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Logistic Regression ICT FVG🚀 OVERVIEW Welcome to the Logistic Regression Fair Value Gap (FVG) System — a next-gen trading tool that blends precision gap detection with machine learning intelligence. Unlike traditional FVG indicators, this one evolves with each bar of price action, scoring and filtering gaps based on real market behavior. 🔧 CORE FEATURES ✨ Smart Gap Detection Automatically identifies bullish and bearish Fair Value Gaps using volatility-aware candle logic. 📊 Probability-Based Filtering Uses logistic regression to assign each gap a confidence score (0 to 1), showing only high-probability setups. 🔁 Real-Time Retest Tracking Continuously watches how price interacts with each gap to determine if it deserves respect. 📈 Multi-Factor Assessment Evaluates RSI, MACD, and body size at gap formation to build a full context snapshot. 🧠 Self-Learning Engine The logistic regression model updates on each bar using gradient descent, refining its predictions over time. 📢 Built-In Alerts Get instant alerts when a gap forms, gets retested, or breaks. 🎨 Custom Display Options Control the color of bullish/bearish zones, and toggle on/off probability labels for cleaner charts. 🚩 WHAT MAKES IT DIFFERENT This isn’t just another box-drawing indicator. While others mark every imbalance, this system thinks before it draws — using statistical modeling to filter out noise and prioritize high-impact zones. By learning from how price behaves around gaps (not just how they form), it helps you trade only what matters — not what clutters. ⚙️ HOW IT WORKS 1️⃣ Detection FVGs are identified using ATR-based thresholds and sharp wick imbalances. 2️⃣ Behavior Monitoring Every gap is tracked — and if respected enough times, it becomes part of the elite training set. 3️⃣ Context Capture Each new FVG logs RSI, MACD, and body size to provide a feature-rich context for prediction. 4️⃣ Prediction (Logistic Regression) The model predicts how likely the gap is to be respected and assigns it a probability score. 5️⃣ Classification & Alerts Gaps above the threshold are plotted with score labels, and alerts trigger for entry/respect/break. ⚙️ CONFIGURATION PANEL 🔧 System Inputs • Max Retests – How many times a gap must be respected to train the model • Prediction Threshold – Minimum score to show a gap on the chart • Learning Rate – Controls how fast the model adapts (default: 0.009) • Max FVG Lifetime – Expiration duration for unused gaps • Show Historic Gaps – Show/hide expired or invalidated gaps 🎨 Visual Options • Bullish/Bearish Colors – Set gap colors to fit your chart style • Confidence Labels – Show probability scores next to FVGs • Alert Toggles – Enable alerts for:   – New FVG detected   – FVG respected (entry)   – FVG invalidated (break) 💡 WHY LOGISTIC REGRESSION? Traditional FVG tools rely on candle shapes. This system relies on probability — by training on RSI, MACD, and price behavior, it predicts whether a gap will act as a true liquidity zone. Logistic regression lets the system continuously adapt using new data, making it more accurate the longer it runs. That means smarter signals, fewer false positives, and a clearer view of where real opportunities lie.
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PalgoTrading tarafından
Floor and Roof Indicator with SignalsFloor and Roof Indicator with Trading Signals A comprehensive support and resistance indicator that identifies premium and discount zones with automated signal generation. Key Features: Dynamic Support/Resistance Zones: Calculates floor (support) and roof (resistance) levels using price action and volatility Premium/Discount Zone Identification: Highlights areas where price may find resistance or support Customizable Signal Frequency: Control how often signals are displayed (every Nth occurrence) Visual Signal Table: Optional table showing the last 5 long and short signal prices Multiple Timeframe Compatibility: Works across all timeframes Technical Details: Uses ATR-based calculations for dynamic zone width adjustment Combines Bollinger Bands with highest/lowest price analysis Smoothing options for cleaner signal generation Fully customizable colors and display options How to Use: Floor Zones (Blue): Potential support areas where long positions may be considered Roof Zones (Pink): Potential resistance areas where short positions may be considered Signal Crosses: Visual markers when price interacts with key levels Signal Table: Track recent signal prices for analysis Settings: Length: Period for calculations (default: 200) Smooth: Smoothing factor for cleaner signals Zone Width: Adjust the thickness of support/resistance zones Signal Frequency: Control signal display frequency Visual Options: Customize colors and table position Alerts Available: Long signal alerts when price touches discount zones Short signal alerts when price reaches premium zones Educational Purpose: This indicator is designed to help traders identify potential support and resistance areas. Always combine with proper risk management and additional analysis. This description focuses on the technical aspects and educational value while avoiding any language that could be interpreted as financial advice or guaranteed profits.
