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חפש סקריפטים עבור "ai"

VWAP Pro v6 (Color + Bands)AI helped me code VWAP When price goes above VWAP line, VWAP line will turn green to indicate buyers are in control. When price goes below VWAP line, VWAP line will turn red to indicate sellers are in control. VWAP line stays blue when price is considered fair value.

אינדיקטור Pine Script®

מאת ‎oscarclausson1959‎

AI - 200 EMA with Offsets Strategy Long when close price crosses above +4% offset 200 day EMA Sell when close price crosses below -6.5% offset 200 day EMA

אסטרטגיית Pine Script®

מאת ‎ChrisBerg‎

Ai Golden Support and Resistance Adaptive Support & Resistance (ADR-scaled ABCD + Breakout/Retest Zones) What it does This indicator detects actionable support/resistance zones from swing structure and breakout events, then keeps each zone active until it’s invalidated by price. It adapts zone sensitivity using Average Daily Range (ADR) so the same rules scale across symbols and vol regimes. Core Logic (high level) Swing & ABCD pattern seed Detects alternating pivots (high–low–high–low or low–high–low–high) using a user-selected lookback. Validates basic AB–BC–CD proportions: BC must retrace a portion of AB; CD must extend BC within a set range. From a valid sequence, sets a candidate level (top for bearish, bottom for bullish). Breakout confirmation A level becomes confirmed when price closes beyond it (crossover/crossunder). On confirmation, the script draws a dotted reference line and records how many bars elapsed from the seed pivot to breakout. That count defines the lookback window used for local extremes. Zone construction Supply (bearish): builds a box around the most recent local range near the bearish seed; Demand (bullish): builds a box around the most recent local range near the bullish seed. Each zone’s height is derived from nearby extremes and the seed swing, so boxes reflect local structure rather than fixed pip widths. Volatility normalization (ADR%) ADR is computed from daily candles. The Risk Profile input (“High/Medium/Low”) scales required move sizes using ADR%, and adjusts pivot sensitivity (fewer/more bars). Higher risk → more sensitive (smaller ADR %, tighter pivot lookback). Lower risk → stricter filters (larger ADR %, wider pivot lookback). Explosive-move filter (streak logic) Searches the seeded lookback for consecutive same-color candles (config via the risk profile). Requires the cumulative % move of that streak to exceed an ADR-scaled threshold. When found, the zone is tagged as originating from an “explosive” move (potentially higher reaction probability). Zone persistence & invalidation Zones persist and auto-extend to the right until invalidated. Invalidation occurs when price closes through a rule-based threshold derived from the seed structure (stored per zone). Once invalidated, the zone is marked inactive and stops updating. Inputs & Controls Risk Profile: High / Medium / Low (sets pivot lookback, streak length, and ADR% thresholds). Labels & Visuals: Toggle labels and level lines; set line width. Colors/Boxes: Supply (red), Demand (green); dotted breakout references. No broker/session settings are required; the script adapts per symbol via ADR. On-Chart Elements Dotted breakout lines at confirmed levels (with measured bars-to-breakout). Supply/Demand boxes that extend until invalidation. Optional labels for clarity; minimal clutter by default. How to Use Context: Use higher-TF context for bias; apply zones on your trading TF. Confluence: Combine zones with your own triggers (structure breaks, rejection wicks, momentum shifts). Invalidation: If price closes beyond a zone’s invalidation threshold, treat that zone as inactive. Sensitivity: If too many zones appear, switch to Medium/Low Risk (stricter ADR% & pivots); if too few, use High Risk. Notes & Limitations Logic is rule-based; there is no machine learning. Daily ADR is computed from D timeframe, so intraday charts inherit daily volatility context. Results vary by symbol and timeframe; validate settings per market. This is an indicator (no orders or P/L).

אינדיקטור Pine Script®

מאת ‎GoldenCryptoSignals‎

AI Fib Strategy (Full Trade Plan)This indicator automatically plots Fibonacci retracements and a Golden Zone box (61.8%–65% retracement) based on the 4H candle body high/low. Features: Auto-detects session breaks or daily breaks (configurable). Draws standard Fib retracement levels (0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%). Highlights the Golden Zone for high-probability trade entries. Optional Take Profit extensions (TP1, TP2, TP3). Fully compatible with Pine Script v6. Usage: Best applied on intraday charts (15m, 30m, 1H). Use the Golden Zone for entry confirmations. Combine with candlestick patterns, order blocks, or volume for stronger signals.

אינדיקטור Pine Script®

מאת ‎FOREXINSIGHT69‎

AI-123's BTC vs Gold (Lag Correlation) DISCLAIMER I made this indicator with the help of ChatGPT and using what I have learned so far from The Pine Script Mastery Course, LOTS of edits based on what I have learned so far had to be made as well as additions and modifications to my liking thanks to what I have learned so far. I am aware this already exists but I have done my best to make a first ever script/indicator while learning how to properly publish as well, so please bear that in mind. Overview This indicator analyzes the correlation between Bitcoin (BTC) and Gold (XAUUSD), with a customizable lag applied to the Gold price, providing insight into the macro relationship between these two assets. It is designed for traders and investors who want to track how Bitcoin and Gold move in relation to each other, particularly when Gold is lagged by a specific number of days. Key Features: BTC and Gold (Lagged) Price Overlay: Display Bitcoin (BTC) and Gold (XAUUSD) prices on the chart, with an adjustable lag applied to the Gold price. Rolling Correlation Calculation: Measures the correlation between Bitcoin and lagged Gold prices over a customizable lookback period. Adjustable Lag: The number of days that Gold is lagged relative to Bitcoin is fully customizable (default: 20 days). Customizable Correlation Length: Allows you to choose the lookback period for the correlation (default: 50 days), providing flexibility for short-term or long-term analysis. Normalized Plotting: Prices of Bitcoin and Gold are normalized for better visual alignment with the correlation values. BTC is divided by 1000, and Gold by 100. Correlation Scaling: The correlation value is amplified by 10 for better visual clarity and comparison with price data. Zero Line: Horizontal line representing a correlation of 0, making it easier to identify positive or negative correlation shifts. Maximum Correlation Lines: Horizontal lines at +10 and -10 values for extreme correlation scenarios. Input Settings: Gold Symbol: Customize the Gold ticker (default: OANDA:XAUUSD). Bitcoin Symbol: Customize the Bitcoin ticker (default: BINANCE:BTCUSDT). Lag (in trading days): Adjust the number of trading days to lag the Gold price relative to Bitcoin (default: 20). Correlation Length (days): Set the number of days over which the rolling correlation is calculated (default: 50). How to Use: Price Comparison: The BTC (Spot) and Lagged Gold plots give you a side-by-side visual comparison of the two assets, normalized for clarity. Correlation Line: The correlation line helps you gauge the strength and direction of the relationship between BTC and lagged Gold. Positive values indicate a strong positive correlation, while negative values indicate a negative correlation. Visual Analysis: Watch how the correlation shifts with changes in lag and correlation length to identify potential market dynamics between Bitcoin and Gold. Potential Applications: Macro Trading: Track how Bitcoin and Gold behave in relation to each other during periods of economic uncertainty or inflation. Sentiment Analysis: Use the correlation data to understand the sentiment between digital and traditional assets. Strategic Timing: Identify potential opportunities where Bitcoin and Gold show a strong correlation or diverge based on the lag adjustment. Understanding Macro Trends/Correlations. Disclaimer: This indicator is for informational purposes only. The correlation between Bitcoin and Gold does not guarantee future performance, and users should conduct their own research and use risk management strategies when making trading decisions. Notes: This script uses historical data, so results may vary across different timeframes. Customization options allow users to adjust the lag and correlation length to better fit their trading strategy. Future Enhancements: Additional Correlation Line: A second correlation line for different lengths of lag or different assets. Color-Coding of Correlation: Future updates may include color-coded correlation strength, visually indicating positive or negative correlation more effectively.

אינדיקטור Pine Script®

מאת ‎alphainvestor123‎

Enhanced Order Flow Pressure Gauge Short Description: Estimates bullish/bearish pressure by analyzing each candle’s close position within its range, then weighting that by volume. Detects potential trend shifts and provides real-time signals. Full Description: 1. Purpose The Enhanced Order Flow Pressure Gauge (OFPG+) is designed to approximate buy vs. sell pressure within each bar, even if you don’t have full Level II / order flow data. By measuring the candle’s close relative to its high-low range and multiplying by volume, OFPG+ provides insights into which side of the market (bulls or bears) is more aggressive in a given interval. 2. Key Components Pressure Score (Histogram): Raw measure of each bar’s close position (rangePos) minus midpoint, multiplied by volume. If the bar closes near its high with decent volume, the score is positive (bullish). Conversely, a close near its low yields a negative (bearish) reading. Cumulative Pressure: Sum of all pressure readings over time (similar to cumulative delta), reflecting the overall market bias. Pressure Delta: The change in cumulative pressure from one bar to the next, plotted as a line. Rising values suggest increasing bullish momentum, while falling values show growing bearish influence. 3. Visual Cues & Signals Histogram (Pressure Profile): A color-coded bar for each candle, indicating net bullish (blue) or bearish (gray) intrabar pressure. Pressure Delta Line: Plotted over the histogram. Turns bullish (blue) when net buy pressure is increasing, or bearish (gray) when net selling accelerates. Background Highlights: Turns lightly blue if the smoothed pressure line exceeds the positive threshold, or lightly gray if it goes below the negative threshold. Bullish / Bearish Signals: Bullish Signal occurs when the smoothed pressure line crosses above the positive threshold, combined with a positive Delta. Bearish Signal occurs when the smoothed pressure line crosses below the negative threshold, combined with a negative Delta. Confirmed Signals: After a bullish/bearish signal, OFPG+ checks the highest or lowest smoothed pressure values over a user-defined number of bars (signalLookback) to confirm momentum. Plotshapes (diamond icons) appear on the chart to mark these confirmed reversals. 4. Usage Scenarios Trend-Following / Momentum: Watch for transitions from negative to positive net pressure or vice versa. Helps identify potential turning points. Reversal Confirmation: The threshold-based signals plus the “confirmed” checks can help filter choppy conditions. Volume-Weighted Insights: By factoring in volume, strong closes near the highs or lows are weighted more heavily, capturing sentiment shifts. 5. Inputs & Parameters Smoothing Length (length): The EMA period for smoothing the raw pressure score. Volume Weight (volWeight): Scales the volume impact on pressure calculations. Pressure Threshold (threshold): Defines when pressure is considered significantly bullish or bearish. Signal Lookback (signalLookback): Number of bars to confirm momentum after a signal. 6. Alerts Bullish Signal & Confirmed Bullish Bearish Signal & Confirmed Bearish These alerts can notify you in real-time about potential shifts in the market’s buying or selling pressure. 7. Disclaimer This script provides an approximation of order flow by analyzing candle structure and volume. It does not represent actual exchange-level order data. Past performance is not necessarily indicative of future results. Always conduct thorough analysis and use proper risk management. Not financial advice. Use at your own discretion.

אינדיקטור Pine Script®

מאת ‎AI-signals‎

מעודכן

AI indicator This script is a trading indicator designed for future trading signals on the TradingView platform. It uses a combination of the Relative Strength Index (RSI) and a Simple Moving Average (SMA) to generate buy and sell signals. Here's a breakdown of its components and logic: 1. Inputs The script includes configurable inputs to make it adaptable for different market conditions: RSI Length: Determines the number of periods for calculating RSI. Default is 14. RSI Overbought Level: Signals when RSI is above this level (default 70), indicating potential overbought conditions. RSI Oversold Level: Signals when RSI is below this level (default 30), indicating potential oversold conditions. Moving Average Length: Defines the SMA length used to confirm price trends (default 50). 2. Indicators Used RSI (Relative Strength Index): Measures the speed and change of price movements. A value above 70 typically indicates overbought conditions. A value below 30 typically indicates oversold conditions. SMA (Simple Moving Average): Used to smooth price data and identify trends. Price above the SMA suggests an uptrend, while price below suggests a downtrend. 3. Buy and Sell Signal Logic Buy Condition: The RSI value is below the oversold level (e.g., 30), indicating the market might be undervalued. The current price is above the SMA, confirming an uptrend. Sell Condition: The RSI value is above the overbought level (e.g., 70), indicating the market might be overvalued. The current price is below the SMA, confirming a downtrend. These conditions ensure that trades align with market trends, reducing false signals. 4. Visual Features Buy Signals: Displayed as green labels (plotshape) below the price bars when the buy condition is met. Sell Signals: Displayed as red labels (plotshape) above the price bars when the sell condition is met. Moving Average Line: A blue line (plot) added to the chart to visualize the SMA trend. 5. How It Works When the buy condition is true (RSI < 30 and price > SMA), a green label appears below the corresponding price bar. When the sell condition is true (RSI > 70 and price < SMA), a red label appears above the corresponding price bar. The blue SMA line helps to visualize the overall trend and acts as confirmation for signals. 6. Advantages Combines Momentum and Trend Analysis: RSI identifies overbought/oversold conditions. SMA confirms whether the market is trending up or down. Simple Yet Effective: Reduces noise by using well-established indicators. Easy to interpret for beginners and experienced traders alike. Customizable: Parameters like RSI length, oversold/overbought levels, and SMA length can be adjusted to fit different assets or timeframes. 7. Limitations Lagging Indicator: SMA is a lagging indicator, so it may not capture rapid market reversals quickly. Not Foolproof: No trading indicator can guarantee 100% accuracy. False signals can occur in choppy or sideways markets. Needs Volume Confirmation: The script does not consider trading volume, which could enhance signal reliability. 8. How to Use It Copy the script into TradingView's Pine Editor. Save and add it to your chart. Adjust the RSI and SMA parameters to suit your preferred asset and timeframe. Look for buy signals (green labels) in uptrends and sell signals (red labels) in downtrends.

אינדיקטור Pine Script®

מאת ‎ihadi08‎

Dynamic ALMA with signals Enhanced ALMA with Signals This TradingView indicator is designed to enhance your trading strategy by utilizing the Arnaud Legoux Moving Average (ALMA), a unique moving average that provides smoother price action while minimizing lag. The script not only plots the ALMA line but also dynamically adjusts its parameters based on market volatility to adapt to different trading conditions. Additionally, it highlights potential bounce points off the line, as well as breakout points, giving traders clear signals for potential support, resistance levels, and breakouts. Key Features: Dynamic ALMA Line with Glow Effect: The core of this indicator is the ALMA line, which is dynamically adjusted to market volatility, providing more accurate signals in varying conditions. The line adapts to both trending and consolidating markets by adjusting its sensitivity in real time. A glow effect is created by plotting the ALMA line multiple times with increasing transparency, making it visually distinct. Bounce Detection Signals with Volatility Filter: The script detects and labels potential support and resistance bounces based on the crossover and crossunder of the price with the ALMA line, further filtered by a volatility condition. This helps in filtering out false signals during low-volatility conditions, making the signals more reliable. Visual Enhancements: Custom glow effects and labels for bounce detection enhance chart readability and help traders quickly identify key levels. Inputs: Base Window Size: Sets the number of bars used in calculating the ALMA, allowing traders to adjust the sensitivity of the moving average. This parameter is dynamically adjusted based on current market volatility. Offset: Determines the position of the ALMA curve. Higher values move the curve further away from the price. This value remains constant for stability. Sigma: Controls the smoothness of the ALMA curve; a higher sigma results in a smoother curve. This value also remains constant. ATR Period and Threshold Multiplier: Used to calculate the Average True Range (ATR) for the volatility filter, which determines whether the market conditions are sufficiently volatile to consider bounce signals. How It Works: Dynamic ALMA Calculation: The script calculates the ALMA (Arnaud Legoux Moving Average) using the ta.alma function, dynamically adjusting the window size based on market volatility measured by the ATR (Average True Range). This ensures that the ALMA line remains responsive in high-volatility environments and smooth in low-volatility conditions. Glow Effect: To create a glow effect around the ALMA line, the script plots the ALMA multiple times with varying degrees of transparency. This visual enhancement helps the ALMA line stand out on the chart. Bounce Detection with Volatility Filter: The script uses two conditions to detect potential bounces: Support Bounce: Detected when the low of the bar crosses above the ALMA line (ta.crossover(low, alma)) and the close is above the ALMA, while the volatility filter confirms sufficient market activity. This suggests potential support at the ALMA line. Resistance Bounce: Detected when the high of the bar crosses below the ALMA line (ta.crossunder(high, alma)) and the close is below the ALMA, while the volatility filter confirms sufficient market activity. This indicates potential resistance at the ALMA line. Labeling Bounce Points: When a bounce is detected, the script labels it on the chart: Support Bounces (S): Labeled with a blue "S" below the bar where a support bounce is detected. Resistance Bounces (R): Labeled with a white "R" above the bar where a resistance bounce is detected. Usage: This enhanced indicator helps traders visualize key support and resistance levels more effectively by dynamically adjusting the ALMA moving average to market conditions. By detecting and labeling potential bounce points and filtering these signals based on volatility, traders can better identify entry and exit points in their trading strategy. The dynamic adjustments and visual enhancements make it easier to spot critical levels quickly and adapt to changing market conditions. Customize the inputs to fit your trading style, and use this enhanced ALMA indicator to gain a more refined understanding of market trends, potential reversals, and breakouts.

אינדיקטור Pine Script®

מאת ‎AI-signals‎

AI-Bank-Nifty Tech Analysis This code is a TradingView indicator that analyzes the Bank Nifty index of the Indian stock market. It uses various inputs to customize the indicator's appearance and analysis, such as enabling analysis based on the chart's timeframe, detecting bullish and bearish engulfing candles, and setting the table position and style. The code imports an external script called BankNifty_CSM, which likely contains functions that calculate technical indicators such as the RSI, MACD, VWAP, and more. The code then defines several table cell colors and other styling parameters. Next, the code defines a table to display the technical analysis of eight bank stocks in the Bank Nifty index. It then defines a function called get_BankComponent_Details that takes a stock symbol as input, requests the stock's OHLCV data, and calculates several technical indicators using the imported CSM_BankNifty functions. The code also defines two functions called get_EngulfingBullish_Detection and get_EngulfingBearish_Detection to detect bullish and bearish engulfing candles. Finally, the code calculates the technical analysis for each bank stock using the get_BankComponent_Details function and displays the results in the table. If the engulfing input is enabled, the code also checks for bullish and bearish engulfing candles and displays buy/sell signals accordingly. The FRAMA stands for "Fractal Adaptive Moving Average," which is a type of moving average that adjusts its smoothing factor based on the fractal dimension of the price data. The fractal dimension reflects self-similarity at different scales. The FRAMA uses this property to adapt to the scale of price movements, capturing short-term and long-term trends while minimizing lag. The FRAMA was developed by John F. Ehlers and is commonly used by traders and analysts in technical analysis to identify trends and generate buy and sell signals. I tried to create this indicator in Pine. In this context, "RS" stands for "Relative Strength," which is a technical indicator that compares the performance of a particular stock or market sector against a benchmark index. The "Alligator" is a technical analysis tool that consists of three smoothed moving averages. Introduced by Bill Williams in his book "Trading Chaos," the three lines are called the Jaw, Teeth, and Lips of the Alligator. The Alligator indicator helps traders identify the trend direction and its strength, as well as potential entry and exit points. When the three lines are intertwined or close to each other, it indicates a range-bound market, while a divergence between them indicates a trending market. The position of the price in relation to the Alligator lines can also provide signals, such as a buy signal when the price crosses above the Alligator lines and a sell signal when the price crosses below them. In addition to these, we have several other commonly used technical indicators, such as MACD, RSI, MFI (Money Flow Index), VWAP, EMA, and Supertrend. I used all the built-in functions for these indicators from TradingView. Thanks to the developer of this TradingView Indicator. I also created a BankNifty Components Table and checked it on the dashboard.

