Empiece

Machinelearning

Machine Learning PSAR [BOSWaves]Machine Learning PSAR - Adaptive Parabolic Stop and Reverse with K-Means Regime Detection and KNN Signal Validation Overview Machine Learning PSAR is a regime-aware trend reversal system that tracks directional price movement through an adaptive Parabolic SAR, where acceleration parameters dynamically adjust based on market regime classification and each reversal signal is validated against historically similar setups using a K-Nearest Neighbors scoring model. Instead of relying on fixed acceleration factors or unfiltered SAR flips, trend state, parameter scaling, and signal confidence are determined through K-Means flip-frequency clustering, KNN outcome weighting, and Kalman-filtered output smoothing that maintains visual clarity without sacrificing reversal responsiveness. This creates a SAR system that reflects actual market conditions rather than applying the same parameters regardless of context - tightening in trending environments where acceleration should build quickly, relaxing in choppy conditions where early flips are noise, and scoring every reversal against the historical record so confidence is quantified rather than assumed. Price is therefore evaluated relative to a SAR that adapts to regime dynamics and historically validated reversal patterns rather than conventional fixed-parameter parabolic logic. Conceptual Framework Machine Learning PSAR is founded on the principle that meaningful reversal signals emerge when the SAR acceleration factor is calibrated to current market conditions, and when each flip is cross-referenced against similar historical flips to assess its probability of success. Traditional PSAR implementations use fixed start, increment, and maximum AF values that ignore whether the market is trending or ranging. This framework replaces static acceleration logic with regime-driven parameter adaptation informed by flip frequency clustering, then layers a KNN validation pass on top to score each signal before it is presented. Three core principles guide the design: Acceleration factor behavior should adapt to the detected market regime, becoming more aggressive during trending conditions and more conservative during choppy ones. Every SAR flip should be scored against historically similar setups so confidence is expressed as a quantified probability rather than a binary signal. The displayed SAR line should be smooth enough for clean visual interpretation while remaining responsive enough that reversals are never delayed. This shifts SAR analysis from a fixed-parameter trailing stop into an adaptive, regime-anchored reversal system with integrated signal confidence measurement. Theoretical Foundation The indicator combines classical Parabolic SAR logic, K-Means-inspired regime classification, K-Nearest Neighbors outcome scoring, exponential AF smoothing, and Kalman filter output processing. Flip frequency over a configurable training period provides the feature for regime classification, with centroid distances determining whether the market is trending, neutral, or choppy. KNN validation uses a five-dimensional feature vector at each flip — prior trend duration, bars since last flip, AF at flip, flip frequency, and EP progress — to find the most similar historical flips and weight their outcomes by proximity. The Kalman filter then smooths the final SAR output while snapping to new values instantly on every reversal. Four internal systems operate in tandem: Adaptive PSAR Engine : Computes classical parabolic SAR with optional AF smoothing and minimum bars filter to suppress whipsaw flips. K-Means Regime Classifier : Measures flip frequency relative to its historical range, assigns the current bar to the nearest of three regime centroids, and adjusts AF start, increment, and maximum accordingly. KNN Signal Validator : On each flip, searches historical flips for the k most similar setups by Euclidean distance, computes an inverse-distance-weighted confidence score, and filters low-confidence signals from high-confidence alerts. Kalman Smoothing Layer : Applies a recursive Kalman filter to the SAR output for display, balancing smoothness with responsiveness and resetting on every reversal so flips are never visually delayed. This design allows reversal signals to reflect actual market behavior and historical precedent rather than reacting mechanically to fixed acceleration rules. How It Works Machine Learning PSAR evaluates price through a sequence of regime-aware and historically-validated processes: PSAR Initialization : Classical parabolic SAR begins with base AF start value, tracking EP and advancing the stop in the trend direction. AF Smoothing : Instead of stepping AF in discrete increments, exponential smoothing ramps it gradually toward the target, producing a more fluid SAR trajectory. Minimum Bars Filter : Trend must persist for a configurable minimum number of bars before a flip is allowed, preventing immediate whipsaw reversals. Flip Detection : Price crossing the SAR triggers a raw flip, resetting AF, capturing the new EP, and recording trend duration and context features. Flip Frequency Measurement : Rolling count of flips over the training period, normalized to its historical range, provides the regime classification feature. Regime Assignment : Flip frequency is compared against three percentile-anchored centroids; the nearest centroid determines whether the market is choppy, neutral, or trending. Parameter Adaptation : Regime assignment scales AF start, increment, and maximum — reducing them in choppy conditions to slow the SAR, increasing them in trending conditions to accelerate it. KNN Feature Construction : At each flip, a five-dimensional vector is built from current context and compared against all historical flips of the same direction within the lookback window. Neighbor Scoring : The k closest historical flips by Euclidean distance are retrieved; each is weighted by inverse distance and its five-bar forward outcome determines a weighted success rate. Confidence Assignment : Weighted success rate expressed as a 0–100% confidence score, with flips below the minimum threshold classified as low-confidence. Kalman Filtering : SAR value is passed through a Kalman filter for display smoothing, with process and measurement noise configurable; filter snaps to new SAR position on every flip. Confidence Fill : Fill opacity between SAR and price anchor reflects current confidence score — denser fill indicates higher conviction in the active trend. Together, these elements form a continuously updating reversal framework anchored in regime awareness and historically validated signal quality. Interpretation Machine Learning PSAR should be interpreted as a confidence-weighted trend reversal system with regime-adaptive sensitivity: Bullish State (Blue) : Established when price closes above the SAR after a validated bullish flip, with SAR acting as a dynamic trailing support level below price. Bearish State (Red) : Established when price closes below the SAR after a validated bearish flip, with SAR acting as a dynamic trailing resistance level above price. Confidence Fill : Gradient zone between SAR and price reflects KNN confidence — vivid, dense fill indicates high historical precedent for the current flip; faint fill indicates low confidence. Confidence Score Labels : Percentage label at each flip displays the KNN confidence score. Green (70%+) indicates strong historical backing; orange (50–69%) indicates moderate backing; red (below 50%) indicates low historical support. Regime Labels : Numbers displayed alongside the SAR indicate current market regime — 3 for trending, 2 for neutral, 1 for choppy — reflecting the K-Means classifier output in real time. High-Confidence Flips : Flips meeting or exceeding the minimum confidence threshold trigger alerts and represent the primary actionable signals. Low-Confidence Flips : Flips below the confidence threshold are still displayed but excluded from high-confidence alerts, flagging setups with weak historical precedent. Regime classification, KNN confidence, and Kalman-smoothed SAR position together outweigh any isolated price movement against the stop. Signal Logic & Visual Cues Machine Learning PSAR presents two primary signal categories: High-Confidence Flip : SAR reversal with KNN score at or above the minimum confidence threshold. These represent setups where historically similar conditions produced successful reversals at a statistically meaningful rate and form the basis for alert-driven systematic monitoring. Low-Confidence Flip : SAR reversal with KNN score below the minimum confidence threshold. The signal is displayed for awareness but is not included in high-confidence alert conditions, reflecting limited historical precedent. Regime labels provide continuous market context between flips, allowing real-time awareness of whether the K-Means system is operating in a trending, neutral, or choppy environment. Confidence fill intensity provides a passive, non-disruptive view of trend conviction without requiring active label reading. Alert generation covers high-confidence bullish and bearish flips, separately triggerable 70%+ confidence signals, and regime transition events for systematic monitoring of market state changes. Strategy Integration Machine Learning PSAR fits within adaptive trend-following and signal-quality-filtered reversal approaches: Confidence-Gated Entries : Enter reversals only on high-confidence flips, using the minimum confidence threshold as a quality gate that filters historically weak setups. Regime-Aware Sizing : Increase position sizing during trending regime (label 3) where the K-Means system detects low flip frequency and sustained directional conviction. Choppy Market Avoidance : Reduce or pause activity during choppy regime (label 1) where frequent flips indicate low directional conviction and elevated whipsaw risk. SAR as Stop Placement : Use the Kalman-smoothed SAR as a trailing stop reference — exit longs when price closes below the bullish SAR, exit shorts when price closes above the bearish SAR. Confidence Fill Monitoring : Use fill intensity as a passive conviction gauge — fading fill during an active trend may indicate the next flip is likely to be lower confidence. Multi-Timeframe Regime Alignment : Apply higher-timeframe regime label as a directional filter, entering signals only when the regime aligns across timeframes. Alert-Based Systematic Monitoring : Configure high-confidence and regime-change alerts for systematic notification without requiring active chart monitoring. Technical Implementation Details Core Engine : Classical Parabolic SAR with configurable base AF start, increment, and maximum, optional exponential AF smoothing, and minimum bars flip filter Regime Model : Flip frequency normalized to training-period range with three percentile-anchored centroids (choppy, neutral, trending) and nearest-centroid assignment KNN Validator : Five-dimensional feature vector with configurable k and lookback, inverse-distance weighting, and five-bar forward outcome labeling Smoothing Layer : Kalman filter with configurable process and measurement noise, hard snap to SAR on every flip to preserve reversal timing Visualization : Dual-plot SAR circles with confidence-opacity fill, percentage confidence labels, every-other-bar regime labels Signal Logic : High/low confidence classification with configurable minimum threshold, raw flip detection decoupled from display Performance Profile : Optimized for real-time execution across all timeframes with efficient array-based KNN search and FIFO distance sorting Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Scalping with tighter AF values, shorter KNN lookback, and lower minimum confidence threshold 15 - 60 min : Intraday trend following with balanced regime sensitivity and moderate confidence filtering 4H - Daily : Swing and position trading with wider AF maximum, longer training period, and higher confidence threshold Suggested Baseline Configuration: Base AF Start : 0.02 Base AF Increment : 0.02 Base AF Maximum : 0.10 Training Data Period : 100 Choppy Regime Percentile : 0.75 Trending Regime Percentile : 0.25 K - Number of Neighbors : 8 Historical Lookback Period : 200 Minimum Confidence Filter : 15% AF Smoothing Factor : 0.01 Kalman Process Noise : 0.015 Kalman Measurement Noise : 0.5 Minimum Bars Before Flip : 3 These suggested parameters should be used as a baseline; their effectiveness depends on the asset's volatility profile, trending characteristics, and preferred signal frequency, so fine-tuning is expected for optimal performance. Parameter Calibration Notes Use the following adjustments to refine behavior without altering the core logic: Too many flips in ranging markets : Increase Minimum Bars Before Flip to require longer trend duration before a reversal is allowed, or increase Base AF Maximum to widen the SAR distance. SAR too slow to reverse : Decrease Base AF Start and increase Base AF Increment so the acceleration factor builds more quickly during new trends. Excessive low-confidence signals : Increase Minimum Confidence Filter to raise the threshold for high-confidence classification, focusing only on the strongest historical precedents. KNN scores feel unstable : Increase K - Number of Neighbors to average over more historical examples, smoothing out per-flip score variance. Regime changing too rapidly : Increase Training Data Period to smooth regime classification over a longer flip-frequency history. Regime too slow to update : Decrease Training Data Period for more responsive regime detection that reacts faster to market character shifts. SAR line too jumpy visually : Increase Kalman Measurement Noise for more aggressive smoothing, or decrease Kalman Process Noise to make the filter trust its own estimate more. Kalman lagging reversals : Decrease Kalman Measurement Noise or increase Kalman Process Noise to make the filter more responsive to SAR changes between flips. AF ramping too abruptly : Decrease AF Smoothing Factor toward 0.01 for a more gradual exponential ramp from start to target AF on each new trend. Adjustments should be incremental and evaluated across multiple market sessions rather than isolated conditions. Performance Characteristics High Effectiveness: Trending markets with sustained directional conviction where flip frequency remains low and regime classification stabilizes at label 3 Instruments with consistent volatility where ATR-normalized KNN features generalize well across historical flips Momentum continuation strategies using SAR as a trailing stop with confidence-filtered entries at reversals Systematic approaches benefiting from quantified signal confidence and regime-based parameter adaptation Multi-timeframe frameworks where regime labels provide higher-timeframe directional context for lower-timeframe entries Reduced Effectiveness: Choppy, range-bound markets with high flip frequency causing frequent low-confidence signals and regime label 1 classification Extremely thin historical data environments where the KNN lookback contains insufficient comparable flips for reliable scoring News-driven or gapped markets where discrete price discontinuities bypass SAR logic and invalidate ATR-normalized tension features Very low volatility instruments where ATR scaling compresses feature vectors and reduces KNN discriminative power Consolidation phases where mean-reversion dominance causes repeated SAR whipsaws regardless of minimum bars filtering Integration Guidelines Confluence : Combine with BOSWaves volume analysis, structure detection, or supply and demand zone identification for multi-factor confirmation SAR Respect : Honor the trailing SAR as the primary risk boundary — avoid holding positions against the active stop regardless of confidence score Confidence Awareness : Treat confidence scores as probabilistic context, not certainty — high scores improve odds but do not guarantee outcome Regime Discipline : Reduce activity during persistent choppy regime classification rather than fighting repeated low-confidence flips Alert Utilization : Configure high-confidence and regime-change alerts to enable systematic monitoring without requiring active chart observation Lookback Sufficiency : Ensure sufficient historical bars are loaded for the KNN lookback period before relying on confidence scores, particularly on shorter timeframes Multi-Timeframe Alignment : Use higher timeframe regime label and trend direction as a filter for lower timeframe flip entries to ensure directional confluence Disclaimer Machine Learning PSAR is a professional-grade adaptive reversal and trend-following tool. It uses K-Means regime classification and KNN signal validation to adapt classical Parabolic SAR behavior to current market conditions but does not predict future price movements. Results depend on market conditions, volatility characteristics, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates price structure, volume context, and comprehensive risk management.
