper and lower extremity bounds, with higher values widening the bands and lowering the amount of returned turning points. 🔶 RELATED SCRIPTS Machine-Learning-Gaussian-Process-Regression SuperTrend-AI-Clustering
Indicador Pine Script®
por LuxAlgo
20
Market Cycles The Market Cycles indicator transforms market price data into a stochastic wave, offering a unique perspective on market cycles. The wave is bounded between positive and negative values, providing clear visual cues for potential bullish and bearish trends. When the wave turns green, it signals a bullish cycle, while red indicates a bearish cycle. Designed to show clarity and precision, this tool helps identify market momentum and cyclical behavior in an intuitive way. Ideal for fine-tuning entries or analyzing broader trends, this indicator aims to enhance the decision-making process with simplicity and elegance.
Indicador Pine Script®
por Zoe-Yoshev-Bepisgat
1