אינדיקטור Pine Script®

מאת ‎chhagansinghmeena‎

מעודכן

AI-EngulfingCandle This script is the combination of RSI and Engulfing Pattern How it works 1. when RSI > 70 and form the bullish engulfing pattern . it gives sell signal 2. when RSI < 30 and form the bearish engulfing pattern . it gives buy signal settings: basic setting for RSI has been enabled in the script to set the levels accordingly to your trades

אינדיקטור Pine Script®

מאת ‎ahmedirshad419‎

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אינדיקטור Pine Script®

מאת ‎phani0906i‎

Market Regime | NY Session Killzones Indicator [ApexLegion]Market Regime | NY Session Killzones Indicator Introduction and Theoretical Background The Market Regime | NY Session Killzones indicator is designed exclusively for New York market hours (07:00-16:00 ET). Unlike universal indicators that attempt to function across disparate global sessions, this tool employs session-specific calibration to target the distinct liquidity characteristics of the NY trading day: Pre-Market structural formation (08:00-09:30), the Morning breakout window (09:30-12:00), and the Afternoon Killzone (13:30-16:00)—periods when institutional order flow exhibits the highest concentration and most definable technical structure. By restricting its operational scope to these statistically significant time windows, the indicator focuses on signal relevance while filtering the noise inherent in lower-liquidity overnight or extended-hours trading environments. I. TECHNICAL RATIONALE: THE PRINCIPLE OF CONTEXTUAL FUSION 1. The Limitation of Acontextual Indicators Traditional technical indicators often fail because they treat every bar and every market session equally, applying static thresholds (e.g., RSI > 70) without regard for the underlying market structure or liquidity environment. However, institutional volume and market volatility are highly dependent on the time of day (session) and the prevailing long-term risk environment. This indicator was developed to address this "contextual deficit" by fusing three distinct yet interdependent analytical layers: • Time and Structure (Macro): Identifying high-probability trading windows (Killzones) and critical structural levels (Pre-Market Range, PDH/PDL). • Volatility and Scoring (Engine): Normalizing intraday momentum against annual volatility data to create an objective, statistically grounded AI Score. • Risk Management (Execution): Implementing dynamic, volatility-adjusted Stop Loss (SL) and Take Profit (TP) parameters based on the Average True Range (ATR). 2. The Mandate for 252-Day Normalization (Z-Score) What makes this tool unique is its 252-day Z-Score normalization engine that transforms raw momentum readings into statistically grounded probability scores, allowing the same indicator to deliver consistent, context-aware signals across any timeframe—from 1-minute scalping to 1-hour swing trades—without manual recalibration. THE PROBLEM OF SCALE INVARIANCE A high Relative Strength Index (RSI) reading on a 1-minute chart has a completely different market implication than a high RSI reading on a Daily chart. Simple percentage-based thresholds (like 70 or 30) do not provide true contextual significance. A sudden spike in momentum may look extreme on a 5-minute chart, but if it is statistically insignificant compared to the overall volatility of the last year, it may be a poor signal. THE SOLUTION: CROSS-TIMEFRAME Z-SCORE NORMALIZATION This indicator utilizes the Pine Script function request.security to reference the Daily timeframe for calculating the mean (μ) and standard deviation (σ) of a momentum oscillator (RSI) over the past 252 trading days (one year). The indicator then calculates the Z-Score (Z) for the current bar's raw momentum (x): Z = (x - μ) / σ Core Implementation: float raw_rsi = ta.rsi(close, 14) // x = request.security(syminfo.tickerid, "D", , // σ (252 days) lookahead=barmerge.lookahead_on) float cur_rsi_norm = d_rsi_std != 0 ? (raw_rsi - d_rsi_mean) / d_rsi_std : 0.0 // Z This score provides an objective measurement of current intraday momentum significance by evaluating its statistical extremity against the yearly baseline of daily momentum. This standardized approach provides the scoring engine with consistent, global contextual information, independent of the chart's current viewing timeframe. II. CORE COMPONENTS AND TECHNICAL ANALYSIS BREAKDOWN 1. TIME AND SESSION ANALYSIS (KILLZONES AND BIAS) The indicator visually segments the trading day based on New York (NY) trading sessions, aligning the analysis with periods of high institutional liquidity events. Pre-Market (PRE) • Function: Defines the range before the core market opens. This range establishes structural support and resistance levels (PMH/PML). • Technical Implementation: Uses a dedicated Session input (ny_pre_sess). The High and Low values (pm_h_val/pm_l_val) within this session are stored and plotted for structural reference. • Smart Extension Logic: PMH/PML lines are automatically extended until the next Pre-Market session begins, providing continuous support/resistance references overnight. NY Killzones (AM/PM) • Function: Highlights high-probability volatility windows where institutional liquidity is expected to be highest (e.g., NY open, lunch, NY close). • Technical Implementation: Separate session inputs (kz_ny_am, kz_ny_pm) are utilized to draw translucent background fills, providing a clear visual cue for timing. Market Regime Bias • Function: Determines the initial directional premise for the trading day. The bias is confirmed when the price breaks either the Pre-Market High (PMH) or the Pre-Market Low (PML). • Technical Implementation: Involves the comparison of the close price against the predefined structural levels (check_h for PMH, check_l for PML). The variable active_bias is set to Bullish or Bearish upon confirmed breakout. Trend Bar Coloring • Function: Applies a visual cue to the bars based on the established regime (Bullish=Cyan, Bearish=Red). This visual filter helps mitigate noise from counter-trend candles. • Technical Implementation: The Pine Script barcolor() function is tied directly to the value of the determined active_bias. 2. VOLATILITY NORMALIZED SCORING ENGINE The internal scoring mechanism accumulates points from multiple market factors to determine the strength and validity of a signal. The purpose is to apply a robust filtering mechanism before generating an entry. The score accumulation logic is based on the following factors: • Market Bias Alignment (+3 Points): Points are awarded for conformance with the determined active_bias (Bullish/Bearish). • VWAP Alignment (+2 Points): Assesses the position of the current price relative to the Volume-Weighted Average Price (VWAP). Alignment suggests conformity with the average institutional transaction price. • Volume Anomaly (+2 Points): Detects a price move accompanied by an abnormally high relative volume (odd_vol_spike). This suggests potential institutional participation or significant order flow. • VIX Integration (+2 Points): A score derived from the CBOE VIX index, assessing overall market stability and stress. Stable VIX levels add points, while high VIX levels (stress regimes) remove points or prevent signal generation entirely. • ML Probability Score (+3 Points): This is the core predictive engine. It utilizes a Log-Manhattan Distance Kernel to compare the current market state against historical volatility patterns. The script implements a Log-linear distance formula (log(1 + |Δ|) ). This approach mathematically dampens the impact of extreme volatility spikes (outliers), ensuring that the similarity score reflects true structural alignment rather than transient market noise. Core Technical Logic (Z-Score Normalization) float cur_rsi_norm = d_rsi_std != 0 ? (raw_rsi - d_rsi_mean) / d_rsi_std : 0.0 • Technical Purpose: This line calculates the Z-Score (cur_rsi_norm) of the current momentum oscillator reading (raw_rsi) by normalizing it against the mean (d_rsi_mean) and standard deviation (d_rsi_std) derived from 252 days of Daily momentum data. If the standard deviation is zero (market is perfectly flat), it safely returns 0.0 to prevent division by zero runtime errors. This allows the AI's probability score to be based on the current signal's significance within the context of the entire trading year. 3. EXECUTION AND RISK MANAGEMENT (ATR MODEL) The indicator utilizes the Average True Range (ATR) volatility model. This helps risk management scale dynamically with market volatility by allowing users to define TP/SL distances independently based on the current ATR. Stop Loss Multiplier (sl_mult) • Function: Sets the Stop Loss (SL) distance as a configurable multiple of the current ATR (e.g., 1.5 × ATR). • Technical Logic: The price level is calculated as: last_sl_price := close - (atr_val * sl_mult). The mathematical sign is reversed for short trades. Take Profit Multiplier (tp_mult) • Function: Sets the Take Profit (TP) distance as a configurable multiple of the current ATR (e.g., 3.0 × ATR). • Technical Logic: The price level is calculated as: last_tp_price := close + (atr_val * tp_mult). The mathematical sign is reversed for short trades. Structural SL Option • Function: Provides an override to the ATR-based SL calculation. When enabled, it forces the Stop Loss to the Pre-Market High/Low (PMH/PML) level, aligning the stop with a key institutional structural boundary. • Technical Logic: The indicator checks the use_struct_sl input. If true, the calculated last_sl_price is overridden with either pm_h_val or pm_l_val, dependent on the specific trade direction. Trend Continuation Logic • Function: Enables signal generation in established, strong trends (typically in the Afternoon session) based on follow-through momentum (a new high/low of the previous bar) combined with a high Signal Score, rather than exclusively relying on the initial PMH/PML breakout. • Technical Logic: For a long signal, the is_cont_long logic specifically requires checks like active_bias == s_bull AND close > high , confirming follow-through momentum within the established regime. Smart Snapping & Cleanup (16:00 Market Close) • Function: To maintain chart cleanliness, all trade boxes (TP/SL), AI Prediction zones, Killzone overlays (NY AM/PM), and Liquidity lines (PDH/PDL) are automatically "snapped" and cut off precisely at 16:00 NY Time (Market Close). • Technical Logic: When is_market_close condition is met (hour == 16 and minute == 0), the script executes cleanup logic that: ◦ Closes active trades and evaluates final P&L ◦ Snaps all TP/SL box widths to current bar ◦ Truncates AI Prediction ghost boxes at market close ◦ Cuts off NY AM/PM Killzone background fills ◦ Terminates PDH/PDL line extensions ◦ Prevents visual clutter from extending into post-market sessions 4. LIQUIDITY AND STRUCTURAL ANALYSIS The indicator plots key structural levels that serve as high-probability magnet zones or areas of potential liquidity absorption. • Pre-Market High/Low (PMH/PML): These are the high and low established during the configured pre-market session (ny_pre_sess). They define the primary structural breakout level for the day, often serving as the initial market inflection point or the key entry level for the morning session. • PDH (Previous Day High): The high of the calendar day immediately preceding the current bar. This represents a key Liquidity Pool; large orders are often placed above this level, making it a frequent target for stop hunts or liquidity absorption by market makers. • PDL (Previous Day Low): The low of the calendar day immediately preceding the current bar. This also represents a key Liquidity Pool and a high-probability reversal or accumulation point, particularly during the Killzones. FIFO Array Management The indicator uses FIFO (First-In-First-Out) array structures to manage liquidity lines and labels, automatically deleting the oldest objects when the count exceeds 500 to comply with drawing object limits. 5. AI PREDICTION BOX (PREDICTIVE MODEL) Function: Analyzes AI scores and volatility to project predicted killzone ranges and duration with asymmetric directional bias. A. DIRECTIONAL BIAS (ASYMMETRIC EXPANSION) The prediction model calculates directional probability using the ML kernel's 252-day Normalized RSI (Z-Score) and Relative Volume (RVOL). The prediction box dynamically adjusts its range based on this probability to provide immediate visual feedback on high-probability direction. Bullish Scenario (ml_prob > 1.0): • Upper Range: Expands significantly (1.5x multiplier) to show the aggressive upside target • Lower Range: Tightens (0.5x multiplier) to show the invalidation level • Visual Intent: The box is visibly skewed upward, immediately communicating bullish bias without requiring numerical analysis. Bearish Scenario (ml_prob < -1.0): • Upper Range: Tightens (0.5x multiplier) to show the invalidation level • Lower Range: Expands significantly (1.5x multiplier) to show the aggressive downside target • Visual Intent: The box is visibly skewed downward, immediately communicating bearish bias. Neutral Scenario (-1.0 < ml_prob < 1.0): Both ranges use balanced multipliers, creating a symmetrical box that indicates uncertainty. B. DYNAMIC VOLATILITY BOOSTER (SESSION-BASED ADAPTATION) The prediction box adjusts its volatility multiplier based on the current session and market conditions to account for intraday volatility patterns. AM Session (Morning: 07:00-12:00): • Base Multiplier: 1.0x (Neutral Base) • Logic: Morning sessions often contain false breakouts and noise. The base multiplier starts neutral to avoid over-projecting during consolidation. • Trend Booster: Multiplier jumps to 1.5x when: Price > London Session Open AND AI is Bullish (ml_prob > 0), OR Price < London Session Open AND AI is Bearish (ml_prob < 0) • Logic: When the London trend (typically 03:00-08:00 NY time) aligns with the AI model's directional conviction, the indicator aggressively targets higher volatility expansion. This filters for "institutional follow-through" rather than random morning chop. PM Session (Afternoon: 13:00-16:00): • Fixed Multiplier: 1.8x • Logic: The PM session, particularly the 13:30-16:00 ICT Silver Bullet window, often contains the "True Move" of the day. A higher baseline multiplier is applied to emphasize this session's significance over morning noise. Safety Floor: A minimum range of 0.2% of the current price is enforced regardless of volatility conditions. • Purpose: Maintains the prediction box visibility during extreme low-volatility consolidation periods where ATR might collapse to near-zero values. Volatility Clamp Protection: Maximum volatility is capped at three times the current ATR value. During flash crashes, circuit breaker halts, or large overnight gaps, raw volatility calculations can spike to extreme levels. This clamp prevents prediction boxes from expanding to unrealistic widths. Technical Implementation: f_get_ai_multipliers(float _prob) => float _abs_prob = math.abs(_prob) float _range_mult = 1.0 float _dur_mult = 1.0 if _abs_prob > 30 _range_mult := 1.8 else if _abs_prob > 10 _range_mult := 1.2 else _range_mult := 0.7 C. PRACTICAL INTERPRETATION • Wide Upper Range + Tight Lower Range: Strong bullish conviction. The model expects significant upside with limited downside risk. • Tight Upper Range + Wide Lower Range: Strong bearish conviction. The model expects significant downside with limited upside. • Symmetrical Range: Neutral/uncertain market. Wait for directional confirmation before entry. • Large Box (Extended Duration): High-confidence prediction expecting sustained movement. • Small Box (Short Duration): Low-confidence or choppy conditions. Expect quick resolution. III. PRACTICAL USAGE GUIDE: METHODOLOGY AND EXECUTION A. ESTABLISHING TRADING CONTEXT (THE THREE CHECKS) The primary goal of the dashboard is to filter out low-probability trade setups before they occur. • Timeframe Selection: Although the core AI is normalized to the Daily context, the indicator performs optimally on intraday timeframes (e.g., 5m, 15m) where session-based volatility is most pronounced. • PHASE Check (Timing): Always confirm the current phase. The highest probability signals typically occur within the visually highlighted NY AM/PM Killzones because this is when institutional liquidity and volume are at their peak. Signals outside these zones should be treated with skepticism. • MARKET REGIME Check (Bias): Ensure the signal (BUY/SELL arrow) aligns with the established MARKET REGIME bias (BULLISH/BEARISH). Counter-bias signals are technically allowed if the score is high, but they represent a higher risk trade. • VIX REGIME Check (Risk): Review the VIX REGIME for overall market stress. Periods marked DANGER (high VIX) indicate elevated volatility and market uncertainty. During DANGER regimes, reducing position size or choosing a wider SL Multiplier is advisable. B. DASHBOARD INTERPRETATION (THE REAL-TIME STATUS DISPLAY) The indicator features a non-intrusive dashboard that provides real-time, context-aware information based on the core analytical engines. PHASE: (PRE-MARKET, NY-AM, LUNCH, NY-PM) • Meaning: Indicates the current institutional session time. This is derived from the customizable session inputs. • Interpretation: Signals generated during NY-AM or NY-PM (Killzones) are generally considered higher-probability due to increased institutional participation and liquidity. MARKET REGIME: (BULLISH, BEARISH, NEUTRAL) • Meaning: The established directional bias for the trading day, confirmed by the price breaking above the Pre-Market High (PMH) or below the Pre-Market Low (PML). • Interpretation: Trading with the established regime (e.g., taking a BUY signal when the regime is BULLISH) is the primary method. NEUTRAL indicates that the PMH/PML boundary has not yet been broken, suggesting market ambiguity. VIX REGIME: (STABLE, DANGER) • Meaning: A measure of overall market stress and stability, based on the CBOE VIX index integration. The thresholds (20.0 and 35.0 default) are customizable by the user. • Interpretation: STABLE indicates stable volatility, favoring momentum trades. DANGER (VIX > 35.0) indicates extreme stress; signals generated in this environment require caution and often necessitate smaller position sizing. SIGNAL SCORE: (0 to 10+ Points) • Meaning: The accumulated score derived from the VOLATILITY NORMALIZED AI SCORING ENGINE, factoring in bias, VWAP alignment, volume, and the Z-Score probability. • Interpretation: The indicator generates a signal when this score meets or exceeds the Minimum Entry Score (default 3). A higher score (e.g., 7+) indicates greater statistical confluence and a stronger potential entry. AI PROBABILITY: (Bull/Bear %) • Meaning: Directional probability derived from the ML kernel, expressed as a percentage with Bull/Bear label. • Interpretation: Higher absolute values (>20%) indicate stronger directional conviction from the ML model. LIVE METRICS SECTION: • STATUS: Shows current trade state (LONG, SHORT, or INACTIVE) • ENTRY: Displays the entry price for active trades • TARGET: Shows the calculated Take Profit level • ROI | KILL ZONE: ◦ For Active Trades: Displays real-time P&L percentage during NY session hours. ◦ At Market Close (16:00 NY): Since this is a NY session-specific indicator, any active position is automatically evaluated and closed at 16:00. The final result (VALIDATED or INVALIDATED) is determined based on whether the trade reached profit or loss at market close. ◦ Result Persistence: The killzone result (VALIDATED/INVALIDATED) remains displayed on the dashboard until the next NY AM KILLZONE session begins, providing a clear performance reference for the previous trading day. Note: If a trade is still trending at 16:00, it will be force-closed and evaluated at that moment, as the indicator operates strictly within NY trading hours. C. SIGNAL GENERATION AND ENTRY LOGIC The indicator generates signals based on two distinct technical setups, both of which require the accumulated SIGNAL SCORE to be above the configured Minimum Entry Score. Breakout Entry • Trigger Condition: Price closes beyond the Pre-Market High (PMH) or Low (PML). • Rationale: This setup targets the initial directional movement for the day. A breakout confirms the institutional bias by decisively breaking the first major structural boundary, making the signal high-probability. Continuation Entry • Trigger Condition: The market is already in an established regime (e.g., BULLISH), and the price closes above the high (or below the low) of the previous bar, while the SIGNAL SCORE remains high. Requires the Allow Trend Continuation parameter to be active. • Rationale: This setup targets follow-through trades, typically in the afternoon session, capturing momentum after the morning's direction has been confirmed. This filters for sustainability in the established trend. Execution: Execute the trade immediately upon the close of the bar that prints the BUY or SELL signal arrow. D. MANAGING RISK AND EXITS 1. RISK PARAMETER SELECTION The indicator immediately draws the dynamic TP/SL zones upon entry. • Volatility-Based (Recommended Default): By setting the SL Multiplier (e.g., 1.5) and the TP Multiplier (e.g., 3.0), the indicator enforces a constant, dynamically sized risk-to-reward ratio (e.g., 1:2 in this example). This helps that risk management scales proportionally with the current market volatility (ATR). • Structural Override: Selecting the Use Structural SL parameter fixes the stop-loss not to the ATR calculation, but to the more significant structural level of the PMH or PML. This is utilized by traders who favor institutional entry rules where the stop is placed behind the liquidity boundary. 2. EXIT METHODS • Hard Exit: Price hits the visual TP or SL box boundary. • Soft Exit (Momentum Decay Filter): If the trade is active and the SIGNAL SCORE drops below the Exit Score Threshold (default 3), it indicates that the momentum supporting the trade has significantly collapsed. This serves as a momentum decay filter, prompting the user to consider a manual early exit even if the SL/TP levels have not been hit, thereby preserving capital during low-momentum consolidation. • Market Close Auto-Exit: At 16:00 NY time, any active trade is automatically closed and classified as VALIDATED (profit) or INVALIDATED (loss) based on current price vs. entry price. IV. PARAMETER REFERENCE AND CONFIGURATION A. GLOBAL SETTINGS • Language (String, Default: English): Selects the language for the dashboard and notification text. Options: English, Korean, Chinese, Spanish, Portuguese, Russian, Ukrainian, Vietnamese. B. SESSION TIMES (3 BOX SYSTEM) • PRE-MARKET (Session, Default: 0800-0930): Defines the session range used for Pre-Market High/Low (PMH/PML) structural calculation. • REGULAR (Morning) (Session, Default: 0930-1200): Defines the core Morning trading session. • AFTERNOON (PM) (Session, Default: 1300-1600): Defines the main Afternoon trading session. • Timezone (String, Default: America/New_York): Sets the timezone for all session and time-based calculations. C. NY KILLZONES (OVERLAYS) • Show NY Killzones (Bool, Default: True): Toggles the translucent background fills that highlight high-probability trading times (Killzones). • NY AM Killzone (Session, Default: 0700-1000): Defines the specific time window for the first key liquidity surge (Open overlap). • NY PM Killzone (Session, Default: 1330-1600): Defines the afternoon liquidity window, aligned with the ICT Silver Bullet and PM Trend entry timing. • Allow Entry in Killzones (Bool, Default: True): Enables or disables signal generation specifically during the defined Killzone hours. • Activate AI Prediction Box (Bool, Default: True): Toggles the drawing of the predicted target range boxes on the chart. D. CORE SCORING ENGINE • Minimum Entry Score (Int, Default: 3): The lowest accumulated score required for a Buy/Sell signal to be generated and plotted. • Allow Trend Continuation (Bool, Default: True): Enables the secondary entry logic that fires signals based on momentum in an established trend. • Force Ignore Volume (Bool, Default: False): Overrides the volume checks in the scoring engine. Useful for markets where volume data is unreliable or nonexistent. • Force Show Signals (Ignore Score) (Bool, Default: False): Debug mode that displays all signals regardless of score threshold. • Integrate CBOE:VIX (Bool, Default: True): Enables the connection to the VIX index for market stress assessment. • Stable VIX (<) (Float, Default: 20.0): VIX level below which market stress is considered low (increases score). • Stress VIX (>) (Float, Default: 35.0): VIX level above which market stress is considered high (decreases score/flags DANGER). • Use ML Probability (Bool, Default: True): Activates the volatility-normalized AI Z-Score kernel. Disabling this removes the cross-timeframe normalization filter. • Max Learning History (Int, Default: 2000): Maximum number of bars stored in the ML training arrays. • Normalization Lookback (252 Days) (Int, Default: 252): The number of DAILY bars used to calculate the Z-Score mean and standard deviation (representing approximately 1 year of data). E. RISK MANAGEMENT (ATR MODEL) • Use Structural SL (Bool, Default: False): Overrides the ATR-based Stop Loss distance to use the Pre-Market High/Low as the fixed stop level. • Stop Loss Multiplier (x ATR) (Float, Default: 1.5): Defines the Stop Loss distance in multiples of the current Average True Range (ATR). • Take Profit Multiplier (x ATR) (Float, Default: 3.0): Defines the Take Profit distance in multiples of the current Average True Range (ATR). • Exit Score Threshold (<) (Int, Default: 3): The minimum score below which an active trade is flagged for a Soft Exit due to momentum collapse. F. VISUAL SETTINGS • Show Dashboard (Bool, Default: True): Toggles the real-time data panel. • Show NY Killzones (Bool, Default: True): Toggles killzone background fills. • Show TP/SL Zones (Bool, Default: True): Toggles the drawing of Take Profit and Stop Loss boxes. • Show Pre-Market Extensions (Bool, Default: True): Extends PM High/Low lines across the entire chart for support/resistance reference. • Activate AI Prediction Box (Bool, Default: True): Enable or disable the predictive range projection. • Light Mode Optimization (Bool, Default: True): Toggles dashboard and plot colors for optimal visibility on white (light) chart backgrounds. • Enforce Trend Coloring (Bool, Default: True): Forces candle colors based on Market Regime (Bullish=Cyan, Bearish=Pink) to emphasize trend direction. • Label Size (String, Default: Normal): Options: Tiny, Small, Normal. G. LIQUIDITY POOLS (PDH/PDL) • Show Liquidity Lines (Bool, Default: True): Toggles the display of the Previous Day High (PDH) and Low (PDL) lines. • Liquidity High Color (Color, Default: Green): Color setting for the PDH line. • Liquidity Low Color (Color, Default: Red): Color setting for the PDL line. 🔔 ALERT CONFIGURATION GUIDE The indicator is equipped with specific alert conditions. How to Set Up an Alert: Click the "Alert" (Clock icon) in the top TradingView toolbar. Select "Market Regime NY Session " from the Condition dropdown menu. Choose one of the specific trigger conditions below depending on your strategy: 🚀 Available Alert Conditions 1. BUY (Long Entry) Trigger: Fires immediately when a confirmed Bullish Setup is detected. Conditions: Market Bias is Bullish (or valid Continuation) + Signal Score ≥ Minimum Entry Score. Usage: Use this alert to open new Long positions or close existing Short positions. 2. SELL (Short Entry) Trigger: Fires immediately when a confirmed Bearish Setup is detected. Conditions: Market Bias is Bearish (or valid Continuation) + Signal Score ≥ Minimum Entry Score. Usage: Use this alert to open new Short positions or close existing Long positions. V. IMPORTANT TECHNICAL LIMITATIONS ⚠️ Intraday Only (Timeframe Compatibility) This indicator is strictly designed for Intraday Timeframes (1m to 4h). Daily/Weekly Charts: The session logic (e.g., "09:30-16:00") cannot function on Daily bars because a single bar encompasses the entire session. Session boxes, TP/SL zones, and AI prediction boxes will NOT draw on the Daily timeframe. Only the PDH/PDL liquidity lines remain visible on Daily charts. This is expected behavior, not a limitation. Maximum Supported Timeframe: All visual components (session boxes, killzone overlays, TP/SL zones, AI prediction boxes) are displayed up to the 4-hour timeframe. Above this timeframe, only PDH/PDL lines and the dashboard remain functional. ⚠️ Drawing Object Limit (Max 500) A single script can display a maximum of 500 drawing objects (boxes/lines) simultaneously. On lower timeframes (e.g., 1-minute), where many signals and session boxes are generated, older history (typically beyond 10-14 days) will automatically disappear to make room for new real-time data. For deeper historical backtesting visualization, switch to higher timeframes (e.g., 15m, 1h). The indicator implements FIFO array management to comply with this limit while maintaining the most recent and relevant visual data. VI. PRACTICAL TRADING TIPS AND BEST PRACTICES • Killzone Confirmation: The highest statistical validity is observed when a high-score signal occurs directly within a visible NY AM/PM Killzone. Use the Killzones as a strict time filter. • Liquidity Awareness (PDH/PDL): Treat the Previous Day High (PDH) and Low (PDL) lines as magnets. If your dynamic Take Profit (TP) is placed just above PDH, consider adjusting your target slightly below PDH or utilizing the Soft Exit, as liquidity absorption at these levels often results in sudden, sharp reversals that stop out a trade just before the target is reached. • VIX as a Position Sizer: During DANGER VIX regimes, the resulting high volatility means the ATR value will be large. It is prudent to either reduce the SL Multiplier or, more commonly, reduce the overall position size to maintain a constant currency risk exposure per trade. • Continuation Filter Timing: Trend Continuation signals are most effective during the Afternoon (PM) session when the morning's directional breakout has had time to establish a strong, clear, and sustainable trend. Avoid using them in the initial AM session when the direction is still being contested. • 16:00 Market Close Rule: All trades, boxes, and lines are automatically cleaned up at 16:00 NY time. This prevents overnight chart clutter and maintains visual clarity. VII. DISCLAIMER & RISK WARNINGS • Educational Purpose Only This indicator, including all associated code, documentation, and visual outputs, is provided strictly for educational and informational purposes. It does not constitute financial advice, investment recommendations, or a solicitation to buy or sell any financial instruments. • No Guarantee of Performance Past performance is not indicative of future results. All metrics displayed on the dashboard (including "ROI" and trade results) are theoretical calculations based on historical data. These figures do not account for real-world trading factors such as slippage, liquidity gaps, spread costs, or broker commissions. • High-Risk Warning Trading cryptocurrencies, futures, and leveraged financial products involves a substantial risk of loss. The use of leverage can amplify both gains and losses. Users acknowledge that they are solely responsible for their trading decisions and should conduct independent due diligence before executing any trades. • Software Limitations The software is provided "as is" without warranty. Users should be aware that market data feeds on analysis platforms may experience latency or outages, which can affect signal generation accuracy.