Indicador Pine Script®
por BOSWaves
1
KNN Trend Forecaster [UAlgo]KNN Trend Forecaster is a chart overlay forecasting tool that uses a K Nearest Neighbors style similarity engine to estimate the next directional bias and project a probabilistic price path. It converts the current market state into a compact feature vector, compares it to a rolling memory of historical states, and computes an expected forward change as a weighted consensus of the most similar past observations. The indicator is built for decision support rather than signal chasing. It provides a projected path, a volatility aware tunnel around that path, and optional ghost structures that visualize how the forecast could evolve bar by bar. A minimal UI panel summarizes the current projection and sentiment, while the plot color adapts to bullish, bearish, or neutral expectation. This script is most effective when treated as a contextual layer. It can help align trade selection with the dominant statistical bias implied by recent conditions, while still leaving execution to your own confirmation rules. 🔹 Features 1) KNN Similarity Forecasting Engine The core model is a K Nearest Neighbors approach. For each bar, the script builds a three dimensional feature set from momentum, volatility, and relative strength. It then measures the distance between the current feature set and each stored historical feature set. The closest K neighbors are selected, and their realized forward returns are combined into a single prediction. Model Sensitivity controls K. Lower values behave more reactive and can change bias quickly. Higher values behave more stable and tend to smooth the projection. 2) Feature Design Focused On Trading Context The feature set is intentionally practical: RSI captures directional pressure and mean reversion tendencies ROC captures normalized momentum ATR captures normalized volatility regime Normalization ensures that ROC and ATR values are scaled into comparable ranges so the distance metric remains balanced and does not get dominated by raw magnitude differences. 3) Rolling Memory With Outcome Labels The script builds a training memory in real time. On each confirmed bar, it stores the features from ten bars ago and labels them with the percentage change over the next ten bars. This creates a consistent supervised learning target: Feature snapshot at time t Outcome equals return from time t to t plus ten bars Memory is capped to a fixed size to keep performance stable. 4) Weighted Neighbor Voting For Robust Predictions Rather than using a simple average of neighbor outcomes, the script assigns higher weight to closer neighbors. Weight is the inverse of distance, which prioritizes highly similar historical states and reduces the influence of weaker matches. This helps stabilize results when the market is transitioning and the feature landscape becomes noisier. 5) Forecast Path With Adaptive Step Decay Once a prediction is produced, the script generates a forward path for a user selected Forecast Horizon. The step applied to the path decays with the square root of the forecast index, which makes the projection more confident near the present and more conservative further out. The result is a smooth curve rather than an aggressive linear extrapolation. 6) Multi Layer Volatility Tunnel A tunnel can be drawn around the projected path. Its width scales with ATR and expands over the forecast horizon using a square root growth profile. Tunnel Volatility controls how wide the envelope becomes. This provides a practical view of expected dispersion around the forecast rather than a single deterministic line. 7) Ghost Structures For Bar To Bar Projection Framing Optional ghost boxes are printed for each forward step. Each box visualizes the projected candle body from the current projected close to the next projected close. The ghost color adapts to whether the step is rising or falling, making momentum and path rhythm easier to read. 8) Neon Glow Path And Target Tag When enabled, the path is rendered twice using polylines: A wider glow stroke for visual depth A thinner main stroke for precision A target label is placed at the end of the horizon, showing the model predicted change in percent. 9) Projection Basis And Dashboard A 21 period EMA is plotted as a reference basis, colored by the current prediction bias. A compact table displays the projection value and a sentiment label that classifies the forecast into bullish, bearish, or neutral ranges. 🔹 Calculations 1) Feature Construction The model uses three features built from common market analytics. RSI uses standard 14 period RSI: float f_rsi = ta.rsi(close, 14) ROC and ATR are normalized into a 0 to 100 style range using rolling min max scaling: normalize(float src, int len) => float mn = ta.lowest(src, len) float mx = ta.highest(src, len) (src - mn) / (math.max(mx - mn, 0.000001)) * 100 float f_roc = normalize(ta.roc(close, 10), 100) float f_atr = normalize(ta.atr(14), 100) The current feature vector: FeatureSet current_f = FeatureSet.new(f_rsi, f_roc, f_atr) 2) Training Memory And Outcome Labeling On each confirmed bar, the script stores the feature snapshot from ten bars earlier and labels it with the forward ten bar return. Outcome in percent: float outcome = (close - close ) / close * 100 Training point created from the past feature snapshot: memory.push(TrainingPoint.new(FeatureSet.new(f_rsi , f_roc , f_atr ), outcome)) Memory is limited for stability: if memory.size() > 1000 memory.shift() 3) Distance Metric Between Feature Vectors Similarity is computed using Euclidean distance in three dimensions: method distance(FeatureSet v1, FeatureSet v2) => math.sqrt(math.pow(v1.f1 - v2.f1, 2) + math.pow(v1.f2 - v2.f2, 2) + math.pow(v1.f3 - v2.f3, 2)) Smaller distance means greater similarity. 4) Neighbor Selection And Weighted Prediction The script computes distances from the current state to each stored training point and gathers the outcomes. It then selects the closest K entries by repeatedly taking the minimum distance. Each neighbor is weighted by inverse distance: float w = 1.0 / math.max(td.get(idx), 0.0001) twc += tc.get(idx) * w ws += w pred := twc / ws This produces pred, a percentage change estimate inferred from the most similar historical contexts. 5) Signal Color Classification The display color adapts to the sign and magnitude of the prediction. Small values map to a neutral tone, stronger positive values map to bullish tone, and stronger negative values map to bearish tone. color sig_col = pred > 0.005 ? THEME_UP : pred < -0.005 ? THEME_DN : THEME_MID 6) Forecast Path Generation Path construction begins from the current close. A base step is derived from the prediction and then decayed across the horizon. Base step: float step = (pred / 10.0) * 0.01 Forward projection with square root decay: float next_c = cur_c * (1 + step * (1.0 / math.sqrt(i))) This produces a smooth forecast curve where early steps carry more weight than later steps. 7) Volatility Tunnel Width Model The tunnel uses ATR as the volatility anchor and expands across the horizon: Outer width: float v_outer = (atr * expansion * 0.3 * math.sqrt(i)) Inner width is half of the outer width: float v_inner = v_outer * 0.5 Upper and lower bounds are then computed around the projected close: float h_out = next_c + v_outer float l_out = next_c - v_outer float h_in = next_c + v_inner float l_in = next_c - v_inner 8) Ghost Structures For each forecast step, a box is drawn between the current projected close and the next projected close. Its color reflects whether the path step is rising or falling. color g_col = next_c >= cur_c ? color.new(THEME_UP, 40) : color.new(THEME_DN, 40) box b = box.new(x1, math.max(cur_c, next_c), x2, math.min(cur_c, next_c), border_color=color.new(g_col, 20), bgcolor=g_col, border_width=1) 9) Path Rendering And Target Tag When glow is enabled, the script renders a thick glow polyline and a thinner main polyline over the same projected points. A label is placed at the final horizon index showing the predicted percent change. path_glow := polyline.new(pts, curved=true, line_color=color.new(sig_col, 70), line_width=8) path_main := polyline.new(pts, curved=true, line_color=sig_col, line_width=2) target_tag := label.new(bar_index + forecast_len, cur_c, "TARGET: " + str.tostring(pred, "#.##") + "%") 10) UI Summary And Basis Plot A 21 period EMA is plotted and colored by the current bias. A table panel prints the projection value and a sentiment classification: Bullish when pred is above 0.01 Bearish when pred is below minus 0.01 Neutral otherwise This gives an at a glance readout that matches the on chart color theme.
Indicador Pine Script®
por UAlgo
1
Anchored Clusters Volume Profile [LuxAlgo]The Anchored Clusters Volume Profile indicator utilizes K-Means clustering to categorize historical price action into distinct groups within a user-defined range 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 selected range defined by a starting and ending date. 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 Unlike traditional anchored volume profiles that provide a single histogram, this script employs a K-Means clustering algorithm to segment the range. This process involves: Identifying the specific range of bars between the user-selected Start Time and End Time . Initializing "centroids" across the price range of that 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 🔹 Anchor Settings Start Time / End Time : Sets the beginning and end of the analysis range. These use the "Confirm" feature, allowing you to select the range directly on the chart after adding the indicator or changing settings. Range Highlight : Adjusts the color and transparency of the background shading that identifies the analyzed range. 🔹 Clustering Settings 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
Neural Probability Channel [AlgoPoint]The Neural Probability Channel (NPC) is a next-generation volatility and trend analysis tool designed to overcome the limitations of traditional bands (like Bollinger Bands) and smoothing filters (like standard Moving Averages). Unlike traditional indicators that rely on linear deviation or simple averages, the NPC utilizes a Rational Quadratic Kernel—a concept derived from machine learning regression models—to calculate a non-repainting, highly adaptive baseline (Fair Value). This allows the indicator to distinguish between market noise and genuine trend shifts with superior accuracy. The volatility bands are dynamically calculated using a hybrid of Standard Error (Mean Deviation) and ATR, ensuring the channels adapt organically to market conditions—expanding during high-impact moves and contracting during consolidation. How It Works - The Neural Baseline (Center Line): Instead of a standard Moving Average, the NPC uses a Rational Quadratic Kernel weighting system. This assigns "importance" to price data based on both recency and similarity. It acts as a "Center of Gravity" for price, providing a smoother yet responsive trend detection line without the lag associated with SMAs or EMAs. Crucially, the math is causal (no lookahead), meaning it does not repaint. - Adaptive Volatility Bands: The channel width is not fixed. It uses a Hybrid Volatility Model: - Inner Channel: Represents the "Probability Zone" (approx. 70% confidence). Price staying here indicates a stable trend. - Outer Channel: Represents "Extreme Deviation" (Statistical Anomalies). When price touches or breaches these outer bands, it is statistically overextended (Overbought/Oversold). Signal Generation: - Reversion Signals: Generated when price breaches the Outer Bands and closes back inside. This suggests a "Snap-back" or Mean Reversion event. - Trend Confirmation: The color of the baseline and the fill zones changes based on the slope of the Kernel, giving an instant visual read on market bias. How to Use It - Mean Reversion Strategy: Look for price action extending beyond the Outer Bands (Thinner lines). If price leaves a wick and closes back inside, it signals a high-probability reversal toward the Neural Baseline. - Green Signal: Potential Long (Reversal from Lows). - Red Signal: Potential Short (Reversal from Highs). - Trend Following: Use the Neural Baseline (Thick Center Line) as a dynamic support/resistance level. If price is holding above the baseline and the cloud is green, the trend is Bullish. If price is holding below the baseline and the cloud is red, the trend is Bearish. - Squeeze Detection: When the Inner and Outer bands compress significantly, it indicates low volatility and often precedes an explosive breakout. Settings - Lookback Window: Determines the depth of the Kernel analysis. - Smoothness (Bandwidth): Higher values create a smoother baseline (better for trends), while lower values make it more reactive (better for scalping). - Regression Alpha: Controls the weight distribution of the Kernel. - Channel Multipliers: Adjust the width of the Inner and Outer bands to fit your specific asset's volatility profile.
