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ค้นหาในสคริปต์สำหรับ "algo"

Forex Master v4.0 (EUR/USD Mean-Reversion Algorithm)DESCRIPTION Forex Master v4.0 is a mean-reversion algorithm currently optimized for trading the EUR/USD pair on the 5M chart interval. All indicator inputs use the period's closing price and all trades are executed at the open of the period following the period where the trade signal was generated. There are 3 main components that make up Forex Master v4.0: I. Trend Filter The algorithm uses a version of the ADX indicator as a trend filter to trade only in certain time periods where price is more likely to be range-bound (i.e., mean-reverting). This indicator is composed of a Fast ADX and a Slow ADX, both using the same look-back period of 50. However, the Fast ADX is smoothed with a 6-period EMA and the Slow ADX is smoothed with a 12-period EMA. When the Fast ADX is above the Slow ADX, the algorithm does not trade because this indicates that price is likelier to trend, which is bad for a mean-reversion system. Conversely, when the Fast ADX is below the Slow ADX, price is likelier to be ranging so this is the only time when the algorithm is allowed to trade. II. Bollinger Bands When allowed to trade by the Trend Filter, the algorithm uses the Bollinger Bands indicator to enter long and short positions. The Bolliger Bands indicator has a look-back period of 20 and a standard deviation of 1.5 for both upper and lower bands. When price crosses over the lower band, a Long Signal is generated and a long position is entered. When price crosses under the upper band, a Short Signal is generated and a short position is entered. III. Money Management Rule 1 - Each trade will use a limit order for a fixed quantity of 50,000 contracts (0.50 lot). The only exception is Rule Rule 2 - Order pyramiding is enabled and up to 10 consecutive orders of the same signal can be executed (for example: 14 consecutive Long Signals are generated over 8 hours and the algorithm sends in 10 different buy orders at various prices for a total of 350,000 contracts). Rule 3 - Every order will include a bracket with both TP and SL set at 50 pips (note: the algorithm only closes the current open position and does not enter the opposite trade once a TP or SL has been hit). Rule 4 - When a new opposite trade signal is generated, the algorithm sends in a larger order to close the current open position as well as open a new one (for example: 14 consecutive Long Signals are generated over 8 hours and the algorithm sends in 10 different buy orders at various prices for a total of 350,000 contracts. A Short Signal is generated shortly after the 14th Long Signal. The algorithm then sends in a sell order for 400,000 contracts to close the 350,000 contracts long position and open a new short position of 50,000 contracts).
กลยุทธ์ Pine Script®
โดย Stable_Camel
16
Position and Risk Calculator (for Indices) [dR-Algo]Position and Risk Calculator : Your Ultimate Risk Management Tool for Indices The difference between a novice and a seasoned trader often comes down to one essential element: risk management. While trading indices, the challenges are even more intense due to market volatility and leverage. The Position and Risk Calculator steps in here to bridge the gap, providing you with an efficient tool designed exclusively for indices trading. Key Features: User-Friendly Interface: Designed to integrate effortlessly with your TradingView chart, this tool's interface is intuitive and clutter-free. Dynamic Price Level Adjustment: Move your Entry, Stop Loss, and Take Profit levels directly on the chart for an interactive experience. Account Balance Input: Customize the tool to understand your unique financial situation by inputting your current account balance. Trade Risk Customization: Define how much you're willing to risk per trade, and the tool will do the rest. Automated Calculations: The indicator calculates the maximum monetary risk and translates it into the maximum lot size you can afford. It delivers a full-integer lot size to make your trading decisions easier. Comprehensive Risk Evaluation: Beyond lot sizes, it provides you with the Cost-to-Reward Ratio (CRV) of your trade, the actual monetary risk according to the calculated lot size, and the potential profit. How To Use: Once you add the Position and Risk Calculator to your TradingView chart, a new interactive panel appears. Here’s how it works: Set Price Levels: Using draggable lines on the chart, set your Entry Price, Stop Loss, and Take Profit levels. Account Details: Go to settings and enter your Account Balance and your desired risk percentage per trade. Automatic Calculations: As soon as the above details are set, the indicator goes to work. It first calculates your maximum risk in monetary terms and then translates that into the maximum lot size you can take for the trade. Review and Trade: The indicator shows you all the vital statistics - CRV of the trade, the money at risk according to the calculated lot size, and the possible profit. Why Choose This Tool? Informed Decisions: Your trading decisions will be based on concrete numbers, removing guesswork. Time-saving: No need for manual calculations or using separate tools; everything is in one place. Focus on Trading: By automating the risk management aspect, this tool allows you to focus more on your trading strategy and market analysis. Tailor-Made for Indices: Unlike many other tools that try to serve all markets, the Position and Risk Calculator is designed specifically for indices trading. Remember, effective risk management is what separates successful traders from those who burn out. The Position and Risk Calculator not only helps you define your risk but also helps you understand it, empowering you to trade with confidence. So why not give yourself the best chance of success? Add the Position and Risk Calculator to your TradingView setup and experience the difference it can make.
อินดิเคเตอร์ Pine Script®
โดย dR-Algo
6
ISM Indicator As a Strategy Here's a very easy code, plotting the ISM against the SPX. In this exercise, i wanted to see if one could use the ISM indicator only to generate buy/sell signal, and what would be the performance. What is the ISM The ISM Manufacturing Index monitors employment, production inventories, new orders and supplier deliveries.By monitoring the ISM Manufacturing Index, investors are able to better understand national economic conditions. When this index is increasing, investors can assume that the stock markets should increase because of higher corporate profits. The opposite can be thought of the bond markets, which may decrease as the ISM Manufacturing Index increases because of sensitivity to potential inflation. Buy/Sell Signal ISM above 50 usually good economic condition and vice versa when below 50 . For this code I used 48.50 as my buy/sell signal line. Results To test this on a longer time period, I use the SPX index instead of SPY. The results are surprisingly good. 76.92% profitability with 3.03 profit factor. Conclusion Investors could use the ISM with other indicators to determine better entry and exit point. I will see if combining the ISM with other custom indicators , could generate better result. Feel free to share your results here. Cheers Algo.