אינדיקטור Pine Script®

מאת ‎ApexLegion‎

מעודכן

SCTI V28 Indicator Overview | 指标概述 English: SCTI V28 (Smart Composite Technical Indicator) is a multi-functional composite technical analysis tool that integrates various classic technical analysis methods. It contains 7 core modules that can be flexibly configured to show or hide components based on traders' needs, suitable for various trading styles and market conditions. 中文: SCTI V28 (智能复合技术指标) 是一款多功能复合型技术分析指标，整合了多种经典技术分析工具于一体。该指标包含7大核心模块，可根据交易者的需求灵活配置显示或隐藏各个组件，适用于多种交易风格和市场环境。 Main Functional Modules | 主要功能模块 1. Basic Indicator Settings | 基础指标设置 English: EMA Display: 13 configurable EMA lines (default shows 8/13/21/34/55/144/233/377/610/987/1597/2584 periods) PMA Display: 11 configurable moving averages with multiple MA types (ALMA/EMA/RMA/SMA/SWMA/VWAP/VWMA/WMA) VWAP Display: Volume Weighted Average Price indicator Divergence Indicator: Detects divergences across 12 technical indicators ATR Stop Loss: ATR-based stop loss lines Volume SuperTrend AI: AI-powered super trend indicator 中文: EMA显示：13条可配置EMA均线，默认显示8/13/21/34/55/144/233/377/610/987/1597/2584周期 PMA显示：11条可配置移动平均线，支持多种MA类型(ALMA/EMA/RMA/SMA/SWMA/VWAP/VWMA/WMA) VWAP显示：成交量加权平均价指标背离指标：12种技术指标的背离检测系统 ATR止损：基于ATR的止损线 Volume SuperTrend AI：基于AI预测的超级趋势指标 2. EMA Settings | EMA设置 English: 13 independent EMA lines, each configurable for visibility and period length Default shows 21/34/55/144/233/377/610/987/1597/2584 period EMAs Customizable colors and line widths for each EMA 中文: 13条独立EMA均线，每条均可单独配置显示/隐藏和周期长度默认显示21/34/55/144/233/377/610/987/1597/2584周期的EMA 每条EMA可设置不同颜色和线宽 3. PMA Settings | PMA设置 English: 11 configurable moving averages, each with: Selectable types (default EMA, options: ALMA/RMA/SMA/SWMA/VWAP/VWMA/WMA) Independent period settings (12-1056) Special ALMA parameters (offset and sigma) Configurable data source and plot offset Support for fill areas between MAs Price lines and labels can be added 中文: 11条可配置移动平均线，每条均可：选择不同类型(默认EMA，可选ALMA/RMA/SMA/SWMA/VWAP/VWMA/WMA) 独立设置周期长度(12-1056) 设置ALMA的特殊参数(偏移量和sigma) 配置数据源和绘图偏移支持MA之间的填充区域显示可添加价格线和标签 4. VWAP Settings | VWAP设置 English: Multiple anchor period options (Session/Week/Month/Quarter/Year/Decade/Century/Earnings/Dividends/Splits) 3 configurable standard deviation bands Option to hide on daily and higher timeframes Configurable data source and offset settings 中文: 多种锚定周期选择(会话/周/月/季/年/十年/世纪/财报/股息/拆股) 3条可配置标准差带可选择在日线及以上周期隐藏支持数据源选择和偏移设置 5. Divergence Indicator Settings | 背离指标设置 English: 12 detectable indicators: MACD, MACD Histogram, RSI, Stochastic, CCI, Momentum, OBV, VWmacd, Chaikin Money Flow, MFI, Williams %R, External Indicator 4 divergence types: Regular Bullish/Bearish, Hidden Bullish/Bearish Multiple display options: Full name/First letter/Hide indicator name Configurable parameters: Pivot period, data source, maximum bars checked, etc. Alert functions: Independent alerts for each divergence type 中文: 检测12种指标：MACD、MACD柱状图、RSI、随机指标、CCI、动量、OBV、VWmacd、Chaikin资金流、MFI、威廉姆斯%R、外部指标 4种背离类型：正/负常规背离，正/负隐藏背离多种显示选项：完整名称/首字母/不显示指标名称可配置参数：枢轴点周期、数据源、最大检查柱数等警报功能：各类背离的独立警报 6. ATR Stop Loss Settings | ATR止损设置 English: Configurable ATR length (default 13) 4 smoothing methods (RMA/SMA/EMA/WMA) Adjustable multiplier (default 1.618) Displays long and short stop loss lines 中文: 可配置ATR长度(默认13) 4种平滑方法(RMA/SMA/EMA/WMA) 可调乘数(默认1.618) 显示多头和空头止损线 7. Volume SuperTrend AI Settings | Volume SuperTrend AI设置 English: AI Prediction: Configurable neighbors (1-100) and data points (1-100) Price trend length and prediction trend length settings SuperTrend Parameters: Length (default 3) Factor (default 1.515) 5 MA source options (SMA/EMA/WMA/RMA/VWMA) Signal Display: Trend start signals (circle markers) Trend confirmation signals (triangle markers) 6 Alerts: Various trend start and confirmation signals 中文: AI预测功能：可配置邻居数(1-100)和数据点数(1-100) 价格趋势长度和预测趋势长度设置 SuperTrend参数：长度(默认3) 因子(默认1.515) 5种MA源选择(SMA/EMA/WMA/RMA/VWMA) 信号显示：趋势开始信号(圆形标记) 趋势确认信号(三角形标记) 6种警报：各类趋势开始和确认信号 Usage Recommendations | 使用建议 English: Trend Analysis: Use EMA/PMA combinations to determine market trends, with long-period EMAs (e.g., 144/233) as primary trend references Divergence Trading: Look for potential reversals using price-indicator divergences Stop Loss Management: Use ATR stop loss lines for risk management AI Assistance: Volume SuperTrend AI provides machine learning-based trend predictions Multiple Timeframes: Verify signals across different timeframes 中文: 趋势分析：使用EMA/PMA组合判断市场趋势，长周期EMA(如144/233)作为主要趋势参考背离交易：结合价格与指标的背离寻找潜在反转点止损设置：利用ATR止损线管理风险 AI辅助：Volume SuperTrend AI提供基于机器学习的趋势预测多时间框架：建议在不同时间框架下验证信号 Parameter Configuration Tips | 参数配置技巧 English: For short-term trading: Focus on 8-55 period EMAs and shorter divergence detection periods For long-term investing: Use 144-2584 period EMAs with longer detection parameters In ranging markets: Disable some EMAs, mainly rely on VWAP and divergence indicators In trending markets: Enable more EMAs and SuperTrend AI 中文: 对于短线交易：可重点关注8-55周期的EMA和较短的背离检测周期对于长线投资：建议使用144-2584周期的EMA和较长的检测参数在震荡市：可关闭部分EMA，主要依靠VWAP和背离指标在趋势市：可启用更多EMA和SuperTrend AI Update Log | 更新日志 English: V28 main updates: Added Volume SuperTrend AI module Optimized divergence detection algorithm Added more EMA period options Improved UI and parameter grouping 中文: V28版本主要更新：新增Volume SuperTrend AI模块优化背离检测算法增加更多EMA周期选项改进用户界面和参数分组 Final Note | 最后说明 English: This indicator is suitable for technical traders with some experience. We recommend practicing with demo trading to familiarize yourself with all features before live trading. 中文: 该指标适合有一定经验的技术分析交易者使用，建议先通过模拟交易熟悉各项功能后再应用于实盘。

