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Probabilistic Breakout Forecaster [LuxAlgo]The Probabilistic Breakout Forecaster indicator calculates the statistical probability of price breaking out of a defined range within a specific future time horizon. Using a log-normal random walk model, it provides traders with a quantitative estimate of whether current price action is likely to exceed local highs or lows. 🔶 USAGE The indicator is displayed in a separate pane, showing two oscillating probability lines: a bullish breakout probability (positive values) and a bearish breakout probability (negative values). When the bullish probability line approaches or exceeds the 50% threshold, it indicates a higher statistical likelihood that the price will break above the recent range high within the specified forecast horizon. Conversely, when the bearish probability line (plotted negatively) reaches the -50% threshold, it suggests a high probability of a breakdown below the recent range low. The tool is particularly useful for: Identifying potential breakout candidates before the price movement occurs. Assessing the risk of "fakeouts" by comparing price proximity to the range boundary versus the statistical probability of a sustained move. Determining if current volatility levels are sufficient to support a trend continuation. 🔹 Dashboard The real-time dashboard provides a concise summary of the current forecast: Bullish/Bearish Probabilities: The exact percentage chance of a breakout within the selected horizon. Squeeze Intensity: A metric indicating how compressed the current volatility is relative to historical averages. High squeeze intensity (above 50%) often precedes significant expansion. Horizon: The number of bars the current forecast is looking into the future. 🔶 DETAILS The script utilizes a Log-Normal Random Walk model to forecast price distributions. This approach assumes that price returns follow a normal distribution, allowing the script to calculate the Z-score for the distance between the current price and the range boundaries. 🔹 Breakout Probability The probability is derived using the Normal Cumulative Distribution Function (CDF). It calculates the area under the bell curve beyond the upper and lower range boundaries, adjusted for the square root of time (Forecast Horizon) and the standard deviation of log returns (Volatility Lookback). 🔹 Volatility Squeeze The Squeeze Intensity metric compares the current Average True Range (ATR) to its 100-period simple moving average. When the ATR is significantly lower than its historical average, the intensity increases, signaling that the market is in a period of low-volatility consolidation that often leads to a "volatility explosion." 🔶 SETTINGS 🔹 Calculation Settings Range Length: The lookback period used to determine the highest high and lowest low that act as the breakout boundaries. Forecast Horizon (Bars): The number of bars into the future the model is predicting. A longer horizon generally increases the probability of hitting a boundary but decreases the precision of the timing. Volatility Lookback: The period used to calculate the standard deviation of log returns, which informs the "width" of the expected price distribution. 🔹 Visualization Bullish/Bearish Color: Customizes the colors for the respective probability plots and fills. Fill Transparency: Adjusts the visibility of the area between the probability lines and the zero baseline. 🔹 Dashboard Enable Dashboard: Toggles the visibility of the on-screen information table. Position: Moves the dashboard to different corners of the chart. Size: Adjusts the scale of the dashboard text and cells.
Pine Script® göstergesi
LuxAlgo tarafından
3
RSI Prediction by Range Segmentation [LuxAlgo]The RSI Prediction by Range Segmentation indicator projects a future path for the Relative Strength Index (RSI) by analyzing and averaging historical patterns that originated from similar RSI levels. This tool provides a probabilistic forecast based on how the RSI has historically behaved after reaching specific value segments. 🔶 USAGE The indicator segments the RSI range (0-100) into multiple horizontal zones. When the current RSI value falls into a specific zone, the script identifies all historical instances where the RSI was in that same zone and calculates the average path it took over a subsequent period. Users can observe the dynamic polyline forecast extending from the current RSI value to anticipate potential overbought or oversold conditions before they occur. The RSI line itself changes color based on its position relative to the 50 level, providing an immediate visual cue for bullish or bearish momentum. 🔹 Range Segmentation The RSI scale is divided into "Range Segments" (e.g., 10 segments of 10 points each). This allows the indicator to categorize market momentum into specific states. By increasing the number of segments, you make the historical matching more precise but may have fewer historical samples to average. A step-line is plotted to visualize the base of the current segment being analyzed. 🔹 The Forecast The forecast is generated only on the most recent bar using a polyline. It looks at the current RSI segment, retrieves the "Historical Limit" of stored patterns for that specific segment, and plots the mathematical average of those paths. The forecast color is dynamic: it appears bullish if the predicted endpoint is higher than the current RSI, and bearish if it is lower. 🔹 Overbought/Oversold Fills To highlight extreme momentum, the script includes conditional vertical gradient fills. When the RSI rises above the user-defined Overbought Level, a green gradient fills the space between the RSI and the level. Conversely, when it drops below the Oversold Level, a red gradient appears. 🔶 DETAILS The script utilizes User-Defined Types (UDTs) to store sequences of RSI values (Segments) within specific RangeData objects. This architecture allows the script to efficiently manage memory while maintaining a deep history of price momentum patterns. Every time a new bar is processed, the script "looks back" at a pattern of a specific length and stores it in the bucket corresponding to where that pattern started (the anchor point). This creates a library of outcomes categorized by their starting momentum state, which is then accessed on the real-time bar to generate the forecast. 🔶 SETTINGS 🔹 General Settings Historical Limit : Determines the maximum number of historical segments stored for each RSI range. A higher limit provides a more "smoothed" average by including more historical data. Forecast Length : The number of bars into the future the prediction will extend. This also defines the length of the historical patterns being recorded. Range Segments : The number of divisions for the 0-100 RSI scale. For example, setting this to 10 creates segments of 10 units (0-10, 10-20, etc.). RSI Length : The lookback period for the standard RSI calculation. 🔹 Levels Overbought Level : The threshold above which the RSI is considered overbought and the bullish gradient fill is triggered. Oversold Level : The threshold below which the RSI is considered oversold and the bearish gradient fill is triggered. 🔹 Colors Bullish Color : The color used for the RSI line (when > 50), the overbought gradient, and bullish forecasts. Bearish Color : The color used for the RSI line (when < 50), the oversold gradient, and bearish forecasts. Level Color : The color of the Overbought, Oversold, and Center (50) horizontal levels.
