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ALT FINAL ABCD PRO V62. Key Improvements and Performance Optimization of Version v6 Faster Large-Scale Computation: The v6 engine processes large-scale computations more quickly and minimizes delays that occur when pulling Bitcoin and dominance data simultaneously. Enhanced Repainting: By using the f_secure_data function to check Bitcoin trends, I eliminated 'future reference errors' at the source, ensuring that backtest returns match actual trading results. Automation of Risk-Reward (R:R): Utilizing ATR multiples, I configured the stop loss to be short (0.8x) and the take profit to be long (1.5x), allowing for automatic responses to the volatility of altcoins. 3. Supplementary Guide for Trading Altcoins Meaning of VWAP Sweep: In the crypto market, when the price briefly dips below the VWAP and then recovers, it is interpreted as a signal that institutions are absorbing the stop-loss volumes of retail investors. This indicator captures that moment and helps traders enter at the most favorable price level. Utilizing the Dominance Filter: An altcoin buying signal occurs only when Bitcoin's dominance is below the moving average. This mechanism ensures trading only in 'tailwind' situations where the flow of funds is directed towards altcoins. Time Zone Focus: The U.S. session (22:30–01:30), marked in orange, is when global liquidity is at its highest. Outside of this time frame, the reliability of patterns decreases, so it is recommended to refrain from trading as much as possible.
Strategi Pine Script®
oleh minovsajh
Three Green Candles Screener - % Move & Volume1️⃣ Core purpose (big picture) The indicator identifies stocks that: Have 2 or 3 consecutive green candles Are above a 21-EMA (trend filter) Have reasonable % price movement (not overextended) Show current volume, average volume, and turnover Show daily and weekly % price change It’s meant for short-term momentum screening (swing / positional / breakout prep). 2️⃣ Trend filter (EMA) ema21 = ta.ema(close, emaLength) Uses a 21-period EMA All buy signals require price > EMA This avoids counter-trend setups 3️⃣ Three Green Candles logic (main signal) threeGreen = (close > open) and (close > open ) and (close > open ) This checks for three consecutive bullish candles. Then it calculates: % change for each candle (open → close) Average % change across the 3 candles avgChg = (chg0 + chg1 + chg2) / 3 ✅ 3-Green signal triggers when: 3 consecutive green candles Average % change ≤ user-defined max (default 10%) Price above EMA21 ➡ Output: signal = 1 // Buy flag signal = 0 // No action This avoids parabolic / news-spike candles. 4️⃣ Two Green Candles logic (early signal) This is a lighter, earlier version of the same logic. twoGreen = (close > open) and (close > open ) avgChg2 = (chg0 + chg1) / 2 ✅ 2-Green signal triggers when: 2 consecutive green candles Average % change ≤ maxAvgChange Price above EMA21 ➡ Output: signal2 = 1 // Early momentum This helps catch moves one day earlier than the 3-green setup. 5️⃣ Volume & liquidity context (important) Average volume (7 days) avgVol7 = ta.sma(volume, 7) / 1e6 Shows liquidity trend Units: Millions of shares Today’s volume todayVol = volume / 1e6 Helps confirm participation 6️⃣ Turnover (Price × Volume) priceVolCrore = (close * volume) / 1e7 Measures capital flow, not just volume Output in ₹ Crores Helps filter: Low-value pump candles Illiquid stocks 7️⃣ % price movement Daily move pctDay = (close - close ) / close * 100 Weekly move (5 bars) pctWeek = (close - close ) / close * 100 These give context, not signals: Is this early? Is it already extended? 8️⃣ Visual outputs (what you see) Plots (in the indicator pane) CMP (current price) 3-Green signal (0 / 1) 2-Green signal (0 / 1) Avg 7-day volume (M) Today’s volume (M) Turnover (₹ Cr) Day % move Week % move This makes it usable as a visual screener. 9️⃣ Summary table (top-right) On the latest bar only, it shows: Field Meaning CMP Current price Today Vol (M) Today’s volume Turnover (Cr) Value traded Day / Week % Momentum context Compact, readable, no clutter. 10️⃣ What this indicator is GOOD for ✅ Momentum stock screening ✅ Swing / positional setups ✅ Avoiding overextended candles ✅ Liquidity & capital flow validation ✅ Manual decision support 11️⃣ What it does NOT do ❌ No auto buy/sell ❌ No stop-loss or targets ❌ No relative strength vs index ❌ No intraday scalping logic TL;DR (one-liner) This indicator finds stocks in a healthy uptrend with 2–3 controlled bullish candles, confirms them with EMA and volume/turnover, and presents all key momentum metrics in one clean view.