אינדיקטור Pine Script®

מאת ‎Erens_Freedom‎

מעודכן

DafeRLMLLib DafeRLMLLib: The Reinforcement Learning & Machine Learning Engine This is not an indicator. This is an artificial intelligence. A state-based, self-learning engine designed to bring the power of professional quantitative finance to the Pine Script ecosystem. Welcome to the next frontier of trading analysis. █ CHAPTER 1: THE PHILOSOPHY - FROM STATIC RULES TO DYNAMIC LEARNING Technical analysis has, for a century, been a discipline of static, human-defined rules. "If RSI is below 30, then buy." "If the 50 EMA crosses the 200 EMA, then sell." These are fixed heuristics. They are brittle. They fail to adapt to the market's ever-changing personality—its shifts between trend and range, high and low volatility, risk-on and risk-off sentiment. An indicator built on static rules is an automaton, destined to fail when the environment it was designed for inevitably changes. The DafeRLMLLib was created to shatter this paradigm. It is not a tool with fixed rules; it is a framework for discovering optimal rules. It is a true Reinforcement Learning (RL) and Machine Learning (ML) engine, built from the ground up in Pine Script. Its purpose is not to follow a pre-programmed strategy, but to learn a strategy through trial, error, and feedback. This library provides a complete, professional-grade toolkit for developers to build indicators that think, adapt, and evolve. It observes the market state, selects an action, receives a reward signal based on the outcome, and updates its internal "brain" to improve its future decisions. This is not just a step forward; it is a quantum leap into the future of on-chart intelligence. █ CHAPTER 2: THE CORE INNOVATIONS - WHAT MAKES THIS A TRUE ML ENGINE? This library is not a collection of simple moving averages labeled as "AI." It is a suite of genuine, academically recognized machine learning algorithms, adapted for the unique constraints and opportunities of the Pine Script environment. Multi-Algorithm Architecture: You are not locked into one learning model. The library provides a choice of powerful RL algorithms: Q-Learning with TD(λ) Eligibility Traces: A classic, robust algorithm for learning state-action values. We've enhanced it with eligibility traces (Lambda), allowing the agent to more efficiently assign credit or blame to a sequence of past actions, dramatically speeding up the learning process. REINFORCE Policy Gradient with Baseline: A more advanced method that directly learns a "policy"—a probability distribution over actions—instead of just values. The baseline helps to stabilize learning by reducing variance. Actor-Critic Architecture: The state-of-the-art. This hybrid model combines the best of both worlds. The "Actor" (the policy) decides what to do, and the "Critic" (the value function) evaluates how good that action was. The Critic's feedback is then used to directly improve the Actor's decisions. Prioritized Experience Replay: Like a human, the AI learns more from surprising or significant events. Instead of learning from experiences in a simple chronological order, the library stores them in a ReplayBuffer. It then replays these memories to the learning algorithms, prioritizing experiences that resulted in a large prediction error. This makes learning incredibly efficient. Meta-Learning & Self-Tuning: An AI that cannot learn how to learn is still a dumb machine. The MetaState module is a meta-learning layer that monitors the agent's own performance over time. If it detects that performance is degrading, it will automatically increase the learning rate ("Synaptic Plasticity"). If performance is improving, it will decrease the learning rate to stabilize the learned strategy. It tunes its own hyperparameters. Catastrophic Forgetting Prevention: A common failure mode for simple neural networks is "catastrophic forgetting," where learning a new task completely erases knowledge of a previous one. This library includes mechanisms like soft_reset and L2 regularization to prevent the agent's learned weights from exploding or being wiped out by a single bad run of trades, ensuring more stable, long-term learning. The Universal Socket Interface: How does the AI "see" the market? Through DataSockets. This brilliant, extensible interface allows a developer to connect any data series—an RSI, a volume metric, a volatility reading, a custom calculation—to the AI's "brain." Each socket normalizes its input, tracks its own statistics, and feeds into the state-building process. This makes the library universally adaptable to any trading idea. █ CHAPTER 3: A DUAL-PURPOSE FRAMEWORK - MODES OF OPERATION This library is a foundational component of the DAFE AI ecosystem, designed for ultimate flexibility. It can be used in two primary modes: as a powerful standalone intelligence, or as the core cognitive engine within a larger, bridged super-system. Understanding these modes is key to unlocking its full potential. MODE 1: STANDALONE ENGINE OPERATION (Independent Power The DafeRLMLLib can be used entirely on its own to create a complete, self-learning trading indicator. This approach is perfect for building focused, single-purpose tools that are designed to master a specific task. In this mode, the developer is responsible for creating the full feedback loop within their own indicator script. The Workflow: Your indicator initializes the ML agent. On each bar, it feeds the agent market data via the socket interface. It asks the agent for an action (e.g., Buy, Sell, Hold). Your script then executes its own internal trade logic based on the agent's decision. Your script is responsible for tracking the Profit & Loss (PnL) of the resulting simulated trade. When the trade is closed, your script feeds the final PnL directly back into the agent's learn() function as the "reward" signal. The Result: A pure, state-based learning system. The agent directly learns the consequences of its own actions. This is excellent for discovering novel, micro-level trading patterns and for building indicators that are designed to operate with complete autonomy. MODE 2: BRIDGED SUPER-SYSTEM OPERATION (Synergistic Intelligence) This is the pinnacle of the DAFE ecosystem. In this advanced mode, the DafeRLMLLib acts as the core "cognitive engine" or the "tactical brain" within a larger, multi-library system. It can be fused with a strategic portfolio management engine (like the DafeSPALib) via a master communication protocol (the DafeMLSPABridge). The Workflow: The ML engine (this library) generates a set of creative, state-based proposals or predictions. The Bridge Library translates these proposals into a portfolio of micro-strategies. The SPA (Strategy Portfolio Allocation) engine, acting as a high-level manager, analyzes the real-time performance of these micro-strategies and selects the one it trusts the most. This becomes the final decision. The PnL from the SPA's final, performance-vetted decision is then routed back through the Bridge as a highly-qualified reward signal for the ML engine. The Result: A hybrid intelligence that is more robust and adaptive than either system alone. The ML engine provides tactical creativity, while the SPA engine provides ruthless, strategic, performance-based oversight. The ML proposes, the SPA disposes, and the ML learns from the SPA's wisdom. This creates a system of checks, balances, and continuous, synergistic learning, perfect for building an ultimate, all-in-one "drawing indicator" or trading system. As a developer, the choice is yours. Use this library independently to build powerful, specialized learning tools, or use it as the foundational brain for a truly comprehensive trading AI. █ CHAPTER 4: A GUIDE FOR DEVELOPERS - INTEGRATING THE BRAIN We have made it incredibly simple to bring your indicators to life with the DAFE AI. This is the true purpose of the library—to empower you. This section provides the full, unabridged input template and usage guide. PART I: THE INPUTS TEMPLATE To give your users full control over the AI, copy this entire block of inputs into your indicator script. It is professionally organized with groups and detailed tooltips. // ╔═════════════════════════════════════════════════════╗ // ║ INPUTS TEMPLATE (COPY INTO YOUR SCRIPT) ║ // ╚═════════════════════════════════════════════════════╝ // INPUT GROUPS string G_RL_AGENT = "═══════════ 🧠 AGENT CONFIGURATION ════════════" string G_RL_LEARN = "═══════════ 📚 LEARNING PARAMETERS ═══════════" string G_RL_REWARD = "═══════════ 💰 REWARD SYSTEM ═══════════════" string G_RL_REPLAY = "═══════════ 📼 EXPERIENCE REPLAY ════════════" string G_RL_META = "═══════════ 🔮 META-LEARNING ═══════════════" string G_RL_DASH = "═══════════ 📋 DIAGNOSTICS DASHBOARD ═════════" // AGENT CONFIGURATION string i_rl_algorithm = input.string("Actor-Critic", "🤖 Algorithm", options= , group=G_RL_AGENT, tooltip="Selects the core learning algorithm.\n\n" + "• Q-Learning: Classic, robust, and fast for discrete states. Learns the 'value' of actions.\n" + "• Policy Gradient: Learns a direct probability distribution over actions.\n" + "• Actor-Critic: The state-of-the-art. The 'Actor' decides, the 'Critic' evaluates.\n" + "• Ensemble: Runs both Q-Learning and Policy Gradient and chooses the action with the highest confidence.\n\n" + "RECOMMENDATION: Start with 'Q-Learning' for stability or 'Actor-Critic' for performance.") int i_rl_num_features = input.int(8, "Number of Features (Sockets)", minval=2, maxval=12, group=G_RL_AGENT, tooltip="Defines the size of the AI's 'vision'. This MUST match the number of sockets you connect.") int i_rl_num_actions = input.int(3, "Number of Actions", minval=2, maxval=5, group=G_RL_AGENT, tooltip="Defines what the AI can do. 3 is standard (0=Neutral, 1=Buy, 2=Sell).") // LEARNING PARAMETERS float i_rl_learning_rate = input.float(0.05, "🎓 Learning Rate (Alpha)", minval=0.001, maxval=0.2, step=0.005, group=G_RL_LEARN, tooltip="How strongly the AI updates its knowledge. Low (0.01-0.03) is stable. High (0.1+) is aggressive.") float i_rl_discount = input.float(0.95, "🔮 Discount Factor (Gamma)", minval=0.8, maxval=0.99, step=0.01, group=G_RL_LEARN, tooltip="Determines the agent's 'foresight'. High (0.95+) for trend following. Low (0.85) for scalping.") float i_rl_epsilon = input.float(0.15, "🧭 Exploration Rate (Epsilon)", minval=0.01, maxval=0.5, step=0.01, group=G_RL_LEARN, tooltip="For Q-Learning. The probability of taking a random action to explore. Decays automatically over time.") float i_rl_lambda = input.float(0.7, "⚡ Eligibility Trace (Lambda)", minval=0.0, maxval=0.95, step=0.05, group=G_RL_LEARN, tooltip="For Q-Learning. A powerful accelerator that allows a reward to be 'traced' back through a sequence of actions.") // REWARD SYSTEM string i_rl_reward_mode = input.string("Normalized", "💰 Reward Shaping Mode", options= , group=G_RL_REWARD, tooltip="Modifies the raw PnL reward signal to guide learning.\n\n" + "• Normalized: Creates a stable reward signal (Recommended).\n" + "• Asymmetric: Punishes losses more than it rewards gains. Teaches risk aversion.\n" + "• Risk-Adjusted: Divides PnL by risk (e.g., ATR). Teaches better risk/reward.") // EXPERIENCE REPLAY bool i_rl_use_replay = input.bool(true, "📼 Enable Experience Replay", group=G_RL_REPLAY, tooltip="Allows the agent to store and re-learn from past experiences. Dramatically improves learning stability. HIGHLY RECOMMENDED.") int i_rl_replay_capacity = input.int(500, "Replay Buffer Size", minval=100, maxval=2000, group=G_RL_REPLAY) int i_rl_replay_batch = input.int(4, "Replay Batch Size", minval=1, maxval=10, group=G_RL_REPLAY) // META-LEARNING bool i_rl_use_meta = input.bool(true, "🔮 Enable Meta-Learning", group=G_RL_META, tooltip="Allows the agent to self-tune its own learning rate based on performance trends.") // DIAGNOSTICS DASHBOARD bool i_rl_show_dash = input.bool(true, "📋 Show Diagnostics Dashboard", group=G_RL_DASH) PART II: THE IMPLEMENTATION LOGIC This is the boilerplate code you will adapt to your indicator. It shows the complete Observe-Act-Learn loop. // ╔═══════════════════════════════════════════════════════╗ // ║ USAGE EXAMPLE (ADAPT TO YOUR SCRIPT) ║ // ╚═══════════════════════════════════════════════════════╝ // 1. INITIALIZE THE AGENT (happens only on the first bar) int algo_id = i_rl_algorithm == "Q-Learning" ? 0 : i_rl_algorithm == "Policy Gradient" ? 1 : i_rl_algorithm == "Actor-Critic" ? 2 : 3 int reward_id = i_rl_reward_mode == "Raw PnL" ? 0 : i_rl_reward_mode == "Normalized" ? 1 : i_rl_reward_mode == "Asymmetric" ? 2 : 3 var rl.RLAgent agent = rl.init(algo_id, i_rl_num_features, i_rl_num_actions, i_rl_learning_rate, 54, i_rl_replay_capacity, i_rl_epsilon, i_rl_discount, i_rl_lambda, reward_id) // 2. CONNECT THE "SENSES" (happens only on the first bar) if barstate.isfirst // Connect your indicator's data series to the AI's sockets. The number MUST match 'i_rl_num_features'. agent := rl.connect_socket(agent, "rsi", ta.rsi(close, 14), "oscillator", 1.0) agent := rl.connect_socket(agent, "atr_norm", ta.atr(14)/close*100, "custom", 0.8) // ... connect all other features ... // 3. THE MAIN LOOP (Observe -> Act -> Learn) - runs on every bar var bool in_trade = false var int trade_direction = 0 var float entry_price = 0.0 var int last_state_hash = 0 var int last_action_taken = 0 // --- OBSERVE: Build the current market state --- rl.RLState current_state = rl.build_state(agent) // --- ACT: Ask the AI for a decision --- = rl.select_action(agent, current_state) agent := updated_agent // CRITICAL: Always update the agent state // --- EXECUTE: Your custom trade logic goes here --- if not in_trade and ai_action.action != 0 // Assuming 0 is "Hold" in_trade := true trade_direction := ai_action.action == 1 ? 1 : -1 // Assuming 1=Buy, 2=Sell entry_price := close last_state_hash := current_state.hash // Store the state at the moment of entry last_action_taken := ai_action.action // --- LEARN: Check for trade closure and provide feedback --- bool trade_is_closed = false float reward = 0.0 if in_trade // Your custom exit condition here (e.g., stop loss, take profit, opposite signal) bool exit_condition = bar_index > ta.valuewhen(in_trade, bar_index, 0) + 20 if exit_condition trade_is_closed := true pnl = trade_direction == 1 ? (close - entry_price) / entry_price : (entry_price - close) / entry_price reward := pnl * 100 in_trade := false // If a trade was closed on THIS bar, feed the experience to the AI if trade_is_closed agent := rl.learn(agent, last_state_hash, last_action_taken, reward, current_state, true) // 4. DISPLAY DIAGNOSTICS if i_rl_show_dash and barstate.islast string diag_text = rl.diagnostics(agent) label.new(bar_index, high, diag_text, style=label.style_label_down, color=color.new(#0A0A14, 10), textcolor=#00FF41, size=size.small, textalign=text.align_left) █ DEVELOPMENT PHILOSOPHY The DafeRLMLLib was born from a desire to push the boundaries of Pine Script and to empower the entire TradingView developer community. We believe that the future of technical analysis is not just in creating more complex algorithms, but in building systems that can learn, adapt, and optimize themselves. This library is an open-source framework designed to be a launchpad for a new generation of truly intelligent indicators on TradingView. This library is designed to help you and your users discover what "the best trades" are, not by following a fixed set of rules, but by learning from the market's own feedback, one trade at a time. █ DISCLAIMER & IMPORTANT NOTES THIS IS A LIBRARY FOR ADVANCED DEVELOPERS: This script does nothing on its own. It is a powerful engine that must be integrated into other indicators. REINFORCEMENT LEARNING IS COMPLEX: RL is not a magic bullet. It requires careful feature engineering (choosing the right sockets), a well-defined reward signal, and a sufficient amount of training data (trades) to converge on a profitable strategy. ALL TRADING INVOLVES RISK: The AI's decisions are based on statistical probabilities learned from past data. It does not predict the future with certainty. "The goal of a successful trader is to make the best trades. Money is secondary." — Alexander Elder Taking you to school. - Dskyz, Create with RL.

ספריית Pine Script®

מאת ‎DskyzInvestments‎

FVG & Order Block Sync Pro - Enhanced 🏦 FVG & Order Block Sync Pro Enhanced The AI-Powered Institutional Trading System That Changes Everything Tired of Guessing Where Price Will Go Next? What if you could see EXACTLY where banks and institutions are placing their orders? Introducing the FVG & Order Block Sync Pro Enhanced - the first indicator that combines institutional Smart Money Concepts with next-generation AI technology to reveal the hidden blueprint of the market. 🎯 Finally, Trade Alongside the Banks - Not Against Them For years, retail traders have been fighting a losing battle. Why? Because they can't see what the institutions see. Until now. Our revolutionary indicator exposes: 🏛️ Institutional Order Blocks - The exact zones where banks accumulate positions 💰 Fair Value Gaps - Price inefficiencies that act as magnets for future price movement 📊 Real-Time Structure Breaks - Know instantly when smart money shifts direction 🎯 Banker Candle Patterns - Spot institutional rejection zones before reversals 🤖 Next-Level AI Technology That Thinks Like a Bank Trader This isn't just another indicator with arrows. Our advanced AI engine: Analyzes 100+ Data Points Per Second across multiple timeframes Machine Learning Pattern Recognition that improves with every trade Multi-Symbol Correlation Analysis to confirm institutional flow Predictive Sentiment Scoring that gauges market momentum in real-time Confluence Algorithm that rates every signal from 0-10 for probability Result? You're not following indicators - you're following institutional order flow. 📈 Perfect for Forex & Futures Markets Whether you're trading: Major Forex Pairs (EUR/USD, GBP/USD, USD/JPY) Futures Contracts (ES, NQ, CL, GC) Indices (S&P 500, NASDAQ, DOW) Commodities (Gold, Oil, Silver) The indicator adapts to any market that institutions trade - because it tracks THEIR footprints. 💎 What Makes This Different? 1. SMC + Market Structure Fusion First indicator to combine Order Blocks, FVG, BOS, and CHOCH in one system Shows not just WHERE to trade, but WHY price will move there 2. The "Sync" Advantage Only signals when BOTH Fair Value Gap AND Order Block align Filters out 73% of false signals that single-concept indicators miss 3. Institutional-Grade Dashboard See what a bank trader sees: 5 timeframes at once Real-time strength meters showing institutional momentum Multi-symbol analysis for correlation confirmation AI-powered signal strength scoring 4. No More Analysis Paralysis Clear BUY/SELL signals with exact entry zones Built-in stop loss and take profit levels Signal strength rating tells you position size 📊 Real Traders, Real Results "I went from a 45% win rate to 78% in just 3 weeks. The ability to see where banks are operating completely changed my trading." - Sarah T., Forex Trader "The AI signal strength feature alone paid for this indicator 10x over. I only take 8+ scores now and my account has never been more consistent." - Mike D., Futures Trader "Finally an indicator that shows market structure properly. The CHOCH alerts saved me from countless losing trades." - Alex R., Day Trader 🚀 Everything You Get: ✅ Institutional Zone Detection - FVG, Order Blocks, Liquidity Zones ✅ AI-Powered Analysis - ML patterns, sentiment scoring, predictive algorithms ✅ Market Structure Mastery - BOS/CHOCH with visual trend lines ✅ Multi-Timeframe Dashboard - 5 timeframes updated in real-time ✅ Banker Candle Recognition - Spot institutional reversals ✅ Advanced Alert System - Never miss a high-probability setup ✅ Risk Management Built-In - Automatic position sizing guidance ✅ Works on ALL Timeframes - From 1-minute scalping to daily swing trading 🎓 Who This Is Perfect For: Frustrated Traders tired of indicators that lag behind price Serious Traders ready to level up with institutional concepts Forex Traders wanting to catch major pair movements Futures Traders seeking precise ES/NQ entries Anyone who wants to stop gambling and start trading with the banks ⚡ The Bottom Line: Every day, institutions move billions through the markets. They leave footprints. This indicator reveals them. Stop trading blind. Start trading with institutional vision. While other traders are still drawing trend lines and hoping for the best, you'll be entering positions at the exact zones where smart money operates. 🔥 Limited Time Bonus Features: Multi-Symbol Analysis - Track 3 correlated pairs simultaneously AI Confidence Scoring - Know exactly when NOT to trade Volume Confluence Filters - Confirm institutional participation Custom Alert Templates - Set up once, trade anywhere Free Updates Forever - As the AI learns, your edge grows 💪 Make the Decision That Changes Your Trading Forever Every day you trade without seeing institutional zones is a day you're trading with a massive disadvantage. The banks aren't smarter than you. They just see things you don't. Until you add this indicator to your chart. Join thousands of traders who've discovered what it feels like to trade WITH the flow of institutional money instead of against it. Because when you can see what the banks see, you can trade like the banks trade. ⚠️ Risk Disclaimer: Trading forex and futures carries significant risk. Past performance doesn't guarantee future results. This indicator is a tool for analysis, not a guarantee of profits. Always use proper risk management. 🎯 Transform your trading. See the market through institutional eyes. Get the FVG & Order Block Sync Pro Enhanced today. The difference between amateur and professional trading is information. Now you can have both.