Pine Script® göstergesi
LuxAlgo tarafından
Quant Market Predictor v3 (No Signals)Quant Market Predictor – Lite is a streamlined version of the full Quant Market Predictor model. This version focuses purely on market structure analysis and statistical forecasting, without generating trading signals. The indicator combines multiple quantitative techniques commonly used in financial modeling to provide a clearer view of trend dynamics, volatility regimes, and short-term price projections. It is designed for traders and analysts who want model-driven market context while making their own discretionary decisions. Core Models 1. Kalman Filter Trend A recursive Kalman filter smooths price data to estimate the underlying trend. This approach reduces noise while remaining more responsive than traditional moving averages. 2. Adaptive AR(2) Price Model An autoregressive model estimates short-term price momentum using recent price changes. The coefficients adapt to volatility conditions to prevent overreaction during unstable markets. 3. GARCH-Inspired Volatility Model A simplified volatility estimator tracks conditional variance similar to GARCH models. This allows the indicator to identify high-volatility trend regimes and low-volatility consolidation regimes. 4. Multi-Bar Forecast The AR model generates a short-term projection path for the next few bars. The forecast gradually decays to reflect increasing uncertainty further into the future. What the Lite Version Shows • Kalman trend line (dynamic trend estimate) • Volatility bands derived from conditional variance • Regime background highlighting trend vs. consolidation environments • Short-term statistical forecast path • Information table displaying model diagnostics Important This Lite version does not produce buy or sell signals. It is intended as an analytical tool to help interpret market conditions rather than a complete trading system. Typical Use Cases • Understanding whether the market is in a trend or mean-reversion regime • Visualizing volatility expansion and contraction • Viewing a probabilistic short-term forecast path • Supporting discretionary trading decisions
Pine Script® göstergesi
liebmann_ tarafından
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.
Pine Script® göstergesi
UAlgo tarafından
1
Monte Carlo Mean Reversion Heatmap [LuxAlgo]The Monte Carlo Mean Reversion Heatmap indicator is a statistical forecasting tool that uses Geometric Brownian Motion (GBM) to simulate 100+ potential future price paths and visualize the mathematical probability of price returning to a specific mean. 🔶 USAGE The indicator provides a visual "probability cloud" projecting from the current price into the future. It helps traders identify statistical overextensions and the likelihood of a trend reversal toward a long-term average. 🔹 1. Assessing Mean Reversion Probability The dashboard shows a Mean Reversion % . This tells you how many of the 100 simulated paths "touched" or "crossed" the EMA ribbon within the projection window (e.g., the next 30 bars). High Probability (>70%): If the current price is far from the EMA but the probability of reversion is high, it suggests the market is "overextended." You might look for a counter-trend trade back toward the EMA. Low Probability (<30%): This suggests that volatility is so high or the trend is so strong that price is statistically unlikely to return to the mean anytime soon. This often happens during "parabolic" runs. 🔹 2. Trading the "Probability Fan" The dotted lines (5%, 50%, 95%) represent the statistical boundaries of where price is expected to stay. Overbought/Oversold: If price moves outside the 5% or 95% lines, it is making a move that only happens in 1 out of 20 scenarios. This is a "statistical extreme." Traders often look for reversals or profit-taking when price enters these outer edges of the cone. The Median Path (50%): This dashed line represents the "most likely" path based on current momentum (drift). It serves as a realistic target for trend-following trades. 🔹 Heatmap Density The heatmap represents the density of the simulated paths. Darker areas indicate a higher concentration of paths, marking the price zones with the highest mathematical probability of being reached according to the model. 🔶 DETAILS The engine behind this script is the Geometric Brownian Motion (GBM) model. GBM is a continuous-time stochastic process used in mathematical finance to model stock prices. The model assumes that price changes follow a random walk with two components: Drift: The deterministic trend of the mean (directional bias). Volatility: The random "noise" or shocks based on historical log-returns. By running 100 individual simulations simultaneously, the script generates a distribution of outcomes rather than a single linear prediction. This allows the user to see the "width" of uncertainty in the current market environment. 🔶 SETTINGS 🔹 Mean Calculation Mean Length: The period of the EMA used as the target for mean reversion analysis. Mean Color: The color of the target EMA line on the chart. 🔹 Monte Carlo Simulation Simulations: The number of random paths to calculate (higher values increase accuracy but may impact performance). Projection Length: How many bars into the future the simulation projects. Volatility Lookback: The window used to calculate historical log-volatility for the simulation. Include Drift: When enabled, the simulation accounts for the slope (trend) of the Mean EMA. 🔹 Visuals Price Bins: Determines the vertical resolution of the heatmap. Heatmap Color: The base color used for the probability density cloud. Show Percentile Lines: Toggles the visibility of the 5%, 50%, and 95% projection lines. 🔹 Dashboard Show Dashboard: Toggles the statistical information table. Position/Size: Controls the location and scale of the dashboard on the chart.
Pine Script® göstergesi
LuxAlgo tarafından
2
Indicator Configuration Forecasting [LuxAlgo]The Indicator Configuration Forecasting tool identifies historical market regimes that share a similar technical configuration to the current market and projects future price action based on those historical outcomes. By encoding multiple technical indicators into a state vector and employing a K-Nearest Neighbors (KNN) search, the script provides a probabilistic forecast including a median path and confidence intervals. 🔶 USAGE The indicator works by "memorizing" the state of various user-selected technical indicators at every bar. When the current bar's configuration matches or closely resembles a previous historical state, the script records how the price moved in the subsequent N bars from that point in time. 🔹 Forecast Interpretation Median Forecast (Dashed Gray): Represents the 50th percentile (median) of all matched historical outcomes. This is the central tendency of the forecast. Upper Bound (Dashed Green): Represents the 75th percentile of historical outcomes, suggesting a bullish boundary for the projected move. Lower Bound (Dashed Red): Represents the 25th percentile of historical outcomes, suggesting a bearish boundary for the projected move. Match Labels: Small labels appearing on the historical price action indicate exactly where the most similar configurations were found in the past. 🔹 Configuration Strategy Users can toggle various indicators to define what constitutes a "similar" market state. For example, if only "SMA Cross" and "Supertrend" are enabled, the script will look for historical periods where the trend relationship and SMA positioning were identical to the current bar, regardless of RSI or MACD values. 🔶 DETAILS The script utilizes a state-encoding methodology to calculate distances between the current market environment and the past. Each enabled indicator is converted into a discrete value (e.g., 1 for bullish, -1 for bearish, 0 for neutral). These values form a vector for the current bar. The algorithm then scans through the "Historical Lookback" period to find the "Top K" neighbors—the points in history where the vector distance to the current state is minimized. To ensure variety in the forecast, the script includes logic to prevent overlapping matches, ensuring that the selected historical points are distinct events. Once the matches are identified, the script calculates the percentage returns for the specified "Forecast Horizon (N)" and projects those returns onto the current price to generate the visual forecast. 🔶 SETTINGS 🔹 Parameters Top K Neighbors: The number of similar historical configurations to include in the forecast calculation. Forecast Horizon (N): How many bars into the future the forecast should extend. Historical Lookback: The maximum number of historical bars the script will search through to find matches. 🔹 Indicator Configuration RSI/SMA/Supertrend/MACD/ADX/etc.: Toggle switches to include or exclude specific technical conditions from the similarity search. RSI: Looks for similar overbought (>70) or oversold (<30) states. SMA Cross: Looks for similar Fast/Slow SMA relationships. Supertrend: Matches the current direction of the Supertrend. MACD: Matches the relationship between the MACD Line and Signal Line. 🔹 Visibility & Style Show Individual Match Paths: When enabled, draws the actual historical price paths from the match points directly on the current chart for visual comparison. Median/Upper/Lower Colors: Customizes the colors of the forecast lines and the shaded confidence intervals. Dashboard: Toggles the information panel showing the number of matches found and forecast confidence.