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profitgang tarafından
Kelly Optimal Leverage IndicatorThe Kelly Optimal Leverage Indicator mathematically applies Kelly Criterion to determine optimal position sizing based on market conditions. This indicator helps traders answer the critical question: "How much capital should I allocate to this trade?" Note that "optimal position sizing" does not equal the position sizing that you should have. The Optima position sizing given by the indicator is based on historical data and cannot predict a crash, in which case, high leverage could be devastating. Originally developed for gambling scenarios with known probabilities, the Kelly formula has been adapted here for financial markets to dynamically calculate the optimal leverage ratio that maximizes long-term capital growth while managing risk. Key Features Kelly Position Sizing: Uses historical returns and volatility to calculate mathematically optimal position sizes Multiple Risk Profiles: Displays Full Kelly (aggressive), 3/4 Kelly (moderate), 1/2 Kelly (conservative), and 1/4 Kelly (very conservative) leverage levels Volatility Adjustment: Automatically recommends appropriate Kelly fraction based on current market volatility Return Smoothing: Option to use log returns and smoothed calculations for more stable signals Comprehensive Table: Displays key metrics including annualized return, volatility, and recommended exposure levels How to Use Interpret the Lines: Each colored line represents a different Kelly fraction (risk tolerance level). When above zero, positive exposure is suggested; when below zero, reduce exposure. Note that this is based on historical returns. I personally like to increase my exposure during market downturns, but this is hard to illustrate in the indicator. Monitor the Table: The information panel provides precise leverage recommendations and exposure guidance based on current market conditions. Follow Recommended Position: Use the "Recommended Position" guidance in the table to determine appropriate exposure level. Select Your Risk Profile: Conservative traders should follow the Half Kelly or Quarter Kelly lines, while more aggressive traders might consider the Three-Quarter or Full Kelly lines. Adjust with Volatility: During high volatility periods, consider using more conservative Kelly fractions as recommended by the indicator. Mathematical Foundation The indicator calculates the optimal leverage (f*) using the formula: f* = μ/σ² Where: μ is the annualized expected return σ² is the annualized variance of returns This approach balances potential gains against risk of ruin, offering a scientific framework for position sizing that maximizes long-term growth rate. Notes The Full Kelly is theoretically optimal for maximizing long-term growth but can experience significant drawdowns. You should almost never use full kelly. Most practitioners use fractional Kelly strategies (1/2 or 1/4 Kelly) to reduce volatility while capturing most of the growth benefits This indicator works best on daily timeframes but can be applied to any timeframe Negative Kelly values suggest reducing or eliminating market exposure The indicator should be used as part of a complete trading system, not in isolation Enjoy the indicator! :) P.S. If you are really geeky about the Kelly Criterion, I recommend the book The Kelly Capital Growth Investment Criterion by Edward O. Thorp and others.
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HenriqueCentieiro tarafından
Crypto Risk-Weighted Allocation SuiteCrypto Risk-Weighted Allocation Suite This indicator is designed to help users explore dynamic portfolio allocation frameworks for the crypto market. It calculates risk-adjusted allocation weights across major crypto sectors and cash based on multi-factor momentum and volatility signals. Best viewed on INDEX:BTCUSD 1D chart. Other charts and timeframes may give mixed signals and incoherent allocations. 🎯 How It Works This model systematically evaluates the relative strength of: BTC Dominance (CRYPTOCAP:BTC.D) Represents Bitcoin’s share of the total crypto market. Rising dominance typically indicates defensive market phases or BTC-led trends. ETH/BTC Ratio (BINANCE:ETHBTC) Gauges Ethereum’s relative performance versus Bitcoin. This provides insight into whether ETH is leading risk appetite. SOL/BTC Ratio (BINANCE:SOLBTC) Measures Solana’s performance relative to Bitcoin, capturing mid-cap layer-1 strength. Total Market Cap excluding BTC and ETH (CRYPTOCAP:TOTAL3ES) Represents Altcoins as a broad category, reflecting appetite for higher-risk assets. Each of these series is: ✅ Converted to a momentum slope over a configurable lookback period. ✅ Standardized into Z-scores to normalize changes relative to recent behavior. ✅ Smoothed optionally using a Hull Moving Average for cleaner signals. ✅ Divided by ATR-based volatility to create a risk-weighted score. ✅ Scaled to proportionally allocate exposure, applying user-configured minimum and maximum constraints. 