אינדיקטור Pine Script®

מאת ‎dkjackson187‎

DafeSPALib DafeSPALib: The Shadow Portfolio Adaptation & Strategy Selection Engine This is not a backtester. This is a live, adaptive portfolio manager. It is a reinforcement learning system that learns which of your strategies to trust in the ever-changing chaos of the market. █ CHAPTER 1: THE PHILOSOPHY - BEYOND A SINGLE STRATEGY The search for a single "holy grail" trading strategy is a fool's errand. No single set of rules can perform optimally in all market conditions. A trend-following system that thrives in a bull run will be decimated by a choppy, range-bound market. A mean-reversion strategy that profits from ranges will be run over by a powerful breakout. The DafeSPALib (Shadow Portfolio Adaptation Library) was created to solve this fundamental problem. It is built on a powerful principle from modern quantitative finance: instead of searching for one perfect strategy, a truly robust system should intelligently allocate to a portfolio of different strategies, dynamically favoring the one that is currently most effective. This is not just a concept; it is a complete, production-grade engine built in Pine Script. It allows a developer to run multiple "shadow portfolios"—hypothetical trading accounts for each of your strategies—in parallel, in real time. The library tracks the actual equity curve, win rate, Sharpe ratio, and drawdown of each strategy. It then uses a sophisticated selection algorithm to determine which strategy is the "alpha" in the current market regime and tells you which one to follow. It is an AI portfolio manager that lives on your chart. █ CHAPTER 2: THE CORE INNOVATIONS - WHAT MAKES THIS A REVOLUTIONARY ENGINE? This library is not a simple strategy switcher. It is a suite of genuine, academically recognized machine learning and statistical concepts, adapted for the Pine Script environment. Shadow Portfolio Tracking: This is the heart of the system. For each of your strategy "arms," the library maintains a complete, independent set of performance analytics. It doesn't just keep a simple "score." It tracks every hypothetical trade, calculates real P&L;, and updates a full suite of institutional metrics, including the Sharpe Ratio (risk-adjusted return), Sortino Ratio (downside-risk-adjusted return), Profit Factor , and Maximum Drawdown . This provides a rich, data-driven foundation for all decision-making. Advanced Selection Algorithms: The library doesn't just pick the strategy with the highest recent win rate. It uses sophisticated, battle-tested algorithms from the "multi-armed bandit" problem in machine learning to solve the critical "explore vs. exploit" dilemma: Thompson Sampling: The default and most powerful. Instead of just picking the "best" arm, it samples from each arm's learned probability distribution of success (its Beta distribution). This naturally balances "exploitation" (using the strategy that works) with "exploration" (giving less-proven strategies a chance to shine), making it incredibly robust against changing conditions. Upper Confidence Bound (UCB): A deterministic algorithm that is "optimistic in the face of uncertainty." It favors strategies that have both a high win rate and a high degree of uncertainty (fewer trades), encouraging intelligent exploration. Epsilon-Greedy: A classic RL algorithm that mostly exploits the best-known strategy but, with a small probability (epsilon), explores a random one to prevent getting stuck on a sub-optimal choice. Trauma-Based Memory Compression: This is a groundbreaking, proprietary concept. When the market experiences a "regime shock" (a sudden explosion in volatility, a violent trend reversal), a simple learning system can be paralyzed or make catastrophic errors. The SPA engine's "trauma" cycle is an intelligent response. It does not erase all learned knowledge. Instead, it compresses the memory : it preserves the direction of what it has learned (e.g., "Strategy A is generally better than B") but it destroys the confidence. The AI "remembers" its experiences but becomes highly uncertain, forcing it to re-learn and adapt to the new market personality with incredible speed. Think of it like PTSD for an AI: the memory of the event remains, but the trust is shattered. Multi-Layer Concept Drift Detection: This is the system's "earthquake detector." It is constantly scanning for signs that the market's fundamental character is changing ("concept drift"). It uses three layers of detection— Structural (trend slope changes), Volatility (ATR explosions), and Participation (volume anomalies)—to identify a regime shock and trigger the trauma compression cycle. █ CHAPTER 3: A DUAL-PURPOSE FRAMEWORK - MODES OF OPERATION This library, along with its companion DAFE libraries, is designed for ultimate flexibility. As a developer, you have complete freedom to use these tools independently or as a fully integrated system. MODE 1: STANDALONE ENGINE OPERATION (Independent Power) The DafeSPALib can be used entirely on its own to build a powerful portfolio-of-strategies indicator without any external ML. This approach is perfect for comparing, validating, and dynamically selecting from your own existing, rule-based trading ideas. The Workflow: Your indicator initializes the SPA engine with a set number of "arms" (e.g., 4). On each bar, you calculate the signals for each of your independent strategies (e.g., an EMA Crossover, an RSI Mean Reversion, a Bollinger Breakout). You feed this array of signals ( ) into the SPA's feed_signals() function. The SPA engine updates the shadow portfolio for each of the four strategies based on these signals. You then call the select() function, and the SPA's chosen algorithm (e.g., Thompson Sampling) will return the index of the single strategy arm that it trusts the most right now. Your indicator's final output signal is the signal from that selected arm. The Result: A complete, self-contained meta-strategy. Your indicator is no longer just one strategy; it is an intelligent manager that dynamically switches between multiple strategies, adapting to the market by selecting the one with the best real-time, risk-adjusted performance. MODE 2: BRIDGED SUPER-SYSTEM OPERATION (The Ultimate AI) This is the pinnacle of the DAFE ecosystem. In this advanced mode, the DafeSPALib acts as the "strategic brain" or "portfolio manager" that is fused with a tactical machine learning engine (like the DafeRLMLLib) via a master communication protocol (the DafeMLSPABridge). The Workflow: The ML engine generates proposals. The Bridge Library translates these proposals into a portfolio of micro-strategies. The SPA engine (this library) receives this portfolio of signals, tracks their shadow performance, and uses its advanced selection algorithms to choose the single best micro-strategy to follow. This becomes the final trade decision. The final P&L; from the SPA's selection is then routed back through the Bridge to the ML engine as a highly qualified reward signal for learning. The Result: A hybrid intelligence that is more robust and adaptive than either system alone. The ML provides tactical creativity, while the SPA provides ruthless, performance-based strategic oversight. █ CHAPTER 4: THE DEVELOPER'S MASTERCLASS - IMPLEMENTATION GUIDE This library is a professional framework. This guide provides the complete, unabridged instructions and templates required to integrate the DAFE SPA engine into your own custom Pine Script indicators. PART I: THE INPUTS TEMPLATE (THE CONTROL PANEL) To give your users full control over the AI, copy this entire block of inputs into your indicator script. It is professionally organized with groups and detailed tooltips. // ╔════════════════════════════════════════════════════════╗ // ║ INPUTS TEMPLATE (COPY INTO YOUR SCRIPT) ║ // ╚════════════════════════════════════════════════════════╝ // INPUT GROUPS string G_SPA_ENGINE = "════════════ 🧠 SPA ENGINE ════════════" string G_SPA_DRIFT = "════════════ 🌊 CONCEPT DRIFT ══════════" string G_SPA_DASH = "════════════ 📋 DIAGNOSTICS ═══════════" // SPA ENGINE int i_spa_num_arms = input.int(4, "Number of Strategy Arms", minval=2, maxval=10, group=G_SPA_ENGINE, tooltip="The number of parallel strategies the SPA will track.") string i_spa_selection = input.string("Thompson Sampling", "🤖 Selection Algorithm", options= , group=G_SPA_ENGINE, tooltip="The machine learning algorithm used to select the best arm.\n\n" + "• Thompson Sampling: Bayesian approach, samples from each arm's success probability. Balances explore/exploit perfectly (Recommended).\n" + "• UCB: Optimistic approach that favors arms with high uncertainty. Excellent for exploration.\n" + "• Epsilon-Greedy: Mostly exploits the best arm, but explores randomly with a small probability (epsilon).\n" + "• Softmax: Selects arms based on a probability distribution weighted by their performance.") float i_spa_epsilon = input.float(0.15, "🧭 Epsilon (for Epsilon-Greedy)", minval=0.01, maxval=0.5, step=0.01, group=G_SPA_ENGINE, tooltip="The probability of taking a random action to explore. This value automatically decays over time.") float i_spa_decay = input.float(0.995, "🧠 Memory Decay Rate", minval=0.98, maxval=0.9999, step=0.0005, group=G_SPA_ENGINE, tooltip="Controls recency bias. A value of 0.995 means the AI gives slightly more weight to recent performance. Lower values create a very short-term memory.") // CONCEPT DRIFT & TRAUMA bool i_spa_use_drift = input.bool(true, "🌊 Enable Concept Drift & Trauma", group=G_SPA_DRIFT, tooltip="Allows the engine to detect market regime shocks and trigger a 'Trauma Compression' cycle to accelerate re-learning.") float i_spa_trauma_sens = input.float(2.0, "Trauma Sensitivity", minval=1.2, maxval=4.0, step=0.1, group=G_SPA_DRIFT, tooltip="How sensitive the shock detector is. A lower value will trigger trauma cycles more frequently on smaller volatility/volume spikes.") // DIAGNOSTICS bool i_spa_show_dash = input.bool(true, "📋 Show Diagnostics Dashboard", group=G_SPA_DASH) PART II: THE IMPLEMENTATION LOGIC (THE HEART OF YOUR SCRIPT) This is the boilerplate code you will adapt to your indicator. It shows the complete loop of feeding signals, detecting drift, and selecting the best strategy. // ╔═══════════════════════════════════════════════════════╗ // ║ USAGE EXAMPLE (ADAPT TO YOUR SCRIPT) ║ // ╚═══════════════════════════════════════════════════════╝ // 1. INITIALIZE THE ENGINE (happens only on the first bar) int sel_method_id = i_spa_selection == "Thompson Sampling" ? 0 : i_spa_selection == "Upper Confidence Bound (UCB)" ? 1 : i_spa_selection == "Epsilon-Greedy" ? 2 : 3 var spa.SPAEngine engine = spa.init( num_arms = i_spa_num_arms, arm_names = array.from("TrendArm", "ReversionArm", "BreakoutArm", "MomentumArm"), // Give your arms names! selection_method = sel_method_id, decay_rate = i_spa_decay, trauma_sensitivity = i_spa_trauma_sens, epsilon = i_spa_epsilon ) // 2. DEFINE YOUR STRATEGY SIGNALS (runs on every bar) // These are your own custom, rule-based strategies. The signal should be +1 for Buy, -1 for Sell, 0 for Neutral. int trend_signal = close > ta.ema(close, 200) and ta.crossover(ta.ema(close, 20), ta.ema(close, 50)) ? 1 : close < ta.ema(close, 200) and ta.crossunder(ta.ema(close, 20), ta.ema(close, 50)) ? -1 : 0 int reversion_signal = ta.crossunder(ta.rsi(close, 14), 30) ? 1 : ta.crossover(ta.rsi(close, 14), 70) ? -1 : 0 int breakout_signal = ta.crossover(close, ta.highest(high, 20) ) ? 1 : ta.crossunder(close, ta.lowest(low, 20) ) ? -1 : 0 int momentum_signal = ta.crossover(ta.mom(close, 10), 0) ? 1 : ta.crossunder(ta.mom(close, 10), 0) ? -1 : 0 // Create an array of your signals. The order MUST be consistent. array all_signals = array.from(trend_signal, reversion_signal, breakout_signal, momentum_signal) // 3. THE MAIN LOOP (Feed -> Detect -> Select) - runs on every bar // --- FEED: Update the shadow portfolios with the latest signals and price --- engine := spa.feed_signals(engine, all_signals, close) // --- DETECT: Run the concept drift engine --- if i_spa_use_drift float trend_slope = ta.linreg(close, 20, 0) - ta.linreg(close, 20, 1) engine := spa.detect_drift(engine, close, volume, ta.atr(14), trend_slope) engine := spa.apply_trauma_cycle(engine) // This will compress memory if a shock was detected // --- SELECT: Ask the engine for its best choice --- = spa.select(engine) engine := updated_engine // CRITICAL: Always update the engine state // --- ACT: Use the final, selected signal for your indicator's logic --- int final_signal = array.get(all_signals, selected_arm) string selected_name = spa.get_name(engine, selected_arm) // Example: Color bars based on the final, SPA-vetted signal barcolor(final_signal == 1 ? color.new(color.green, 70) : final_signal == -1 ? color.new(color.red, 70) : na) // 4. DISPLAY DIAGNOSTICS if i_spa_show_dash and barstate.islast string diag_text = spa.diagnostics(engine) label.new(bar_index, high, diag_text, style=label.style_label_down, color=color.new(#0A0A14, 10), textcolor=#00E5FF, size=size.small, textalign=text.align_left) █ DEVELOPMENT PHILOSOPHY The DafeSPALib was born from the realization that market adaptation is the true holy grail of trading. While any single strategy is brittle, a portfolio of strategies, managed by an intelligent selection algorithm, is antifragile—it can learn, adapt, and potentially thrive in the face of chaos. This library is an open-source tool for the systems thinker, the quantitative analyst, and the professional developer. It is designed to provide the foundational architecture for building the most robust, adaptive, and intelligent trading systems on the TradingView platform. This library is a tool for that wisdom. It is not about having the single smartest algorithm, but about having a disciplined, data-driven process for selecting the one that is working right now. █ DISCLAIMER & IMPORTANT NOTES THIS IS A LIBRARY FOR ADVANCED DEVELOPERS: This script does nothing on its own. It is a powerful engine that must be integrated into other indicators and fed with valid strategy signals. PERFORMANCE IS HYPOTHETICAL: The shadow portfolio tracking is a simulation. It does not account for slippage, fees (unless manually added to P&L;), or the psychological pressure of live trading. LEARNING REQUIRES DATA: The selection algorithms require a sufficient number of trades (at least 20-30 per arm) to make statistically meaningful decisions. The engine will be less reliable during the initial "warm-up" period. "You don't need to be a rocket scientist. Investing is not a game where the guy with the 160 IQ beats the guy with the 130 IQ." — Warren Buffett Taking you to school. - Dskyz, Create with RL.

ספריית Pine Script®

מאת ‎DskyzInvestments‎

Volume Profile - Density of Density [DAFE]Volume Profile - Density of Density The Art & Science of Market Architecture: An AI-Enhanced Volume Profile & Order Flow Engine with a Revolutionary Visualization Core. █ PHILOSOPHY: BEYOND THE PROFILE, INTO THE DENSITY Standard Volume Profile shows you a one-dimensional story: where volume was traded. It shows you the first layer of density. But this is like looking at a galaxy and only seeing the stars, completely missing the gravitational forces, the dark matter, and the nebulae that give it structure. Volume Profile - Density of Density (VP-DoD) is a revolutionary leap forward. It was engineered to analyze the second order of market data: the properties of the density itself . We don't just ask "Where did volume trade?" We ask " Why did it trade there? What was the character of that volume? What is the statistical significance of its shape? What is the probability of what happens next?" This is a complete, institutional-grade analytical framework built on the DAFE principle: Data Analysis For Execution . It fuses a higher-timeframe structural engine, a proprietary microstructure delta engine, and a Bayesian AI into a single, cohesive intelligence system. It is designed to transform your chart from a flat, lagging record of the past into a living, three-dimensional map of market structure and intention. █ WHAT MAKES VP-DoD ULTIMATE UNLIKE ANY OTHER PROFILE TOOL? This is not just another volume profile script. It stands apart due to a suite of proprietary features previously unseen on this platform. Higher Timeframe (HTF) Core: While other profiles are trapped by the noise of your current chart, VP-DoD builds its foundation on a higher timeframe of your choice (e.g., Daily data on a 15m chart). This is its greatest strength. It filters out intraday noise to reveal the true, macro architectural levels where institutions have built their positions. Microstructure Hybrid Delta Engine: Standard delta is primitive. Our engine provides a far more accurate picture of order flow by simulating tick data and analyzing the battle between candle bodies (aggression) and wicks (absorption). It sees the hidden story inside the volume. Bayesian AI Confidence Model: This is not a simple weighted score. VP-DoD incorporates a genuine Bayesian inference model. It starts with a neutral "belief" about the market and continuously updates its Bullish/Bearish Confidence percentage based on new evidence from delta, POC velocity, and price action. It thinks like a professional quant, providing you with a real-time statistical edge. Advanced Statistical Analysis: It calculates metrics found nowhere else, such as Profile Entropy (a measure of market disorder) and Volatility Skew (a measure of fear vs. greed from the derivatives market), and normalizes them with Z-Scores for universal applicability. Revolutionary Visualization Engine: Data should be intuitive and beautiful. VP-DoD features 14 distinct, animated, and theme-aware rendering modes . From "Nebula Plasma" and "Liquid Metal" to "DNA Helix" and "Constellation Map," you can transform raw data into interactive data art, allowing you to perceive market structure in a way that resonates with your unique analytical style. █ THE ART OF ANALYSIS: A REVOLUTIONARY VISUALIZATION CORE Data is useless if it isn't intuitive. VP-DoD shatters the mold of boring, static indicators with a state-of-the-art visualization engine. This is where data analysis becomes data art. The Profile Itself: 14 Modes of Perception Choose how you want to see the market's architecture: Nebula Plasma & Quantum Matrix: Futuristic, cyberpunk aesthetics with vibrant glow effects that make HVNs and POCs pulse with energy. Thermal Vision & Heat Shimmer: Renders the profile as a heatmap, instantly drawing your eye to the "hottest" zones of institutional liquidity. Liquid Metal & Crystalline: Creates a tangible, almost physical representation of volume with metallic sheens, animated light flows, and faceted structures. 3D Depth Map & Prismatic Refraction: Uses layering and color channel separation to create a stunning illusion of depth, separating the profile into its core components. Particle Field & Constellation Map: Abstract, beautiful data art modes that represent volume as animated particles or glowing stars, connecting major nodes like celestial bodies. DNA Helix & Magnetic Field: Dynamic, animated modes that visualize the forces of attraction and repulsion around the POC and Value Area, representing the market's underlying code. The POC & Value Area: A Living, Breathing Structure The POC and VA are no longer static lines. They are a dynamic, interactive system designed for immediate contextual awareness: Multi-Layered Glow Effects: The POC and VA lines are rendered with multiple layers of glowing, pulsating light, giving them a vibrant, three-dimensional presence on your chart. Dynamic Labels & Badges: Each key level (POC, VAH, VAL) features an advanced label block showing not just the price, but the real-time distance from the current price, and a status badge (e.g., "▲ ABOVE", "◆ INSIDE") that changes color and text based on price interaction. Intelligent Color Adaptation: The color of the VAH and VAL lines dynamically changes. A VAH line will glow bright green when price is breaking above it, but will appear dim and neutral when price is far below it, providing instant visual cues about market context. █ ACTIONABLE INTELLIGENCE: THE SIGNAL & ALERT SYSTEM VP-DoD is not just an analytical tool; it's a complete trading framework with a built-in, context-aware signal system. Absorption/Distribution Signals (🏦): The "Whale Signal." Triggers when price and delta are in stark divergence, indicating large passive orders are absorbing the market—a classic institutional maneuver. Coiling Signals (⚡): A high-probability setup that alerts you when the market is compressing (VA contracting, low entropy), storing energy for a significant breakout. POC Shift & VA Breakout Signals: Trend-initiation signals that fire when value is migrating and the market breaks out of its established balance area with conviction. Delta Extreme Signals: Contrarian reversal signals that detect capitulation at the extremes of buying or selling pressure, often marking key turning points. █ THE DASHBOARD: YOUR INSTITUTIONAL COMMAND CENTER The professional-grade dashboard provides a real-time, comprehensive overview of the market's hidden state. Market Regime: Instantly know if the market is BALANCED, COILING, TRENDING , or VOLATILE . Advanced Metrics: Monitor Entropy (disorder), Volatility Skew (fear/greed), and a composite Risk Score . Institutional Score: See the calculated Liquidity Score and Conviction Level , grading the quality of the current market structure. Bayesian AI: The crown jewel. See the real-time, AI-calculated Bull vs. Bear Confidence percentages, giving you a statistical edge on the probable direction of the next move. Breakout Gauge: A forward-looking metric that calculates the Breakout Probability and its likely Bias (Bullish/Bearish). █ DEVELOPMENT PHILOSOPHY VP-DoD Ultimate was created out of a passion for revealing the hidden architecture of the market. We believe that the most profound truths are found at the intersection of rigorous science and intuitive art. This tool is the culmination of thousands of hours of research into market microstructure, statistical analysis, and data visualization. It is for the trader who is no longer satisfied with lagging indicators and seeks a deeper, more contextual understanding of the market auction. It is for the trader who believes that analysis should be not only effective but also beautiful. VP-DoD Ultimate is designed to help you ride the trend with confidence, but more importantly, to give you the data-driven intelligence to anticipate that final, critical bend. █ DISCLAIMER AND BEST PRACTICES CONTEXT IS KING: This is an advanced contextual tool, not a simple "buy/sell" signal indicator. Use its intelligence to frame your trades within your own strategy. RISK MANAGEMENT IS PARAMOUNT: All trading involves substantial risk. The signals and levels provided are based on historical data and statistical probability, not guarantees. HTF IS YOUR GUIDE: For the highest probability setups, use the HTF feature (e.g., 240m or Daily) to identify macro structure. Then, execute trades on a lower timeframe based on interactions with these key macro levels. ALIGN WITH THE REGIME: Pay close attention to the "Regime" and "Entropy" readouts on the dashboard. Trading a breakout strategy during a high-entropy "RANGING" regime is a low-probability endeavor. Align your strategy with the market's current state. "The trend is your friend, except at the end where it bends." — Ed Seykota, Market Wizard Taking you to school. - Dskyz, Trade with Volume. Trade with Density. Trade with DAFE

אינדיקטור Pine Script®

מאת ‎DskyzInvestments‎

BTC - ALSI: Altcoin Season Index (Dynamic Eras)Title: BTC - ALSI: Altcoin Season Index (Dynamic Eras) Overview & Philosophy The Altcoin Season Index (ALSI) is a quantitative tool designed to answer the most critical question in crypto capital rotation: "Is it time to hold Bitcoin, or is it time to take risks on Altcoins?" Most "Altseason" indicators suffer from Survivor Bias or Obsolescence. They either track a static list of coins that includes "dead" assets from previous cycles (ghosts of 2017), or they break completely when major tokens collapse (like LUNA or FTT). This indicator solves this by using a Time-Varying Basket. The indicator automatically adjusts its reference list of Top 20 coins based on historical eras. This ensures the index tracks the winners of the moment—capturing the DeFi summer of 2020, the NFT craze of 2021, and the AI/Meme narratives of 2024/2025. Methodology The indicator calculates the percentage of the Top 20 Altcoins that are outperforming Bitcoin over a rolling window (Default: 90 Days). The "Win" Count: For every major Altcoin performing better than BTC, the index adds a point. Dynamic Eras: The basket of coins changes depending on the date: 2020 Era (DeFi Summer): Tracks the "Blue Chips" of the DeFi revolution like UNI, LINK, DOT, and early movers like VET and FIL. 2021 Era (Layer 1 Wars): Tracks the explosion of alternative smart contract platforms, adding winners like SOL, AVAX, MATIC, and ALGO. 2022 Era (The Survivors): Filters for resilience during the Bear Market, solidifying the status of established assets like SHIB and ATOM. 2023 Era (Infrastructure & Scale): Captures the rise of "Next-Gen" tech leading into the pre-halving year, introducing TON, APT (Aptos), and ARB (Arbitrum). 2024/25 Era (AI & Speed): Tracks the current Super-Cycle leaders, focusing on the AI narrative (TAO, RNDR), High-Performance L1s (SUI), and modern Memes (PEPE). Chart Analysis & Strategy ( The "Alpha" ) As seen in the chart above, there is a strong correlation between ALSI Peaks and local tops in TOTAL3 (The Crypto Market Cap excluding BTC & ETH). The Entry (Rotation): When the indicator rises above the neutral 50 line, it signals that capital is beginning to rotate out of Bitcoin and into Altcoins. This has historically been a strong confirmation signal to increase exposure to high-beta assets. The Exit (Saturation): When the indicator hits 100 (or sustains in the Red Zone > 75), it means every single Altcoin is beating Bitcoin. Historically, this extreme exuberance often marks a local top in the TOTAL3 chart. This is the zone where smart money typically sells into strength, rather than opening new positions. How to Read the Visuals 🚀 Altcoin Season (Red Zone > 75): Strong Altcoin dominance. The market is "Risk On." 🛡️ Bitcoin Season (Blue Zone < 25): Bitcoin dominance. Alts are bleeding against BTC. Historically, this is a defensive zone to hold BTC or Stablecoins. Data Dashboard: A status table in the bottom-right corner displays the live Index Value, current Regime, and a System Check to ensure all 20 data feeds are active. Settings Lookback Period: Default 90 Days. Lowering this (e.g., to 30) makes the index faster but noisier. Thresholds: Adjustable zones for Altcoin Season (Default: 75) and Bitcoin Season (Default: 25). Credits & Attribution This open-source indicator is built on the shoulders of giants. I acknowledge the original creators of the concept and the pioneers of its implementation on TradingView: Original Concept: BlockchainCenter.net. - They established the industry standard definition: 75% of the Top 50 coins outperforming Bitcoin over 90 days = Altseason.. TradingView Implementation: Adam_Nguyen - He implemented the "Dynamic Era" logic (updating the coin list annually) on TradingView. Our code structure for the time-based switching is inspired by his methodology. See also his implementation in the chart. ( Altcoin Season Index - Adam) . Comparison: Why use ALSI | RM? While inspired by the above, ALSI introduces three key improvements: Open Source: Unlike other popular TradingView versions (which are closed-source), this script is fully transparent. You can see exactly which coins are triggering the signal. Sanitized History (Anti-Fragile): Historical Top 20 snapshots are not blindly used. "Dead" coins (like LUNA and FTT) from previous eras are manually filtered out. A raw index would crash during the Terra/FTX collapses, giving a false "Bitcoin Season" signal purely due to bad actors. The curated list preserves the integrity of the market structure signal. Narrative Relevance: The 2024/25 basket was updated to include TAO (Bittensor) and RNDR, ensuring the index captures the dominant AI narrative, rather than tracking fading assets from the previous cycle. You can compare the ALSI indicator with other available tradingview indicators in the chart: Different indicators for the same idea are shown in the 3 Pane window below the BTC and Total3 chart, whereas ALSI is the top pane indicator. Important Note on Coin Selection Baskets are highly curated: Dead/irrelevant coins (FTT, LUNA, BSV) are excluded for clean signals. This prevents historical breaks and ensures Era T5 captures current narratives (AI, Memes) via TAO/RNDR. See above. Users are free to adjust the source code to test their own baskets. Disclaimer This script is for research and educational purposes only. Past correlations between ALSI and TOTAL3 do not guarantee future results. Market regimes can change, and "Altseasons" can be cut short by macro events. Tags bitcoin, btc, altseason, dominance, total3, rotation, cycle, index, alsi, Rob Maths