Pine Script® göstergesi
LuxAlgo tarafından
Pulse Mean AcceleratorPulse Mean Accelerator (PMA) | MisinkoMaster Pulse Mean Accelerator (PMA) is a high-speed adaptive trend engine designed to dynamically accelerate or stabilize its behavior depending on how aggressively price moves relative to its underlying structure. Instead of acting like a traditional moving average that simply lags behind price, PMA attempts to anticipate momentum expansion by accelerating when price pulses strengthen and stabilizing when market movement slows. The result is a responsive yet smooth trend-following tool that adapts to both trending and consolidating markets. PMA is particularly useful for traders who want earlier participation in expanding trends without sacrificing structural clarity. By combining adaptive acceleration, volatility awareness, and layered smoothing, PMA balances speed and stability to help traders remain aligned with developing momentum. Key Features Adaptive acceleration that reacts when price movement intensifies Automatically slows down during consolidation to reduce noise Multiple moving average types supported for flexibility Volatility-aware responsiveness adjustment Optional confirmation logic to filter weak signals Multiple smoothing modes for balancing speed vs stability Dynamic candle coloring reflecting active trend state Automatic Long and Short markers when direction changes Works across fast intraday and slower swing environments Designed to reduce lag while preserving structure How It Works Pulse Mean Accelerator begins with a moving average structure but enhances it by measuring how aggressively price moves relative to that baseline. When price starts moving faster than the average, acceleration increases, allowing the indicator to catch up quickly. When price slows or becomes erratic, acceleration reduces, preventing excessive reaction to noise. Volatility measurements are incorporated to scale this acceleration, ensuring that responsiveness adapts naturally to current market conditions. Strong moves result in quicker adaptation, while quiet markets lead to smoother, calmer behavior. Additional smoothing layers can then be applied, allowing traders to choose between faster responsiveness or more stable structure depending on their trading style. Optional confirmation logic ensures that signals are not triggered solely by temporary price spikes, helping filter weaker moves. The outcome is a moving average framework that behaves more like a dynamic trend engine rather than a static lagging indicator. Trend Detection Logic Trend direction is determined by how price behaves relative to the accelerated mean structure. Bullish phases occur when price maintains strength above the adaptive mean while momentum confirms upward pressure. Bearish phases occur when price weakens below the structure and downward momentum dominates. Signals appear when participation shifts strongly enough to confirm directional change, helping traders detect transitions from consolidation to expansion phases. Acceleration Behavior A defining characteristic of PMA is its pulse acceleration mechanism. • Strong price pulses increase responsiveness • Weak or slow price movement reduces acceleration • Volatility conditions influence adaptation speed • Structure remains smooth when momentum is weak This dynamic adjustment helps traders enter trends earlier while avoiding excessive reactions during sideways markets. Smoothing Modes PMA includes multiple smoothing options so users can tune responsiveness: • Raw acceleration for fastest reaction • Exponential stabilization for balanced behavior • Additional smoothing layers for structural clarity • Double smoothing for maximum noise reduction This flexibility allows PMA to be tailored for scalping, intraday trading, or higher-timeframe trend following. Visual Signals The indicator provides several visual cues for ease of interpretation: • Candle coloring reflects active trend direction • Adaptive mean and accelerated mean are plotted together • Long and Short markers appear when trend shifts occur • Filled areas highlight separation between price and structure These features help traders read market structure quickly without relying on numerical interpretation. Inputs Overview Users can customize behavior through adjustable components including: • Price source selection used in calculations • Moving average type controlling base structure • Length settings affecting responsiveness • Acceleration sensitivity determining reaction speed • Volatility measurement type influencing adaptation • Smoothing mode selection for stability control • Optional confirmation filtering for signal validation These controls allow the tool to be tuned for both aggressive and conservative trading approaches. Usage Notes Ideal for traders needing faster adaptation to momentum expansion Helps detect early stages of trend acceleration Useful for filtering sideways noise while remaining reactive to breakouts Works well in volatile assets where traditional averages lag Can be combined with support/resistance or volume tools for confirmation Higher smoothing settings suit swing traders, lower smoothing benefits intraday traders Confirmation mode reduces false signals in choppy markets Parameter tuning improves performance across different assets Best Use Scenarios Pulse Mean Accelerator performs particularly well in: • Momentum expansion phases • Breakouts from consolidation ranges • Trend continuation environments • High-volatility market conditions • Assets showing periodic acceleration bursts • Markets transitioning from low to high volatility It is especially effective where traditional moving averages react too slowly to developing moves. Summary Pulse Mean Accelerator transforms traditional moving average logic into an adaptive trend engine capable of accelerating when price momentum expands and stabilizing during calm conditions. By blending acceleration, volatility awareness, and flexible smoothing, it provides traders with a faster yet structured view of market direction. PMA is best suited for traders seeking earlier trend participation while maintaining smooth, readable structure across both fast-moving and consolidating markets.
Pine Script® göstergesi
MisinkoMaster tarafından
Next Candle PredictorAdvanced TradingView Indicator for Precise Buy and Sell Signals Overview: The Predicta Futures - Next Candle Predictor is a cutting-edge TradingView indicator designed to forecast the next candle's direction in futures and cryptocurrency markets. Leveraging a multi-indicator confluence strategy, this tool provides traders with actionable long and short prediction percentages, enhanced by dynamic ADX-based thresholds and visual projection candles. Ideal for scalping, day trading, or swing trading on platforms like MEXC or Binance futures, it combines Supertrend, MACD, RSI, Stochastic, ADX, and volume analysis to deliver high-probability buy and sell signals while minimizing false positives. Key Features: • Multi-Indicator Confluence Scoring: Integrates Supertrend for trend direction, EMAs (8, 21, 50) for alignment, MACD for momentum crossovers, RSI for overbought/oversold conditions, Stochastic for divergence detection, ADX for trend strength, and volume ratios for confirmation. A customizable confluence score (0-6) ensures signals meet user-defined criteria, reducing whipsaws in volatile markets. • Dynamic Prediction Thresholds: ADX-driven adjustments lower the required prediction percentage (e.g., 60% in strong trends) for "PERFECT TIME" entries, adapting to market conditions like ranging or trending phases. • Visual Analysis Table: A sleek, color-coded dashboard displays progress bars for each indicator, prediction percentages, and status (e.g., "PERFECT TIME" or "WAIT"). Supports long and short analyses with intuitive ASCII bars for quick scans. • Projection Candles: Simulates potential next-candle outcomes with volatility-scaled (via Bollinger Bands width) green long and red short candles, aiding in visualizing price targets. • Buy/Sell Signals and Alerts: Generates labeled "BUY" and "SELL" arrows on EMA crossovers within confirmed trends, with separate alerts for basic signals and high-confluence "PERFECT TIME" opportunities. • Customizable Inputs: Adjust ATR periods, Supertrend factors, minimum confluence scores, and volume ratios to tailor the indicator for stocks, forex, or crypto perpetual futures. How It Works: This TradingView script calculates long and short scores using weighted contributions from key indicators, normalizing them into prediction percentages. A confluence check—factoring trend, EMA alignment, MACD, Stochastic, volume, and ADX—triggers "PERFECT TIME" only when conditions align robustly. For example: • In a downtrend (Supertrend red), with bearish MACD and Stochastic, and sufficient volume, the indicator highlights short opportunities. • Dynamic thresholds ensure aggressive entries in strong trends (ADX >25) and conservative ones in weak trends. • Backtested for reliability, it excels in identifying reversals and continuations, making it a must-have for traders seeking an edge in futures trading strategies. Usage Instructions: 1. Add the indicator to your TradingView chart. (Search: Next Candle Predictor) 2. Customize settings via the inputs panel (e.g., set minConfluence to 5 for stricter signals). 3. Monitor the analysis table for predictions and confluence scores. 4. Act on "BUY/SELL" labels or "PERFECT TIME" alerts, combining with your risk management. 5. Enable projection candles for visual forecasting of the next bar. Compatible with all timeframes, from 1-minute scalping to daily swings. Note: This is not financial advice; always verify signals with additional analysis. Join thousands of traders enhancing their strategies—add it to your charts today and elevate your trading performance! Please rate and review if it boosts your trades! Thank you!