Indicador Pine Script®
por Iamalala
10
Gold AI: Hyper-Frequency 1minute Scalper The Gold AI: Hyper-Frequency is a high-precision scalping tool specifically engineered for the unique volatility of XAUUSD on lower timeframes (1m, 5m). Unlike traditional lagging indicators, this script utilizes a Lorentzian Distance Classifier —a machine learning approach that finds historical price fractals similar to current market conditions to predict the next high-probability move. This indicator is designed for "Hyper-Frequency" trading, meaning it prioritizes capturing multiple micro-moves while using a three-stage Dynamic Take Profit system to let "runners" extend during strong momentum bursts. █ THE AI CORE: LORENTZIAN CLASSIFICATION Instead of standard Euclidean distance, this script employs Lorentzian Distance . This metric is superior for financial time series because it is less sensitive to extreme outliers, allowing the AI to identify patterns in "noisy" 1-minute gold charts that other models might miss. The AI evaluates three key "features" to find matches: 1 — RSI (7): Momentum and overbought/oversold status. 2 — MFI (7): Money Flow Index to track institutional volume. 3 — ROC (3): Rate of Change to detect immediate velocity. █ DYNAMIC PROFIT SCALING To solve the "Gold Problem"—where price often spikes past targets or reverses instantly—we implemented a 3-Stage Profit Extender: • Stage 1 (Initial TP): Targets a quick scalp (default 1.0 ATR). • Stage 2 (Extension): If momentum remains strong, the TP is automatically pushed further. The Stop Loss moves to Breakeven at this stage. • Stage 3 (Moonshot): For high-velocity moves, the TP extends to a major multiplier while the Stop Loss trails the previous candle's high/low. █ KEY FEATURES • K-Neighbors Engine: Analyzes the 'K' most similar historical instances to generate buy/sell "votes." • Sensitivity Control: Adjust how strictly the AI matches patterns. Lower values = higher accuracy; higher values = more frequent trades. • Volatility Filter (ATR): All targets and stops are mathematically pegged to current volatility, ensuring the script adapts whether the market is quiet or explosive. • Volume Gatekeeper: Filters out "fake-outs" that occur on low-volume retail spikes. █ HOW TO USE Asset: Optimized for XAUUSD (Gold). Timeframe: Best performance on 1m or 5m charts. Session: High-frequency signals are most reliable during London and New York sessions. Confirmation: For maximum safety, enable the "Filter by 200 EMA" in settings to only take trades in the direction of the macro trend. Disclaimer: Trading Gold involves high risk. This AI tool is designed to assist in decision-making and pattern recognition. Always perform your own due diligence and use proper risk management. Past performance does not guarantee future results.
Indicador Pine Script®
por YuvarajNg
Adaptive Pullbacks ML v2.5Adaptive Pullbacks ML - Context-Aware Trend Trading Overview Adaptive Pullbacks ML is a sophisticated trend-following tool that solves the biggest problem in pullback trading: "Is this a dip to buy, or the start of a reversal?" Unlike standard indicators that use fixed percentages or static moving averages, this script uses a 5-Dimensional k-Nearest Neighbors (k-NN) machine learning engine to learn the specific characteristics of successful pullbacks for the asset you are trading. The 5-Dimensional ML Engine The market is dynamic. A pullback depth that works in a low-volatility lunch session might fail during a high-volatility news event. This indicator tracks 5 key dimensions for every pullback: Depth (ATR Normalized): How deep is the pullback relative to volatility? Trend Slope: Is the trend steep (parabolic) or flat (grinding)? ADX: How strong is the directional energy? VWAP Distance: Is price extended or close to value? Time of Day: Is this a morning drive or an afternoon fade? When a new pullback occurs, the k-NN engine finds the 5 most similar historical events across these dimensions and predicts the probability of success. Core Features 1. Fractal Normalization The indicator speaks the language of ATR (Average True Range). It doesn't care if you trade the 15-second chart or the Daily chart. A "1.5 ATR Pullback" is a statistically comparable event across all timeframes, allowing for robust, scale-invariant analysis. 2. HTF Stats Bridge (Higher Timeframe Data) You can trade on lower timeframes (e.g., 1-minute) while using statistics derived from higher timeframes (e.g., 15-minute). This ensures your signals are based on significant market structure, not microstructure noise. 3. Smart Zones The indicator plots dynamic "Value Zones" based on learning: Cyan Zone (Avg Depth): The "Sweet Spot". High probability bounce area. Yellow Zone (Sigma): The "Extension". Price is stretching elastic limits. Red Zone (Deep): The "Danger/Opportunity". Statistical anomaly. 4. PQS & k-NN Filters Two layers of filtering protect your capital: PQS (Probability Qualification Score): Based on raw win-rate of the zone. k-NN Probability: Based on similarity to past winners. Settings Guide Stats Timeframe: The timeframe to learn from (Leave empty for Chart). Trend/Trigger Settings: Define what constitutes a trend for your strategy. k-Neighbors: Number of historical twins to compare (Default: 5). Min PQS / k-NN: Thresholds for filtering weak signals. Disclaimer: This tool is for educational purposes. Past performance of the k-NN engine does not guarantee future results.
Indicador Pine Script®
por Selfsimilarity
1
Adaptive ML VWAP v1.0Overview Adaptive ML VWAP is a next-generation "Smart Indicator" that moves beyond static deviations (Standard Deviation). Instead of assuming market volatility is distributed normally (Bell Curve), this indicator uses a k-Nearest Neighbors (k-NN) machine learning engine to learn the specific volatility behavior of the asset you are trading. It answers the question: "When price extends away from VWAP, how far does it actually go before reversing?" The Adaptive ML Engine This script features a 5-Dimensional ML Engine that tracks every major extension or pullback event. It records: Deviation Depth (Normalized to ATR) Trend Slope (Is the trend steep or flat?) ADX (Trend Strength) VWAP Deviation (Relative Position) Time of Day (Session Context) When a new setup occurs, the k-NN engine instantly searches its memory for the 5 most similar historical events and calculates the probability of success based on what happened last time. Two Strategy Modes You can toggle the logic to suit your trading style: 1. Mean Reversion Mode (Default) "Fade The Move" Goal: Catch price at an exhaustion point returning to VWAP. Signal: Triggers when price touches a Smart Band and reverses back toward the center. k-NN Learning: Learns which conditions favor a snap-back. Best For: Ranging markets, Lunch hours, Choppy sessions. 2. Trend Following Mode "Ride The Move" Goal: Catch breakouts that are launching away from value. Signal: Triggers when price breaks out of the Inner Band (1.0). k-NN Learning: Learns which breakouts tend to extend to the Outer Bands. Best For: Morning Drives, News Events, Strong Trends. Visual Guide The indicator uses a Dynamic Gradient system to visualize risk/reward: Cyan Mist (0.5 - 1.0): The Value Zone. Noise area. Safe for trend entries. Deep Cyan (1.0 - 2.0): The Trend Zone. Price is moving proactively. Orange Glow (2.0 - 3.0): The Danger Zone. Price is statistically overextended. Reversals are highly probable here. "Fractal" Math Unlike standard indicators that break when you change timeframes, Adaptive ML VWAP uses Fractal Normalization. A "2.0 Band" on a 15-second chart means the same statistical extreme as a "2.0 Band" on a 4-hour chart. Auto-Adaptive Lookback: The indicator automatically boosts the ML memory (Lookback) on lower timeframes (seconds/minutes) where more noise requires larger sample sizes, ensuring robust predictions without manual tweaking. Settings Auto-Adapting Lookback: (Default: True) automatically increases Lookback to 100+ for seconds charts and 50+ for minute charts. Lookback (Events): Manual override base value (Default: 100). Strategy Mode: Toggle between Mean Reversion and Trend Following. k-Neighbors: The number of similar past events to structurally compare (Default: 5). Disclaimer: This tool is for educational purposes. Machine learning performance is dependent on market conditions and historical recursion.
Indicador Pine Script®
por Selfsimilarity
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
AI-Enhanced MSS HunterAI-Enhanced MSS Hunter This indicator is a hybrid trading system that merges Mechanical Price Action (ICT Concepts) with Statistical Machine Learning (K-Nearest Neighbors). It is designed to assist traders in identifying high-probability reversals after liquidity sweeps, as well as trend-continuation entries during specific "Kill Zone" sessions. How It Works The script operates on a strict 3-step validation process to filter out false signals during choppy market conditions. 1. Liquidity Sweep (The Trigger) The system automatically plots the Previous Day High (PDH) and Previous Day Low (PDL). The logic begins only when price "sweeps" (breaks) one of these key levels. State Persistence: Once a level is swept, the system remembers this event for the remainder of the session (or until a signal fires), waiting for the market to reverse. 2. Market Structure Shift (The Setup) After a sweep, the indicator hunts for a Market Structure Shift (MSS). It tracks dynamic Swing Highs and Swing Lows. A signal is prepared only if price breaks a recent structural swing point in the opposite direction of the sweep (e.g., Sweep PDL -> Break Swing High). 3. AI / Machine Learning Filter (The Confirmation) To reduce false positives, the signal must be confirmed by a K-Nearest Neighbors (KNN) algorithm. The Logic: The script analyzes the current values of RSI (14), CCI (14), and ROC (10). The Comparison: It looks back at the last ~1,000 bars of history to find similar market conditions (neighbors). The Prediction: If the majority of those historical "neighbors" resulted in a favorable move, the AI confirms the trade. If historical data suggests chop or reversal, the signal is blocked. Key Features 🎯 Primary Reversal Signals (Circles) Green Circle: Price swept PDL + Bullish MSS + AI Confirmation. Red Circle: Price swept PDH + Bearish MSS + AI Confirmation. ♻️ Golden Zone Re-Entries (Triangles) Once a Primary Signal is active, the script tracks the new trend leg. It automatically draws a dynamic Golden Zone (0.5 – 0.618 Fibonacci Retracement). If price pulls back into this zone and forms a new MSS, a Re-Entry Triangle is plotted. Invalidation: If the pullback breaks the original setup's low/high, the zone is removed to prevent bad trades. ⏰ Kill Zone Time Filters Signals are filtered by time to ensure you are trading during high-volume sessions. Default AM Session: 08:30 – 10:00 (New York Time) Default PM Session: 14:00 – 15:00 (New York Time) Fully customizable in settings. Settings Guide Key Levels: Toggle PDH/PDL lines and customize colors. Kill Zones: Enable/Disable time filtering and highlight background colors. AI Settings: K-Nearest Neighbors (k): Number of historical neighbors to compare (Default: 5). Training Window: How far back the AI looks for patterns (Default: 1000 bars). Visuals: Turn on/off the Golden Zone fib clouds or text labels. Disclaimer This tool is for educational purposes only. The "AI" component is a statistical classification algorithm based on historical momentum and does not guarantee future results. Always manage risk and use this indicator as part of a comprehensive trading plan.