🪙 Dynamic Allocation Logic All signals are normalized to sum to 100% if fully confident. An overall confidence factor (based on total signal strength) scales the allocation up or down. Any residual is allocated to cash (unallocated capital) for conservative exposure. The script automatically avoids “all-in” bias and prevents negative allocations. 📊 Outputs The indicator displays: Market Phase Detection (which asset class is currently leading) Risk Mode (Risk On, Neutral, Risk Off) Dynamic Allocations for BTC, ETH, SOL, Alts, and Cash Optional momentum plots for transparency 🧠 Why This Is Unique Unlike simple dominance indicators or crossovers, this model: Integrates multiple cross-asset signals (BTC, ETH, SOL, Alts) Adjusts exposure proportionally to signal strength Normalizes by volatility, dynamically scaling risk Includes configurable constraints to reflect your own risk tolerance Provides a cash fallback allocation when conviction is low Is entirely non-repainting and based on daily closing data ⚠️ Disclaimer This script is provided for educational and informational purposes only. It is not financial advice and should not be relied upon to make investment decisions. Past performance does not guarantee future results. Always consult a qualified financial advisor before acting on any information derived from this tool. 🛠 Recommended Use As a framework to visualize relative momentum and risk-adjusted allocations For research and backtesting ideas on portfolio allocation across crypto sectors To help build your own risk management process This script is not a turnkey strategy and should be customized to fit your goals. ✅ Enjoy exploring dynamic crypto allocations responsibly!
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RWCS_LTD tarafından
Jumping watermark# Jumping watermark ## Function description - Dynamic watermark: Mainly used to add dynamic watermarks to prevent theft and transfer when recording videos. - Static watermark: Sharing opinions can easily include information such as trading pairs, cycles, current time, and individual signatures. ### Static watermark: Display the watermark related to the current trading pair in the center of the chart. - Configuration items: - You can choose to configure the display content: current trading pair code and name, cycle, date, time, and individual signature content ### Dynamic watermark Display the configured watermark content in a dynamic random position. - Configuration items: - Turn on or off the display of watermark jumping - Modify the display text content and style by yourself ----- 中文简介----- # 跳动水印 ## 功能描述 - 动态水印： 主要可用于视频录制时添加动态水印防盗、防搬运。 - 静态水印：观点分享是可方便的带上交易对、周期、当前时间、个签等信息。 ### 静态水印： 在图表中心位置显示当前交易对相关信息水印。 - 配置项： - 可选择配置显示内容：当前交易对代码及名称、周期、日期、时间、个签内容 ### 动态水印 动态随机位置显示配置水印内容。 - 配置项： - 开启或关闭显示水印跳动 - 自行修改配置显示文字内容和样式
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v50me tarafından
EVaR Indicator and Position SizingThe Problem: Financial markets consistently show "fat-tailed" distributions where extreme events occur with higher frequency than predicted by normal distributions (Gaussian or even log-normal). These fat tails manifest in sudden price crashes, volatility spikes, and black swan events that traditional risk measures like volatility can underestimate. Standard deviation and conventional VaR calculations assume normally distributed returns, leaving traders vulnerable to severe drawdowns during market stress. Cryptocurrencies and volatile instruments display particularly pronounced fat-tailed behavior, with extreme moves occurring 5-10 times more frequently than normal distribution models would predict. This reality demands a more sophisticated approach to risk measurement and position sizing. The Solution: Entropic Value at Risk (EVAR) EVaR addresses these limitations by incorporating principles from statistical mechanics and information theory through Tsallis entropy. This advanced approach captures the non-linear dependencies and power-law distributions characteristic of real financial markets. Entropy is more adaptive than standard deviations and volatility measures. I was inspired to create this indicator after reading the paper " The End of Mean-Variance? Tsallis Entropy Revolutionises Portfolio Optimisation in Cryptocurrencies " by by Sana Gaied Chortane and Kamel Naoui. Key advantages of EVAR over traditional risk measures: Superior tail risk capture: More accurately quantifies the probability of extreme market moves Adaptability to market regimes: Self-calibrates to changing volatility environments Non-parametric flexibility: Makes less assumptions about the underlying return distribution Forward-looking risk assessment: Better anticipates potential market changes (just look at the charts :) Mathematically, EVAR is defined as: EVAR_α(X) = inf_{z>0} {z * log(1/α * M_X(1/z))} Where the moment-generating function is calculated using q-exponentials rather than conventional exponentials, allowing precise modeling of fat-tailed behavior. Technical Implementation This indicator implements EVAR through a q-exponential approach from Tsallis statistics: Returns Calculation: Price returns are calculated over the lookback period Moment Generating Function: Approximated using q-exponentials to account for fat tails EVAR Computation: Derived from the MGF and confidence parameter Normalization: Scaled to for intuitive visualization Position Sizing: Inversely modulated based on normalized EVAR The q-parameter controls tail sensitivity—higher values (1.5-2.0) increase the weighting of extreme events in the calculation, making the model more conservative during potentially turbulent conditions. Indicator Components 1. EVAR Risk Visualization Dynamic EVAR Plot: Color-coded from red to green normalized risk measurement (0-1) Risk Thresholds: Reference lines at 0.3, 0.5, and 0.7 delineating risk zones 2. Position Sizing Matrix Risk Assessment: Current risk level and raw EVAR value Position Recommendations: Percentage allocation, dollar value, and quantity Stop Parameters: Mathematically derived stop price with percentage distance Drawdown Projection: Maximum theoretical loss if stop is triggered Interpretation and Application The normalized EVAR reading provides a probabilistic risk assessment: < 0.3: Low risk environment with minimal tail concerns 0.3-0.5: Moderate risk with standard tail behavior 0.5-0.7: Elevated risk with increased probability of significant moves > 0.7: High risk environment with substantial tail risk present Position sizing is automatically calculated using an inverse relationship to EVAR, contracting during high-risk periods and expanding during low-risk conditions. This is a counter-cyclical approach that ensures consistent risk exposure across varying market regimes, especially when the market is hyped or overheated. Parameter Optimization For optimal risk assessment across market conditions: Lookback Period: Determines the historical window for risk calculation Q Parameter: Controls tail sensitivity (higher values increase conservatism) Confidence Level: Sets the statistical threshold for risk assessment For cryptocurrencies and highly volatile instruments, a q-parameter between 1.5-2.0 typically provides the most accurate risk assessment because it helps capturing the fat-tailed behavior characteristic of these markets. You can also increase the q-parameter for more conservative approaches. Practical Applications Adaptive Risk Management: Quantify and respond to changing tail risk conditions Volatility-Normalized Positioning: Maintain consistent exposure across market regimes Black Swan Detection: Early identification of potential extreme market conditions Portfolio Construction: Apply consistent risk-based sizing across diverse instruments This indicator is my own approach to entropy-based risk measures as an alterative to volatility and standard deviations and it helps with fat-tailed markets. Enjoy!
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HenriqueCentieiro tarafından
BANKNIFTY Contribution Table [GSK-VIZAG-AP-INDIA]1. Overview This indicator provides a real-time visual contribution table of the 12 constituent stocks in the BANKNIFTY index. It displays key metrics for each stock that help traders quickly understand how each component is impacting the index at any given moment. 2. Purpose / Trading Use Case The tool is designed for intraday and short-term traders who rely on index movement and its internal strength or weakness. By seeing which stocks are contributing positively or negatively, traders can: Confirm trend strength or divergence within the index. Identify whether a BANKNIFTY move is broad-based or driven by a few heavyweights. Detect reversals when individual components decouple from index direction. 3. Key Features and Logic Live LTP: Current price of each BANKNIFTY stock. Price Change: Difference between current LTP and previous day’s close. % Change: Percentage move from previous close. Weight %: Static weight of each stock within the BANKNIFTY index (user-defined). This estimates how much each stock contributes to the BANKNIFTY’s point change. Sorted View: The stocks are sorted by their weight (descending), so high-impact movers are always at the top. 4. User Inputs / Settings Table Position (tableLocationOpt): Choose where the table appears on the chart: top_left, top_right, bottom_left, or bottom_right. This helps position the table away from your price action or indicators. 