กลยุทธ์ Pine Script®
โดย Algokid
4
AK TREND ID v1.00Hello, "Are we at the top yet ? "........ " Is it a good time to invest ? " ......." Should I buy or sell ? " These are the many questions I hear and get on the daily basis. 1000's of investors do not know when to go in and out of the market. Most of them rely on the opinion of "experts" on television to make their investment decisions. Bad idea.Taking a systematic approach when investing, could save you a lot of time and headache. If there was only a way to know when to get in and out of the market !! hmmmm. The good news is that there many ways to do that. The bad news is , are you disciplined enough to follow it ? I coded the AK_TREND ID specifically to identified trends in the SPX or SPY only . How does it work ? very simply , I simply plot the spread between the 3 month and 8 month moving average on the chart. If the spread > 0 @ month end = BUY if the spread < 0 @ month end = SELL The AK TREND ID is a LAGGING Indicator , so it will not get you in at the very bottom or get you out at the very top. I did a backtest on the SPX from 1984 to 7/2/2014 (yesterday), The rule was to buy only when the AK TREND ID was green. let's look at the result: 14 trades : 11 W 3 L , 78.75 % winning % Biggest winner (%) = 108 % Biggest loser (%) = -10.7 % Average Return = 27 % Total Return since 1984 = 351.3 % You can see the result in detail here : docs.google.com Although the backtesting results are good, the AK TREND ID is not to be used as a trading system. It is simply design to let you know when to invest and when to get out. I'm working a more accurate version of this Indicator , that will use both technical and fundamental data. In the mean time , I hope this will give some of you piece of mind, and eliminate emotions from your trading decision. Feel free to modify the code as you wish, but please share your finding with the rest of Trading View community. All the best Algo
อินดิเคเตอร์ Pine Script®
โดย Algokid
35
Risk Recommender — (Heatmap)📊 Risk Recommender — Per-Trade & Annualized (Heatmap Columns) Estimate the optimal risk percentage for any market regime. This tool dynamically recommends how much of your account equity to risk — either per trade or at a portfolio (annualized) level — using volatility as the guide. ⚙️ How it works Two distinct modes give you flexibility: 1️⃣ Per-Trade (ATR-based) • Calculates the current Average True Range (ATR) compared to its long-term baseline. • When volatility is high (ATR ↑), risk per trade decreases to maintain constant dollar risk. • When volatility is low (ATR ↓), risk per trade increases within your defined floor and ceiling. • The display is normalized by stop distance (× ATR) and smoothed to avoid noise. 2️⃣ Annualized (Volatility Targeting) • Computes realized volatility (standard deviation of log returns) and an EWMA forecast of future volatility. • Blends current and forecast volatilities to estimate “effective” volatility. • Scales your base risk so that portfolio volatility converges toward your chosen annual target (e.g., 20%). • Useful for portfolio-level or systematic strategies that maintain constant volatility exposure. 🎨 Heatmap Visualization The vertical column graph acts like a thermometer: • 🟥 Red → “Reduce risk” (volatility high). • 🟩 Green → “Increase risk” (volatility low). • Smoothed and bounded between your Floor and Ceiling risk levels. • Optional dotted guides mark those bounds. • Label shows the current mode, recommended risk %, and key metrics (ATR ratio or effective volatility). 🔧 Key Inputs • Base max risk per trade (%) — your normal per-trade risk budget. • ATR length / Baseline ATR length — control sensitivity to short- vs. long-term volatility. • Target annualized volatility (%) — portfolio volatility target for quant mode. • λ (lambda) — smoothing factor for the EWMA volatility forecast (0.90–0.99 typical). • Floor & Ceiling — clamps the output to avoid extreme sizing. • Smoothing & Hysteresis — prevent rapid changes in risk recommendations. 🧮 Interpreting the Output • “Recommended Risk (%)” = suggested portion of equity to risk on the next trade (or current exposure). • In Per-Trade mode: reflects current ATR ÷ baseline ATR . • In Annualized mode: reflects target volatility ÷ effective volatility . • Use the color and height of the column as a quick visual cue for aggressiveness. 💡 Typical Use Cases • Position-sizing overlay for discretionary traders. • Volatility-targeting component for algorithmic or multi-asset systems. • Educational tool to understand how volatility governs prudent risk management. 📘 Notes • This indicator provides risk suggestions only ; it does not place trades. • Works on any symbol or timeframe. • Combine with your own strategy or alerts for full automation. • All calculations use built-in Pine functions; no proprietary logic. Tags: #RiskManagement #ATR #Volatility #Quant #PositionSizing #SystematicTrading #AlgorithmicTrading #Portfolio #TradingStrategy #Heatmap #EWMA #Risk
อินดิเคเตอร์ Pine Script®
โดย Algorific
Murrey Math The Murrey Math indicator is a set of horizontal price levels, calculated from an algorithm developed by stock trader T.J. Murray. The main concept behind Murrey Math is that prices tend to react and rotate at specific price levels. These levels are calculated by dividing the price range into fixed segments called "ranges", usually using a number of 8, 16, 32, 64, 128 or 256. Murrey Math levels are calculated as follows: 1. A particular price range is taken, for example, 128. 2. Divide the current price by the range (128 in this example). 3. The result is rounded to the nearest whole number. 4. Multiply that whole number by the original range (128). This results in the Murrey Math level closest to the current price. More Murrey levels are calculated and drawn by adding and subtracting multiples of the range to the initially calculated level. Traders use Murrey Math levels as areas of possible support and resistance as it is believed that prices tend to react and pivot at these levels. They are also used to identify price patterns and possible entry and exit points in trading. The Murrey Math indicator itself simply calculates and draws these horizontal levels on the price chart, allowing traders to easily visualize them and use them in their technical analysis. HOW TO USE THIS INDICATOR? To use the Murrey Math indicator effectively, here are some tips: 1. Choose the appropriate Murrey Math range : The Murrey Math range input (128 by default in the provided code) determines the spacing between the levels. Common ranges used are 8, 16, 32, 64, 128, and 256. A smaller range will give you more levels, while a larger range will give you fewer levels. Choose a range that suits the volatility and trading timeframe you're working with. 2. Identify potential support and resistance levels: The horizontal lines drawn by the indicator represent potential support and resistance levels based on the Murrey Math calculation. Prices often react or reverse at these levels, so they can be used to spot areas of interest for entries and exits. 3. Look for price reactions at the levels: Watch for price action like rejections, bounces, or breakouts at the Murrey Math levels. These reactions can signal potential trend continuation or reversal setups. 4. Trail stop-loss orders: You can place stop-loss orders just below/above the nearest Murrey Math level to manage risk if the price moves against your trade. 5. Set targets at future levels: Project potential profit targets by looking at upcoming Murrey Math levels in the direction of the trend. 7. Adjust range as needed: If prices are consistently breaking through levels without reacting, try adjusting the range input to a different value to see if it provides better levels. In which asset can this indicator perform better? The Murrey Math indicator can potentially perform well on any liquid financial asset that exhibits some degree of mean-reversion or trading range behavior. However, it may be more suitable for certain asset classes or trading timeframes than others. Here are some assets and scenarios where the Murrey Math indicator can potentially perform better: 1. Forex Markets: The foreign exchange market is known for its ranging and mean-reverting nature, especially on higher timeframes like the daily or weekly charts. The Murrey Math levels can help identify potential support and resistance levels within these trading ranges. 2. Futures Markets: Futures contracts, such as those for commodities (e.g., crude oil, gold, etc.) or equity indices, often exhibit trading ranges and mean-reversion trends. The Murrey Math indicator can be useful in identifying potential turning points within these ranges. 3. Stocks with Range-bound Behavior: Some stocks, particularly those of large-cap companies, can trade within well-defined ranges for extended periods. The Murrey Math levels can help identify the boundaries of these ranges and potential reversal points. 4. I ntraday Trading: The Murrey Math indicator may be more effective on lower timeframes (e.g., 1-hour, 30-minute, 15-minute) for intraday trading, as prices tend to respect support and resistance levels more closely within shorter time periods. 5. Trending Markets: While the Murrey Math indicator is primarily designed for range-bound markets, it can also be used in trending markets to identify potential pullback or continuation levels.