Indicador Pine Script®
por DivergentTrades
Adaptive ML Trailing Stop [BOSWaves]Adaptive ML Trailing Stop – Regime-Aware Risk Control with KAMA Adaptation and Pattern-Based Intelligence Overview Adaptive ML Trailing Stop is a regime-sensitive trailing stop and risk control system that adjusts stop placement dynamically as market behavior shifts, using efficiency-based smoothing and pattern-informed biasing. Instead of operating with fixed ATR offsets or rigid trailing rules, stop distance, responsiveness, and directional treatment are continuously recalculated using market efficiency, volatility conditions, and historical pattern resemblance. This creates a live trailing structure that responds immediately to regime change - contracting during orderly directional movement, relaxing during rotational conditions, and applying probabilistic refinement when pattern confidence is present. Price is therefore assessed relative to adaptive, condition-aware trailing boundaries rather than static stop levels. Conceptual Framework Adaptive ML Trailing Stop is founded on the idea that effective risk control depends on regime context rather than price location alone. Conventional trailing mechanisms apply constant volatility multipliers, which often results in trend suppression or delayed exits. This framework replaces static logic with adaptive behavior shaped by efficiency state and observed historical outcomes. Three core principles guide the design: Stop distance should adjust in proportion to market efficiency. Smoothing behavior must respond to regime changes. Trailing logic benefits from probabilistic context instead of fixed rules. This shifts trailing stops from rigid exit tools into adaptive, regime-responsive risk boundaries. Theoretical Foundation The indicator combines adaptive averaging techniques, volatility-based distance modeling, and similarity-weighted pattern analysis. Kaufman’s Adaptive Moving Average (KAMA) is used to quantify directional efficiency, allowing smoothing intensity and stop behavior to scale with trend quality. Average True Range (ATR) defines the volatility reference, while a K-Nearest Neighbors (KNN) process evaluates historical price patterns to introduce directional weighting when appropriate. Three internal systems operate in tandem: KAMA Efficiency Engine : Evaluates directional efficiency to distinguish structured trends from range conditions and modulate smoothing and stop behavior. Adaptive ATR Stop Engine : Expands or contracts ATR-derived stop distance based on efficiency, tightening during strong trends and widening in low-efficiency environments. KNN Pattern Influence Layer : Applies distance-weighted historical pattern outcomes to subtly influence stop placement on both sides. This design allows stop behavior to evolve with market context rather than reacting mechanically to price changes. How It Works Adaptive ML Trailing Stop evaluates price through a sequence of adaptive processes: Efficiency-Based Regime Identification : KAMA efficiency determines whether conditions favor trend continuation or rotational movement, influencing stop sensitivity. Volatility-Responsive Scaling : ATR-based stop distance adjusts automatically as efficiency rises or falls. Pattern-Weighted Adjustment : KNN compares recent price sequences to historical analogs, applying confidence-based bias to stop positioning. Adaptive Stop Smoothing : Long and short stop levels are smoothed using KAMA logic to maintain structural stability while remaining responsive. Directional Trailing Enforcement : Stops advance only in the direction of the prevailing regime, preserving invalidation structure. Gradient Distance Visualization : Gradient fills reflect the relative distance between price and the active stop. Controlled Interaction Markers : Diamond markers highlight meaningful stop interactions, filtered through cooldown logic to reduce clustering. Together, these elements form a continuously adapting trailing stop system rather than a fixed exit mechanism. Interpretation Adaptive ML Trailing Stop should be interpreted as a dynamic risk envelope: Long Stop (Green) : Acts as the downside invalidation level during bullish regimes, tightening as efficiency improves. Short Stop (Red) : Serves as the upside invalidation level during bearish regimes, adjusting width based on efficiency and volatility. Trend State Changes : Regime flips occur only after confirmed stop breaches, filtering temporary price spikes. Gradient Depth : Deeper gradient penetration indicates increased extension from the stop rather than imminent reversal. Pattern Influence : KNN weighting affects stop behavior only when historical agreement is strong and remains neutral otherwise. Distance, efficiency, and context outweigh isolated price interactions. Signal Logic & Visual Cues Adaptive ML Trailing Stop presents two primary visual signals: Trend Transition Circles : Display when price crosses the opposing trailing stop, confirming a regime change rather than anticipating one. Stop Interaction Diamonds : Indicate controlled contact with the active stop, subject to cooldown filtering to avoid excessive signals. Alert generation is limited to confirmed trend transitions to maintain clarity. Strategy Integration Adaptive ML Trailing Stop fits within trend-following and risk-managed trading approaches: Dynamic Risk Framing : Use adaptive stops as evolving invalidation levels instead of fixed exits. Directional Alignment : Base execution on confirmed regime state rather than speculative reversals. Efficiency-Based Tolerance : Allow greater price fluctuation during inefficient movement while enforcing tighter control during clean trends. Pattern-Guided Refinement : Let KNN influence adjust sensitivity without overriding core structure. Multi-Timeframe Context : Apply higher-timeframe efficiency states to inform lower-timeframe stop responsiveness. Technical Implementation Details Core Engine : KAMA-based efficiency measurement with adaptive smoothing Volatility Model : ATR-derived stop distance scaled by regime Machine Learning Layer : Distance-weighted KNN with confidence modulation Visualization : Directional trailing stops with layered gradient fills Signal Logic : Regime-based transitions and controlled interaction markers Performance Profile : Optimized for real-time chart execution Optimal Application Parameters Timeframe Guidance: 1 - 5 min : Tight adaptive trailing for short-term momentum control 15 - 60 min : Structured intraday trend supervision 4H - Daily : Higher-timeframe regime monitoring Suggested Baseline Configuration: KAMA Length : 20 Fast/Slow Periods : 15 / 50 ATR Period : 21 Base ATR Multiplier : 2.5 Adaptive Strength : 1.0 KNN Neighbors : 7 KNN Influence : 0.2 These suggested parameters should be used as a baseline; their effectiveness depends on the asset volatility, liquidity, and preferred entry frequency, so fine-tuning is expected for optimal performance. Parameter Calibration Notes Use the following adjustments to refine behavior without altering the core logic: Excessive chop or overreaction : Increase KAMA Length, Slow Period, and ATR Period to reinforce regime filtering. Stops feel overly permissive : Reduce the Base ATR Multiplier to tighten invalidation boundaries. Frequent false regime shifts : Increase KNN Neighbors to demand stronger historical agreement. Delayed adaptation : Decrease KAMA Length and Fast Period to improve responsiveness during regime change. Adjustments should be incremental and evaluated over multiple market cycles rather than isolated sessions. Performance Characteristics High Effectiveness: Markets exhibiting sustained directional efficiency Instruments with recurring structural behavior Trend-oriented, risk-managed strategies Reduced Effectiveness: Highly erratic or event-driven price action Illiquid markets with unreliable volatility readings Integration Guidelines Confluence : Combine with BOSWaves structure or trend indicators Discipline : Follow adaptive stop behavior rather than forcing exits Risk Framing : Treat stops as adaptive boundaries, not forecasts Regime Awareness : Always interpret stop behavior within efficiency context Disclaimer Adaptive ML Trailing Stop is a professional-grade adaptive risk and regime management tool. It does not forecast price movement and does not guarantee profitability. Results depend on market conditions, parameter selection, and disciplined execution. BOSWaves recommends deploying this indicator within a broader analytical framework that incorporates structure, volatility, and contextual risk management.
Indicador Pine Script®
por BOSWaves
3
SHAP-Aligned BUY Signal (Daily, Edge-Triggered)Based on the XGBoost + SHAP interpretation report, I'll explain which indicators to monitor for buying NVO. However, I must emphasize that this model performed poorly (47.5% accuracy) and should NOT be used for actual trading! That said, here's what the model learned (for educational purposes): 📊 Top Indicators to Monitor for BUY Signals 1. Days_Since_Low (Most Important - 1.264) Direction: BULLISH ↑ Interpretation: Higher values → UP prediction What to monitor: Track how many days since the stock hit its recent low Buy signal: When the stock has been recovering for an extended period (e.g., 100+ days from low) Why it matters: The model learned that stocks in long-term recovery tend to continue rising 2. SMA_50 (50-day Moving Average) (0.413) Direction: BULLISH ↑ Interpretation: Higher absolute SMA_50 values → UP prediction What to monitor: The 50-day simple moving average price level Buy signal: When SMA_50 is at higher levels (e.g., above $80-90) Why it matters: Higher moving averages indicate stronger long-term trends 3. SMA_200 (200-day Moving Average) (0.274) Direction: BULLISH ↑ Interpretation: Higher SMA_200 → UP prediction What to monitor: The 200-day simple moving average Buy signal: When SMA_200 is trending upward and at elevated levels Why it matters: Long-term trend indicator; golden cross (SMA_50 > SMA_200) is traditionally bullish 4. BB_Width (Bollinger Band Width) (0.199) Direction: BULLISH ↑ Interpretation: WIDER Bollinger Bands → UP prediction What to monitor: The distance between upper and lower Bollinger Bands Buy signal: When BB_Width is expanding (increasing volatility often precedes trend moves) Why it matters: Widening bands can signal the start of a new trend 5. Price_SMA_50_Ratio (0.158) Direction: BULLISH ↑ Interpretation: When price is ABOVE the 50-day MA → UP prediction What to monitor: Current price ÷ SMA_50 Buy signal: When ratio > 1.0 (price is above the 50-day average) Why it matters: Price above moving averages indicates uptrend 6. Momentum_21D (21-day Momentum) (0.152) Direction: BULLISH ↑ Interpretation: Positive 21-day momentum → UP prediction What to monitor: 21-day rate of change Buy signal: When momentum is positive and increasing Why it matters: Positive momentum suggests continuation 7. Stoch_K (Stochastic Oscillator) (0.142) Direction: BULLISH ↑ Interpretation: Higher Stochastic K → UP prediction What to monitor: Stochastic oscillator (0-100 scale) Buy signal: When Stoch_K is rising from oversold (<20) or in mid-range (40-60) Why it matters: Measures momentum and overbought/oversold conditions
Indicador Pine Script®
por yellowpierid
RSI Forecast Colorful [DiFlip]RSI Forecast Colorful Introducing one of the most complete RSI indicators available — a highly customizable analytical tool that integrates advanced prediction capabilities. RSI Forecast Colorful is an evolution of the classic RSI, designed to anticipate potential future RSI movements using linear regression. Instead of simply reacting to historical data, this indicator provides a statistical projection of the RSI’s future behavior, offering a forward-looking view of market conditions. ⯁ Real-Time RSI Forecasting For the first time, a public RSI indicator integrates linear regression (least squares method) to forecast the RSI’s future behavior. This innovative approach allows traders to anticipate market movements based on historical trends. By applying Linear Regression to the RSI, the indicator displays a projected trendline n periods ahead, helping traders make more informed buy or sell decisions. ⯁ Highly Customizable The indicator is fully adaptable to any trading style. Dozens of parameters can be optimized to match your system. All 28 long and short entry conditions are selectable and configurable, allowing the construction of quantitative, statistical, and automated trading models. Full control over signals ensures precise alignment with your strategy. ⯁ Innovative and Science-Based This is the first public RSI indicator to apply least-squares predictive modeling to RSI calculations. Technically, it incorporates machine-learning logic into a classic indicator. Using Linear Regression embeds strong statistical foundations into RSI forecasting, making this tool especially valuable for traders seeking quantitative and analytical advantages. ⯁ Scientific Foundation: Linear Regression Linear regression is a fundamental statistical method that models the relationship between a dependent variable y and one or more independent variables x. The general formula for simple linear regression is: y = β₀ + β₁x + ε where: y  = predicted variable (e.g., future RSI value) x  = explanatory variable (e.g., bar index or time) β₀ = intercept (value of y when x = 0) β₁ = slope (rate of change of y relative to x) ε   = random error term The goal is to estimate β₀ and β₁ by minimizing the sum of squared errors. This is achieved using the least squares method, ensuring the best linear fit to historical data. Once the coefficients are calculated, the model extends the regression line forward, generating the RSI projection based on recent trends. ⯁ Least Squares Estimation To minimize the error between predicted and observed values, we use the formulas: β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²) β₀ = ȳ - β₁x̄ Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables. ⯁ Linear Regression in Machine Learning Linear regression is a foundational component of supervised learning. Its simplicity and precision in numerical prediction make it essential in AI, predictive algorithms, and time-series forecasting. Applying regression to RSI is akin to embedding artificial intelligence inside a classic indicator, adding a new analytical dimension. ⯁ Visual Interpretation Imagine a time series of RSI values like this: Time → RSI    → The regression line smooths these historical values and projects itself n periods forward, creating a predictive trajectory. This projected RSI line can cross the actual RSI, generating sophisticated entry and exit signals. In summary, the RSI