5. Visual and Plotting Elements Table Layout: 6 columns Stock | Contribution | Weight % | LTP | Change | % Change Color Coding: Green/red for positive/negative price changes and contributions. Alternating background rows for better visibility. BANKNIFTY row is highlighted separately at the top. Text & Background Colors are chosen for both readability and direction indication. 6. Tips for Effective Use Use this table on 1-minute or 5-minute intraday charts to see near real-time market structure. Watch for: A few heavyweight stocks pulling the index alone (can signal weak internal breadth). Broad green/red across all rows (signals strong directional momentum). Combine this with price action or volume-based strategies for confirmation. Best used during market hours for live updates. 7. What Makes It Unique Unlike other contribution tables that show only static data or require paid feeds, this script: Updates in real time. Uses dynamic calculated contributions. Places BANKNIFTY at the top and presents the entire internal structure clearly. Doesn’t repaint or rely on lagging indicators. 8. Alerts / Additional Features No alerts are added in this version. (Optional: Alerts can be added to notify when a certain stock contributes above/below a threshold.) 9. Technical Concepts Used request.security() to pull both 1-minute and daily close data. Conditional color formatting based on price change direction. Dynamic table rendering using table.new() and table.cell(). Static weights assigned manually for BANKNIFTY stocks (can be updated if index weights change). 10. Disclaimer This script is intended for educational and informational purposes only. It does not constitute financial advice or a buy/sell recommendation. Users should test and validate the tool on paper or demo accounts before applying it to live trading. 📌 Note: Due to internet connectivity, data delays, or broker feeds, real-time values (LTP, change, contribution, etc.) may slightly differ from other platforms or terminals. Use this indicator as a supportive visual tool, not a sole decision-maker. Script Title: BANKNIFTY Contribution Table - Author: GSK-VIZAG-AP-INDIA Version: Final Public Release
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prowelltraders tarafından
Frahm Factor Position Size CalculatorThe Frahm Factor Position Size Calculator is a powerful evolution of the original Frahm Factor script, leveraging its volatility analysis to dynamically adjust trading risk. This Pine Script for TradingView uses the Frahm Factor’s volatility score (1-10) to set risk percentages (1.75% to 5%) for both Margin-Based and Equity-Based position sizing. A compact table on the main chart displays Risk per Trade, Frahm Factor, and Average Candle Size, making it an essential tool for traders aligning risk with market conditions. Calculates a volatility score (1-10) using true range percentile rank over a customizable look-back window (default 24 hours). Dynamically sets risk percentage based on volatility: Low volatility (score ≤ 3): 5% risk for bolder trades. High volatility (score ≥ 8): 1.75% risk for caution. Medium volatility (score 4-7): Smoothly interpolated (e.g., 4 → 4.3%, 5 → 3.6%). Adjustable sensitivity via Frahm Scale Multiplier (default 9) for tailored volatility response. Position Sizing: Margin-Based: Risk as a percentage of total margin (e.g., $175 for 1.75% of $10,000 at high volatility). Equity-Based: Risk as a percentage of (equity - minimum balance) (e.g., $175 for 1.75% of ($15,000 - $5,000)). Compact 1-3 row table shows: Risk per Trade with Frahm score (e.g., “$175.00 (Frahm: 8)”). Frahm Factor (e.g., “Frahm Factor: 8”). Average Candle Size (e.g., “Avg Candle: 50 t”). Toggles to show/hide Frahm Factor and Average Candle Size rows, with no empty backgrounds. Four sizes: XL (18x7, large text), L (13x6, normal), M (9x5, small, default), S (8x4, tiny). Repositionable (9 positions, default: top-right). Customizable cell color, text color, and transparency. Set Frahm Factor: Frahm Window (hrs): Pick how far back to measure volatility (e.g., 24 hours). Shorter for fast markets, longer for chill ones. Frahm Scale Multiplier: Set sensitivity (1-10, default 9). Higher makes the score jumpier; lower smooths it out. Set Margin-Based: Total Margin: Enter your account balance (e.g., $10,000). Risk auto-adjusts via Frahm Factor. Set Equity-Based: Total Equity: Enter your total account balance (e.g., $15,000). Minimum Balance: Set to the lowest your account can go before liquidation (e.g., $5,000). Risk is based on the difference, auto-adjusted by Frahm Factor. Customize Display: Calculation Method: Pick Margin-Based or Equity-Based. Table Position: Choose where the table sits (e.g., top_right). Table Size: Select XL, L, M, or S (default M, small text). Table Cell Color: Set background color (default blue). Table Text Color: Set text color (default white). Table Cell Transparency: Adjust transparency (0 = solid, 100 = invisible, default 80). Show Frahm Factor & Show Avg Candle Size: Check to show these rows, uncheck to hide (default on).