อินดิเคเตอร์ Pine Script®
โดย AlgoriZate
4
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion . Core Architecture Why These Components Are Combined: The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention. The Four Trading Agents 1. Spoofing Detector Agent 🎭 Detects iceberg orders through persistent volume at similar price levels over 5 bars Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average) Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model) Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation Best Markets: Consolidations, institutional trading windows, low-liquidity hours 2. Exhaustion Detector Agent ⚡ Calculates RSI divergence between price movement and momentum indicator over 5-bar window Detects VWAP exhaustion (price at 2σ bands with declining volume) Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure Signal Logic: Counter-trend—enters when momentum extreme shows weakness Best Markets: Trending markets reaching climax points, over-extended moves 3. Liquidity Void Detector Agent 💧 Measures Bollinger Band squeeze (width <60% of 50-period average) Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR) Signal Logic: Breakout anticipation—enters after liquidity grab but before main move Best Markets: Range-bound pre-breakout, volatility compression zones 4. Mean Reversion Agent 📊 Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ) Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model) Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability) Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments Adaptive Selection: Multi-Armed Bandit System The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance: Thompson Sampling (Default - Recommended): Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent) Balances exploration (trying underused agents) vs. exploitation (using proven winners) Each agent's win/loss history informs its selection probability Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation UCB1 (Upper Confidence Bound): Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls) Deterministic algorithm favoring agents with high uncertainty (potential upside) More conservative than Thompson Sampling Epsilon-Greedy: Exploits best-performing agent (1-ε)% of the time Explores randomly ε% of the time (default 10%, configurable 1-50%) Simple, transparent, easily tuned via epsilon parameter Gradient Bandit: Uses softmax probability distribution over agent preference weights Updates weights via gradient ascent based on rewards Best for Blend mode where all agents contribute Selection Modes: Switch Mode: Uses only the selected agent's signal (clean, decisive) Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified) Lock Agent Feature: Optional manual override to force one specific agent Useful after identifying which agent dominates your specific instrument Only applies in Switch mode Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion Memory System Dual-Layer Architecture: Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50) Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars) Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20% Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds) How Memory Works: Agent generates signal → outcome tracked after 8 bars (performance horizon) Result stored in short-term memory (win = 1.0, loss = 0.0) Short-term average influences agent's future scores (positive feedback loop) After threshold met (default 10 results), episode averaged into long-term storage Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost Market Microstructure Analysis These advanced metrics quantify institutional order flow dynamics: Order Flow Toxicity (Simplified VPIN): Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol) Detects informed trading activity (institutional players with non-public information) Values >0.4 indicate "toxic flow" (informed traders active) Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification Price Impact Analysis (Simplified Kyle's Lambda): Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10) Low values = large orders with minimal price impact ( stealth accumulation ) High values = retail-dominated moves with high slippage Lite Approximation: Uses simplified denominator instead of regression-based signed order flow Market Randomness (Entropy Analysis): Counts unique price changes over 20 bars / 20 Measures market predictability High entropy (>0.6) = human-driven, chaotic price action Low entropy (<0.25) = algorithmic trading dominance (predictable patterns) Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x)) Order Clustering (Simplified Hawkes Process): Tracks self-exciting event intensity (coordinated order activity) Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average High intensity (>0.7) indicates clustering (potential spoofing/accumulation) Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE Signal Generation Process Multi-Stage Validation: Stage 1: Agent Scoring Each agent calculates internal score based on its detection criteria Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier) Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0) Stage 2: Memory Boost Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance) Successful agents get amplified, failing agents get dampened Stage 3: Bandit Selection/Blending If Adaptive Mode ON: Switch: Bandit selects single best agent, uses only its signal Blend: All agents combined using softmax-weighted confidence scores If Adaptive Mode OFF: Traditional consensus voting with confidence-squared weighting Signal fires when consensus exceeds threshold (default 70%) Stage 4: Confirmation Filter Raw signal must repeat for consecutive bars (default 3, configurable 2-4) Minimum confidence threshold: 0.25 (25%) enforced regardless of mode Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2 Stage 5: Cooldown Enforcement Minimum bars between signals (default 10, configurable 5-15) Prevents over-trading during choppy conditions Stage 6: Performance Tracking After 8 bars (performance horizon), signal outcome evaluated Win = price moved in signal direction, Loss = price moved against Results fed back into memory and bandit statistics Trading Modes (Presets) Pre-configured parameter sets: Conservative: 85% consensus, 4 confirmations, 15-bar cooldown Expected: 60-70% win rate, 3-8 signals/week Best for: Swing trading, capital preservation, beginners Balanced: 70% consensus, 3 confirmations, 10-bar cooldown Expected: 55-65% win rate, 8-15 signals/week Best for: Day trading, most traders, general use Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown Expected: 50-58% win rate, 15-30 signals/week Best for: Scalping, high-frequency trading, active management Elite: 75% consensus, 3 confirmations, 12-bar cooldown Expected: 58-68% win rate, 5-12 signals/week Best for: Selective trading, high-conviction setups Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown Expected: Varies based on learning Best for: Experienced users leveraging bandit system How to Use 1. Initial Setup (5 Minutes): Select Trading Mode matching your style (start with Balanced) Enable Adaptive Learning (recommended for automatic agent selection) Choose Thompson Sampling algorithm (best all-around performance) Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume) 2. Agent Tuning (Optional): Adjust Agent