Pine Script® göstergesi
PredictaFutures tarafından
39
Next Candle PredictorAdvanced TradingView Indicator for Precise Buy and Sell Signals Overview: The Predicta Futures - Next Candle Predictor is a cutting-edge TradingView indicator designed to forecast the next candle's direction in futures and cryptocurrency markets. Leveraging a multi-indicator confluence strategy, this tool provides traders with actionable long and short prediction percentages, enhanced by dynamic ADX-based thresholds and visual projection candles. Ideal for scalping, day trading, or swing trading on platforms like MEXC or Binance futures, it combines Supertrend, MACD, RSI, Stochastic, ADX, and volume analysis to deliver high-probability buy and sell signals while minimizing false positives. Key Features: * Multi-Indicator Confluence Scoring: Integrates Supertrend for trend direction, EMAs (8, 21, 50) for alignment, MACD for momentum crossovers, RSI for overbought/oversold conditions, Stochastic for divergence detection, ADX for trend strength, and volume ratios for confirmation. A customizable confluence score (0-6) ensures signals meet user-defined criteria, reducing whipsaws in volatile markets. * Dynamic Prediction Thresholds: ADX-driven adjustments lower the required prediction percentage (e.g., 60% in strong trends) for "PERFECT TIME" entries, adapting to market conditions like ranging or trending phases. * Visual Analysis Table: A sleek, color-coded dashboard displays progress bars for each indicator, prediction percentages, and status (e.g., "PERFECT TIME" or "WAIT"). Supports long and short analyses with intuitive ASCII bars for quick scans. * Projection Candles: Simulates potential next-candle outcomes with volatility-scaled (via Bollinger Bands width) green long and red short candles, aiding in visualizing price targets. Buy/Sell Signals and Alerts: Generates labeled "BUY" and "SELL" arrows on EMA crossovers within confirmed trends, with separate alerts for basic signals and high-confluence "PERFECT TIME" opportunities. * Customizable Inputs: Adjust ATR periods, Supertrend factors, minimum confluence scores, and volume ratios to tailor the indicator for stocks, forex, or crypto perpetual futures. How It Works: This TradingView script calculates long and short scores using weighted contributions from key indicators, normalizing them into prediction percentages. A confluence check—factoring trend, EMA alignment, MACD, Stochastic, volume, and ADX—triggers "PERFECT TIME" only when conditions align robustly. For example: In a downtrend (Supertrend red), with bearish MACD and Stochastic, and sufficient volume, the indicator highlights short opportunities. Dynamic thresholds ensure aggressive entries in strong trends (ADX >25) and conservative ones in weak trends. Backtested for reliability, it excels in identifying reversals and continuations, making it a must-have for traders seeking an edge in futures trading strategies. Usage Instructions: 1. Add the indicator to your TradingView chart. 2. Customize settings via the inputs panel (e.g., set minConfluence to 5 for stricter signals). 3. Monitor the analysis table for predictions and confluence scores. 4. Act on "BUY/SELL" labels or "PERFECT TIME" alerts, combining with your risk management. 5. Enable projection candles for visual forecasting of the next bar. Compatible with all timeframes, from 1-minute scalping to daily swings. Note: This is not financial advice; always verify signals with additional analysis. Rate and review if it boosts your trades! Thank you!
Pine Script® göstergesi
PredictaFutures tarafından
Pivot Points Standard w/ Future PivotsPivot Points Standard with Future Projections This indicator displays traditional pivot point levels with an added feature to project future pivot levels based on the current period's price action. Key Features: Multiple Pivot Types: Choose from Traditional, Fibonacci, Woodie, Classic, DM, and Camarilla pivot calculations Flexible Timeframes: Auto-detect or manually select Daily, Weekly, Monthly, Quarterly, Yearly, and multi-year periods Future Pivot Projections: Visualize potential pivot levels for the next period based on current price movement Custom Price Scenarios: Test "what-if" scenarios by entering a custom close price to see resulting pivot levels Customizable Display: Adjust line styles, colors, opacity, and label positioning for both historical and future pivots Historical Pivots: View up to 200 previous pivot periods for context Future Pivot Options: The unique future pivot feature calculates what the next period's support and resistance levels would be using the current period's High, Low, Open, and either the current price or a custom price you specify for the closing value. Future pivots are displayed with customizable line styles (solid, dashed, dotted) and opacity to distinguish them from historical levels. Use Cases: Plan entries and exits based on projected support/resistance Scenario analysis with custom price targets Identify key levels before the period closes Multi-timeframe pivot analysis Works on all timeframes and instruments.
Pine Script® göstergesi
CryptoPl3be tarafından
Bollinger Bands Regression Forecast [BigBeluga]🔵 OVERVIEW The Bollinger Bands Regression Forecast combines volatility envelopes from Bollinger Bands with a linear regression-based projection model . It visualizes both current and future price zones by extrapolating the Bollinger channel forward in time, giving traders a statistical forecast of probable support and resistance behavior. 🔵 CONCEPTS Classic Bollinger Bands use a moving average (basis) and standard deviation (deviation) to form dynamic envelopes around price. This indicator enhances them with linear regression slope detection , allowing it to forecast how the band may expand or contract in the future. Regression is applied to both the band’s basis and deviation components to predict their trajectory for a user-defined number of Forecast Bars . The resulting forecast creates a smoothed, funnel-shaped projection that dynamically adapts to volatility. ▲ and ▼ markers highlight potential mean reversion points when price crosses the outer bounds of the bands. 🔵 FEATURES Forecast Engine : Uses linear regression to project Bollinger Band movement into the future. Dynamic Channel Width : Adapts standard deviation and slope for realistic volatility modeling. Auto-Labeled Levels : Displays live upper and lower forecast values for quick reference. Cross Signals : Marks potential overbought and oversold zones with ▲/▼ signals when price exits the band. Trend-Adaptive Basis Color : Basis line automatically switches color to represent short-term trend direction. Customizable Colors and Widths for complete visual control. 🔵 HOW TO USE Apply the indicator to visualize both current Bollinger structure and its forward projection. Use ▲/▼ breakout markers to identify short-term reversals or volatility shifts. When price consistently rides the upper band forecast, the trend is strong and likely continuing. When regression shows narrowing bands ahead, expect a volatility contraction or consolidation period. For range traders, outer projected bands can be used as potential mean reversion entry points . Combine with volume or momentum filters to confirm whether breakouts are genuine or fading. 🔵 CONCLUSION Bollinger Bands Regression Forecast transforms classic Bollinger analysis into a predictive forecasting model . By merging volatility dynamics with regression-based extrapolation, it provides traders with a forward-looking visualization of likely price boundaries — revealing not only where volatility is but also where it’s heading next.