Forecast Colorful indicator provides both the current RSI and the forecasted RSI, allowing comparison between past and future trend behavior. ⯁ Summary of Scientific Concepts Used Linear Regression: Models relationships between variables using a straight line. Least Squares: Minimizes squared prediction errors for optimal fit. Time-Series Forecasting: Predicts future values from historical patterns. Supervised Learning: Predictive modeling based on known output values. Statistical Smoothing: Reduces noise to highlight underlying trends. ⯁ Why This Indicator Is Revolutionary Scientifically grounded: Built on statistical and mathematical theory. First of its kind: The first public RSI with least-squares predictive modeling. Intelligent: Incorporates machine-learning logic into RSI interpretation. Forward-looking: Generates predictive, not just reactive, signals. Customizable: Exceptionally flexible for any strategic framework. ⯁ Conclusion By combining RSI and linear regression, the RSI Forecast Colorful allows traders to predict market momentum rather than simply follow it. It's not just another indicator: it's a scientific advancement in technical analysis technology. Offering 28 configurable entry conditions and advanced signals, this open-source indicator paves the way for innovative quantitative systems. ⯁ Example of simple linear regression with one independent variable This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables. ⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x) This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component. ⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models. ⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model. ⯁ Result of fitting a set of data points with a quadratic function This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line. ⯁ What Is RSI? The RSI (Relative Strength Index) is a technical indicator developed by J. Welles Wilder. It measures the velocity and magnitude of recent price movements to identify overbought and oversold conditions. The RSI ranges from 0 to 100 and is commonly used to identify potential reversals and evaluate trend strength. ⯁ How RSI Works RSI is calculated from average gains and losses over a set period (commonly 14 bars) and plotted on a 0–100 scale. It consists of three key zones: Overbought: RSI above 70 may signal an overbought market. Oversold: RSI below 30 may signal an oversold market. Neutral Zone: RSI between 30 and 70, indicating no extreme condition. These zones help identify potential price reversals and confirm trend strength. ⯁ Entry Conditions All conditions below are fully customizable and allow detailed control over entry signal creation. 📈 BUY 🧲 Signal Validity: Signal remains valid for X bars. 🧲 Signal Logic: Configurable using AND or OR. 🧲 RSI > Upper 🧲 RSI < Upper 🧲 RSI > Lower 🧲 RSI < Lower 🧲 RSI > Middle 🧲 RSI < Middle 🧲 RSI > MA 🧲 RSI < MA 🧲 MA > Upper 🧲 MA < Upper 🧲 MA > Lower 🧲 MA < Lower 🧲 RSI (Crossover) Upper 🧲 RSI (Crossunder) Upper 🧲 RSI (Crossover) Lower 🧲 RSI (Crossunder) Lower 🧲 RSI (Crossover) Middle 🧲 RSI (Crossunder) Middle 🧲 RSI (Crossover) MA 🧲 RSI (Crossunder) MA 🧲 MA (Crossover)Upper 🧲 MA (Crossunder)Upper 🧲 MA (Crossover) Lower 🧲 MA (Crossunder) Lower 🧲 RSI Bullish Divergence 🧲 RSI Bearish Divergence 🔮 RSI (Crossover) Forecast MA 🔮 RSI (Crossunder) Forecast MA 📉 SELL 🧲 Signal Validity: Signal remains valid for X bars. 🧲 Signal Logic: Configurable using AND or OR. 🧲 RSI > Upper 🧲 RSI < Upper 🧲 RSI > Lower 🧲 RSI < Lower 🧲 RSI > Middle 🧲 RSI < Middle 🧲 RSI > MA 🧲 RSI < MA 🧲 MA > Upper 🧲 MA < Upper 🧲 MA > Lower 🧲 MA < Lower 🧲 RSI (Crossover) Upper 🧲 RSI (Crossunder) Upper 🧲 RSI (Crossover) Lower 🧲 RSI (Crossunder) Lower 🧲 RSI (Crossover) Middle 🧲 RSI (Crossunder) Middle 🧲 RSI (Crossover) MA 🧲 RSI (Crossunder) MA 🧲 MA (Crossover)Upper 🧲 MA (Crossunder)Upper 🧲 MA (Crossover) Lower 🧲 MA (Crossunder) Lower 🧲 RSI Bullish Divergence 🧲 RSI Bearish Divergence 🔮 RSI (Crossover) Forecast MA 🔮 RSI (Crossunder) Forecast MA 🤖 Automation All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies. ⯁ Unique Features Linear Regression Forecast Signal Validity: Keep signals active for X bars Signal Logic: AND/OR configuration Condition Table: BUY/SELL Condition Labels: BUY/SELL Chart Labels: BUY/SELL markers above price Automation & Alerts: BUY/SELL Background Colors: bgcolor Fill Colors: fill Linear Regression Forecast Signal Validity: Keep signals active for X bars Signal Logic: AND/OR configuration Condition Table: BUY/SELL Condition Labels: BUY/SELL Chart Labels: BUY/SELL markers above price Automation & Alerts: BUY/SELL Background Colors: bgcolor Fill Colors: fill
Indicador Pine Script®
por DiFlip
2
SNP420/INDI/support_resist_future_levelFunctionality – short description The indicator automatically detects the latest pivot highs/lows and builds the current resistance and support levels from them. New levels start as candidate levels (dotted lines). Using an ATR-based tolerance, it counts how many times price precisely tests and rejects the level (touch + reversal). Once the minimum number of touches is reached, the level is marked as validated (solid line). The indicator also detects breakouts of S/R, colors breakout candles, projects a target level after the breakout, and highlights retests of the broken levels with boxes. autor: SNP_420 project: FNXS ps: Piece a love
Indicador Pine Script®
por SNP_420
2
Lorentzian Length Adaptive Moving Average [LLAMA] Adaptation of "Machine Learning: Lorentzian Classification" by Gradient color by base on work by LLAMA: A regime-aware adaptive moving average that bends with the market. Start with a problem traders know: Traditional moving averages are either too slow (EMA200) or too fast (EMA9) Adaptive MAs exist, but they often hug price too tightly or smooth too much, failing to balance bias and tactics LLAMA uses a Lorentzian distance function to adapt its length dynamically. Instead of a fixed smoothing window, it stretches or contracts depending on market conditions. This distortion reduces lag while still providing a clear bias line. The indicator looks back at recent bars and measures how similar they are using a Lorentzian distance (a log‑scaled absolute difference). It keeps track of the “nearest neighbors” — bars that most resemble the current regime. Each neighbor carries a label (long, short, neutral) based on simple price comparisons. By averaging these labels, LLAMA predicts whether the market is leaning bullish or bearish. That prediction is then mapped into a dynamic length between and . Bullish bias -> length stretches toward max (smoother, more stable). Bearish bias -> length contracts toward min (snappier, more reactive). During breakouts, LLAMA tightens and comes into contact with bars, giving actionable signals. During chop, it stretches to avoid false triggers. It covers both ends of the spectrum (bias and tactics) in one line, something static MA's can't do. Think of LLAMA as a lens that bends with the market: Wide lens (max length) for big picture bias. Narrow lens (min length) for tactical precision. The "Lorentzian Loop" is the math that decides when to widen or narrow.
Indicador Pine Script®
por Starcruiser
2
Machine Learning Moving Average [BackQuant]Machine Learning Moving Average A powerful tool combining clustering, pseudo-machine learning, and adaptive prediction, enabling traders to understand and react to price behavior across multiple market regimes (Bullish, Neutral, Bearish). This script uses a dynamic clustering approach based on percentile thresholds and calculates an adaptive moving average, ideal for forecasting price movements with enhanced confidence levels. What is Percentile Clustering? Percentile clustering is a method that sorts and categorizes data into distinct groups based on its statistical distribution. In this script, the clustering process relies on the percentile values of a composite feature (based on technical indicators like RSI, CCI, ATR, etc.). By identifying key thresholds (lower and upper percentiles), the script assigns each data point (price movement) to a cluster (Bullish, Neutral, or Bearish), based on its proximity to these thresholds. This approach mimics aspects of machine learning, where we “train” the model on past price behavior to predict future movements. The key difference is that this is not true machine learning; rather, it uses data-driven statistical techniques to "cluster" the market into patterns. Why Percentile Clustering is Useful Clustering price data into meaningful patterns (Bullish, Neutral, Bearish) helps traders visualize how price behavior can be grouped over time. By leveraging past price behavior and technical indicators, percentile clustering adapts dynamically to evolving market conditions. It helps you understand whether price behavior today aligns with past bullish or bearish trends, improving market context. Clusters can be used to predict upcoming market conditions by identifying regimes with high confidence, improving entry/exit timing. What This Script Does Clustering Based on Percentiles : The script uses historical price data and various technical features to compute a "composite feature" for each bar. This feature is then sorted and clustered based on predefined percentile thresholds (e.g., 10th percentile for lower, 90th percentile for upper). Cluster-Based Prediction : Once clustered, the script uses a weighted average, cluster momentum, or regime transition model to predict future price behavior over a specified number of bars. Dynamic Moving Average : The script calculates a machine-learning-inspired moving average (MLMA) based on the current cluster, adjusting its behavior according to the cluster regime (Bullish, Neutral, Bearish). Adaptive Confidence Levels : Confidence in the predicted return is calculated based on the distance between the current value and the other clusters. The further it is from the next closest cluster, the higher the confidence. Visual Cluster Mapping : The script visually highlights different clusters on the chart with distinct colors for Bullish, Neutral, and Bearish regimes, and plots the MLMA line. Prediction Output : It projects the predicted price based on the selected method and shows both predicted price and confidence percentage for each prediction horizon. Trend Identification : Using the clustering output, the script colors the bars based on the current cluster to reflect whether the market is trending Bullish (green), Bearish (red), or is Neutral (gray). How Traders Use It Predicting Price Movements : The script provides traders with an idea of where prices might go based on past market behavior. Traders can use this forecast for short-term and long-term predictions, guiding their trades. Clustering for Regime Analysis : Traders can identify whether the market is in a Bullish, Neutral, or Bearish regime, using that information to adjust trading strategies. Adaptive Moving Average for Trend Following : The adaptive moving average can be used as a trend-following indicator, helping traders stay in the market when it’s aligned with the current trend (Bullish or Bearish). Entry/Exit Strategy : By understanding the current cluster and its associated trend, traders can time entries and exits with higher precision, taking advantage of favorable conditions when the confidence in the predicted price is high. Confidence for Risk Management : The confidence level associated with the predicted returns allows traders to manage risk better. Higher confidence levels indicate stronger market conditions, which can lead to higher position sizes. Pseudo Machine Learning Aspect While the script does not use conventional machine learning models (e.g., neural networks or decision trees), it mimics certain aspects of machine learning in its approach. By using clustering and the dynamic adjustment of a moving average, the model learns from historical data to adjust predictions for future price behavior. The "learning" comes from how the script uses past price data (and technical indicators) to create patterns (clusters) and predict future market movements based on those patterns. Why This Is Important for Traders Understanding market regimes helps to adjust trading strategies in a way that adapts to current market conditions. Forecasting price behavior provides an additional edge, enabling traders to time entries and exits based on predicted price movements. By leveraging the clustering technique, traders can separate noise from signal, improving the reliability of trading signals. The combination of clustering and predictive modeling in one tool reduces the complexity for traders, allowing them to focus on actionable insights rather than manual analysis. How to Interpret the Output Bullish (Green) Zone : When the price behavior clusters into the Bullish zone, expect upward price movement. The MLMA line will help confirm if the trend remains upward. Bearish (Red) Zone : When the price behavior clusters into the Bearish zone, expect downward price movement. The MLMA line will assist in tracking any downward trends. Neutral (Gray) Zone : A neutral market condition signals indecision or range-bound behavior. The MLMA line can help track any potential breakouts or trend reversals. Predicted Price : The projected price is shown on the chart, based on the cluster's predicted behavior. This provides a useful reference for where the price might move in the near future. Prediction Confidence : The confidence percentage helps you gauge the reliability of the predicted price. A higher percentage indicates stronger market confidence in the forecasted move. Tips for Use Combining with Other Indicators : Use the output of this indicator in combination with your existing strategy (e.g., RSI, MACD, or moving averages) to enhance signal accuracy. Position Sizing with Confidence : Increase position size when the prediction confidence is high, and decrease size when it’s low, based on the confidence interval. Regime-Based Strategy : Consider developing a multi-strategy approach where you use this tool for Bullish or Bearish regimes and a separate strategy for Neutral markets. Optimization : Adjust the lookback period and percentile settings to optimize the clustering algorithm based on your asset’s characteristics. Conclusion The Machine Learning Moving Average offers a novel approach to price prediction by leveraging percentile clustering and a dynamically adapting moving average. While not a traditional machine learning model, this tool mimics the adaptive behavior of machine learning by adjusting to evolving market conditions, helping traders predict price movements and identify trends with improved confidence and accuracy.