Indikator Pine Script®
oleh ahamed_thanzeer
Stochastic Dynamic BandsStochastic is a purpose-built oscillator designed for clean, actionable signals instead of noisy crosses. It lets you switch between Classic Stochastic and StochRSI, then adapts to current market conditions with optional dynamic percentile bands rather than fixed 80/20 levels. You can trigger signals by standard K/D crosses, by higher-quality re-entry logic when momentum exits an extreme zone, or both. A built-in EMA trend filter helps align signals with the dominant direction, and the indicator keeps a classic stochastic panel layout with clear active bands and alert support for every signal type.
Indikator Pine Script®
oleh EVLabs
Adaptive ML VWAP v1.0Overview Adaptive ML VWAP is a next-generation "Smart Indicator" that moves beyond static deviations (Standard Deviation). Instead of assuming market volatility is distributed normally (Bell Curve), this indicator uses a k-Nearest Neighbors (k-NN) machine learning engine to learn the specific volatility behavior of the asset you are trading. It answers the question: "When price extends away from VWAP, how far does it actually go before reversing?" The Adaptive ML Engine This script features a 5-Dimensional ML Engine that tracks every major extension or pullback event. It records: Deviation Depth (Normalized to ATR) Trend Slope (Is the trend steep or flat?) ADX (Trend Strength) VWAP Deviation (Relative Position) Time of Day (Session Context) When a new setup occurs, the k-NN engine instantly searches its memory for the 5 most similar historical events and calculates the probability of success based on what happened last time. Two Strategy Modes You can toggle the logic to suit your trading style: 1. Mean Reversion Mode (Default) "Fade The Move" Goal: Catch price at an exhaustion point returning to VWAP. Signal: Triggers when price touches a Smart Band and reverses back toward the center. k-NN Learning: Learns which conditions favor a snap-back. Best For: Ranging markets, Lunch hours, Choppy sessions. 2. Trend Following Mode "Ride The Move" Goal: Catch breakouts that are launching away from value. Signal: Triggers when price breaks out of the Inner Band (1.0). k-NN Learning: Learns which breakouts tend to extend to the Outer Bands. Best For: Morning Drives, News Events, Strong Trends. Visual Guide The indicator uses a Dynamic Gradient system to visualize risk/reward: Cyan Mist (0.5 - 1.0): The Value Zone. Noise area. Safe for trend entries. Deep Cyan (1.0 - 2.0): The Trend Zone. Price is moving proactively. Orange Glow (2.0 - 3.0): The Danger Zone. Price is statistically overextended. Reversals are highly probable here. "Fractal" Math Unlike standard indicators that break when you change timeframes, Adaptive ML VWAP uses Fractal Normalization. A "2.0 Band" on a 15-second chart means the same statistical extreme as a "2.0 Band" on a 4-hour chart. Auto-Adaptive Lookback: The indicator automatically boosts the ML memory (Lookback) on lower timeframes (seconds/minutes) where more noise requires larger sample sizes, ensuring robust predictions without manual tweaking. Settings Auto-Adapting Lookback: (Default: True) automatically increases Lookback to 100+ for seconds charts and 50+ for minute charts. Lookback (Events): Manual override base value (Default: 100). Strategy Mode: Toggle between Mean Reversion and Trend Following. k-Neighbors: The number of similar past events to structurally compare (Default: 5). Disclaimer: This tool is for educational purposes. Machine learning performance is dependent on market conditions and historical recursion.