אינדיקטור Pine Script®

מאת ‎Rob_Maths‎

AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend)The AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend) is a cutting-edge indicator that combines advanced mathematical modeling, AI-driven analytics, and segment-based pattern recognition to forecast price movements with precision. This tool is designed to provide traders with deep insights into market dynamics by leveraging multivariate pattern detection and sophisticated predictive algorithms. 👽 Core Features Segment-Based Pattern Recognition At its heart, the indicator divides price data into discrete segments, capturing key elements like candle bodies, high-low ranges, and wicks. These segments are normalized using ATR-based volatility adjustments to ensure robustness across varying market conditions. AI-Powered k-Nearest Neighbors (kNN) Prediction The predictive engine uses the kNN algorithm to identify the closest historical patterns in a multivariate dictionary. By calculating the distance between current and historical segments, the algorithm determines the most likely outcomes, weighting predictions based on either proximity (distance) or averages. Dynamic Dictionary of Historical Patterns The indicator maintains a rolling dictionary of historical patterns, storing multivariate data for: Candle body ranges, High-low ranges, Wick highs and lows. This dynamic approach ensures the model adapts continuously to evolving market conditions. Volatility-Normalized Forecasting Using ATR bands, the indicator normalizes patterns, reducing noise and enhancing the reliability of predictions in high-volatility environments. AI-Driven Trend Detection The indicator not only predicts price levels but also identifies market regimes by comparing current conditions to historically significant highs, lows, and midpoints. This allows for clear visualizations of trend shifts and momentum changes. 👽 Deep Dive into the Core Mathematics 👾 Segment-Based Multivariate Pattern Analysis The indicator analyzes price data by dividing each bar into distinct segments, isolating key components such as: Body Ranges: Differences between the open and close prices. High-Low Ranges: Capturing the full volatility of a bar. Wick Extremes: Quantifying deviations beyond the body, both above and below. Each segment contributes uniquely to the predictive model, ensuring a rich, multidimensional understanding of price action. These segments are stored in a rolling dictionary of patterns, enabling the indicator to reference historical behavior dynamically. 👾 Volatility Normalization Using ATR To ensure robustness across varying market conditions, the indicator normalizes patterns using Average True Range (ATR). This process scales each component to account for the prevailing market volatility, allowing the algorithm to compare patterns on a level playing field regardless of differing price scales or fluctuations. 👾 k-Nearest Neighbors (kNN) Algorithm The AI core employs the kNN algorithm, a machine-learning technique that evaluates the similarity between the current pattern and a library of historical patterns. Euclidean Distance Calculation: The indicator computes the multivariate distance across four distinct dimensions: body range, high-low range, wick low, and wick high. This ensures a comprehensive and precise comparison between patterns. Weighting Schemes: The contribution of each pattern to the forecast is either weighted by its proximity (distance) or averaged, based on user settings. 👾 Prediction Horizon and Refinement The indicator forecasts future price movements (Y_hat) by predicting logarithmic changes in the price and projecting them forward using exponential scaling. This forecast is smoothed using a user-defined EMA filter to reduce noise and enhance actionable clarity. 👽 AI-Driven Pattern Recognition Dynamic Dictionary of Patterns: The indicator maintains a rolling dictionary of N multivariate patterns, continuously updated to reflect the latest market data. This ensures it adapts seamlessly to changing market conditions. Nearest Neighbor Matching: At each bar, the algorithm identifies the most similar historical pattern. The prediction is based on the aggregated outcomes of the closest neighbors, providing confidence levels and directional bias. Multivariate Synthesis: By combining multiple dimensions of price action into a unified prediction, the indicator achieves a level of depth and accuracy unattainable by single-variable models. Visual Outputs Forecast Line (Y_hat_line): A smoothed projection of the expected price trend, based on the weighted contribution of similar historical patterns. Trend Regime Bands: Dynamic high, low, and midlines highlight the current market regime, providing actionable insights into momentum and range. Historical Pattern Matching: The nearest historical pattern is displayed, allowing traders to visualize similarities 👽 Applications Trend Identification: Detect and follow emerging trends early using dynamic trend regime analysis. Reversal Signals: Anticipate market reversals with high-confidence predictions based on historically similar scenarios. Range and Momentum Trading: Leverage multivariate analysis to understand price ranges and momentum, making it suitable for both breakout and mean-reversion strategies. Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.

אינדיקטור Pine Script®

מאת ‎aibitcointrend‎

QTechLabs Machine Learning Logistic Regression Indicator [Lite]QTechLabs Machine Learning Logistic Regression Indicator Ver5.1 1st January 2026 Author: QTechLabs Description A lightweight logistic-regression-based signal indicator (Q# ML Logistic Regression Indicator ) for TradingView. It computes two normalized features (short log-returns and a synthetic nonlinear transform), applies fixed logistic weights to produce a probability score, smooths that score with an EMA, and emits BUY/SELL markers when the smoothed probability crosses configurable thresholds. Quick analysis (how it works) - Price source: selectable (Open/High/Low/Close/HL2/HLC3/OHLC4). - Features: - ret = log(ds / ds ) — short log-return over ret_lookback bars. - synthetic = log(abs(ds^2 - 1) + 0.5) — a nonlinear “synthetic” feature. - Both features normalized over a 20‑bar window to range ~0–1. - Fixed logistic regression weights: w0 = -2.0 (bias), w1 = 2.0 (ret), w2 = 1.0 (synthetic). - Probability = sigmoid(w0 + w1*norm_ret + w2*norm_synthetic). - Smoothed probability = EMA(prob, smooth_len). - Signals: - BUY when sprob > threshold. - SELL when sprob < (1 - threshold). - Visual buy/sell shapes plotted and alert conditions provided. - Defaults: threshold = 0.6, ret_lookback = 3, smooth_len = 3. User instructions 1. Add indicator to chart and pick the Price Source that matches your strategy (Close is default). 2. Verify weight of ret_lookback (default 3) — increase for slower signals, decrease for faster signals. 3. Threshold: default 0.6 — higher = fewer signals (more confidence), lower = more signals. Recommended range 0.55–0.75. 4. Smoothing: smooth_len (EMA) reduces chattiness; increase to reduce whipsaws. 5. Use the indicator as a directional filter / signal generator, not a standalone execution system. Combine with trend confirmation (e.g., higher-timeframe MA) and risk management. 6. For alerts: enable the built-in Buy Signal and Sell Signal alertconditions and customize messages in TradingView alerts. 7. Do NOT mechanically polish/modify the code weights unless you backtest — weights are pre-set and tuned for the Lite heuristic. Practical tips & caveats - The synthetic feature is heuristic and may behave unpredictably on extreme price values or illiquid symbols (watch normalization windows). - Normalization uses a 20-bar lookback; on very low-volume or thinly traded assets this can produce unstable norms — increase normalization window if needed. - This is a simple model: expect false signals in choppy ranges. Always backtest on your instrument and timeframe. - The indicator emits instantaneous cross signals; consider adding debounce (e.g., require confirmation for N bars) or a position-sizing rule before live trading. - For non-destructive testing of performance, run the indicator through TradingView’s strategy/backtest wrapper or export signals for out-of-sample testing. Recommended starter settings - Swing / daily: Price Source = Close, ret_lookback = 5–10, threshold = 0.62–0.68, smooth_len = 5–10. - Intraday / scalping: Price Source = Close or HL2, ret_lookback = 1–3, threshold = 0.55–0.62, smooth_len = 2–4. A Quantum-Inspired Logistic Regression Framework for Algorithmic Trading Overview This description introduces a quantum-inspired logistic regression framework developed by QTechLabs for algorithmic trading, implementing logistic regression in Q# to generate robust trading signals. By integrating quantum computational techniques with classical predictive models, the framework improves both accuracy and computational efficiency on historical market data. Rigorous back-testing demonstrates enhanced performance and reduced overfitting relative to traditional approaches. This methodology bridges the gap between emerging quantum computing paradigms and practical financial analytics, providing a scalable and innovative tool for systematic trading. Our results highlight the potential of quantum enhanced machine learning to advance applied finance. Introduction Algorithmic trading relies on computational models to generate high-frequency trading signals and optimize portfolio strategies under conditions of market uncertainty. Classical statistical approaches, including logistic regression, have been extensively applied for market direction prediction due to their interpretability and computational tractability. However, as datasets grow in dimensionality and temporal granularity, classical implementations encounter limitations in scalability, overfitting mitigation, and computational efficiency. Quantum computing, and specifically Q#, provides a framework for implementing quantum inspired algorithms capable of exploiting superposition and parallelism to accelerate certain computational tasks. While theoretical studies have proposed quantum machine learning models for financial prediction, practical applications integrating classical statistical methods with quantum computing paradigms remain sparse. This work presents a Q#-based implementation of logistic regression for algorithmic trading signal generation. The framework leverages Q#’s simulation and state-space exploration capabilities to efficiently process high-dimensional financial time series, estimate model parameters, and generate probabilistic trading signals. Performance is evaluated using historical market data and benchmarked against classical logistic regression, with a focus on predictive accuracy, overfitting resistance, and computational efficiency. By coupling classical statistical modeling with quantum-inspired computation, this study provides a scalable, technically rigorous approach for systematic trading and demonstrates the potential of quantum enhanced machine learning in applied finance. Methodology 1. Data Acquisition and Pre-processing Historical financial time series were sourced from , spanning . The dataset includes OHLCV (Open, High, Low, Close, Volume) data for multiple equities and indices. Feature Engineering: ○ Log-returns: ○ Technical indicators: moving averages (MA), exponential moving averages (EMA), relative strength index (RSI), Bollinger Bands ○ Lagged features to capture temporal dependencies Normalization: All features scaled via z-score normalization: z = \frac{x - \mu}{\sigma} ● Data Partitioning: ○ Training set: 70% of chronological data ○ Validation set: 15% ○ Test set: 15% Temporal ordering preserved to avoid look-ahead bias. Logistic Regression Model The classical logistic regression model predicts the probability of market movement in a binary framework (up/down). Mathematical formulation: P(y_t = 1 | X_t) = \sigma(X_t \beta) = \frac{1}{1 + e^{-X_t \beta}} is the feature matrix at time is the vector of model coefficients is the logistic sigmoid function Loss Function: Binary cross-entropy: \mathcal{L}(\beta) = -\frac{1}{N} \sum_{t=1}^{N} \left MLLR Trading System Implementation Framework: Utilizes the Microsoft Quantum Development Kit (QDK) and Q# language for quantum-inspired computation. Simulation Environment: Q# simulator used to represent quantum states for parallel evaluation of logistic regression updates. Parameter Update Algorithm: Quantum-inspired gradient evaluation using amplitude encoding of feature vectors ○ Parallelized computation of gradient components leveraging superposition ○ Classical post-processing to update coefficients: \beta_{t+1} = \beta_t - \eta \nabla_\beta \mathcal{L}(\beta_t) Back-Testing Protocol Signal Generation: Model outputs probability ; threshold used for binary signal assignment. ○ Trading positions: ■ Long if ■ Short if Performance Metrics: Accuracy, precision, recall ○ Profit and loss (PnL) ○ Sharpe ratio: \text{Sharpe} = \frac{\mathbb{E} }{\sigma_{R_t}} Comparison with baseline classical logistic regression Risk Management: Transaction costs incorporated as a fixed percentage per trade ○ Stop-loss and take-profit rules applied ○ Slippage simulated via historical intraday volatility Computational Considerations QTechLabs simulations executed on classical hardware due to quantum simulator limitations Parallelized batch processing of data to emulate quantum speedup Memory optimization applied to handle high-dimensional feature matrices Results Model Training and Convergence Logistic regression parameters converged within 500 iterations using quantum-inspired gradient updates. Learning rate , batch size = 128, with L2 regularization to mitigate overfitting. Convergence criteria: change in loss over 10 consecutive iterations. Observation: Q# simulation allowed parallel evaluation of gradient components, resulting in ~30% faster convergence compared to classical implementation on the same dataset. Predictive Performance Test set (15% of data) performance: Metric Q# Logistic Regression Classical Logistic Regression Accuracy 72.4% 68.1% Precision 70.8% 66.2% Recall 73.1% 67.5% F1 Score 71.9% 66.8% Interpretation: Q# implementation improved predictive metrics across all dimensions, indicating better generalization and reduced overfitting. Trading Signal Performance Signals generated based on threshold applied to historical OHLCV data. ● Key metrics over test period: Metric Q# LR Classical LR Cumulative PnL ($) 12,450 9,320 Sharpe Ratio 1.42 1.08 Max Drawdown ($) 1,120 1,780 Win Rate (%) 58.3 54.7 Interpretation: Quantum-enhanced framework demonstrated higher cumulative returns and lower drawdown, confirming risk-adjusted improvement over classical logistic regression. Computational Efficiency Q# simulation allowed simultaneous evaluation of multiple gradient components via amplitude encoding: ○ Effective speedup ~30% on classical hardware with 16-core CPU. Memory utilization optimized: feature matrix dimension . Numerical precision maintained at to ensure stable convergence. Statistical Significance McNemar’s test for classification improvement: \chi^2 = 12.6, \quad p < 0.001 Visual Analysis Figures / charts to include in manuscript: ROC curves comparing Q# vs. classical logistic regression Cumulative PnL curve over test period Coefficient evolution over iterations Feature importance analysis (via absolute values) Discussion The experimental results demonstrate that the Q#-enhanced logistic regression framework provides measurable improvements in both predictive performance and trading signal quality compared to classical logistic regression. The increase in accuracy (72.4% vs. 68.1%) and F1 score (71.9% vs. 66.8%) reflects enhanced model generalization and reduced overfitting, likely due to the quantum-inspired parallel evaluation of gradient components. The trading performance metrics further reinforce these findings. Cumulative PnL increased by approximately 33%, while the Sharpe ratio improved from 1.08 to 1.42, indicating superior risk adjusted returns. The reduction in maximum drawdown (1,120$ vs. 1,780$) demonstrates that the Q# framework not only enhances profitability but also mitigates downside risk, critical for systematic trading applications. Computationally, the Q# simulation enables parallel amplitude encoding of feature vectors, effectively accelerating the gradient computation and reducing iteration time by ~30%. This supports the hypothesis that quantum-inspired architectures can provide tangible efficiency gains even when executed on classical hardware, offering a bridge between theoretical quantum advantage and practical implementation. From a methodological perspective, this study demonstrates a hybrid approach wherein classical logistic regression is augmented by quantum computational techniques. The results suggest that quantum-inspired frameworks can enhance both algorithmic performance and model stability, opening avenues for further exploration in high-dimensional financial datasets and other predictive analytics domains. Limitations: The framework was tested on historical datasets; live market conditions, slippage, and dynamic market microstructure may affect real-world performance. The Q# implementation was run on a classical simulator; access to true quantum hardware may alter efficiency and scalability outcomes. Only logistic regression was tested; extension to more complex models (e.g., deep learning or ensemble methods) could further exploit quantum computational advantages. Implications for Future Research: Expansion to multi-class classification for portfolio allocation decisions Integration with reinforcement learning frameworks for adaptive trading strategies Deployment on quantum hardware for benchmarking real quantum advantage In conclusion, the Q#-enhanced logistic regression framework represents a technically rigorous and practical quantum-inspired approach to systematic trading, demonstrating improvements in predictive accuracy, risk-adjusted returns, and computational efficiency over classical implementations. This work establishes a foundation for future research at the intersection of quantum computing and applied financial machine learning. Conclusion and Future Work This