Indicador Pine Script®
por BackQuant
10
Machine Learning Price Predictor: Ridge AR [Bitwardex]🔹Machine Learning Price Predictor: Ridge AR is a research-oriented indicator demonstrating the use of Regularized AutoRegression (Ridge AR) for short-term price forecasting. The model combines autoregressive structure with Ridge regularization , providing stability under noisy or volatile market conditions. The latest version introduces Bull and Bear signals , visually representing the current momentum phase and model direction directly on the chart. Unlike traditional linear regression, Ridge AR minimizes overfitting, stabilizes coefficient dynamics, and enhances predictive consistency in correlated datasets. The script plots: Fit Line — in-sample fitted data; Forecast Line — out-of-sample projection; Trend Segments — color-coded bullish/bearish sections; Bull/Bear Labels 🐂🐻 — dynamic visual signals showing directional bias. Designed for researchers, students, and developers, this tool helps explore regularized time-series forecasting in Pine Script™. 🧩 Ridge AR Settings Training Window — number of bars used for model training; Forecast Horizon — forecast length (bars ahead); AR Order — number of lags used as features; Ridge Strength (λ) — regularization coefficient; Damping Factor — exponential trend decay rate; Trend Length — period for trend/volatility estimation; Momentum Weight — strength of the recent move; Mean Reversion — pullback intensity toward the mean. 🧮 Data Processing Prefilter: None — raw close price; EMA — exponential smoothing; SuperSmoother — Ehlers filter for noise reduction. EMA Length, SuperSmoother Length — smoothing parameters. 🖥️ Display Settings Update Mode: Lock — static model; Update Once Reached — rebuild after forecast horizon; Continuous — update every bar. Forecast Color — projection line color; Bullish/Bearish Colors — colors for trend segments. 🐂🐻 Bull/Bear Signal System The Bull/Bear Signal System adds directional visual cues to highlight local momentum shifts and model-based trend confirmation. Bull (🐂) — appears when upward momentum is confirmed (momentum > 0) . Displayed below the bar, colored with Bullish Color. Bear (🐻) — appears when downward momentum is dominant (momentum < 0) . Displayed above the bar, colored with Bearish Color. Signals are generated during model recalculations or when the directional bias changes in Continuous mode. These visual markers are analytical aids , not trading triggers. 🧠 Core Algorithmic Components Regularized AutoRegression (Ridge AR): Solves: (X′X+λI)−1X′y to derive stable regression coefficients. Matrix and Pseudoinverse Operations — implemented natively in Pine Script™. Prefiltering (EMA / Ehlers SuperSmoother) — stabilizes noisy data. Forecast Dynamics — integrates damping, momentum, and mean reversion. Trend Visualization — color-coded bullish/bearish line segments. Bull/Bear Signal Engine — visualizes real-time impulse direction. 📊 Applications Academic and educational purposes; Demonstration of Ridge Regression and AR models; Analysis of bull/bear market phase transitions; Visualization of time-series dependencies. ⚠️ Disclaimer This script is provided for educational and research purposes only. It does not provide trading or investment advice. The author assumes no liability for financial losses resulting from its use. Use responsibly and at your own risk.
Indicador Pine Script®
por bitwardex
Adaptive Machine Learning Trading System [PhenLabs]📊Adaptive ML Trading System Version: PineScript™v6 📌Description The Adaptive ML Trading System is a sophisticated machine learning indicator that combines ensemble modeling with advanced technical analysis. This system uses XGBoost, Random Forest, and Neural Network algorithms to generate high-confidence trading signals while incorporating robust risk management features. Traders benefit from objective, data-driven decision-making that adapts to changing market conditions. 🚀Points of Innovation • Machine Learning Ensemble - Three integrated models (XGBoost, Random Forest, Neural Network) • Confidence-Based Trading - Only executes trades when ML confidence exceeds threshold • Dynamic Risk Management - ATR-based stop loss and max drawdown protection • Adaptive Position Sizing - Volatility-adjusted position sizing with confidence weighting • Real-Time Performance Metrics - Live tracking of win rate, Sharpe ratio, and performance • Multi-Timeframe Feature Analysis - Adaptive lookback periods for different market regimes 🔧Core Components • ML Ensemble Engine - Weighted combination of XGBoost, Random Forest, and Neural Network outputs • Feature Normalization System - Advanced preprocessing with custom tanh/sigmoid activation • Risk Management Module - Dynamic position sizing and drawdown protection • Performance Dashboard - Real-time metrics and risk status monitoring • Alert System - Comprehensive alert conditions for entries, exits, and risk events 🔥Key Features • High-confidence ML signals with customizable confidence thresholds • Multiple trading modes (Conservative, Balanced, Aggressive) for different risk profiles • Integrated stop loss and risk management with ATR-based calculations • Real-time performance metrics including win rate and Sharpe ratio • Comprehensive alert system with entry, exit, and risk management notifications • Visual confidence bands and threshold indicators for easy signal interpretation 🎨Visualization • ML Signal Line - Primary signal output ranging from -1 to +1 • Confidence Bands - Visual representation of model confidence levels • Threshold Lines - Customizable buy/sell threshold levels • Position Histogram - Current market position visualization • Performance Tables - Real-time metrics display in customizable positions 📖Usage Guidelines Model Configuration • Confidence Threshold: Default 0.55, Range 0.5-0.95 - Minimum confidence for signals • Model Sensitivity: Default 0.9, Range 0.1-2.0 - Adjusts signal sensitivity • Ensemble Mode: Conservative/Balanced/Aggressive - Trading style preference • Signal Threshold: Default 0.55, Range 0.3-0.9 - ML signal threshold for entries Risk Management • Position Size %: Default 10%, Range 1-50% - Portfolio percentage per trade • Max Drawdown %: Default 15%, Range 5-30% - Maximum allowed drawdown • Stop Loss ATR: Default 2.0, Range 0.5-5.0 - Stop loss in ATR multiples • Dynamic Sizing: Default true - Volatility-based position adjustment Display Settings • Show Signals: Default true - Display entry/exit signals • Show Threshold Signals: Default true - Display ±0.6 threshold crosses • Show Confidence Bands: Default true - Display ML confidence levels • Performance Dashboard: Default true - Show metrics table ✅Best Use Cases • Swing trading with 1-5 day holding periods • Trend-following strategies in established trends • Volatility breakout trading during high-confidence periods • Risk-adjusted position sizing for portfolio management • Multi-timeframe confirmation for existing strategies ⚠️Limitations • Requires sufficient historical data for accurate ML predictions • May experience low confidence periods in choppy markets • Performance varies across different asset classes and timeframes • Not suitable for very short-term scalping strategies • Requires understanding of basic risk management principles 💡What Makes This Unique • True machine learning ensemble with multiple model types • Confidence-based trading rather than simple signal generation • Integrated risk management with dynamic position sizing • Real-time performance tracking and metrics • Adaptive parameters that adjust to market conditions 🔬How It Works Feature Calculation: Computes 20+ technical features from price/volume data Feature Normalization: Applies custom normalization for ML compatibility Ensemble Prediction: Combines XGBoost, Random Forest, and Neural Network outputs Signal Generation: Produces confidence-weighted trading signals Risk Management: Applies position sizing and stop loss rules Execution: Generates alerts and visual signals based on thresholds 💡Note: This indicator works best on daily and 4-hour timeframes for most assets. Ensure you understand the risk management settings before live trading. The system includes automatic risk-off modes that halt trading during excessive drawdown periods.
Indicador Pine Script®
por PhenLabs
5
Institutional Levels (CNN) - [PhenLabs]📊Institutional Levels (Convolutional Neural Network-inspired) Version : PineScript™v6 📌Description The CNN-IL Institutional Levels indicator represents a breakthrough in automated zone detection technology, combining convolutional neural network principles with advanced statistical modeling. This sophisticated tool identifies high-probability institutional trading zones by analyzing pivot patterns, volume dynamics, and price behavior using machine learning algorithms. The indicator employs a proprietary 9-factor logistic regression model that calculates real-time reaction probabilities for each detected zone. By incorporating CNN-inspired filtering techniques and dynamic zone management, it provides traders with unprecedented accuracy in identifying where institutional money is likely to react to price action. 🚀Points of Innovation ● CNN-Inspired Pivot Analysis - Advanced binning system using convolutional neural network principles for superior pattern recognition ● Real-Time Probability Engine - Live reaction probability calculations using 9-factor logistic regression model ● Dynamic Zone Intelligence - Automatic zone merging using Intersection over Union (IoU) algorithms ● Volume-Weighted Scoring - Time-of-day volume Z-score analysis for enhanced zone strength assessment ● Adaptive Decay System - Intelligent zone lifecycle management based on touch frequency and recency ● Multi-Filter Architecture - Optional gradient, smoothing, and Difference of Gaussians (DoG) convolution filters 🔧Core Components ● Pivot Detection Engine - Advanced pivot identification with configurable left/right bars and ATR-normalized strength calculations ● Neural Network Binning - Price level clustering using CNN-inspired algorithms with ATR-based bin sizing ● Logistic Regression Model - 9-factor probability calculation including distance, width, volume, VWAP deviation, and trend analysis ● Zone Management System - Intelligent creation, merging, and decay algorithms for optimal zone lifecycle control ● Visualization Layer - Dynamic line drawing with opacity-based scoring and optional zone fills 🔥Key Features ● High-Probability Zone Detection - Automatically identifies institutional levels with reaction probabilities above configurable thresholds ● Real-Time Probability Scoring - Live calculation of zone reaction likelihood using advanced statistical modeling ● Session-Aware Analysis - Optional filtering to specific trading sessions for enhanced accuracy during active market hours ● Customizable Parameters - Full control over lookback periods, zone sensitivity, merge thresholds, and probability models ● Performance Optimized - Efficient processing with controlled update frequencies and pivot processing limits ● Non-Repainting Mode - Strict mode available for backtesting accuracy and live trading reliability 🎨Visualization ● Dynamic Zone Lines - Color-coded support and resistance levels with opacity reflecting zone strength and confidence scores ● Probability Labels - Real-time display of reaction probabilities, touch counts, and historical hit rates for active zones ● Zone Fills - Optional semi-transparent zone highlighting for enhanced visual clarity and immediate pattern recognition ● Adaptive Styling - Automatic color and opacity adjustments based on zone scoring and statistical significance 📖Usage Guidelines ● Lookback Bars - Default 500, Range 100-1000, Controls the historical data window for pivot analysis and zone calculation ● Pivot Left/Right - Default 3, Range 1-10, Defines the pivot detection sensitivity and confirmation requirements ● Bin Size ATR units - Default 0.25, Range 0.1-2.0, Controls price level clustering granularity for zone creation ● Base Zone Half-Width ATR units - Default 0.25, Range 0.1-1.0, Sets the minimum zone width in ATR units for institutional level boundaries ● Zone Merge IoU Threshold - Default 0.5, Range 0.1-0.9, Intersection over Union threshold for automatic zone merging algorithms ● Max Active Zones - Default 5, Range 3-20, Maximum number of zones displayed simultaneously to prevent chart clutter ● Probability Threshold for Labels - Default 0.6, Range 0.3-0.9, Minimum reaction probability required for zone label display and alerts ● Distance Weight w1 - Controls influence of price distance from zone center on reaction probability ● Width Weight w2 - Adjusts impact of zone width on probability calculations ● Volume Weight w3 - Modifies volume Z-score influence on zone strength assessment ● VWAP Weight w4 - Controls VWAP deviation impact on institutional level significance ● Touch Count Weight w5 - Adjusts influence of historical zone interactions on probability scoring ● Hit Rate Weight w6 - Controls prior success rate impact on future reaction likelihood predictions ● Wick Penetration Weight w7 - Modifies wick penetration analysis influence on probability calculations ● Trend Weight w8 - Adjusts trend context impact using ADX analysis for directional bias assessment ✅Best Use Cases ● Swing Trading Entries - Enter positions at high-probability institutional zones with 60%+ reaction scores ● Scalping Opportunities - Quick entries and exits around frequently tested institutional levels ● Risk Management - Use zones as dynamic stop-loss and take-profit levels based on institutional behavior ● Market Structure Analysis - Identify key institutional levels that define current market structure and sentiment ● Confluence Trading - Combine with other technical indicators for high-probability trade setups ● Session-Based Strategies - Focus analysis during high-volume sessions for maximum effectiveness ⚠️Limitations ● Historical Pattern Dependency - Algorithm effectiveness relies on historical patterns that may not repeat in changing market conditions ● Computational Intensity - Complex calculations may impact chart performance on lower-end devices or with multiple indicators ● Probability Estimates - Reaction probabilities are statistical estimates and do not guarantee actual market outcomes ● Session Sensitivity - Performance may vary significantly between different market sessions and volatility regimes ● Parameter Sensitivity - Results can be highly dependent on input parameters requiring optimization for different instruments 💡What Makes This Unique ● CNN Architecture - First indicator to apply convolutional neural network principles to institutional-level detection ● Real-Time ML Scoring - Live machine learning probability calculations for each zone interaction ● Advanced Zone Management - Sophisticated algorithms for zone lifecycle management and automatic optimization ● Statistical Rigor - Comprehensive 9-factor logistic regression model with extensive backtesting validation ● Performance Optimization - Efficient processing algorithms designed for real-time trading applications 🔬How It Works ● Multi-timeframe pivot identification - Uses configurable sensitivity parameters for advanced pivot detection ● ATR-normalized strength calculations - Standardizes pivot significance across different volatility regimes ● Volume Z-score integration - Enhanced pivot weighting based on time-of-day volume patterns ● Price level clustering - Neural network binning algorithms with ATR-based sizing for zone creation ● Recency decay applications - Weights recent pivots more heavily than historical data for relevance ● Statistical filtering - Eliminates low-significance price levels and reduces market noise ● Dynamic zone generation - Creates zones from statistically significant pivot clusters with minimum support thresholds ● IoU-based merging algorithms - Combines overlapping zones while maintaining accuracy using Intersection over Union ● Adaptive decay systems - Automatic removal of outdated or low-performing zones for optimal performance ● 9-factor logistic regression - Incorporates distance, width, volume, VWAP, touch history, and trend analysis ● Real-time scoring updates - Zone interaction calculations with configurable threshold filtering ● Optional CNN filters - Gradient detection, smoothing, and Difference of Gaussians processing for enhanced accuracy 💡Note This indicator represents advanced quantitative analysis and should be used by traders familiar with statistical modeling concepts. The probability scores are mathematical estimates based on historical patterns and should be combined with proper risk management and additional technical analysis for optimal trading decisions.