Sensitivity multipliers (0.5-2.0): <0.8 = Highly selective (fewer signals, higher quality) 0.9-1.2 = Balanced (recommended starting point) 1.3 = Aggressive (more signals, lower individual quality) Monitor dashboard for 20-30 signals to identify dominant agent If one agent consistently outperforms, consider using Lock Agent feature 3. Bandit Configuration (Advanced): Blend Temperature (0.1-2.0): 0.3 = Sharp decisions (best agent dominates) 0.5 = Balanced (default) 1.0+ = Smooth (equal weighting, democratic) Memory Decay (0.8-0.99): 0.90 = Fast adaptation (volatile markets) 0.95 = Balanced (most instruments) 0.97+ = Long memory (stable trends) 4. Signal Interpretation: Green triangle (▲): Long signal confirmed Red triangle (▼): Short signal confirmed Dashboard shows: Active agent (highlighted row with ► marker) Win rate per agent (green >60%, yellow 40-60%, red <40%) Confidence bars (█████ = maximum confidence) Memory size (short-term buffer count) Colored zones display: Entry level (current close) Stop-loss (1.5× ATR) Take-profit 1 (2.0× ATR) Take-profit 2 (3.5× ATR) 5. Risk Management: Never risk >1-2% per signal (use ATR-based stops) Signals are entry triggers, not complete strategies Combine with your own market context analysis Consider fundamental catalysts and news events Use "Confirming" status to prepare entries (not to enter early) 6. Memory Persistence (Optional): After 50-100 trades, check Memory Export Panel Record displayed alpha/beta/weight values for each agent Record VPIN and Kyle threshold values Enable "Restore From Memory" and input saved values to continue learning Useful when switching timeframes or restarting indicator Visual Components On-Chart Elements: Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend) Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers) Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR) Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal) Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets Signal Markers: Triangle up/down at validated entry points Dashboard (Configurable Position & Size): Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear) Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus) Agent Performance Table: Real-time win%, confidence, and memory stats Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled) Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress Memory Panel (Configurable Position & Size): Live Parameter Export: Alpha, beta, and weight values per agent Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold Save Reminder: Visual indicator if parameters should be recorded What Makes This Original This script's originality lies in three key innovations: 1. Genuine Meta-Learning Framework: Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning. 2. Episodic Memory Architecture with Transfer Learning: The dual-layer memory system mimics human learning patterns: Short-term memory captures recent performance (recency bias) Long-term memory preserves historical patterns (experience) Automatic transfer mechanism consolidates knowledge Memory boost creates positive feedback loops (successful strategies become stronger) This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates. 3. Institutional Microstructure Integration: Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis. Mashup Justification: The four agents are combined specifically for risk diversification across failure modes: Spoofing Detector: Prevents false breakout losses from manipulation Exhaustion Detector: Prevents chasing extended trends into reversals Liquidity Void: Exploits volatility compression (different regime than trending) Mean Reversion: Provides mathematical anchoring when patterns fail The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals. Why "ML-lite"? Simplifications and Approximations This is the "lite" version due to necessary simplifications for Pine Script execution: 1. Simplified VPIN Calculation: Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume) Impact: May misclassify volume during ranging/choppy markets; works best in directional moves 2. Pseudo-Random Sampling: Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100 Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection 3. Hawkes Process Approximation: Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event) Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments 4. Kyle's Lambda Simplification: Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals 5. Entropy Calculation: Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude This Implementation: Simple ratio of unique price changes to total observations (variety measure) Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape 6. Memory System Constraints: Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking 7. Limited Feature Engineering: Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations 8. Single-Instrument Data: Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes This Implementation: Only OHLCV data from displayed instrument Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets 9. Fixed Performance Horizon: Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement This Implementation: Fixed 8-bar evaluation window Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach Performance Impact Summary: These simplifications make the script: ✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations ✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers ✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models) ✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint ⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations ⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows ⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized When to Upgrade to Full Implementation: Consider professional Python/C++ versions with institutional data feeds if: Trading with >$100K capital where precision differences materially impact returns Operating in microsecond-competitive environments (HFT, market making) Requiring regulatory-grade audit trails and reproducibility Backtesting with tick-level precision for strategy validation Need true real-time adaptation with neural network-based learning For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact. Technical Notes All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap ) VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above) Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling No repainting: All calculations use confirmed bar data (no forward-looking) Maximum lookback: 500 bars (set via max_bars_back parameter) Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed) Confidence threshold: Minimum 0.25 (25%) enforced on all signals Memory arrays: Dynamic sizing with FIFO queue management Limitations and Disclaimers Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty. Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context. Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms. Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts. Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets. No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss. Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics. Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes. Recommended Settings Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning) Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices) Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable) Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens) Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0) After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent Alert Configuration The script includes built-in alert conditions: Long Signal: Fires when validated long entry confirmed Short Signal: Fires when validated short entry confirmed Alerts fire once per bar (after confirmation requirements met) Set alert to "Once Per Bar Close" for reliability Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