Indikator Pine Script®
oleh Selfsimilarity
RSI Divergence by EVRSI Divergence is a clean, non-repainting RSI built to look and feel like the standard oscillator while adding automatic divergence detection. It identifies Regular Bullish and Regular Bearish divergences using confirmed pivot points, helping you spot momentum weakening against price action without the guesswork. Optional Hidden divergences are included for continuation reads, and you can filter signals by requiring RSI to be inside Overbought/Oversold zones. The script keeps a classic 70/30 layout with optional OB/OS shading, draws divergence lines directly on the RSI for instant visual confirmation, and includes ready-to-use alerts for each divergence type.
Indikator Pine Script®
oleh EVLabs
Vietnam Asset Monitoring ToolVietNamese Asset Correlation Tracking Chart This chart monitors the relationship between major asset classes which Vietnamese analyst should monitor: Gold Price: Gold is currently in the spotlight as international trade tensions show no signs of easing. U.S. Dollar Index (DXY): The strength of the U.S. dollar, a key indicator that has relative correlations with most asset classes. VN30: Vietnam’s benchmark stock index, representing the top 30 leading listed companies. VNREAL: The stock index tracking Vietnam’s real estate–related companies. Hanoi Residential Property Prices: Apartment and landed house prices in Hanoi, compiled and aggregated by **VietRealty.Pro** Development in progress..
Indikator Pine Script®
oleh VietNC
Position Size Gev_Risk ($) The amount of money you are willing to lose if the stop-loss is hit. Base Stop Price The stop-loss price you set. The stop is recalculated in real time as price moves. Fee (%) round-trip Estimated total trading fees for opening and closing the position. Stop Mode With Buffer: adds extra distance to the stop, resulting in a smaller position size No Buffer: uses the Base Stop Price exactly as entered Stop Buffer (%) The percentage added to the stop distance when Stop Mode is set to With Buffer. Live adjustment behavior The stop price updates continuously as price moves, and the position size is recalculated live to stay aligned with the selected Risk ($).
Indikator Pine Script®
oleh gevanv
NodialTreesLows2: ML Random Forest / Pivot Lows (Part 2 of 2)Title: `Library: ML Random Forest / Pivot Lows (Part 2 of 2)` Description: This library contains the second half (Trees 6-11) of the Random Forest Classifier designed to validate Pivot Lows (Long setups). It is a direct extension of NodialTreesL1 and cannot be used alone. Due to Pine Script's compilation limits on complexity and file size, the 12-tree ensemble model has been split into two separate libraries. ### 🧩 Library Contents This module exports the following methods representing the specific decision paths of the trained AI model: - `tree_6(array f)` - `tree_7(array f)` - `tree_8(array f)` - `tree_9(array f)` - `tree_10(array f)` - `tree_11(array f)` ### ⚠️ Implementation Guide To use this library, you must combine it with Part 1. Please refer to the NodialTreesLows1 library description for: 1. The full Integration Code Example (how to average the votes). 2. The exact Input Feature List (the 27 required metrics). 3. Detailed explanation of the Machine Learning logic. How to finish the integration: Import this library alongside Part 1 and add the results of `tree_6` through `tree_11` to your voting sum, as shown in the Part 1 documentation.