Indicador Pine Script®
por PhenLabs
4
Machine Learning Gaussian Mixture Model | AlphaNattMachine Learning Gaussian Mixture Model | AlphaNatt A revolutionary oscillator that uses Gaussian Mixture Models (GMM) with unsupervised machine learning to identify market regimes and automatically adapt momentum calculations - bringing statistical pattern recognition techniques to trading. "Markets don't follow a single distribution - they're a mixture of different regimes. This oscillator identifies which regime we're in and adapts accordingly." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🤖 THE MACHINE LEARNING Gaussian Mixture Models (GMM): Unlike K-means clustering which assigns hard boundaries, GMM uses probabilistic clustering : Models data as coming from multiple Gaussian distributions Each market regime is a different Gaussian component Provides probability of belonging to each regime More sophisticated than simple clustering Expectation-Maximization Algorithm: The indicator continuously learns and adapts using the E-M algorithm: E-step: Calculate probability of current market belonging to each regime M-step: Update regime parameters based on new data Continuous learning without repainting Adapts to changing market conditions ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 THREE MARKET REGIMES The GMM identifies three distinct market states: Regime 1 - Low Volatility: Quiet, ranging markets Uses RSI-based momentum calculation Reduces false signals in choppy conditions Background: Pink tint Regime 2 - Normal Market: Standard trending conditions Uses Rate of Change momentum Balanced sensitivity Background: Gray tint Regime 3 - High Volatility: Strong trends or volatility events Uses Z-score based momentum Captures extreme moves Background: Cyan tint ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 KEY INNOVATIONS 1. Probabilistic Regime Detection: Instead of binary regime assignment, provides probabilities: 30% Regime 1, 60% Regime 2, 10% Regime 3 Smooth transitions between regimes No sudden indicator jumps 2. Weighted Momentum Calculation: Combines three different momentum formulas Weights based on regime probabilities Automatically adapts to market conditions 3. Confidence Indicator: Shows how certain the model is (white line) High confidence = strong regime identification Low confidence = transitional market state Line transparency changes with confidence ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ PARAMETER OPTIMIZATION Training Period (50-500): 50-100: Quick adaptation to recent conditions 100: Balanced (default) 200-500: Stable regime identification Number of Components (2-5): 2: Simple bull/bear regimes 3: Low/Normal/High volatility (default) 4-5: More granular regime detection Learning Rate (0.1-1.0): 0.1-0.3: Slow, stable learning 0.3: Balanced (default) 0.5-1.0: Fast adaptation ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📊 TRADING STRATEGIES Visual Signals: Cyan gradient: Bullish momentum Magenta gradient: Bearish momentum Background color: Current regime Confidence line: Model certainty 1. Regime-Based Trading: Regime 1 (pink): Expect mean reversion Regime 2 (gray): Standard trend following Regime 3 (cyan): Strong momentum trades 2. Confidence-Filtered Signals: Only trade when confidence > 70% High confidence = clearer market state Avoid transitions (low confidence) 3. Adaptive Position Sizing: Regime 1: Smaller positions (choppy) Regime 2: Normal positions Regime 3: Larger positions (trending) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🚀 ADVANTAGES OVER OTHER ML INDICATORS vs K-Means Clustering: Soft clustering (probabilities) vs hard boundaries Captures uncertainty and transitions More mathematically robust vs KNN (K-Nearest Neighbors): Unsupervised learning (no historical labels needed) Continuous adaptation Lower computational complexity vs Neural Networks: Interpretable (know what each regime means) No overfitting issues Works with limited data ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📈 PERFORMANCE CHARACTERISTICS Best Market Conditions: Markets with clear regime shifts Volatile to trending transitions Multi-timeframe analysis Cryptocurrency markets (high regime variation) Key Strengths: Automatically adapts to market changes No manual parameter adjustment needed Smooth transitions between regimes Probabilistic confidence measure ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔬 TECHNICAL BACKGROUND Gaussian Mixture Models are used extensively in: Speech recognition (Google Assistant) Computer vision (facial recognition) Astronomy (galaxy classification) Genomics (gene expression analysis) Finance (risk modeling at investment banks) The E-M algorithm was developed at Stanford in 1977 and is one of the most important algorithms in unsupervised machine learning. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 PRO TIPS Watch regime transitions: Best opportunities often occur when regimes change Combine with volume: High volume + regime change = strong signal Use confidence filter: Avoid low confidence periods Multi-timeframe: Compare regimes across timeframes Adjust position size: Scale based on identified regime ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚠️ IMPORTANT NOTES Machine learning adapts but doesn't predict the future Best used with other confirmation indicators Allow time for model to learn (100+ bars) Not financial advice - educational purposes Backtest thoroughly on your instruments ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🏆 CONCLUSION The GMM Momentum Oscillator brings institutional-grade machine learning to retail trading. By identifying market regimes probabilistically and adapting momentum calculations accordingly, it provides: Automatic adaptation to market conditions Clear regime identification with confidence levels Smooth, professional signal generation True unsupervised machine learning This isn't just another indicator with "ML" in the name - it's a genuine implementation of Gaussian Mixture Models with the Expectation-Maximization algorithm, the same technology used in: Google's speech recognition Tesla's computer vision NASA's data analysis Wall Street risk models "Let the machine learn the market regimes. Trade with statistical confidence." ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Developed by AlphaNatt | Machine Learning Trading Systems Version: 1.0 Algorithm: Gaussian Mixture Model with E-M Classification: Unsupervised Learning Oscillator Not financial advice. Always DYOR.
Indicador Pine Script®
por AlphaNatt
3
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-Inspired Supply & Demand Zones [AlgoPoint]This indicator is a Smart Supply & Demand Zone tool, developed with principles inspired by Machine Learning (ML). It intelligently filters out market noise, allowing you to focus only on the most significant zones where institutional order flow is likely present. 💡 How It Works: Why Is This Indicator "Smart"? Unlike traditional indicators that only measure simple price movements, this script uses an algorithm that asks the same critical questions an experienced market analyst would to qualify a zone: - 1. Price Imbalance: How fast and aggressively did the price leave the zone? Our algorithm measures the body size of the "departure candle" relative to the current market volatility (ATR). A zone is only considered if it was formed by an explosive move that is statistically significant, indicating a major imbalance between buyers and sellers. - 2. Volume Confirmation: Did the "smart money" participate in this move? The script checks if the volume on the departure candle was significantly higher than the recent average volume. A spike in volume confirms that the move was backed by institutional interest, adding strength and validity to the zone. - 3. Valid Pivot Structure: Did the zone originate from a meaningful swing high or low? The algorithm first identifies a valid pivot structure, ensuring that zones are not drawn from insignificant or random price fluctuations. Only when a potential zone passes these three critical tests—our "quality filter"—is it drawn on your chart. 🚀 Features & How to Use Using the indicator is straightforward. You will see two primary types of boxes on your chart: * 🟥 Red Box (Supply Zone): An area of potential resistance where selling pressure is likely to be strong. Look for potential shorting opportunities as the price approaches this zone. * 🟩 Green Box (Demand Zone): An area of potential support where buying pressure is likely to be strong. Look for potential long opportunities as the price pulls back into this zone. Dynamic Zone Management This indicator is not static; it lives and breathes with the market: - Fresh Zone: A newly formed zone appears in its full, vibrant color. These are the highest-probability zones as they have not yet been re-tested. - Broken / Flipped Zone: You have full control over what happens when a zone is broken! In the settings, you can choose: - Delete Zone: The zone will be removed completely when the price closes through it. - Show as Broken (Flip): When broken, the zone will turn gray, stop extending, and remain on your chart. This is extremely useful for identifying Support/Resistance Flips, where a broken demand zone becomes new resistance, or a broken supply zone becomes new support. ⚙️ Settings & Customization Fine-tune the indicator to match your personal trading style via the settings menu: - Breakout Behavior: The most powerful feature. Choose between Delete Zone and Show as Broken (Flip) to customize your chart. - Zone Finding Logic: Control the indicator's sensitivity. - Selective: Requires both strong imbalance and high volume. Finds fewer, but higher-quality, zones. - Moderate: Requires either strong imbalance or high volume. Finds more potential zones. - Sensitivity Settings: Adjust the ATR Multiplier and Volume Multiplier to make the criteria for a "strong" zone stricter or looser.