study presents a quantum-inspired framework for algorithmic trading by implementing logistic regression in Q#. The methodology integrates classical predictive modeling with quantum computational paradigms, leveraging amplitude encoding and parallel gradient evaluation to enhance predictive accuracy and computational efficiency. Empirical evaluation using historical financial data demonstrates statistically significant improvements in predictive performance (accuracy, precision, F1 score), risk-adjusted returns (Sharpe ratio), and maximum drawdown reduction, relative to classical logistic regression benchmarks. The results confirm that quantum-inspired architectures can provide tangible benefits in systematic trading applications, even when executed on classical hardware simulators. This establishes a scalable and technically rigorous approach for high-dimensional financial prediction tasks, bridging the gap between theoretical quantum computing concepts and applied financial analytics. Future Work: Model Extension: Investigate quantum-inspired implementations of more complex machine learning algorithms, including ensemble methods and deep learning architectures, to further enhance predictive performance. Live Market Deployment: Test the framework in real-time trading environments to evaluate robustness against slippage, latency, and dynamic market microstructure. Quantum Hardware Implementation: Transition from classical simulation to quantum hardware to quantify real quantum advantage in computational efficiency and model performance. Multi-Asset and Multi-Class Predictions: Expand the framework to multi-class classification for portfolio allocation and risk diversification. In summary, this work provides a practical, technically rigorous, and scalable quantumenhanced logistic regression framework, establishing a foundation for future research at the intersection of quantum computing and applied financial machine learning. Q# ML Logistic Regression Trading System Summary Problem: Classical logistic regression for algorithmic trading faces scalability, overfitting, and computational efficiency limitations on high-dimensional financial data. Solution: Quantum-inspired logistic regression implemented in Q#: Leverages amplitude encoding and parallel gradient evaluation Processes high-dimensional OHLCV data Generates robust trading signals with probabilistic classification Methodology Highlights: Feature engineering: log-returns, MA, EMA, RSI, Bollinger Bands Logistic regression model: P(y_t = 1 | X_t) = \frac{1}{1 + e^{-X_t \beta}} 4. Back-testing: thresholded signals, Sharpe ratio, drawdown, transaction costs Key Results: Accuracy: 72.4% vs 68.1% (classical LR) Sharpe ratio: 1.42 vs 1.08 Max Drawdown: 1,120$ vs 1,780$ Statistically significant improvement (McNemar’s test, p < 0.001) Impact: Bridges quantum computing and financial analytics Enhances predictive performance, risk-adjusted returns, computational efficiency ● Scalable framework for systematic trading and applied finance research Future Work: Extend to ensemble/deep learning models ● Deploy in live trading environments ● Benchmark on quantum hardware. Appendix Q# Implementation Partial Code operation LogisticRegressionStep(features: Double , beta: Double , learningRate: Double) : Double { mutable updatedBeta = beta; // Compute predicted probability using sigmoid let z = Dot(features, beta); let p = 1.0 / (1.0 + Exp(-z)); // Compute gradient for (i in 0..Length(beta)-1) { let gradient = (p - Label) * features ; set updatedBeta w/= i <- updatedBeta - learningRate * gradient; { return updatedBeta; } Notes: ○ Dot() computes inner product of feature vector and coefficient vector ○ Label is the observed target value ○ Parallel gradient evaluation simulated via Q# superposition primitives Supplementary Tables Table S1: Feature importance rankings (|β| values) Table S2: Iteration-wise loss convergence Table S3: Comparative trading performance metrics (Q# vs. classical LR) Figures (Suggestions) ROC curves for Q# and classical LR Cumulative PnL curves Coefficient evolution over iterations Feature contribution heatmaps Machine Learning Trading Strategy: Literature Review and Methodology Authors: QTechLabs Date: December 2025 Abstract This manuscript presents a machine learning-based trading strategy, integrating classical statistical methods, deep reinforcement learning, and quantum-inspired approaches. Forward testing over multi-year datasets demonstrates robust alpha generation, risk management, and model stability. Introduction Machine learning has transformed quantitative finance (Bishop, 2006; Hastie, 2009; Hosmer, 2000). Classical methods such as logistic regression remain interpretable while deep learning and reinforcement learning offer predictive power in complex financial systems (Moody & Saffell, 2001; Deng et al., 2016; Li & Hoi, 2020). Literature Review 2.1 Foundational Machine Learning and Statistics Foundational ML frameworks guide algorithmic trading system design. Key references include Bishop (2006), Hastie (2009), and Hosmer (2000). 2.2 Financial Applications of ML and Algorithmic Trading Technical indicator prediction and automated trading leverage ML for alpha generation (Frattini et al., 2022; Qiu et al., 2024; QuantumLeap, 2022). Deep learning architectures can process complex market features efficiently (Heaton et al., 2017; Zhang et al., 2024). 2.3 Reinforcement Learning in Finance Deep reinforcement learning frameworks optimize portfolio allocation and trading decisions (Moody & Saffell, 2001; Deng et al., 2016; Jiang et al., 2017; Li et al., 2021). RL agents adapt to non-stationary markets using reward-maximizing policies. 2.4 Quantum and Hybrid Machine Learning Approaches Quantum-inspired techniques enhance exploration of complex solution spaces, improving portfolio optimization and risk assessment (Orus et al., 2020; Chakrabarti et al., 2018; Thakkar et al., 2024). 2.5 Meta-labelling and Strategy Optimization Meta-labelling reduces false positives in trading signals and enhances model robustness (Lopez de Prado, 2018; MetaLabel, 2020; Bagnall et al., 2015). Ensemble models further stabilize predictions (Breiman, 2001; Chen & Guestrin, 2016; Cortes & Vapnik, 1995). 2.6 Risk, Performance Metrics, and Validation Sharpe ratio, Sortino ratio, expected shortfall, and forward-testing are critical for evaluating trading strategies (Sharpe, 1994; Sortino & Van der Meer, 1991; More, 1988; Bailey & Lopez de Prado, 2014; Bailey & Lopez de Prado, 2016; Bailey et al., 2014). 2.7 Portfolio Optimization and Deep Learning Forecasting Portfolio optimization frameworks integrate deep learning for time-series forecasting, improving allocation under uncertainty (Markowitz, 1952; Bertsimas & Kallus, 2016; Feng et al., 2018; Heaton et al., 2017; Zhang et al., 2024). Methodology The methodology combines logistic regression, deep reinforcement learning, and quantum inspired models with walk-forward validation. Meta-labeling enhances predictive reliability while risk metrics ensure robust performance across diverse market conditions. Results and Discussion Sample forward testing demonstrates out-of-sample alpha generation, risk-adjusted returns, and model stability. Hyper parameter tuning, cross-validation, and meta-labelling contribute to consistent performance. Conclusion Integrating classical statistics, deep reinforcement learning, and quantum-inspired machine learning provides robust, adaptive, and high-performing trading strategies. Future work will explore additional alternative datasets, ensemble models, and advanced reinforcement learning techniques. References Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer. Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. Wiley. Frattini, A. et al. (2022). Financial Technical Indicator and Algorithmic Trading Strategy Based on Machine Learning and Alternative Data. Risks, 10(12), 225. doi.org Qiu, Y. et al. (2024). 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Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org Chakrabarti, S. et al. (2018). Quantum Algorithms for Finance: Portfolio Optimization and Option Pricing. Quantum Information Processing. doi.org Thakkar, S. et al. (2024). Quantum-inspired Machine Learning for Portfolio Risk Estimation. Quantum Machine Intelligence, 6, 27. doi.org Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. doi.org Lopez de Prado, M. (2020). The Use of MetaLabeling to Enhance Trading Signals. Journal of Financial Data Science, 2(3), 15–27. doi.org Bagnall, A. et al. (2015). The UEA & UCR Time Series Classification Repository. arXiv:1503.04048. arxiv.org Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. doi.org Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD, 2016. doi.org Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. doi.org Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49–58. doi.org Sortino, F. A., & Van der Meer, R. (1991). Downside Risk. Journal of Portfolio Management, 17(4), 27–31. doi.org More, R. (1988). Estimating the Expected Shortfall. Risk, 1, 35–39. Bailey, D. H., & Lopez de Prado, M. (2014). Forward-Looking Backtests and Walk-Forward Optimization. Journal of Investment Strategies, 3(2), 1–20. doi.org Bailey, D. H., & Lopez de Prado, M. (2016). The Deflated Sharpe Ratio. Journal of Portfolio Management, 42(5), 45–56. doi.org Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91. doi.org Bertsimas, D., & Kallus, J. N. (2016). Optimal Classification Trees. Machine Learning, 106, 103– 132. doi.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755– 15790. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of machine learning in quantitative trading. Applied and Computational Engineering, 82. direct.ewa.pub 6616 Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for HumanCentric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224– 2245. doi.org Cover, T. M. (1991). Universal Portfolios. Mathematical Finance. en.wikipedia.org rithm Wikipedia. Meta-Labeling. en.wikipedia.org Chakrabarti, S. et al. (2018). Quantum Algorithms for Finance: Portfolio Optimization and Option Pricing. Quantum Information Processing. doi.org Thakkar, S. et al. (2024). Quantum-inspired Machine Learning for Portfolio Risk Estimation. Quantum Machine Intelligence, 6, 27. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224–2245. doi.org Cover, T. M. (1991). Universal Portfolios. Mathematical Finance. en.wikipedia.org Wikipedia. Meta-Labeling. en.wikipedia.org Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for Quantitative Finance. arXiv:2111.05188. arxiv.org Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management. arXiv:2003.00613. arxiv.org Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Lopez de Prado, M. (2018). Advances in Financial Machine Learning. Wiley. doi.org Lopez de Prado, M. (2020). The Use of Meta-Labeling to Enhance Trading Signals. Journal of Financial Data Science, 2(3), 15–27. doi.org Bagnall, A. et al. (2015). The UEA & UCR Time Series Classification Repository. arXiv:1503.04048. arxiv.org Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. doi.org Chen, T., & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. KDD, 2016. doi.org Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273– 297. doi.org Sharpe, W. F. (1994). The Sharpe Ratio. Journal of Portfolio Management, 21(1), 49–58. doi.org Sortino, F. A., & Van der Meer, R. (1991). Downside Risk. Journal of Portfolio Management, 17(4), 27–31. doi.org More, R. (1988). Estimating the Expected Shortfall. Risk, 1, 35–39. Bailey, D. H., & Lopez de Prado, M. (2014). Forward-Looking Backtests and WalkForward Optimization. Journal of Investment Strategies, 3(2), 1–20. doi.org Bailey, D. H., & Lopez de Prado, M. (2016). The Deflated Sharpe Ratio. Journal of Portfolio Management, 42(5), 45–56. doi.org Bailey, D. H., Borwein, J., Lopez de Prado, M., & Zhu, Q. J. (2014). Pseudo- Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-ofSample Performance. Notices of the AMS, 61(5), 458–471. www.ams.org Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77–91. doi.org Bertsimas, D., & Kallus, J. N. (2016). Optimal Classification Trees. Machine Learning, 106, 103–132. doi.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790. doi.org Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org Gao, J. (2024). Applications of Machine Learning in Quantitative Trading. Applied and Computational Engineering, 82. direct.ewa.pub Niu, H. et al. (2022). MetaTrader: An RL Approach Integrating Diverse Policies for Portfolio Optimization. arXiv:2210.01774. arxiv.org Dutta, S. et al. (2024). QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction. arXiv:2408.03088. arxiv.org Bagarello, F., Gargano, F., & Khrennikova, P. (2025). Quantum Logic as a New Frontier for Human-Centric AI in Finance. arXiv:2510.05475. arxiv.org Herman, D. et al. (2022). A Survey of Quantum Computing for Finance. arXiv:2201.02773. ideas.repec.org Financial Innovation (2025). From portfolio optimization to quantum blockchain and security: a systematic review of quantum computing in finance. Financial Innovation, 11, 88. doi.org Cheng, C. et al. (2024). Quantum Finance and Fuzzy RL-Based Multi-agent Trading System. International Journal of Fuzzy Systems, 7, 2224–2245. doi.org Cover, T. M. (1991). Universal Portfolios. Mathematical Finance. en.wikipedia.org Wikipedia. Meta-Labeling. en.wikipedia.org Orus, R., Mugel, S., & Lizaso, E. (2020). Quantum Computing for Finance: Overview and Prospects. Reviews in Physics, 4, 100028. doi.org FinRL-Podracer, Z. L. et al. (2021). Scalable Deep Reinforcement Learning for Quantitative Finance. arXiv:2111.05188. arxiv.org Li, X., & Hoi, S. C. H. (2020). Deep Reinforcement Learning in Portfolio Management. arXiv:2003.00613. arxiv.org Jiang, Z. et al. (2017). A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem. arXiv:1706.10059. arxiv.org Feng, G. et al. (2018). Deep Learning for Time Series Forecasting in Finance. Expert Systems with Applications, 113, 184–199. doi.org Heaton, J., Polson, N., & Witte, J. (2017). Deep Learning in Finance. arXiv:1602.06561. arxiv.org Zhang, L. et al. (2024). Deep Learning Methods for Forecasting Financial Time Series: A Survey. Neural Computing and Applications, 36, 15755–15790. doi.org 100.Rundo, F. et al. (2019). Machine Learning for Quantitative Finance Applications: A Survey. Applied Sciences, 9(24), 5574. doi.org 🔹 MLLR Advanced / Institutional — Framework License Positioning Statement The MLLR Advanced offering provides licensed access to a published quantitative framework, including documented empirical behaviour, retraining protocols, and portfolio-level extensions. This offering is intended for professional researchers, quantitative traders, and institutional users requiring methodological transparency and governance compatibility. Commercial and Practical Implications While the primary contribution of this work is methodological, the proposed framework has practical relevance for real-world trading and research environments. The model is designed to operate under realistic constraints, including transaction costs, regime instability, and limited retraining frequency, making it suitable for both exploratory research and constrained deployment scenarios. The framework has been implemented internally by the authors for live and paper trading across multiple asset classes, primarily as a mechanism to fund continued independent research and development. This self-funded approach allows the research team to remain free from external commercial or grant-driven constraints, preserving methodological independence and transparency. Importantly, the authors do not present the model as a guaranteed alpha-generating strategy. Instead, it should be understood as a probabilistic classification framework whose performance is regime-dependent and subject to the well-documented risks of non-stationary in financial time series. Potential users are encouraged to treat the framework as a research reference implementation rather than a turnkey trading system. From a broader perspective, the work demonstrates how relatively simple machine learning models, when subjected to rigorous validation and forward testing, can still offer practical value without resorting to excessive model complexity or opaque optimisation practices. 🧑‍ 🔬 Reviewer #1 — Quantitative Methods Comment The authors demonstrate commendable restraint in model complexity and provide a clear discussion of overfitting risks and regime sensitivity. The forward-testing methodology is particularly welcome, though additional clarification on retraining frequency would further strengthen the work. What This Does : Validates methodological seriousness Signals anti-overfitting discipline Makes institutional buyers comfortable Justifies premium pricing for “boring but robust” research 🧑‍ 🔬 Reviewer #2 — Empirical Finance Comment Unlike many applied trading studies, this paper avoids exaggerated performance claims and instead focuses on robustness and reproducibility. While the reported returns are modest, the framework’s transparency and adaptability are notable strengths. What This Does: “Modest returns” = credible returns Transparency becomes your product’s USP Supports long-term subscriptions Filters out unrealistic retail users (a good thing) 🧑‍ 🔬 Reviewer #3 — Applied Machine Learning Comment The use of logistic regression may appear simplistic relative to contemporary deep learning approaches; however, the authors convincingly argue that interpretability and stability are preferable in non-stationary financial environments. The discussion of failure modes is particularly valuable. What This Does : Positions MLLR as deliberately chosen, not outdated Interpretability = institutional gold “Failure modes” language is rare and powerful Strongly supports institutional licensing 🧑‍ 🔬 Associate Editor Summary Comment This paper makes a useful applied contribution by demonstrating how constrained machine learning models can be responsibly deployed in financial contexts. The manuscript would benefit from minor clarifications but is suitable for publication. What This Does: “Responsibly deployed” is commercial dynamite Lets you say “peer-reviewed applied framework” Strong pricing anchor for Standard & Institutional tiers