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charleyRT tarafından
Machine Learning Key Levels [AlgoAlpha]🟠 OVERVIEW This script plots Machine Learning Key Levels on your chart by detecting historical pivot points and grouping them using agglomerative clustering to highlight price levels with the most past reactions. It combines a pivot detection, hierarchical clustering logic, and an optional silhouette method to automatically select the optimal number of key levels, giving you an adaptive way to visualize price zones where activity concentrated over time. 🟠 CONCEPTS Agglomerative clustering is a bottom-up method that starts by treating each pivot as its own cluster, then repeatedly merges the two closest clusters based on the average distance between their members until only the desired number of clusters remain. This process creates a hierarchy of groupings that can flexibly describe patterns in how price reacts around certain levels. This offers an advantage over K-means clustering, since the number of clusters does not need to be predefined. In this script, it uses an average linkage approach, where distance between clusters is computed as the average pairwise distance of all contained points. The script finds pivot highs and lows over a set lookback period and saves them in a buffer controlled by the Pivot Memory setting. When there are at least two pivots, it groups them using agglomerative clustering: it starts with each pivot as its own group and keeps merging the closest pairs based on their average distance until the desired number of clusters is left. This number can be fixed or chosen automatically with the silhouette method, which checks how well each point fits in its cluster compared to others (higher scores mean cleaner separation). Once clustering finishes, the script takes the average price of each cluster to create key levels, sorts them, and draws horizontal lines with labels and colors showing their strength. A metrics table can also display details about the clusters to help you understand how the levels were calculated. 🟠 FEATURES Agglomerative clustering engine with average linkage to merge pivots into level groups. Dynamic lines showing each cluster’s price level for clarity. Labels indicating level strength either as percent of all pivots or raw counts. A metrics table displaying pivot count, cluster count, silhouette score, and cluster size data. Optional silhouette-based auto-selection of cluster count to adaptively find the best fit. 🟠 USAGE Add the indicator to any chart. Choose how far back to detect pivots using Pivot Length and set Pivot Memory to control how many are kept for clustering (more pivots give smoother levels but can slow performance). If you want the script to pick the number of levels automatically, enable Auto No. Levels ; otherwise, set Number of Levels . The colored horizontal lines represent the calculated key levels, and circles show where pivots occurred colored by which cluster they belong to. The labels beside each level indicate its strength, so you can see which levels are supported by more pivots. If Show Metrics Table is enabled, you will see statistics about the clustering in the corner you selected. Use this tool to spot areas where price often reacts and to plan entries or exits around levels that have been significant over time. Adjust settings to better match volatility and history depth of your instrument.
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AlgoAlpha tarafından
35
GER40 Opening Range Breakout (Advanced)🔥 GER40 (DAX40) Opening Range Breakout Strategy 📌 Overview: This strategy takes advantage of the high volatility and liquidity during the Frankfurt and London session openings (8:00–10:00 CET). It’s especially suitable for day traders who want to capitalize on early momentum. ✅ Strategy Steps: 1. Mark the Opening Range (08:00–08:15 CET) Wait for the first 15 minutes after the Frankfurt open (08:00 CET). Draw horizontal lines at the high and low of this range. 2. Entry Rules: Buy when price breaks above the opening range high with strong volume. Sell (short) when price breaks below the opening range low with strong volume. 3. Confirmation (optional but helpful): Use a momentum indicator like RSI (above 50 for long, below 50 for short) or MACD crossing above/below the signal line. Look for volume spike at breakout for validation. 4. Stop-Loss: Set just below the range low (for long) or above the range high (for short). Or use a fixed pip/point stop-loss like 15–25 points depending on current volatility. 5. Take Profit / Exit: 1:1.5 to 1:2 Risk/Reward Ratio. Or scale out at fixed points (e.g., +20, +40). Or trail stop after price moves in favor by +20 points. 📊 Additional Filters to Improve Accuracy: Check macroeconomic calendar (avoid entering during red news like ECB, German CPI, etc.). Use VWAP as a dynamic support/resistance for bias direction. Use 5-min or 15-min charts for better signal clarity. 📈 Example: Let’s say the DAX opens at 08:00 CET, and by 08:15, the high is 18,000 and the low is 17,950. If price breaks above 18,000 with volume and RSI > 50, enter long. Place stop at 17,950 or slightly below. Take profit at 18,030–18,050 or trail stop. 🧠 Pro Tips: GER40 is highly volatile, so ensure your risk per trade is small (e.g., 1% or less). Avoid trading around major news (ECB rate decisions, German GDP, etc.). Best sessions for GER40: Frankfurt Open (08:00 CET) and London Open (09:00 CET).