อินดิเคเตอร์ Pine Script®
โดย DskyzInvestments
10
Algorithmic Kalman Filter [CRYPTIK1]Price action is chaos. Markets are driven by high-frequency algorithms, emotional reactions, and raw speculation, creating a constant stream of noise that obscures the true underlying trend. A simple moving average is too slow, too primitive to navigate this environment effectively. It lags, it gets chopped up, and it fails when you need it most. This script implements an Algorithmic Kalman Filter (AKF), a sophisticated signal processing algorithm adapted from aerospace and robotic guidance systems. Its purpose is singular: to strip away market noise and provide a hyper-adaptive, self-correcting estimate of an asset's true trajectory. The Concept: An Adaptive Intelligence Unlike a moving average that mindlessly averages past data, the Kalman Filter operates on a two-step principle: Predict and Update. Predict: On each new bar, the filter makes a prediction of the true price based on its previous state. Update: It then measures the error between its prediction and the actual closing price. It uses this error to intelligently correct its estimate, learning from its mistakes in real-time. The result is a flawlessly smooth line that adapts to volatility. It remains stable during chop and reacts swiftly to new trends, giving you a crystal-clear view of the market's real intention. How to Wield the Filter: The Core Settings The power of the AKF lies in its two tuning parameters, which allow you to calibrate the filter's "brain" to any asset or timeframe. Process Noise (Q) - Responsiveness: This controls how much you expect the true trend to change. A higher Q value makes the filter more sensitive and responsive to recent price action. Use this for highly volatile assets or lower timeframes. A lower Q value makes the filter smoother and more stable, trusting that the underlying trend is slow-moving. Use this for higher timeframes or ranging markets. Measurement Noise (R) - Smoothness: This controls how much you trust the incoming price data. A higher R value tells the filter that the price is extremely noisy and to be more skeptical. This results in a much smoother, slower-moving line. A lower R value tells the filter to trust the price data more, resulting in a line that tracks price more closely. The interaction between Q and R is what gives the filter its power. The default settings provide a solid baseline, but a true operator will fine-tune these to perfectly match the rhythm of their chosen market. Tactical Application The AKF is not just a line; it's a complete framework for viewing the market. Trend Identification: The primary signal. The filter's color code provides an unambiguous definition of the trend. Teal for an uptrend, Pink for a downtrend. No more guesswork. Dynamic Support & Resistance: The filter itself acts as a dynamic level. Watch for price to pull back and find support on a rising (Teal) filter in an uptrend, or to be rejected by a falling (Pink) filter in a downtrend. A Higher-Order Filter: Use the AKF's trend state to filter signals from your primary strategy. For example, only take long signals when the AKF is Teal. This single rule can dramatically reduce noise and eliminate low-probability trades. This is a professional-grade tool for traders who are serious about gaining a statistical edge. Ditch the lagging averages. Extract the signal from the noise.
อินดิเคเตอร์ Pine Script®
โดย CRYPTIK1
Algo Trading Signals - Buy/Sell System# 📊 Algo Trading Signals - Dynamic Buy/Sell System ## 🎯 Overview **Algo Trading Signals** is a sophisticated intraday trading indicator designed for algorithmic traders and active day traders. This system generates precise buy and sell signals based on a dynamic box breakout strategy with intelligent position management, add-on entries, and automatic target adjustment. The indicator creates a reference price box during a specified time window (default: 9:15 AM - 9:45 AM IST) and generates high-probability signals when price breaks out of this range with confirmation. --- ## ✨ Key Features ### 📍 **Smart Signal Generation** - **Primary Entry Signals**: Clear buy/sell signals on confirmed breakouts above/below the reference box - **Confirmation Bars**: Reduces false signals by requiring multiple bar confirmation before entry - **Cooldown System**: Prevents overtrading with configurable cooldown periods between trades - **Add-On Positions**: Automatically identifies optimal pullback entries for scaling into positions ### 📦 **Dynamic Reference Box** - Creates a high/low range during your chosen time window - Automatically updates after each successful trade - Visual box display with color-coded boundaries (red=resistance, green=support) - Mid-level reference line for market structure analysis ### 🎯 **Intelligent Position Management** - **Automatic Target Calculation**: Sets profit targets based on average move distance - **Add-On System**: Up to 3 additional entries on optimal pullbacks - **Position Tracking**: Monitors active trades and remaining add-on capacity - **Auto Box Shift**: Adjusts reference box after target hits for continued trading ### 📊 **Visual Clarity** - **Color-Coded Labels**: - 🟢 Green for BUY signals - 🔴 Red for SELL signals - 🔵 Blue for ADD-ON buys - 🟠 Orange for ADD-ON sells - ✓ Yellow for Target hits - **TP Level Lines**: Dotted lines showing current profit targets - **Hover Tooltips**: Detailed information on entry prices, targets, and add-on numbers ### 📈 **Real-Time Statistics** Live performance dashboard showing: - Total buy and sell signals generated - Number of add-on positions taken - Take profit hits achieved - Current trade status (LONG/SHORT/None) - Cooldown timer status ### 🔔 **Comprehensive Alerts** Built-in alert conditions for: - Primary buy entry signals - Primary sell entry signals - Add-on buy positions - Add-on sell positions - Buy take profit hits - Sell take profit hits --- ## 🛠️ Configuration Options ### **Time Settings** - **Box Start Hour/Minute**: Define when to begin tracking the reference range - **Box End Hour/Minute**: Define when to lock the reference box - **Default**: 9:15 AM - 9:45 AM (IST) - Perfect for Indian market opening range ### **Trade Settings** - **Target Points (TP)**: Average move distance for profit targets (default: 40 points) - **Breakout Confirmation Bars**: Number of bars to confirm breakout (default: 2) - **Cooldown After Trade**: Bars to wait after closing position (default: 3) - **Add-On Distance Points**: Minimum pullback for add-on entry (default: 40 points) - **Max Add-On Positions**: Maximum additional positions allowed (default: 3) ### **Display Options** - Toggle buy/sell signal labels - Show/hide trading box visualization - Show/hide TP level lines - Show/hide statistics table --- ## 💡 How It Works ### **Phase 1: Box Formation (9:15 AM - 9:45 AM)** The indicator tracks the high and low prices during your specified time window to create a reference box representing the opening range. ### **Phase 2: Breakout Detection** After the box is locked, the system monitors for: - **Bullish Breakout**: Price closes above box high for confirmation bars - **Bearish Breakout**: Price closes below box low for confirmation bars ### **Phase 3: Signal Generation** When confirmation requirements are met: - Entry signal is generated with clear visual label - Target price is calculated (Entry ± Target Points) - Position tracking activates - Cooldown timer starts ### **Phase 4: Position Management** During active trade: - **Add-On Logic**: If price pulls back by specified distance but stays within favorable range, additional entry signal fires - **Target Monitoring**: Continuously checks if price reaches TP level - **Box Adjustment**: After TP hit, box automatically shifts to new range for next opportunity ### **Phase 5: Trade Exit & Reset** On target hit: - Position closes with TP marker - Statistics update - Box repositions for next setup - Cooldown activates - System ready for next signal --- ## 📌 Best Use Cases ### **Ideal For:** - ✅ Intraday breakout trading strategies - ✅ Algorithmic trading systems (via alerts/webhooks) - ✅ Opening range breakout (ORB) strategies - ✅ Index futures (Nifty, Bank Nifty, Sensex) - ✅ High-liquidity stocks with clear ranges - ✅ Automated trading bots - ✅ Scalping and day trading ### **Markets:** - Indian Stock Market (NSE/BSE) - Futures & Options - Forex pairs - Cryptocurrency (adjust timing for 24/7 markets) - Global indices --- ## ⚙️ Integration with Algo Trading This indicator is **algo-ready** and can be integrated with automated trading systems: 1. **TradingView Alerts**: Set up alert conditions for each signal type 2. **Webhook Integration**: Connect alerts to trading platforms via webhooks 3. **API Automation**: Use with brokers supporting TradingView integration (Zerodha, Upstox, Interactive Brokers, etc.) 4. **Signal Data Access**: All signals are plotted for external data retrieval --- ## 📖 Quick Start Guide 1. **Add Indicator**: Apply to your chart (works best on 1-5 minute timeframes) 2. **Configure Time Window**: Set your desired box formation period 3. **Adjust Parameters**: Tune confirmation bars, targets, and add-on settings to your trading style 4. **Set Alerts**: Create alert conditions for automated notifications 5. **Backtest**: Review historical signals to validate strategy performance 6. **Go Live**: Enable alerts and start receiving real-time trading signals --- ## ⚠️ Risk Disclaimer This indicator is a **tool for analysis** and does not guarantee profits. Trading involves substantial risk of loss. Always: - Use proper position sizing - Implement stop losses (not included in this indicator) - Test thoroughly before live trading - Understand market conditions - Never risk more than you can afford to lose - Consider your risk tolerance and trading experience **Past performance does not indicate future results.** ## 🔄 Version History **v1.0** - Initial Release - Dynamic box formation system - Confirmed breakout signals - Add-on position management - Visual signal labels and statistics - Comprehensive alert system - Auto-adjusting target boxes --- ## 📞 Support & Feedback If you find this indicator helpful: - ⭐ Please leave a like/favorite - 💬 Share your feedback in comments - 📊 Share your results and improvements - 🤝 Suggest features for future updates --- ## 🏷️ Tags `breakout` `daytrading` `signals` `algo` `automated` `intraday` `ORB` `opening-range` `buy-sell` `scalping` `futures` `nifty` `banknifty` `algorithmic` `box-strategy` *Remember: The best indicator is combined with proper risk management and trading discipline.* Use it at your own rist, not as financial advie
อินดิเคเตอร์ Pine Script®
โดย vkpk1401
Algorithmic Value Oscillator [CRYPTIK1]Algorithmic Value Oscillator Introduction: What is the AVO? Welcome to the Algorithmic Value Oscillator (AVO), a powerful, modern momentum indicator that reframes the classic "overbought" and "oversold" concept. Instead of relying on a fixed lookback period like a standard RSI, the AVO measures the current price relative to a significant, higher-timeframe Value Zone . This gives you a more contextual and structural understanding of price. The core question it answers is not just "Is the price moving up or down quickly?" but rather, " Where is the current price in relation to its recently established area of value? " This allows traders to identify true "premium" (overbought) and "discount" (oversold) levels with greater accuracy, all presented with a clean, futuristic aesthetic designed for the modern trader. The Core Concept: Price vs. Value The market is constantly trying to find equilibrium. The AVO is built on the principle that the high and low of a significant prior period (like the previous day or week) create a powerful area of perceived value. The Value Zone: The range between the high and low of the selected higher timeframe. Premium Territory (Distribution Zone): When the oscillator moves into the glowing pink/purple zone above +100, it is trading at a premium. Discount Territory (Accumulation Zone): When the oscillator moves into the glowing teal/blue zone below -100, it is trading at a discount. Key Features 1. Glowing Gradient Oscillator: The main oscillator line is a dynamic visual guide to momentum. The line changes color smoothly from light blue to neon teal as bullish momentum increases. It shifts from hot pink to bright purple as bearish momentum increases. Multiple transparent layers create a professional "glow" effect, making the trend easy to see at a glance. 2. Dynamic Volatility Histogram: This histogram at the bottom of the indicator is a custom volatility meter. It has been engineered to be adaptive, ensuring that the visual differences between high and low volatility are always clear and dramatic, no matter your zoom level. It uses a multi-color gradient to visualize the intensity of market volatility. 3. Volatility Regime Dashboard: This simple on-screen table analyzes the histogram and provides a clear, one-word summary of the current market state: Compressing, Stable, or Expanding. How to Use the AVO: Trading Strategies 1. Reversion Trading This is the most direct way to use the indicator. Look for Buys: When the AVO line drops into the teal "Accumulation Zone" (below -100), the price is trading at a discount. Watch for the oscillator to form a bottom and start turning up as a signal that buying pressure is returning. Look for Sells: When the AVO line moves into the pink "Distribution Zone" (above +100), the price is trading at a premium. Watch for the oscillator to form a peak and start turning down as a signal that selling pressure is increasing. 2. Best Practices & Settings Timeframe Synergy: The AVO is most effective when your chart timeframe is lower than your selected "Value Zone Source." For example, if you trade on the 1-hour chart, set your Value Zone to "Previous Day." Confirmation is Key: This indicator provides powerful context, but it should not be used in isolation. Always combine its readings with your primary analysis, such as market structure and support/resistance levels.