Perpustakaan Pine Script®
oleh Nodial
NodialTreesLows1: ML Random Forest / Pivot Lows (Part 1 of 2)Title: `Library: ML Random Forest / Pivot Lows (Part 1 of 2)` Description: This library contains the first half (Trees 0-5) of a Random Forest Classifier designed to validate Pivot Lows (Long setups). Due to Pine Script size constraints, the model is split into two libraries. You must use this library in conjunction with NodialTreesL2 to run the full ensemble. ### 🧩 System Architecture - Model: Random Forest (12 Trees total). - This Library: Contains `tree_0` to `tree_5`. - Logic: Each tree analyzes a feature array and outputs a probability score. - Target: Validating Swing Lows / Support Bounces. ### 📊 Input Requirements The methods expect an `array` of size 27 containing market features (Price Action, Momentum, Volatility, Volume, Structure). The exact order of features is critical for the model's accuracy. ### 🛠️ Integration Example Since this is a modular library, you need to import both parts and average their results to get the final prediction. ### 📋 Feature Mapping (Array Indexing) To get accurate predictions, the input array must contain exactly 27 floats in this specific order: 0. Timeframe (in seconds) 1. RSI (Raw Value) 2. MACD Histogram 3. Relative Volume 4. EMA Distance (%) 5. EMA Slope 6. ATR Ratio 7. ADX 8. Buying/Selling Pressure 9. Wick Ratio 10-16. Divergences & Pattern Flags (Boolean 0.0/1.0) 17-22. Proprietary Momentum Metrics ("Onion" Structure) 23-26. Derived Volatility/Volume Features *Note: For the advanced proprietary metrics (Indices 17-26), users must implement their own calculations or use compatible indicators.* //@version=6 indicator("My ML Long Strategy", overlay=true) // Import BOTH libraries import YourUsername/NodialTreesL1/1 as rf_part1 import YourUsername/NodialTreesL2/1 as rf_part2 // ... (Calculate your 27 features and fill the array) ... // var features = array.from(timeframe, rsi, macd, ...) // Calculate Ensemble Probability (Average of 12 Trees) float vote_sum = 0.0 // Trees from Part 1 vote_sum += rf_part1.tree_0(features) vote_sum += rf_part1.tree_1(features) vote_sum += rf_part1.tree_2(features) vote_sum += rf_part1.tree_3(features) vote_sum += rf_part1.tree_4(features) vote_sum += rf_part1.tree_5(features) // Trees from Part 2 (Trees 6-11) vote_sum += rf_part2.tree_6(features) vote_sum += rf_part2.tree_7(features) vote_sum += rf_part2.tree_8(features) vote_sum += rf_part2.tree_9(features) vote_sum += rf_part2.tree_10(features) vote_sum += rf_part2.tree_11(features) // Final Probability (0.0 to 1.0) float final_prob = vote_sum / 12.0 if final_prob > 0.60 label.new(bar_index, low, "Valid Low", color=color.green)
Perpustakaan Pine Script®
oleh Nodial
NodialTreesHighs2: ML Random Forest / Pivot Highs (Part 2 of 2)Title: `Library: ML Random Forest / Pivot Highs (Part 2 of 2)` Description: This library contains the second half (Trees 6-11) of the Random Forest Classifier designed to validate Pivot Highs (Short setups). It is a direct extension of NodialTreesH1 and cannot be used alone. Due to Pine Script's compilation limits on complexity and file size, the 12-tree ensemble model has been split into two separate libraries. ### 🧩 Library Contents This module exports the following methods representing the specific decision paths of the trained AI model: - `tree_6(array f)` - `tree_7(array f)` - `tree_8(array f)` - `tree_9(array f)` - `tree_10(array f)` - `tree_11(array f)` ### ⚠️ Implementation Guide To use this library, you must combine it with Part 1. Please refer to the NodialTreesH1 library description for: 1. The full Integration Code Example (how to average the votes). 2. The exact Input Feature List (the 27 required metrics). 3. Detailed explanation of the Machine Learning logic. How to finish the integration: Import this library alongside Part 1 and add the results of `tree_6` through `tree_11` to your voting sum, as shown in the Part 1 documentation.
Perpustakaan Pine Script®
oleh Nodial