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Abanobi tarafından
Gabriel's MPT Moving Average RibbonGabriel's MPT Moving Average Ribbon is a cutting-edge, risk-adjusted technical analysis tool that fuses Modern Portfolio Theory (MPT) with adaptive moving average logic to dynamically guide market participants through bullish and bearish conditions. This ribbon is not a simple MA crossover — it leverages Sharpe Ratio, Sortino Ratio, Omega Ratio, and Value at Risk (VaR) to scale and smooth each moving average using real-time probabilistic efficiency metrics. Combined, these elements create a volatility-weighted, risk-optimized visualization of market structure. 🔍 Core Features: Multi-Ratio Adaptive Scaling: Moving averages are dynamically weighted by Omega Ratio, Sortino Stdev, Sharpe Winrate, and VaR conditions for smarter price tracking. Volatility Engine: Supports multiple return models: Close-to-Close Parkinson Garman-Klass Rogers–Satchell Yang–Zhang (default for highest accuracy) Smart Ribbon Construction: Blends 3 different MA types per ribbon (e.g., SMA, EMA, WMA) for each of the 4 ribbons Supports WMA-style dynamic weighting using MPT-derived ratios Sharpe Winrate Estimation: Uses CDF logic to project the probability of success given current Sharpe ratio. Dynamic Risk Phase Detection (VaR): Identifies Risk On, Risk Off, or Neutral states using a triple-model composite VaR framework. 🛎️ Alerts Included: 📈 Bullish Crossover Alert: MA #1 crossing above MA #2 with all ribbons aligned upward. 📉 Bearish Crossunder Alert: MA #1 crossing below MA #2 with all ribbons aligned downward. 📊 Omega Ratio Alert: Triggered when Omega exceeds 1 (profitable risk-adjusted reward). ⚠️ Omega Caution Alert: Triggered when Omega drops below 1. 🟢 Risk On Alert: Market enters a favorable, low-risk zone. Deep Value Zone for Long-Term Investing. 🔴 Risk Off Alert: Market enters a cautionary, high-risk phase. 🎯 Use Cases: Trend Identification: MA ribbon alignment indicates momentum phases. Risk-Tuned Entries/Exits: Combine ribbon crossovers with VaR/Ratio signals for confirmation. Institutional Strategy Overlay: Ideal for portfolio managers integrating risk-adjusted technical overlays. 🧠 Pro Tips: Use "Complete" mode for the most robust risk signal, as it blends Historical, EWMA, and Variance-Covariance ratios. Customize each MA’s type and length to match your trading horizon (e.g., intraday, swing). Toggle Ratios Weighted MA for adaptive weighting when market risk fluctuates. It's set to the settings I use to trade, from MA settings to MPT table. It goes in order: Sharpe Est. Winrate, Deviation of Sortino, Omega Ratio (1 Year), and the Ideal position size according to VaR.
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GabrielAmadeusLau tarafından
RAHA Strategy - Short Roni's Adjusted Hybrid Average – a formula developed by Aharon Roni Pesach. What is RAHA? This is an adjusted hybrid average that gives different weight to outliers: The extreme values (particularly high or low) receive a lower weight. The calculation is based on the standard deviation and average of the data. This results in a more sensitive but stable average that does not ignore outliers – but rather considers them in proportion. The RAHA Short Strategy identifies a negative trend and enters when clear technical conditions are met, such as a downward slope of RAHA 40, RAHA 10 crossing below RAHA 20, and the absence of a sequence of 3 red candles. Entry is also made in the exceptional case of a red candle above the Bollinger Band. The position size is determined by 1% of the capital divided by the stop. The exit is carried out by a stop above the high, or under additional conditions below the profit target (TP). אסטרטגיית השורט RAHA מבוססת על נוסחת ממוצע ייחודית בשם RAHA – ראשי תיבות של: Roni's Adjusted Hybrid Average – נוסחה שפיתח אהרון רוני פסח. מהו RAHA? מדובר בממוצע היברידי מתואם המעניק משקל שונה לנתונים חריגים: הערכים הקיצוניים (גבוהים או נמוכים במיוחד) מקבלים משקל נמוך יותר. החישוב מבוסס על סטיית התקן והממוצע של הנתונים. כך מתקבל ממוצע רגיש אך יציב יותר, שאינו מתעלם מהחריגים – אלא מתחשב בהם בפרופורציה. אסטרטגיית השורט RAHA מזהה מגמה שלילית ומבצעת כניסה כשמתקיימים תנאים טכניים ברורים, כמו שיפוע יורד של RAHA 40, חציית RAHA 10 מתחת ל‑RAHA 20, והיעדר רצף של 3 נרות אדומים. הכניסה מבוצעת גם במקרה חריג של נר אדום מעל רצועת בולינגר. גודל הפוזיציה נקבע לפי 1% מההון חלקי הסטופ. היציאה מבוצעת לפי סטופ מעל הגבוה, או בתנאים נוספים מתחת ליעד הרווח (TP).