อินดิเคเตอร์ Pine Script®
โดย CRYPTIK1
3
Weekly RSI Buy/Sell SignalsWeekly RSI Buy/Sell Signal Indicator This indicator is designed to help traders identify high-probability buy and sell opportunities on the weekly chart by using the Relative Strength Index (RSI). By utilizing weekly RSI values, this indicator ensures signals align with broader market trends, providing a clearer view of potential price reversals and continuation. How It Works: Weekly RSI Calculation: This script calculates the RSI using a 14-period setting, focusing on the weekly timeframe regardless of the user’s current chart view. The weekly RSI is derived using request.security, allowing for consistent signals even on intraday charts. Signal Conditions: Buy Signal: A buy signal appears when the RSI crosses above the oversold threshold of 30, suggesting that price may be gaining momentum after a potential bottom. Sell Signal: A sell signal triggers when the RSI crosses below the overbought threshold of 70, indicating a possible momentum shift downwards. Visual Cues: Buy/Sell Markers: Clear green "BUY" and red "SELL" markers are displayed on the chart when buy or sell conditions are met, making it easy to identify entry and exit points. RSI Line and Thresholds: The weekly RSI value is plotted in real time with color-coded horizontal lines at 30 (oversold) and 70 (overbought), providing a visual reference for key levels. This indicator is ideal for traders looking for reliable, trend-based signals on higher timeframes and can be a helpful tool for filtering out shorter-term market noise.
อินดิเคเตอร์ Pine Script®
โดย ALGO-TECH
4
SPY Master v1.0This is a simple swing trading algorithm that uses a fast RSI-EMA to trigger buy/cover signals and a slow RSI-EMA to trigger sell/short signals for SPY, an xchange-traded fund for the S&P 500. The idea behind this strategy follows the premise that most profitable momentum trades usually occur during periods when price is trending up or down. Periods of flat price actions are usually where most unprofitable trades occur. Because we cannot predict exactly when trending periods will occur, the algorithm basically bets money on all trade opportunities during all market conditions. Despite an accuracy rate of only 40%, the algorithm's asymmetric risk/reward profile allows the average winner to be 2x the average loser. The end result is a positive (profitable) net payout. TRADING RULES: Buy/Cover = EMA3(RSI2) cross> 50 Sell/Short = EMA5(RSI2) cross< 50 BACKTEST SETTINGS: - Period = March 2011 - Present - Initial capital = $10,000 - Dividends excluded - Trading costs excluded PERFORMANCE COMPARISON: There are 657 trades, which means 1,314 orders. Assuming each order costs $2 (what I pay for at Interactive Brokers), total trading costs should be $2,628. -SPY (buy & hold) = 132.73 ---> 193.22 = +45.57% (dividends excluded) -SPY Master v1.0 = $12,649 - $2,628 = $10,021 = +100.21% DISCLAIMER: None of my ideas and posts are investment advice. Past performance is not an indication of future results. This strategy was constructed with the benefit of hindsight and its future performance cannot be guaranteed.
กลยุทธ์ Pine Script®
โดย Stable_Camel
5
Algorithmic Volume Rejection Zones [AVRZ]Hello traders, I am pleased to release the Algorithmic Volume Rejection Zones (AVRZ). This is a specialized decision-support system designed to identify high-probability reversal points by synthesizing candle geometry, market structure, and statistical volume anomalies. Trading reversals often presents a dilemma: wait for confirmation and miss the move, or enter early and get stopped out by noise. AVRZ solves this by quantifying "Institutional Absorption." It filters out weak price probes and highlights only the specific moments where significant volume has stepped in to defend a price level. 🛡️ The Concept: Attacking The Zonesl You will often see price aggressively "attack" a support or resistance level with speed and high volume. To the untrained eye, this looks like a breakout. However, professional analysis reveals that this is often an Efficiency Event—liquidity is being absorbed by passive limit orders. The AVRZ indicator is specifically engineered to detect this phenomenon. When price strikes a level and volume spikes (>2.0 Sigma), it signals that the auction is becoming efficient and a reversal is imminent. The script captures this "Attack" via the Climax Bypass logic, plotting a fresh zone immediately to mark where the liquidity was defended.
อินดิเคเตอร์ Pine Script®
โดย CandlesofKarma
1
Algo + Trendlines :: Medium PeriodThis indicator helps me to avoid overlooking Trendlines / Algolines. So far it doesn't search explicitly for Algolines (I don't consider volume at all), but it's definitely now already not horribly bad. These are meant to be used on logarithmic charts btw! The lines would be displayed wrong on linear charts. The biggest challenge is that there are some technical restrictions in TradingView, f. e. a script stops executing if a for-loop would take longer than 0.5 sec. So in order to circumvent this and still be able to consider as many candles from the past as possible, I've created multiple versions for different purposes that I use like this: Algo + Trendlines :: Medium Period : This script looks for "temporary highs / lows" (meaning the bar before and after has lower highs / lows) on the daily chart, connects them and shows the 5 ones that are the closest to the current price (=most relevant). This one is good to find trendlines more thoroughly, but only up to 4 years ago. Algo + Trendlines :: Long Period : This version looks instead at the weekly charts for "temporary highs / lows" and finds out which days caused these highs / lows and connects them, Taking data from the weekly chart means fewer data points to check whether a trendline is broken, which allows to detect trendlines from up to 12 years ago! Therefore it misses some trendlines. Personally I prefer this one with "Only Confirmed" set to true to really show only the most relevant lines. This means at least 3 candle highs / lows touched the line. These are more likely stronger resistance / support lines compared to those that have been touched only twice. Very important: sometimes you might see dotted lines that suddenly stop after a few months (after 100 bars to be precise). This indicates you need to zoom further out for TradingView to be able to load the full line. Unfortunately TradingView doesn't render lines if the starting point was too long ago, so this is my workaround. This is also the script's biggest advantage: showing you lines that you might have missed otherwise since the starting bars were outside of the screen, and required you to scroll f. e back to 2015.. One more thing to know: Weak colored line = only 2 "collision" points with candle highs/lows (= not confirmed) Usual colored line = 3+ "collision" points (= confirmed) Make sure to move this indicator above the ticker in the Object Tree, so that it is drawn on top of the ticker's candles! More infos: www.reddit.com
อินดิเคเตอร์ Pine Script®
โดย CaressingCandles