Pine Script® göstergesi
BigBeluga tarafından
14
Predicta Futures – Scalping Predictor with Confidence FilterPredicta Futures is an advanced short-term forecasting indicator that combines historical pattern similarity analysis with weighted technical signals to predict price movements 1–10 minutes ahead. **Core Functionality** The script scans up to 5,000 historical bars to identify structurally similar price patterns. It aggregates forward outcomes from matched patterns and integrates real-time signals from RSI, MACD, Bollinger Bands, volume momentum, and volatility. A composite confidence score filters signals, displaying only those meeting the user-defined threshold (default ≥68%). **Key Outputs** - Buy/sell triangles with text labels - Dashed projection line to predicted price - Dotted target and ATR-based stop lines - Info panel showing forecast direction, confidence %, expected move %, pattern count, order book status, and data access details **Customization & Performance** - Execution modes: Fast, Balanced, Accurate - Adaptive sampling with recency bias option - Filters for volatility and market hours - Adjustable weights, lookback period, and prediction horizon **Use Cases** Scalping, intraday trading, futures, cryptocurrencies, equities. *Order book metrics are simulated (platform limitation). Technical analysis tool; not financial advice.*
Pine Script® göstergesi
PredictaFutures tarafından
4
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.
Pine Script® göstergesi
BackQuant tarafından
12
Volume Surprise [LuxAlgo]The Volume Surprise tool displays the trading volume alongside the expected volume at that time, allowing users to spot unexpected trading activity on the chart easily. The tool includes an extrapolation of the estimated volume for future periods, allowing forecasting future trading activity. 🔶 USAGE We define Volume Surprise as a situation where the actual trading volume deviates significantly from its expected value at a given time. Being able to determine if trading activity is higher or lower than expected allows us to precisely gauge the interest of market participants in specific trends. A histogram constructed from the difference between the volume and expected volume is provided to easily highlight the difference between the two and may be used as a standalone. The tool can also help quantify the impact of specific market events, such as news about an instrument. For example, an important announcement leading to volume below expectations might be a sign of market participants underestimating the impact of the announcement. Like in the example above, it is possible to observe cases where the volume significantly differs from the expected one, which might be interpreted as an anomaly leading to a correction. 🔹 Detecting Rare Trading Activity Expected volume is defined as the mean (or median if we want to limit the impact of outliers) of the volume grouped at a specific point in time. This value depends on grouping volume based on periods, which can be user-defined. However, it is possible to adjust the indicator to overestimate/underestimate expected volume, allowing for highlighting excessively high or low volume at specific times. In order to do this, select "Percentiles" as the summary method, and change the percentiles value to a value that is close to 100 (overestimate expected volume) or to 0 (underestimate expected volume). In the example above, we are only interested in detecting volume that is excessively high, we use the 95th percentile to do so, effectively highlighting when volume is higher than 95% of the volumes recorded at that time. 🔶 DETAILS 🔹 Choosing the Right Periods Our expected volume value depends on grouping volume based on periods, which can be user-defined. For example, if only the hourly period is selected, volumes are grouped by their respective hours. As such, to get the expected volume for the hour 7 PM, we collect and group the historical volumes that occurred at 7 PM and average them to get our expected value at that time. Users are not limited to selecting a single period, and can group volume using a combination of all the available periods. Do note that when on lower timeframes, only having higher periods will lead to less precise expected values. Enabling periods that are too low might prevent grouping. Finally, enabling a lot of periods will, on the other hand, lead to a lot of groups, preventing the ability to get effective expected values. In order to avoid changing periods by navigating across multiple timeframes, an "Auto Selection" setting is provided. 🔹 Group Length The length setting allows controlling the maximum size of a volume group. Using higher lengths will provide an expected value on more historical data, further highlighting recurring patterns. 🔹 Recommended Assets Obtaining the expected volume for a specific period (time of the day, day of the week, quarter, etc) is most effective when on assets showing higher signs of periodicity in their trading activity. This is visible on stocks, futures, and forex pairs, which tend to have a defined, recognizable interval with usually higher trading activity. Assets such as cryptocurrencies will usually not have a clearly defined periodic trading activity, which lowers the validity of forecasts produced by the tool, as well as any conclusions originating from the volume to expected volume comparisons. 🔶 SETTINGS Length: Maximum number of records in a volume group for a specific period. Older values are discarded. Smooth: Period of a SMA used to smooth volume. The smoothing affects the expected value. 🔹 Periods Auto Selection: Automatically choose a practical combination of periods based on the chart timeframe. Custom periods can be used if disabling "Auto Selection". Available periods include: - Minutes - Hours - Days (can be: Day of Week, Day of Month, Day of Year) - Months - Quarters 🔹 Summary Method: Method used to obtain the expected value. Options include Mean (default) or Percentile. Percentile: Percentile number used if "Method" is set to "Percentile". A value of 50 will effectively use a median for the expected value. 🔹 Forecast Forecast Window: Number of bars ahead for which the expected volume is predicted. Style: Style settings of the forecast.
Pine Script® göstergesi
LuxAlgo tarafından
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.