Indicador Pine Script®
por Iamalala
5
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
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain? This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions. How does a Markov Chain work, and how should I read this spheres-and-arrows diagram? Think of three weather modes: Sunny, Rainy, Cloudy. Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow). The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy). Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step. Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny. Now relabel the spheres to markets: Bull, Bear, Neutral. Spheres: market regimes (uptrend, downtrend, range). Self‑loop: tendency for the current regime to continue on the next bar. Arrows: the most common next regime if a switch happens. How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear). What states and transitions are shown? The three market states visualized are: Bullish (Bull): Upward or strong-market regime. Bearish (Bear): Downward or weak-market regime. Neutral: Sideways or range-bound regime. Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull). How does the regime detection system work? You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.). Values are statistically normalized (Z-scored) over a configurable lookback period. The normalized outputs are classified into Bull, Bear, or Neutral zones. If using three indicators, their regime signals are averaged and smoothed for robustness. How are transition probabilities calculated? On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions. The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear). Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time. What is unique about the visualization? 3D animated spheres represent each regime and change visually when active. Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows. Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed. All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format. Can I use custom indicators for regime classification? Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement. What does the “Lookback Period” control? Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt. How is this different from a Hidden Markov Model (HMM)? It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust. How is this Markov Chain different from a Hidden Markov Model (HMM)? Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable. Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training. A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data. Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune. HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data. How does the indicator avoid “repainting” or look-ahead bias? All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation. Are there practical tuning tips? Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability. Use custom indicators if your asset has unique regime drivers. Watch for rapid changes in transition probabilities as early warning of a possible regime shift. Who is this indicator for? Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models. Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting: The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear). Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions. Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies. It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes. The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting. Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis. What's the mathematical foundation behind this? The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM). How to use this indicator programmatically? The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading. Step-by-Step Integration with Pine Script (input.source) Add the Markov Chain indicator to your chart. This must be done first, since your custom script will "pull" the bias signal from the indicator's plot. In your strategy, create an input using input.source() Example: //@version=5 strategy("MC Bias Strategy Example") mcBias = input.source(close, "MC Bias Source") After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot). Use the bias in your trading logic Example (long only on Bull, flat otherwise): if mcBias == 1 strategy.entry("Long", strategy.long) else strategy.close("Long") For more advanced workflows, combine mcBias with additional filters or trailing stops. How does this work behind-the-scenes? TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source). The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.). This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code. Integrating 45+ Indicators with Your Markov Chain — How & Why The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots. // Enhanced Custom Indicators Export - 45 Technical Indicators // Comprehensive technical analysis suite for advanced market regime detection //@version=6 indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500) // |----- Input Parameters -----| // momentum_group = "Momentum Indicators" trend_group = "Trend Indicators" volume_group = "Volume Indicators" volatility_group = "Volatility Indicators" oscillator_group = "Oscillator Indicators" display_group = "Display Settings" // Common lengths length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group) length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group) length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group) // Display options show_table = input.bool(true, "Show Values Table", group=display_group) table_size = input.string("Small", "Table Size", options= , group=display_group) // |----- MOMENTUM INDICATORS (15 indicators) -----| // // 1. RSI (Relative Strength Index) rsi_14 = ta.rsi(close, length_14) rsi_centered = rsi_14 - 50 // 2. Stochastic Oscillator stoch_k = ta.stoch(close, high, low, length_14) stoch_d = ta.sma(stoch_k, 3) stoch_centered = stoch_k - 50 // 3. Williams %R williams_r = ta.stoch(close, high, low, length_14) - 100 // 4. MACD (Moving Average Convergence Divergence) = ta.macd(close, 12, 26, 9) // 5. Momentum (Rate of Change) momentum = ta.mom(close, length_14) momentum_pct = (momentum / close ) * 100 // 6. Rate of Change (ROC) roc = ta.roc(close, length_14) // 7. Commodity Channel Index (CCI) cci = ta.cci(close, length_20) // 8. Money Flow Index (MFI) mfi = ta.mfi(close, length_14) mfi_centered = mfi - 50 // 9. Awesome Oscillator (AO) ao = ta.sma(hl2, 5) - ta.sma(hl2, 34) // 10. Accelerator Oscillator (AC) ac = ao - ta.sma(ao, 5) // 11. Chande Momentum Oscillator (CMO) cmo = ta.cmo(close, length_14) // 12. Detrended Price Oscillator (DPO) dpo = close - ta.sma(close, length_20) // 13. Price Oscillator (PPO) ppo = ta.sma(close, 12) - ta.sma(close, 26) ppo_pct = (ppo / ta.sma(close, 26)) * 100 // 14. TRIX trix_ema1 = ta.ema(close, length_14) trix_ema2 = ta.ema(trix_ema1, length_14) trix_ema3 = ta.ema(trix_ema2, length_14) trix = ta.roc(trix_ema3, 1) * 10000 // 15. Klinger Oscillator klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55) // 16. Fisher Transform fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25 fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2)) // 17. Stochastic RSI stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14) stoch_rsi_centered = stoch_rsi - 50 // 18. Relative Vigor Index (RVI) rvi_num = ta.swma(close - open) rvi_den = ta.swma(high - low) rvi = rvi_den != 0 ? rvi_num / rvi_den : 0 // 19. Balance of Power (BOP) bop = (close - open) / (high - low) // |----- TREND INDICATORS (10 indicators) -----| // // 20. Simple Moving Average Momentum sma_20 = ta.sma(close, length_20) sma_momentum = ((close - sma_20) / sma_20) * 100 // 21. Exponential Moving Average Momentum ema_20 = ta.ema(close, length_20) ema_momentum = ((close - ema_20) / ema_20) * 100 // 22. Parabolic SAR sar = ta.sar(0.02, 0.02, 0.2) sar_trend = close > sar ? 1 : -1 // 23. Linear Regression Slope lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1) // 24. Moving Average Convergence (MAC) mac = ta.sma(close, 10) - ta.sma(close, 30) // 25. Trend Intensity Index (TII) tii_sum = 0.0 for i = 1 to length_20 tii_sum += close > close ? 1 : 0 tii = (tii_sum / length_20) * 100 // 26. Ichimoku Cloud Components ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2 ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2 ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1 // 27. MESA Adaptive Moving Average (MAMA) mama_alpha = 2.0 / (length_20 + 1) mama = ta.ema(close, length_20) mama_momentum = ((close - mama) / mama) * 100 // 28. Zero Lag Exponential Moving Average (ZLEMA) zlema_lag = math.round((length_20 - 1) / 2) zlema_data = close + (close - close ) zlema = ta.ema(zlema_data, length_20) zlema_momentum = ((close - zlema) / zlema) * 100 // |----- VOLUME INDICATORS (6 indicators) -----| // // 29. On-Balance Volume (OBV) obv = ta.obv // 30. Volume Rate of Change (VROC) vroc = ta.roc(volume, length_14) // 31. Price Volume Trend (PVT) pvt = ta.pvt // 32. Negative Volume Index (NVI) nvi = 0.0 nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi // 33. Positive Volume Index (PVI) pvi = 0.0 pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi // 34. Volume Oscillator vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10) // 35. Ease of Movement (EOM) eom_distance = high - low eom_box_height = volume / 1000000 eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0 eom_sma = ta.sma(eom, length_14) // 36. Force Index force_index = volume * (close - close ) force_index_sma = ta.sma(force_index, length_14) // |----- VOLATILITY INDICATORS (10 indicators) -----| // // 37. Average True Range (ATR) atr = ta.atr(length_14) atr_pct = (atr / close) * 100 // 38. Bollinger Bands Position bb_basis = ta.sma(close, length_20) bb_dev = 2.0 * ta.stdev(close, length_20) bb_upper = bb_basis + bb_dev bb_lower = bb_basis - bb_dev bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0 bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0 // 39. Keltner Channels Position kc_basis = ta.ema(close, length_20) kc_range = ta.ema(ta.tr, length_20) kc_upper = kc_basis + (2.0 * kc_range) kc_lower = kc_basis - (2.0 * kc_range) kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0 // 40. Donchian Channels Position dc_upper = ta.highest(high, length_20) dc_lower = ta.lowest(low, length_20) dc_basis = (dc_upper + dc_lower) / 2 dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0 // 41. Standard Deviation std_dev = ta.stdev(close, length_20) std_dev_pct = (std_dev / close) * 100 // 42. Relative Volatility Index (RVI) rvi_up = ta.stdev(close > close ? close : 0, length_14) rvi_down = ta.stdev(close < close ? close : 0, length_14) rvi_total = rvi_up + rvi_down rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50 // 43. Historical Volatility hv_returns = math.log(close / close ) hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100 // 44. Garman-Klass Volatility gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open) gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100 // 45. Parkinson Volatility park_vol = math.log(high/low) * math.log(high/low) parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100 // 46. Rogers-Satchell Volatility rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open) rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100 // |----- OSCILLATOR INDICATORS (5 indicators) -----| // // 47. Elder Ray Index elder_bull = high - ta.ema(close, 13) elder_bear = low - ta.ema(close, 13) elder_power = elder_bull + elder_bear // 48. Schaff Trend Cycle (STC) stc_macd = ta.ema(close, 23) - ta.ema(close, 50) stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10) stc_d = ta.ema(stc_k, 3) stc = ta.stoch(stc_d, stc_d, stc_d, 10) // 49. Coppock Curve coppock_roc1 = ta.roc(close, 14) coppock_roc2 = ta.roc(close, 11) coppock = ta.wma(coppock_roc1 + coppock_roc2, 10) // 50. Know Sure Thing (KST) kst_roc1 = ta.roc(close, 10) kst_roc2 = ta.roc(close, 15) kst_roc3 = ta.roc(close, 20) kst_roc4 = ta.roc(close, 30) kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15) // 51. Percentage Price Oscillator (PPO) ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100 ppo_signal = ta.ema(ppo_line, 9) ppo_histogram = ppo_line - ppo_signal // |----- PLOT MAIN INDICATORS -----| // // Plot key momentum indicators plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1) plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1) plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1) plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1) plot(cci, title="05_CCI", color=color.green, linewidth=1) // Plot trend indicators plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1) plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1) plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1) plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1) plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1) // Plot volatility indicators plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1) plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1) plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1) plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1) plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1) // Plot volume indicators plot(vroc, title="16_VROC", color=color.blue, linewidth=1) plot(eom_sma, title="17_EOM", color=color.red, linewidth=1) plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1) plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1) plot(obv, title="20_OBV", color=color.purple, linewidth=1) // Plot additional oscillators plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1) plot(cmo, title="22_CMO", color=color.maroon, linewidth=1) plot(dpo, title="23_DPO", color=color.teal, linewidth=1) plot(trix, title="24_TRIX", color=color.lime, linewidth=1) plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1) // Plot more momentum indicators plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1) plot(ac, title="27_AC", color=color.aqua, linewidth=1) plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1) plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1) plot(klinger, title="30_Klinger", color=color.gray, linewidth=1) // Plot trend continuation plot(tii, title="31_TII", color=color.blue, linewidth=1) plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1) plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1) plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1) plot(bop, title="35_BOP", color=color.purple, linewidth=1) // Plot volume continuation plot(nvi, title="36_NVI", color=color.navy, linewidth=1) plot(pvi, title="37_PVI", color=color.maroon, linewidth=1) plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1) plot(roc, title="39_ROC", color=color.lime, linewidth=1) plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1) // Plot volatility continuation plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1) plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1) plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1) plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1) plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1) // Plot final oscillators plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1) plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1) plot(stc, title="48_STC", color=color.green, linewidth=1) plot(coppock, title="49_Coppock", color=color.orange, linewidth=1) plot(kst, title="50_KST", color=color.purple, linewidth=1) // Plot final indicators plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1) plot(pvt, title="52_PVT", color=color.maroon, linewidth=1) // |----- Reference Lines -----| // hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1) hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1) // |----- Enhanced Information Table -----| // if show_table and barstate.islast table_position = position.top_right table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray) // Headers table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) // Key Momentum Indicators table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90)) table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size) table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size) table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size) table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size) table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size) table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size) table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size) table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size) table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size) // Key Trend Indicators table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90)) table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size) table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size) table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size) table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size) table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size) table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size) table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size) table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size) // Key Volatility Indicators table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90)) table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size) table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size) table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size) table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size) table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size) // Key Volume Indicators table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90)) table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size) // Key Oscillators table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90)) table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size) // Summary Statistics table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70)) table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size) regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL" table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size) This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator. How to use this script with the Markov Chain for research and backtesting: Add the Enhanced Indicator Export to your chart. Every calculated indicator is available as an individual data stream. Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option. In the Markov Chain indicator’s settings, turn ON the custom indicator mode. For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name. This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering. Backtest regime logic directly. Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns. The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration. Export the regime bias for programmatic use. As described above, use input.source() in your Pine Script strategy or system and link the bias output. You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes. With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you. Optimize your signal generation by backtesting across a universe of meaningful indicator blends. Enhance risk management with objective, real-time regime boundaries. Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes. Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic. Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem. References & Further Reading Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182. Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384. Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg. Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544. Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance. Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764. Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC. On Quantitative Finance and Markov Models: Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press. Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press. TradingView Pine Script Documentation: www.tradingview.com TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org What makes this indicator original and unique? - On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm. - Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script. - Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time. - Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window. - Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior. Integrate your own signals (using input.source()) - In settings, choose the Source . This is powered by input.source() . - Feed it price, an indicator you compute inside the script, or a custom composite series. - The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time. Credits: Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community. Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization. Disclaimer This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Indicador Pine Script®
por Fractalyst
10