אינדיקטור Pine Script®

מאת ‎QTechLabsInfo‎

Luxy Momentum, Trend, Bias and Breakout Indicators V7 TABLE OF CONTENTS This is Version 7 (V7) - the latest and most optimized release. If you are using any older versions (V6, V5, V4, V3, etc.), it is highly recommended to replace them with V7. Why This Indicator is Different Who Should Use This Core Components Overview The UT Bot Trading System Understanding the Market Bias Table Candlestick Pattern Recognition Visual Tools and Features How to Use the Indicator Performance and Optimization FAQ --- ### CREDITS & ATTRIBUTION This indicator implements proven trading concepts using entirely original code developed specifically for this project. ### CONCEPTUAL FOUNDATIONS • UT Bot ATR Trailing System - Original concept by @QuantNomad: (search "UT-Bot-Strategy" - Our version is a complete reimplementation with significant enhancements: - Volume-weighted momentum adjustment - Composite stop loss from multiple S/R layers - Multi-filter confirmation system (swing, %, 2-bar, ZLSMA) - Full integration with multi-timeframe bias table - Visual audit trail with freeze-on-touch - NOTE: No code was copied - this is a complete reimplementation with enhancements. • Standard Technical Indicators (Public Domain Formulas): - Supertrend: ATR-based trend calculation with custom gradient fills - MACD: Gerald Appel's formula with separation filters - RSI: J. Welles Wilder's formula with pullback zone logic - ADX/DMI: Custom trend strength formula inspired by Wilder's directional movement concept, reimplemented with volume weighting and efficiency metrics - ZLSMA: Zero-lag formula enhanced with Hull MA and momentum prediction ### Custom Implementations - Trend Strength: Inspired by Wilder's ADX concept but using volume-weighted pressure calculation and efficiency metrics (not traditional +DI/-DI smoothing) - All code implementations are original ### ORIGINAL FEATURES (70%+ of codebase) - Multi-Timeframe Bias Table with live updates - Risk Management System (R-multiple TPs, freeze-on-touch) - Opening Range Breakout tracker with session management - Composite Stop Loss calculator using 6+ S/R layers - Performance optimization system (caching, conditional calcs) - VIX Fear Index integration - Previous Day High/Low auto-detection - Candlestick pattern recognition with interactive tooltips - Smart label and visual management - All UI/UX design and table architecture ### DEVELOPMENT PROCESS **AI Assistance:** This indicator was developed over 2+ months with AI assistance (ChatGPT/Claude) used for: - Writing Pine Script code based on design specifications - Optimizing performance and fixing bugs - Ensuring Pine Script v6 compliance - Generating documentation **Author's Role:** All trading concepts, system design, feature selection, integration logic, and strategic decisions are original work by the author. The AI was a coding tool, not the system designer. **Transparency:** We believe in full disclosure - this project demonstrates how AI can be used as a powerful development tool while maintaining creative and strategic ownership. --- 1. WHY THIS INDICATOR IS DIFFERENT Most traders use multiple separate indicators on their charts, leading to cluttered screens, conflicting signals, and analysis paralysis. The Suite solves this by integrating proven technical tools into a single, cohesive system. Key Advantages: All-in-One Design: Instead of loading 5-10 separate indicators, you get everything in one optimized script. This reduces chart clutter and improves TradingView performance. Multi-Timeframe Bias Table: Unlike standard indicators that only show the current timeframe, the Bias Table aggregates trend signals across multiple timeframes simultaneously. See at a glance whether 1m, 5m, 15m, 1h are aligned bullish or bearish - no more switching between charts. Smart Confirmations: The indicator doesn't just give signals - it shows you WHY. Every entry has multiple layers of confirmation (MA cross, MACD momentum, ADX strength, RSI pullback, volume, etc.) that you can toggle on/off. Dynamic Stop Loss System: Instead of static ATR stops, the SL is calculated from multiple support/resistance layers: UT trailing line, Supertrend, VWAP, swing structure, and MA levels. This creates more intelligent, price-action-aware stops. R-Multiple Take Profits: Built-in TP system calculates targets based on your initial risk (1R, 1.5R, 2R, 3R). Lines freeze when touched with visual checkmarks, giving you a clean audit trail of partial exits. Educational Tooltips Everywhere: Every single input has detailed tooltips explaining what it does, typical values, and how it impacts trading. You're not guessing - you're learning as you configure. Performance Optimized: Smart caching, conditional calculations, and modular design mean the indicator runs fast despite having 15+ features. Turn off what you don't use for even better performance. No Repainting: All signals respect bar close. Alerts fire correctly. What you see in history is what you would have gotten in real-time. What Makes It Unique: Integrated UT Bot + Bias Table: No other indicator combines UT Bot's ATR trailing system with a live multi-timeframe dashboard. You get precision entries with macro trend context. Candlestick Pattern Recognition with Interactive Tooltips: Patterns aren't just marked - hover over any emoji for a full explanation of what the pattern means and how to trade it. Opening Range Breakout Tracker: Built-in ORB system for intraday traders with customizable session times and real-time status updates in the Bias Table. Previous Day High/Low Auto-Detection: Automatically plots PDH/PDL on intraday charts with theme-aware colors. Updates daily without manual input. Dynamic Row Labels in Bias Table: The table shows your actual settings (e.g., "EMA 10 > SMA 20") not generic labels. You know exactly what's being evaluated. Modular Filter System: Instead of forcing a fixed methodology, the indicator lets you build your own strategy. Start with just UT Bot, add filters one at a time, test what works for your style. --- 2. WHO WHOULD USE THIS Designed For: Intermediate to Advanced Traders: You understand basic technical analysis (MAs, RSI, MACD) and want to combine multiple confirmations efficiently. This isn't a "one-click profit" system - it's a professional toolkit. Multi-Timeframe Traders: If you trade one asset but check multiple timeframes for confirmation (e.g., enter on 5m after checking 15m and 1h alignment), the Bias Table will save you hours every week. Trend Followers: The indicator excels at identifying and following trends using UT Bot, Supertrend, and MA systems. If you trade breakouts and pullbacks in trending markets, this is built for you. Intraday and Swing Traders: Works equally well on 5m-1h charts (day trading) and 4h-D charts (swing trading). Scalpers can use it too with appropriate settings adjustments. Discretionary Traders: This isn't a black-box system. You see all the components, understand the logic, and make final decisions. Perfect for traders who want tools, not automation. Works Across All Markets: Stocks (US, international) Cryptocurrency (24/7 markets supported) Forex pairs Indices (SPY, QQQ, etc.) Commodities NOT Ideal For : Complete Beginners: If you don't know what a moving average or RSI is, start with basics first. This indicator assumes foundational knowledge. Algo Traders Seeking Black Box: This is discretionary. Signals require context and confirmation. Not suitable for blind automated execution. Mean-Reversion Only Traders: The indicator is trend-following at its core. While VWAP bands support mean-reversion, the primary methodology is trend continuation. --- 3. CORE COMPONENTS OVERVIEW The indicator combines these proven systems: Trend Analysis: Moving Averages: Four customizable MAs (Fast, Medium, Medium-Long, Long) with six types to choose from (EMA, SMA, WMA, VWMA, RMA, HMA). Mix and match for your style. Supertrend: ATR-based trend indicator with unique gradient fill showing trend strength. One-sided ribbon visualization makes it easier to see momentum building or fading. ZLSMA : Zero-lag linear-regression smoothed moving average. Reduces lag compared to traditional MAs while maintaining smooth curves. Momentum & Filters: MACD: Standard MACD with separation filter to avoid weak crossovers. RSI: Pullback zone detection - only enter longs when RSI is in your defined "buy zone" and shorts in "sell zone". ADX/DMI: Trend strength measurement with directional filter. Ensures you only trade when there's actual momentum. Volume Filter: Relative volume confirmation - require above-average volume for entries. Donchian Breakout: Optional channel breakout requirement. Signal Systems: UT Bot: The primary signal generator. ATR trailing stop that adapts to volatility and gives clear entry/exit points. Base Signals: MA cross system with all the above filters applied. More conservative than UT Bot alone. Market Bias Table: Multi-timeframe dashboard showing trend alignment across 7 timeframes plus macro bias (3-day, weekly, monthly, quarterly, VIX). Candlestick Patterns: Six major reversal patterns auto-detected with interactive tooltips. ORB Tracker: Opening range high/low with breakout status (intraday only). PDH/PDL: Previous day levels plotted automatically on intraday charts. VWAP + Bands : Session-anchored VWAP with up to three standard deviation band pairs. --- 4. THE UT BOT TRADING SYSTEM The UT Bot is the heart of the indicator's signal generation. It's an advanced ATR trailing stop that adapts to market volatility. Why UT Bot is Superior to Fixed Stops: Traditional ATR stops use a fixed multiplier (e.g., "stop = entry - 2×ATR"). UT Bot is smarter: It TRAILS the stop as price moves in your favor It WIDENS during high volatility to avoid premature stops It TIGHTENS during consolidation to lock in profits It FLIPS when price breaks the trailing line, signaling reversals Visual Elements You'll See: Orange Trailing Line: The actual UT stop level that adapts bar-by-bar Buy/Sell Labels: Aqua triangle (long) or orange triangle (short) when the line flips ENTRY Line: Horizontal line at your entry price (optional, can be turned off) Suggested Stop Loss: A composite SL calculated from multiple support/resistance layers: - UT trailing line - Supertrend level - VWAP - Swing structure (recent lows/highs) - Long-term MA (200) - ATR-based floor Take Profit Lines: TP1, TP1.5, TP2, TP3 based on R-multiples. When price touches a TP, it's marked with a checkmark and the line freezes for audit trail purposes. Status Messages: "SL Touched ❌" or "SL Frozen" when the trade leg completes. How UT Bot Differs from Other ATR Systems: Multiple Filters Available: You can require 2-bar confirmation, minimum % price change, swing structure alignment, or ZLSMA directional filter. Most UT implementations have none of these. Smart SL Calculation: Instead of just using the UT line as your stop, the indicator suggests a better SL based on actual support/resistance. This prevents getting stopped out by wicks while keeping risk controlled. Visual Audit Trail: All SL/TP lines freeze when touched with clear markers. You can review your trades weeks later and see exactly where entries, stops, and targets were. Performance Options: "Draw UT visuals only on bar close" lets you reduce rendering load without affecting logic or alerts - critical for slower machines or 1m charts. Trading Logic: UT Bot flips direction (Buy or Sell signal appears) Check Bias Table for multi-timeframe confirmation Optional: Wait for Base signal or candlestick pattern Enter at signal bar close or next bar open Place stop at "Suggested Stop Loss" line Scale out at TP levels (TP1, TP2, TP3) Exit remaining position on opposite UT signal or stop hit --- 5. UNDERSTANDING THE MARKET BIAS TABLE This is the indicator's unique multi-timeframe intelligence layer. Instead of looking at one chart at a time, the table aggregates signals across seven timeframes plus macro trend bias. Why Multi-Timeframe Analysis Matters: Professional traders check higher and lower timeframes for context: Is the 1h uptrend aligning with my 5m entry? Are all short-term timeframes bullish or just one? Is the daily trend supportive or fighting me? Doing this manually means opening multiple charts, checking each indicator, and making mental notes. The Bias Table does it automatically in one glance. Table Structure: Header Row: On intraday charts: 1m, 5m, 15m, 30m, 1h, 2h, 4h (toggle which ones you want) On daily+ charts: D, W, M (automatic) Green dot next to title = live updating Headline Rows - Macro Bias: These show broad market direction over longer periods: 3 Day Bias: Trend over last 3 trading sessions (uses 1h data) Weekly Bias: Trend over last 5 trading sessions (uses 4h data) Monthly Bias: Trend over last 30 daily bars Quarterly Bias: Trend over last 13 weekly bars VIX Fear Index: Market regime based on VIX level - bullish when low, bearish when high Opening Range Breakout: Status of price vs. session open range (intraday only) These rows show text: "BULLISH", "BEARISH", or "NEUTRAL" Indicator Rows - Technical Signals: These evaluate your configured indicators across all active timeframes: Fast MA > Medium MA (shows your actual MA settings, e.g., "EMA 10 > SMA 20") Price > Long MA (e.g., "Price > SMA 200") Price > VWAP MACD > Signal Supertrend (up/down/neutral) ZLSMA Rising RSI In Zone ADX ≥ Minimum These rows show emojis: GREEB (bullish), RED (bearish), GRAY/YELLOW (neutral/NA) AVG Column: Shows percentage of active timeframes that are bullish for that row. This is the KEY metric: AVG > 70% = strong multi-timeframe bullish alignment AVG 40-60% = mixed/choppy, no clear trend AVG < 30% = strong multi-timeframe bearish alignment How to Use the Table: For a long trade: Check AVG column - want to see > 60% ideally Check headline bias rows - want to see BULLISH, not BEARISH Check VIX row - bullish market regime preferred Check ORB row (intraday) - want ABOVE for longs Scan indicator rows - more green = better confirmation For a short trade: Check AVG column - want to see < 40% ideally Check headline bias rows - want to see BEARISH, not BULLISH Check VIX row - bearish market regime preferred Check ORB row (intraday) - want BELOW for shorts Scan indicator rows - more red = better confirmation When AVG is 40-60%: Market is choppy, mixed signals. Either stay out or reduce position size significantly. These are low-probability environments. Unique Features: Dynamic Labels: Row names show your actual settings (e.g., "EMA 10 > SMA 20" not generic "Fast > Slow"). You know exactly what's being evaluated. Customizable Rows: Turn off rows you don't care about. Only show what matters to your strategy. Customizable Timeframes: On intraday charts, disable 1m or 4h if you don't trade them. Reduces calculation load by 20-40%. Automatic HTF Handling: On Daily/Weekly/Monthly charts, the table automatically switches to D/W/M columns. No configuration needed. Performance Smart: "Hide BIAS table on 1D or above" option completely skips all table calculations on higher timeframes if you only trade intraday. --- 6. CANDLESTICK PATTERN RECOGNITION The indicator automatically detects six major reversal patterns and marks them with emojis at the relevant bars. Why These Six Patterns: These are the most statistically significant reversal patterns according to trading literature: High win rate when appearing at support/resistance Clear visual structure (not subjective) Work across all timeframes and assets Studied extensively by institutions The Patterns: Bullish Patterns (appear at bottoms): Bullish Engulfing: Green candle completely engulfs prior red candle's body. Strong reversal signal. Hammer: Small body with long lower wick (at least 2× body size). Shows rejection of lower prices by buyers. Morning Star: Three-candle pattern (large red → small indecision → large green). Very strong bottom reversal. Bearish Patterns (appear at tops): Bearish Engulfing: Red candle completely engulfs prior green candle's body. Strong reversal signal. Shooting Star: Small body with long upper wick (at least 2× body size). Shows rejection of higher prices by sellers. Evening Star: Three-candle pattern (large green → small indecision → large red). Very strong top reversal. Interactive Tooltips: Unlike most pattern indicators that just draw shapes, this one is educational: Hover your mouse over any pattern emoji A tooltip appears explaining: what the pattern is, what it means, when it's most reliable, and how to trade it No need to memorize - learn as you trade Noise Filter: "Min candle body % to filter noise" setting prevents false signals: Patterns require minimum body size relative to price Filters out tiny candles that don't represent real buying/selling pressure Adjust based on asset volatility (higher % for crypto, lower for low-volatility stocks) How to Trade Patterns: Patterns are NOT standalone entry signals. Use them as: Confirmation: UT Bot gives signal + pattern appears = stronger entry Reversal Warning: In a trade, opposite pattern appears = consider tightening stop or taking profit Support/Resistance Validation: Pattern at key level (PDH, VWAP, MA 200) = level is being respected Best combined with: UT Bot or Base signal in same direction Bias Table alignment (AVG > 60% or < 40%) Appearance at obvious support/resistance --- 7. VISUAL TOOLS AND FEATURES VWAP (Volume Weighted Average Price): Session-anchored VWAP with standard deviation bands. Shows institutional "fair value" for the trading session. Anchor Options: Session, Day, Week, Month, Quarter, Year. Choose based on your trading timeframe. Bands: Up to three pairs (X1, X2, X3) showing statistical deviation. Price at outer bands often reverses. Auto-Hide on HTF: VWAP hides on Daily/Weekly/Monthly charts automatically unless you enable anchored mode. Use VWAP as: Directional bias (above = bullish, below = bearish) Mean reversion levels (outer bands) Support/resistance (the VWAP line itself) Previous Day High/Low: Automatically plots yesterday's high and low on intraday charts: Updates at start of each new trading day Theme-aware colors (dark text for light charts, light text for dark charts) Hidden automatically on Daily/Weekly/Monthly charts These levels are critical for intraday traders - institutions watch them closely as support/resistance. Opening Range Breakout (ORB): Tracks the high/low of the first 5, 15, 30, or 60 minutes of the trading session: Customizable session times (preset for NYSE, LSE, TSE, or custom) Shows current breakout status in Bias Table row (ABOVE, BELOW, INSIDE, BUILDING) Intraday only - auto-disabled on Daily+ charts ORB is a classic day trading strategy - breakout above opening range often leads to continuation. Extra Labels: Change from Open %: Shows how far price has moved from session open (intraday) or daily open (HTF). Green if positive, red if negative. ADX Badge: Small label at bottom of last bar showing current ADX value. Green when above your minimum threshold, red when below. RSI Badge: Small label at top of last bar showing current RSI value with zone status (buy zone, sell zone, or neutral). These labels provide quick at-a-glance confirmation without needing separate indicator windows. --- 8. HOW TO USE THE INDICATOR Step 1: Add to Chart Load the indicator on your chosen asset and timeframe First time: Everything is enabled by default - the chart will look busy Don't panic - you'll turn off what you don't need Step 2: Start Simple Turn OFF everything except: UT Bot labels (keep these ON) Bias Table (keep this ON) Moving Averages (Fast and Medium only) Suggested Stop Loss and Take Profits Hide everything else initially. Get comfortable with the basic UT Bot + Bias Table workflow first. Step 3: Learn the Core Workflow UT Bot gives a Buy or Sell signal Check Bias Table AVG column - do you have multi-timeframe alignment? If yes, enter the trade Place stop at Suggested Stop Loss line Scale out at TP levels Exit on opposite UT signal Trade this simple system for a week. Get a feel for signal frequency and win rate with your settings. Step 4: Add Filters Gradually If you're getting too many losing signals (whipsaws in choppy markets), add filters one at a time: Try: "Require 2-Bar Trend Confirmation" - wait for 2 bars to confirm direction Try: ADX filter with minimum threshold - only trade when trend strength is sufficient Try: RSI pullback filter - only enter on pullbacks, not chasing Try: Volume filter - require above-average volume Add one filter, test for a week, evaluate. Repeat. Step 5: Enable Advanced Features (Optional) Once you're profitable with the core system, add: Supertrend for additional trend confirmation Candlestick patterns for reversal warnings VWAP for institutional anchor reference ORB for intraday breakout context ZLSMA for low-lag trend following Step 6: Optimize Settings Every setting has a detailed tooltip explaining what it does and typical values. Hover over any input to read: What the parameter controls How it impacts trading Suggested ranges for scalping, day trading, and swing trading Start with defaults, then adjust based on your results and style. Step 7: Set Up Alerts Right-click chart → Add Alert → Condition: "Luxy Momentum v6" → Choose: "UT Bot — Buy" for long entries "UT Bot — Sell" for short entries "Base Long/Short" for filtered MA cross signals Optionally enable "Send real-time alert() on UT flip" in settings for immediate notifications. Common Workflow Variations: Conservative Trader: UT signal + Base signal + Candlestick pattern + Bias AVG > 70% Enter only at major support/resistance Wider UT sensitivity, multiple filters Aggressive Trader: UT signal + Bias AVG > 60% Enter immediately, no waiting Tighter UT sensitivity, minimal filters Swing Trader: Focus on Daily/Weekly Bias alignment Ignore intraday noise Use ORB and PDH/PDL less (or not at all) Wider stops, patient approach --- 9. PERFORMANCE AND OPTIMIZATION The indicator is optimized for speed, but with 15+ features running simultaneously, chart load time can add up. Here's how to keep it fast: Biggest Performance Gains: Disable Unused Timeframes: In "Time Frames" settings, turn OFF any timeframe you don't actively trade. Each disabled TF saves 10-15% calculation time. If you only day trade 5m, 15m, 1h, disable 1m, 2h, 4h. Hide Bias Table on Daily+: If you only trade intraday, enable "Hide BIAS table on 1D or above". This skips ALL table calculations on higher timeframes. Draw UT Visuals Only on Bar Close: Reduces intrabar rendering of SL/TP/Entry lines. Has ZERO impact on logic or alerts - purely visual optimization. Additional Optimizations: Turn off VWAP bands if you don't use them Disable candlestick patterns if you don't trade them Turn off Supertrend fill if you find it distracting (keep the line) Reduce "Limit to 10 bars" for SL/TP lines to minimize line objects Performance Features Built-In: Smart Caching: Higher timeframe data (3-day bias, weekly bias, etc.) updates once per day, not every bar Conditional Calculations: Volume filter only calculates when enabled. Swing filter only runs when enabled. Nothing computes if turned off. Modular Design: Every component is independent. Turn off what you don't need without breaking other features. Typical Load Times: 5m chart, all features ON, 7 timeframes: ~2-3 seconds 5m chart, core features only, 3 timeframes: ~1 second 1m chart, all features: ~4-5 seconds (many bars to calculate) If loading takes longer, you likely have too many indicators on the chart total (not just this one). --- 10. FAQ Q: How is this different from standard UT Bot indicators? A: Standard UT Bot (originally by @QuantNomad) is just the ATR trailing line and flip signals. This implementation adds: - Volume weighting and momentum adjustment to the trailing calculation - Multiple confirmation filters (swing, %, 2-bar, ZLSMA) - Smart composite stop loss system from multiple S/R layers - R-multiple take profit system with freeze-on-touch - Integration with multi-timeframe Bias Table - Visual audit trail with checkmarks Q: Can I use this for automated trading? A: The indicator is designed for discretionary trading. While it has clear signals and alerts, it's not a mechanical system. Context and judgment are required. Q: Does it repaint? A: No. All signals respect bar close. UT Bot logic runs intrabar but signals only trigger on confirmed bars. Alerts fire correctly with no lookahead. Q: Do I need to use all the features? A: Absolutely not. The indicator is modular. Many profitable traders use just UT Bot + Bias Table + Moving Averages. Start simple, add complexity only if needed. Q: How do I know which settings to use? A: Every single input has a detailed tooltip. Hover over any setting to see: What it does How it affects trading Typical values for scalping, day trading, swing trading Start with defaults, adjust gradually based on results. Q: Can I use this on crypto 24/7 markets? A: Yes. ORB will not work (no defined session), but everything else functions normally. Use "Day" anchor for VWAP instead of "Session". Q: The Bias Table is blank or not showing. A: Check: "Show Table" is ON Table position isn't overlapping another indicator's table (change position) At least one row is enabled "Hide BIAS table on 1D or above" is OFF (if on Daily+ chart) Q: Why are candlestick patterns not appearing? A: Patterns are relatively rare by design - they only appear at genuine reversal points. Check: Pattern toggles are ON "Min candle body %" isn't too high (try 0.05-0.10) You're looking at a chart with actual reversals (not strong trending market) Q: UT Bot is too sensitive/not sensitive enough. A: Adjust "Sensitivity (Key×ATR)". Lower number = tighter stop, more signals. Higher number = wider stop, fewer signals. Read the tooltip for guidance. Q: Can I get alerts for the Bias Table? A: The Bias Table is a dashboard for visual analysis, not a signal generator. Set alerts on UT Bot or Base signals, then manually check Bias Table for confirmation. Q: Does this work on stocks with low volume? A: Yes, but turn OFF the volume filter. Low volume stocks will never meet relative volume requirements. Q: How often should I check the Bias Table? A: Before every entry. It takes 2 seconds to glance at the AVG column and headline rows. This one check can save you from fighting the trend. Q: What if UT signal and Base signal disagree? A: UT Bot is more aggressive (ATR trailing). Base signals are more conservative (MA cross + filters). If they disagree, either: Wait for both to align (safest) Take the UT signal but with smaller size (aggressive) Skip the trade (conservative) There's no "right" answer - depends on your risk tolerance. --- FINAL NOTES The indicator gives you an edge. How you use that edge determines results. For questions, feedback, or support, comment on the indicator page or message the author. Happy Trading!

אינדיקטור Pine Script®

מאת ‎orenluxy‎

מעודכן

Neural Network Buy and Sell Signals Trend Architect Suite Lite - Neural Network Buy and Sell Signals Advanced AI-Powered Signal Scoring This indicator provides neural network market analysis on buy and sell signals designed for scalpers and day traders who use 30s to 5m charts. Signals are generated based on an ATR system and then filtered and scored using an advanced AI-driven system. Features Neural Network Signal Engine 5-Layer Deep Learning analysis combining market structure, momentum, and market state detection AI-based Letter Grade Scoring (A+ through F) for instant signal quality assessment Normalized Input Processing with Z-score standardization and outlier clipping Real-time Signal Evaluation using 5 market dimensions Advanced Candle Types Standard Candlesticks - Raw price action Heikin Ashi - Trend smoothing and noise reduction Linear Regression - Mathematical trend visualization Independent Signal vs Display - Calculate signals on one type, display another Key Settings Signal Configuration - Signal Trigger Sensitivity (Default: 1.7) - Controls signal frequency vs quality - Stop Loss ATR Multiplier (Default: 1.5) - Risk management sizing - Signal Candle Type (Default: Candlesticks) - Data source for signal calculations - Display Candle Type (Default: Linear Regression) - Visual candle display Display Options - Signal Distance (Default: 1.35 ATR) - Label positioning from price - Label Size (Default: Medium) - Optimal readability Trading Applications Scalping - Fast pace signal detection with quality filtering - ATR-based stop management prevents signal overlap - Neural network attempts to reduces false signals in choppy markets Day Trading - Multi-timeframe compatible with adaptation settings - Clear trend visualization with Linear Regression candles - Support/resistance integration for better entries/exits Signal Filtering - Use A+/A grades for highest probability setups - B grades for confirmation in trending markets - C-F grades help identify market uncertainty Why Choose Trend Architect Lite? No Lag - Real-time neural network processing No Repainting - Signals appear and stay fixed Clean Charts - Focus on price action, not indicators Smart Filtering - AI reduces noise and false signals Flexible and customizable - Works across all timeframes and instruments Compatibility - All Timeframes - 1m to Monthly charts - All Instruments - Forex, Crypto, Stocks, Futures, Indices Risk Disclaimer This indicator is a tool for technical analysis and should not be used as the sole basis for trading decisions. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.

אינדיקטור Pine Script®

מאת ‎B3AR_Trades‎

מעודכן

Linear Regression Channel Screener [Daveatt]Hello traders First and foremost, I want to extend a huge thank you to @LonesomeTheBlue for his exceptional Linear Regression Channel indicator that served as the foundation for this screener. Original work can be found here: Overview This project demonstrates how to transform any open-source indicator into a powerful multi-asset screener. The principles shown here can be applied to virtually any indicator you find interesting. How to Transform an Indicator into a Screener Step 1: Identify the Core Logic First, identify the main calculations of the indicator. In our case, it's the Linear Regression Channel calculation: get_channel(src, len) => mid = math.sum(src, len) / len slope = ta.linreg(src, len, 0) - ta.linreg(src, len, 1) intercept = mid - slope * math.floor(len / 2) + (1 - len % 2) / 2 * slope endy = intercept + slope * (len - 1) dev = 0.0 for x = 0 to len - 1 by 1 dev := dev + math.pow(src - (slope * (len - x) + intercept), 2) dev dev := math.sqrt(dev / len) Step 2: Use request.security() Pass the function to request.security() to analyze multiple assets: = request.security(sym, timeframe.period, get_channel(src, len)) Step 3: Scale to Multiple Assets PineScript allows up to 40 request.security() calls, letting you monitor up to 40 assets simultaneously. Features of This Screener The screener provides real-time trend detection for each monitored asset, giving you instant insights into market movements. It displays each asset's position relative to its middle regression line, helping you understand price momentum. The data is presented in a clean, organized table with color-coded trends for easy interpretation. At its core, the screener performs trend detection based on regression slope calculations, clearly indicating whether an asset is in a bullish or bearish trend. Each asset's price is tracked relative to its middle regression line, providing additional context about trend strength. The color-coded visual feedback makes it easy to spot changes at a glance. Built-in alerts notify you instantly when any asset experiences a trend change, ensuring you never miss important market moves. Customization Tips You can easily expand the screener by adding more symbols to the symbols array, adapting it to your watchlist. The regression parameters can be adjusted to match your preferred trading timeframes and sensitivity. The alert system is already configured to notify you of trend changes, but you can customize the alert messages and conditions to your needs. Limitations While powerful, the screener is bound by PineScript's limitation of 40 security calls, capping the maximum number of monitored assets. Using AI to Help With Conversion An interesting tip: You can use AI tools to help convert single-asset indicators to screeners. Simply provide the original code and ask for assistance in transforming it into a screener format. While the AI output might need some syntax adjustments, it can handle much of the heavy lifting in the conversion process. Prompt (example) : " Please make a pinescript version 5 screener out of this indicator below or in attachment to scan 20 instruments " I prefer Claude AI (Opus model) over ChatGPT for pinescript. Conclusion This screener transformation technique opens up endless possibilities for market analysis. By following these steps, you can convert any indicator into a powerful multi-asset scanner, enhancing your trading toolkit significantly. Remember: The power of a screener lies not just in monitoring multiple assets, but in applying consistent analysis across your entire watchlist in real-time. Feel free to fork and modify this screener for your own needs. Happy trading! 🚀📈 Daveatt

אינדיקטור Pine Script®

מאת ‎Daveatt‎

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נתוני שוק נבחרים מסופקים על ידי ICE Data Services. נתוני ייחוס נבחרים מסופקים על ידי FactSet. זכויות יוצרים © 2026 FactSet Research Systems Inc.זכויות יוצרים © 2026, איגוד הבנקאים האמריקאי. מאגר הנתונים של CUSIP מסופק על ידי FactSet Research Systems Inc. כל הזכויות שמורות. דוחות ל־SEC ומסמכים נוספים מסופקים על ידי Quartr.© 2026 TradingView, Inc.

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