Pine Script® göstergesi
bitwardex tarafından
Estimated Manipulation Movement Signal [AlgoPoint]Follow the Footprints of Whale Movements That Drive the Market Overview The market is not always driven by natural supply and demand. Large players—often called "whales" or institutions—can create artificial price movements to trigger stop-losses, induce panic or FOMO, and build their large positions at favorable prices. These events are known as "stop hunts" or "liquidity grabs." The EMMS indicator is a specialized tool designed to detect these specific moments of potential market manipulation. It does not follow trends in a traditional sense; instead, it identifies high-probability reversal points created by the calculated actions of Smart Money trapping other market participants. How It Works: The 3-Module Logic The indicator uses a multi-stage confirmation process to identify a potential stop hunt: 1. Anomaly Detection: The engine first scans the chart for "Anomaly Candles." These are candles with unusually high volume and a very long wick relative to their body. This combination signals a sudden, forceful, and potentially unnatural price push. 2. Liquidity Zone Detection: The indicator automatically identifies and tracks recent significant swing highs and lows. These levels are considered "Liquidity Zones" because they are areas where a large number of stop-loss orders are likely clustered. These are the "hunting grounds" for whales. 3. The Stop Hunt Signal: A final signal is generated only when these two events align in a specific sequence: An Anomaly Candle (high volume, long wick) spikes through a previously identified Liquidity Zone. The same candle then reverses, closing back inside the previous price range. This sequence confirms that the move was likely a "trap" designed to engineer liquidity, and a reversal in the opposite direction is now highly probable. How to Interpret & Use This Indicator BUY Signal: A BUY signal appears after a sharp price drop that pierces a recent swing low (taking out the stops of long positions) and then aggressively reverses to close higher. This suggests that Smart Money has absorbed the panic selling they just induced. The signal indicates a potential move UP. SELL Signal: A SELL signal appears after a sharp price spike that pierces a recent swing high (taking out the stops of short positions) and then aggressively reverses to close lower. This suggests that Smart Money has sold into the FOMO buying they just created. The signal indicates a potential move DOWN. This indicator is best used as a high-probability confirmation tool, ideally in conjunction with your understanding of the overall market trend and structure.
Pine Script® göstergesi
Iamalala tarafından
8
Cyclic Reversal Engine [AlgoPoint]Overview Most indicators focus on price and momentum, but they often ignore a critical third dimension: time. Markets move in rhythmic cycles of expansion and contraction, but these cycles are not fixed; they speed up in trending markets and slow down in choppy conditions. The Cyclic Reversal Engine is an advanced analytical tool designed to decode this rhythm. Instead of relying on static, lagging formulas, this indicator learns from past market behavior to anticipate when the current trend is statistically likely to reach its exhaustion point, providing high-probability reversal signals. It achieves this by combining a sophisticated time analysis with a robust price-action confirmation. How It Works: The Core Logic The indicator operates on a multi-stage process to identify potential turning points in the market. 1. Market Regime Analysis (The Brain): Before analyzing any cycles, the indicator first diagnoses the current "personality" of the market. Using a combination of the ADX, Choppiness Index, and RSI, it classifies the market into one of three primary regimes: - Trending: Strong, directional movement. - Ranging: Sideways, non-directional chop. - Reversal: An over-extended state (overbought/oversold) where a turn is imminent. 2. Adaptive Cycle Learning (The "Machine Learning" Aspect): This is the indicator's smartest feature. It constantly analyzes past cycles by measuring the bar-count between significant swing highs and swing lows. Crucially, it learns the average cycle duration for each specific market regime. For example, it learns that "in a strong trending market, a new swing low tends to occur every 35 bars," while "in a ranging market, this extends to 60 bars." 3. The Countdown & Timing Signal: The indicator identifies the last major swing high or low and starts a bar-by-bar countdown. Based on the current market regime, it selects the appropriate learned cycle length from its memory. When the bar count approaches this adaptive target, the indicator determines that a reversal is "due" from a timing perspective. 4. Price Confirmation (The Trigger): A signal is never generated based on timing alone. Once the timing condition is met (the cycle is "due"), the indicator waits for a final price-action confirmation. The default confirmation is the RSI entering an extreme overbought or oversold zone, signaling momentum exhaustion. The signal is only triggered when Time + Price Confirmation align. How to Use This Indicator - The Dashboard: The panel in the bottom-right corner is your command center. - Market Regime: Shows the current market personality analyzed by the engine. - Adaptive Cycle / Bar Count: This is the core of the indicator. It shows the target cycle length for the current regime (e.g., 50) and the current bar count since the last swing point (e.g., 45). The background turns orange when the bar count enters the "due zone," indicating that you should be on high alert for a reversal. - BUY/SELL Signals: A label appears on the chart only when the two primary conditions are met: The timing is right (Bar Count has reached the Adaptive Cycle target). The price confirms exhaustion (RSI is in an extreme zone). A BUY signal suggests a downtrend cycle is likely complete, and a SELL signal suggests an uptrend cycle is likely complete. Key Settings - Pivot Lookback: Controls the sensitivity of the swing point detection. Higher values will identify more significant, longer-term cycles. - Market Regime Engine: The ADX, Choppiness, and RSI settings can be fine-tuned to adjust how the indicator classifies the market's personality. - Require Price Confirmation: You can toggle the RSI confirmation on or off. It is highly recommended to keep it enabled for higher-quality signals.
Pine Script® göstergesi
Iamalala tarafından
9
HorizonSigma Pro [CHE]HorizonSigma Pro Disclaimer Not every timeframe will yield good results . Very short charts are dominated by microstructure noise, spreads, and slippage; signals can flip and the tradable edge shrinks after costs. Very high timeframes adapt more slowly, provide fewer samples, and can lag regime shifts. When you change timeframe, you also change the ratios between horizon, lookbacks, and correlation windows—what works on M5 won’t automatically hold on H1 or D1. Liquidity, session effects (overnight gaps, news bursts), and volatility do not scale linearly with time. Always validate per symbol and timeframe, then retune horizon, z-length, correlation window, and either the neutral band or the z-threshold. On fast charts, “components” mode adapts quicker; on slower charts, “super” reduces noise. Keep prior-shift and calibration enabled, monitor Hit Rate with its confidence interval and the Brier score, and execute only on confirmed (closed-bar) values. For example, what do “UP 61%” and “DOWN 21%” mean? “UP 61%” is the model’s estimated probability that the close will be higher after your selected horizon—directional probability, not a price target or profit guarantee. “DOWN 21%” still reports the probability of up; here it’s 21%, which implies 79% for down (a short bias). The label switches to “DOWN” because the probability falls below your short threshold. With a neutral-band policy, for example ±7%, signals are: Long above 57%, Short below 43%, Neutral in between. In z-score mode, fixed z-cutoffs drive the call instead of percentages. The arrow length on the chart is an ATR-scaled projection to visualize reach; treat it as guidance, not a promise. Part 1 — Scientific description Objective. The indicator estimates the probability that price will be higher after a user-defined horizon (a chosen number of bars) and emits long, short, or neutral decisions under explicit thresholds. It combines multi‑feature, z‑normalized inputs, adaptive correlation‑based weighting, a prior‑shifted sigmoid mapping, optional rolling probability calibration, and repaint‑safe confirmation. It also visualizes an ATR‑scaled forward projection and prints a compact statistics