Algo + Trendlines :: Long PeriodThis indicator helps me to avoid overlooking Trendlines / Algolines. So far it doesn't search explicitly for Algolines (I don't consider volume at all), but it's definitely now already not horribly bad. These are meant to be used on logarithmic charts btw! The lines would be displayed wrong on linear charts. The biggest challenge is that there are some technical restrictions in TradingView, f. e. a script stops executing if a for-loop would take longer than 0.5 sec. So in order to circumvent this and still be able to consider as many candles from the past as possible, I've created multiple versions for different purposes that I use like this: Algo + Trendlines :: Medium Period : This script looks for "temporary highs / lows" (meaning the bar before and after has lower highs / lows) on the daily chart, connects them and shows the 5 ones that are the closest to the current price (=most relevant). This one is good to find trendlines more thoroughly, but only up to 4 years ago. Algo + Trendlines :: Long Period : This version looks instead at the weekly charts for "temporary highs / lows" and finds out which days caused these highs / lows and connects them, Taking data from the weekly chart means fewer data points to check whether a trendline is broken, which allows to detect trendlines from up to 12 years ago! Therefore it misses some trendlines. Personally I prefer this one with "Only Confirmed" set to true to really show only the most relevant lines. This means at least 3 candle highs / lows touched the line. These are more likely stronger resistance / support lines compared to those that have been touched only twice. Very important: sometimes you might see dotted lines that suddenly stop after a few months (after 100 bars to be precise). This indicates you need to zoom further out for TradingView to be able to load the full line. Unfortunately TradingView doesn't render lines if the starting point was too long ago, so this is my workaround. This is also the script's biggest advantage: showing you lines that you might have missed otherwise since the starting bars were outside of the screen, and required you to scroll f. e back to 2015.. One more thing to know: Weak colored line = only 2 "collision" points with candle highs/lows (= not confirmed) Usual colored line = 3+ "collision" points (= confirmed) Make sure to move this indicator above the ticker in the Object Tree, so that it is drawn on top of the ticker's candles! More infos: www.reddit.com
อินดิเคเตอร์ Pine Script®
โดย CaressingCandles
Algorithmic Signal AnalyzerMeet Algorithmic Signal Analyzer (ASA) v1: A revolutionary tool that ushers in a new era of clarity and precision for both short-term and long-term market analysis, elevating your strategies to the next level. ASA is an advanced TradingView indicator designed to filter out noise and enhance signal detection using mathematical models. By processing price movements within defined standard deviation ranges, ASA produces a smoothed analysis based on a Weighted Moving Average (WMA). The Volatility Filter ensures that only relevant price data is retained, removing outliers and improving analytical accuracy. While ASA provides significant analytical advantages, it’s essential to understand its capabilities in both short-term and long-term use cases. For short-term trading, ASA excels at capturing swift opportunities by highlighting immediate trend changes. Conversely, in long-term trading, it reveals the overall direction of market trends, enabling traders to align their strategies with prevailing conditions. Despite these benefits, traders must remember that ASA is not designed for precise trade execution systems where accuracy in timing and price levels is critical. Its focus is on analysis rather than order management. The distinction is crucial: ASA helps interpret price action effectively but may not account for real-time market factors such as slippage or execution delays. Features and Functionality ASA integrates multiple tools to enhance its analytical capabilities: Customizable Moving Averages: SMA, EMA, and WMA options allow users to tailor the indicator to their trading style. Signal Detection: Identifies bullish and bearish trends using the Relative Exponential Moving Average (REMA) and marks potential buy/sell opportunities. Visual Aids: Color-coded trend lines (green for upward, red for downward) simplify interpretation. Alert System: Notifications for trend swings and reversals enable timely decision-making. Notes on Usage ASA’s effectiveness depends on the context in which it is applied. Traders should carefully consider the trade-offs between analysis and execution. Results may vary depending on market conditions and chart types. Backtesting with ASA on standard charts provides more reliable insights compared to non-standard chart types. Short-term use focuses on rapid trend recognition, while long-term application emphasizes understanding broader market movements. Takeaways ASA is not a tool for precise trade execution but a powerful aid for interpreting price trends. For short-term trading, ASA identifies quick opportunities, while for long-term strategies, it highlights trend directions. Understanding ASA’s limitations and strengths is key to maximizing its utility. ASA is a robust solution for traders seeking to filter noise, enhance analytical clarity, and align their strategies with market movements, whether for short bursts of activity or sustained trading goals.
อินดิเคเตอร์ Pine Script®
โดย ertekinibrahim
4
Algo Structure [ValiantTrader_]Explanation of the "Algo Structure" Trading Indicator This Pine Script indicator, created by ValiantTrader_, is a multi-timeframe swing analysis tool that helps traders identify key price levels and market structure across different timeframes. Here's how it works and how traders can use it: Core Components 1. Multi-Timeframe Swing Analysis The indicator tracks swing highs and lows across: The current chart timeframe A higher timeframe (weekly by default) An even higher timeframe (monthly by default) 2. Swing Detection Logic Current timeframe swings: Identified when price makes a 3-bar high/low pattern Higher timeframe swings: Uses the highest high/lowest low of the last 3 bars on those timeframes 3. Visual Elements Horizontal lines marking swing points Labels showing the timeframe and percentage distance from current price An information table summarizing key levels How Traders Use This Indicator 1. Identifying Key Levels The indicator draws recent swing highs (red) and swing lows (green) These levels act as potential support/resistance areas Traders watch for price reactions at these levels 2. Multi-Timeframe Analysis By seeing swings from higher timeframes (weekly, monthly), traders can: Identify more significant support/resistance zones Understand the broader market context Spot confluence areas where multiple timeframes align 3. Measuring Price Distance The percentage display shows how far current price is from each swing level Helps assess potential reward/risk at current levels Shows volatility between swings (wider % = more volatile moves) 4. Table Summary The info table provides a quick reference for: Exact price levels of swings Percentage ranges between highs and lows Comparison across timeframes 5. Trading Applications Breakout trading: When price moves beyond a swing high/low Mean reversion: Trading bounces between swing levels Trend confirmation: Higher highs/lows in multiple timeframes confirm trends Support/resistance trading: Entering trades at swing levels with other confirmation Customization Options Traders can adjust: The higher timeframes analyzed Whether to show the timeframe labels Whether to display swing levels Whether to show the info table The indicator also includes price alerts for new swing highs/lows on the current timeframe, allowing traders to get notifications when market structure changes. This tool is particularly valuable for traders who incorporate multi-timeframe analysis into their strategy, helping them visualize important price levels across different time perspectives
อินดิเคเตอร์ Pine Script®
โดย ValiantTrader_