panel. Data and labeling. For each bar, the target label is whether price increased over the past chosen horizon. Learning is deliberately backward‑looking to avoid look‑ahead: features are associated with outcomes that are only known after that horizon has elapsed. Feature engineering. The feature set includes momentum, RSI, stochastic %K, MACD histogram slope, a normalized EMA(20/50) trend spread, ATR as a share of price, Bollinger Band width, and volume normalized by its moving average. All features are standardized over rolling windows. A compressed “super‑feature” is available that aggregates core trend and momentum components while penalizing excessive width (volatility). Users can switch between a “components” mode (weighted sum of individual features) and a “super” mode (single compressed driver). Weighting and learning. Weights are the rolling correlations between features (evaluated one horizon ago) and realized directional outcomes, smoothed by an EMA and optionally clamped to a bounded range to stabilize outliers. This produces an adaptive, regime‑aware weighting without explicit machine‑learning libraries. Scoring and probability mapping. The raw score is either the weighted component sum or the weighted super‑feature. The score is standardized again and passed through a sigmoid whose steepness is user‑controlled. A “prior shift” moves the sigmoid’s midpoint to the current base rate of up moves, estimated over the evaluation window, so that probabilities remain well‑calibrated when markets drift bullish or bearish. Probabilities and standardized scores are EMA‑smoothed for stability. Decision policy. Two modes are supported: - Neutral band: go long if the probability is above one half plus a user‑set band; go short if it is below one half minus that band; otherwise stay neutral. - Z‑score thresholds: use symmetric positive/negative cutoffs on the standardized score to trigger long/short. Repaint protection. All values used for decisions can be locked to confirmed (closed) bars. Intrabar updates are available as a preview, but confirmed values drive evaluation and stats. Calibration. An optional rolling linear calibration maps past confirmed probabilities to realized outcomes over the evaluation window. The mapping is clipped to the unit interval and can be injected back into the decision logic if desired. This improves reliability (probabilities that “mean what they say”) without necessarily improving raw separability. Evaluation metrics. The table reports: hit rate on signaled bars; a Wilson confidence interval for that hit rate at a chosen confidence level; Brier score as a measure of probability accuracy; counts of long/short trades; average realized return by side; profit factor; net return; and exposure (signal density). All are computed on rolling windows consistent with the learning scheme. Visualization. On the chart, an arrowed projection shows the predicted direction from the current bar to the chosen horizon, with magnitude scaled by ATR (optionally scaled by the square‑root of the horizon). Labels display either the decision probability or the standardized score. Neutral states can display a configurable icon for immediate recognition. Computational properties. The design relies on rolling means, standard deviations, correlations, and EMAs. Per‑bar cost is constant with respect to history length, and memory is constant per tracked series. Graphical objects are updated in place to obey platform limits. Assumptions and limitations. The method is correlation‑based and will adapt after regime changes, not before them. Calibration improves probability reliability but not necessarily ranking power. Intrabar previews are non‑binding and should not be evaluated as historical performance. Part 2 — Trader‑facing description What it does. This tool tells you how likely price is to be higher after your chosen number of bars and converts that into Long / Short / Neutral calls. It learns, in real time, which components—momentum, trend, volatility, breadth, and volume—matter now, adjusts their weights, and shows you a probability line plus a forward arrow scaled by volatility. How to set it up. 1) Choose your horizon. Intraday scalps: 5–10 bars. Swings: 10–30 bars. The default of 14 bars is a balanced starting point. 2) Pick a feature mode. - components: granular and fast to adapt when leadership rotates between signals. - super: cleaner single driver; less noise, slightly slower to react. 3) Decide how signals are triggered. - Neutral band (probability based): intuitive and easy to tune. Widen the band for fewer, higher‑quality trades; tighten to catch more moves. - Z‑score thresholds: consistent numeric cutoffs that ignore base‑rate drift. 4) Keep reliability helpers on. Leave prior shift and calibration enabled to stabilize probabilities across bullish/bearish regimes. 5) Smoothing. A short EMA on the probability or score reduces whipsaws while preserving turns. 6) Overlay. The arrow shows the call and a volatility‑scaled reach for the next horizon. Treat it as guidance, not a promise. Reading the stats table. - Hit Rate with a confidence interval: your recent accuracy with an uncertainty range; trust the range, not only the point. - Brier Score: lower is better; it checks whether a stated “70%” really behaves like 70% over time. - Profit Factor, Net Return, Exposure: quick triage of tradability and signal density. - Average Return by Side: sanity‑check that the long and short calls each pull their weight. Typical adjustments. - Too many trades? Increase the neutral band or raise the z‑threshold. - Missing the move? Tighten the band, or switch to components mode to react faster. - Choppy timeframe? Lengthen the z‑score and correlation windows; keep calibration on. - Volatility regime change? Revisit the ATR multiplier and enable square‑root scaling of horizon. Execution and risk. - Size positions by volatility (ATR‑based sizing works well). - Enter on confirmed values; use intrabar previews only as early signals. - Combine with your market structure (levels, liquidity zones). This model is statistical, not clairvoyant. What it is not. Not a black‑box machine‑learning model. It is transparent, correlation‑weighted technical analysis with strong attention to probability reliability and repaint safety. Suggested defaults (robust starting point). - Horizon 14; components mode; weight EMA 10; correlation window 500; z‑length 200. - Neutral band around seven percentage points, or z‑threshold around one‑third of a standard deviation. - Prior shift ON, Calibration ON, Use calibrated for decisions OFF to start. - ATR multiplier 1.0; square‑root horizon scaling ON; EMA smoothing 3. - Confidence setting equivalent to about 95%. Disclaimer No indicator guarantees profits. HorizonSigma Pro is a decision aid; always combine with solid risk management and your own judgment. Backtest, forward test, and size responsibly. The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context. Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved. By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk. Enhance your trading precision and confidence 🚀 Best regards Chervolino
Pine Script® göstergesi
chervolino tarafından
Essa - Market Structure Crystal Ball SystemEssa - Market Structure Crystal Ball V2.0 Ever wished you had a glimpse into the market's next move? Stop guessing and start anticipating with the Market Structure Crystal Ball! This isn't just another indicator that tells you what has happened. This is a comprehensive analysis tool that learns from historical price action to forecast the most probable future structure. It combines advanced pattern recognition with essential trading concepts to give you a unique analytical edge. Key Features The Predictive Engine (The Crystal Ball) This is the core of the indicator. It doesn't just identify market structure; it predicts it. Know the Odds: Get a real-time probability score (%) for the next structural point: Higher High (HH), Higher Low (HL), Lower Low (LL), or Lower High (LH). Advanced Analysis: The engine considers the pattern sequence, the speed (velocity) of the move, and its size to find the most accurate historical matches. Dynamic Learning: The indicator constantly updates its analysis as new price data comes in. The All-in-One Dashboard Your command center for at-a-glance information. No need to clutter your screen! Market Phase: Instantly know if the market is in a "🚀 Strong Uptrend," "📉 Steady Downtrend," or "↔️ Consolidation." Live Probabilities: See the updated forecasts for HH, HL, LL, and LH in a clean, easy-to-read format. Confidence Level: The dashboard tells you how confident the algorithm is in its current prediction (Low, Medium, or High). 🎯 Dynamic Prediction Zones Turn probabilities into actionable price areas. Visual Targets: Based on the highest probability outcome, the indicator draws a target zone on your chart where the next structure point is likely to form. Context-Aware: These zones are calculated using recent volatility and average swing sizes, making them adaptive to the current market conditions. 🔍 Fair Value Gap (FVG) Detector Automatically identify and track key price imbalances. Price Magnets: FVGs are automatically detected and drawn, acting as potential targets for price. Smart Tracking: The indicator tracks the status of each FVG (Fresh, Partially Filled, or Filled) and uses this data to refine its predictions. 🌍 Trading Session Analysis Never lose track of key session levels again. Visualize Sessions: See the Asia, London, and New York sessions highlighted with colored backgrounds. Key Levels: Automatically plots the high and low of each session, which are often critical support and resistance levels. Breakout Alerts: Get notified when price breaks a session high or low. 📈 Multi-Timeframe (MTF) Context Understand the bigger picture by integrating higher timeframe analysis directly onto your chart. BOS & MSS: Automatically identifies Breaks of Structure (trend continuation) and Market Structure Shifts (potential reversals) from up to two higher timeframes. Trade with the Trend: Align your intraday trades with the dominant trend for higher probability setups. ⚙️ How It Works in Simple Terms 1️⃣ It Learns: The indicator first identifies all the past swing points (HH, HL, LL, LH) and analyzes their characteristics (speed, size, etc.). 2️⃣ It Finds a Match: It looks at the most recent price action and searches through hundreds of historical bars to find moments that were almost identical. 3️⃣ It Analyzes the Outcome: It checks what happened next in those similar historical scenarios. 4️⃣ It Predicts: Based on that historical data, it calculates the probability of each potential outcome and presents it to you. 🚀 How to Use This Indicator in Your Trading Confirmation Tool: Use a high probability score (e.g., >60% for a HH) to confirm your own bullish analysis before entering a trade. Finding High-Probability Zones: Use the Prediction Zones as potential areas to take profit, or as reversal zones to watch for entries in the opposite direction. Gauging Market Sentiment: Check the "Market Phase" on the dashboard. Avoid forcing trades when the indicator shows "😴 Low Volatility." Confluence is Key: This indicator is incredibly powerful when combined with your existing strategy. Use it alongside supply/demand zones, moving averages, or RSI for ultimate confirmation. We hope this tool gives you a powerful new perspective on the market. Dive into the settings to customize it to your liking! If you find this indicator helpful, please give it a Boost 👍 and leave a comment with your feedback below! Happy trading! Disclaimer: All predictions are probabilistic and based on historical data. Past performance is not indicative of future results. Always use proper risk management.
Pine Script® göstergesi
Essa02 tarafından
2
ADR Plots + OverlayADR Plots + Overlay This tool calculates and displays Average Daily Range (ADR) levels on your chart, giving traders a quick visual reference for expected daily price movement. It plots guide levels above and below the daily open and shows how much of the day's typical range has already been covered—all in one interactive table and on-chart overlay. What It Does ADR Calculation: Uses daily high-low differences over a user-defined period (default 14 days), smoothed via RMA, SMA, EMA, or WMA to calculate the average daily range. Projected Levels: Plots four reference levels relative to the current day's open price: +100% ADR: Open + ADR +50% ADR: Open + 50% of ADR −50% ADR: Open − 50% of ADR −100% ADR: Open − ADR Coverage %: Tracks intraday high and low prices to calculate what percentage of the ADR has already been covered for the current session: Coverage % = (High − Low) ÷ ADR × 100 Interactive Table: Shows the ADR value and today's ADR coverage percentage in a customizable table overlay. The table position, colors, border, transparency, and an optional empty top row can all be adjusted via settings. Customization Options Table Settings: Position the table (top/bottom × left/right). Change background color, text color, border color and thickness. Toggle an empty top row for spacing. Line Settings: Choose color, line style (solid/dotted/dashed), and width. Lines automatically reposition each day based on that day's open price and ADR calculation. General Inputs: ADR length (number of days). Smoothing method (RMA, SMA, EMA, WMA). How to Use It for Trading Measure Daily Movement: Instantly know the expected daily price range based on historical volatility. Identify Overextension: Use the coverage % to see if the market has already moved close to or beyond its typical daily range. Plan Entries & Exits: Align trade targets and stops with ADR levels for more objective intraday planning. Visual Reference: Horizontal guide lines and table update automatically as new data comes in, helping traders stay informed without manual calculations. Ideal For Intraday traders tracking daily volatility limits. Swing traders wanting a quick reference for expected price movement per day. Anyone seeking a volatility-based framework for planning targets, stops, or identifying extended market conditions.
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abbadon9 tarafından
Probability Grid [LuxAlgo]The Probability Grid tool allows traders to see the probability of where and when the next reversal would occur, it displays a 10x10 grid and/or dashboard with the probability of the next reversal occurring beyond each cell or within each cell. 🔶 USAGE By default, the tool displays deciles (percentiles from 0 to 90), users can enable, disable and modify each percentile, but two of them must always be enabled or the tool will display an error message alerting of it. The use of the tool is quite simple, as shown in the chart above, the further the price moves on the grid, the higher the probability of a reversal. In this case, the reversal took place on the cell with a probability of 9%, which means that there is a probability of 91% within the square defined by the last reversal and this cell. 🔹 Grid vs Dashboard The tool can display a grid starting from the last reversal and/or a dashboard at three predefined locations, as shown in the chart above. 🔶 DETAILS 🔹 Raw Data vs Normalized Data By default the tool displays the normalized data, this means that instead of using the raw data (price delta between reversals) it uses the returns between each reversal, this is useful to make an apples to apples comparison of all the data in the dataset. This can be seen in the left side of the chart above (BTCUSD Daily chart) where normalize data is disabled, the percentiles from 0 to 40 overlap and are indistinguishable from each other because the tool uses the raw price delta over the entire bitcoin history, with normalize data enabled as we can see in the right side of the chart we can have a fair comparison of the data over the entire history. 🔹 Probability Beyond or Within Each Cell Two different probability modes are available, the default mode is Probability Beyond Each Cell, the number displayed in each cell is the probability of the next reversal to be located in the area beyond the cell, for example, if the cell displays 20%, it means that in the area formed by the square starting from the last reversal and ending at the cell, there is an 80% probability and outside that square there is a 20% probability for the location of the next reversal. The second probability mode is the probability within each cell, this outlines the chance that the next reversal will be within the cell, as we can see on the right chart above, when using deciles as percentiles (default settings), each cell has the same 1% probability for the 10x10 grid. 🔶 SETTINGS Swing Length: The maximum length in bars used to identify a swing Maximum Reversals: Maximum number of reversals included in calculations Normalize Data: Use returns between swings instead of raw price Probability: Choose between two different probability modes: beyond and inside each cell Percentiles: Enable/disable each of the ten percentiles and select the percentile number and line style 🔹 Dashboard Show Dashboard: Enable or disable the dashboard Position: Choose dashboard location Size: Choose dashboard size 🔹 Style Show Grid: Enable or disable the grid Size: Choose grid text size Colors: Choose grid background colors Show Marks: Enable/disable reversal markers
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LuxAlgo tarafından
15