Pine Script® stratejisi
RoniPesach tarafından
Liquidity Break Probability [PhenLabs]📊 Liquidity Break Probability Version: PineScript™ v6 The Liquidity Break Probability indicator revolutionizes how traders approach liquidity levels by providing real-time probability calculations for level breaks. This advanced indicator combines sophisticated market analysis with machine learning inspired probability models to predict the likelihood of high/low breaks before they happen. Unlike traditional liquidity indicators that simply draw lines, LBP analyzes market structure, volume profiles, momentum, volatility, and sentiment to generate dynamic break probabilities ranging from 5% to 95%. This gives traders unprecedented insight into which levels are most likely to hold or break, enabling more confident trading decisions. 🚀 Points of Innovation Advanced 6-factor probability model weighing market structure, volatility, volume, momentum, patterns, and sentiment Real-time probability updates that adjust as market conditions change Intelligent trading style presets (Scalping, Day Trading, Swing Trading) with optimized parameters Dynamic color-coded probability labels showing break likelihood percentages Professional tiered input system - from quick setup to expert-level customization Smart volume filtering that only highlights levels with significant institutional interest 🔧 Core Components Market Structure Analysis: Evaluates trend alignment, level strength, and momentum buildup using EMA crossovers and price action Volatility Engine: Incorporates ATR expansion, Bollinger Band positioning, and price distance calculations Volume Profile System: Analyzes current volume strength, smart money proxies, and level creation volume ratios Momentum Calculator: Combines RSI positioning, MACD strength, and momentum divergence detection Pattern Recognition: Identifies reversal patterns (doji, hammer, engulfing) near key levels Sentiment Analysis: Processes fear/greed indicators and market breadth measurements 🔥 Key Features Dynamic Probability Labels: Real-time percentage displays showing break probability with color coding (red >70%, orange >50%, white <50%) Trading Style Optimization: One-click presets automatically configure sensitivity and parameters for your trading timeframe Professional Dashboard: Live market state monitoring with nearest level tracking and active level counts Smart Alert System: Customizable proximity alerts and high-probability break notifications Advanced Level Management: Intelligent line cleanup and historical analysis options Volume-Validated Levels: Only displays levels backed by significant volume for institutional-grade analysis 🎨 Visualization Recent Low Lines: Red lines marking validated support levels with probability percentages Recent High Lines: Blue lines showing resistance zones with break likelihood indicators Probability Labels: Color-coded percentage labels that update in real-time Professional Dashboard: Customizable panel showing market state, active levels, and current price Clean Display Modes: Toggle between active-only view for clean charts or historical view for analysis 📖 Usage Guidelines Quick Setup Trading Style Preset Default: Day Trading Options: Scalping, Day Trading, Swing Trading, Custom Description: Automatically optimizes all parameters for your preferred trading timeframe and style Show Break Probability % Default: True Description: Displays percentage labels next to each level showing break probability Line Display Default: Active Only Options: Active Only, All Levels Description: Choose between clean active-only view or comprehensive historical analysis Level Detection Settings Level Sensitivity Default: 5 Range: 1-20 Description: Lower values show more levels (sensitive), higher values show fewer levels (selective) Volume Filter Strength Default: 2.0 Range: 0.5-5.0 Description: Controls minimum volume threshold for level validation Advanced Probability Model Market Trend Influence Default: 25% Range: 0-50% Description: Weight given to overall market trend in probability calculations Volume Influence Default: 20% Range: 0-50% Description: Impact of volume analysis on break probability ✅ Best Use Cases Identifying high-probability breakout setups before they occur Determining optimal entry and exit points near key levels Risk management through probability-based position sizing Confluence trading when multiple high-probability levels align Scalping opportunities at levels with low break probability Swing trading setups using high-probability level breaks ⚠️ Limitations Probability calculations are estimations based on historical patterns and current market conditions High-probability setups do not guarantee successful trades - risk management is essential Performance may vary significantly across different market conditions and asset classes Requires understanding of support/resistance concepts and probability-based trading Best used in conjunction with other analysis methods and proper risk management 💡 What Makes This Unique Probability-Based Approach: First indicator to provide quantitative break probabilities rather than simple S/R lines Multi-Factor Analysis: Combines 6 different market factors into a comprehensive probability model Adaptive Intelligence: Probabilities update in real-time as market conditions change Professional Interface: Tiered input system from beginner-friendly to expert-level customization Institutional-Grade Filtering: Volume validation ensures only significant levels are displayed 🔬 How It Works 1. Level Detection: Identifies pivot highs and lows using configurable sensitivity settings Validates levels with volume analysis to ensure institutional significance 2. Probability Calculation: Analyzes 6 key market factors: structure, volatility, volume, momentum, patterns, sentiment Applies weighted scoring system based on user-defined factor importance Generates probability score from 5% to 95% for each level 3. Real-Time Updates: Continuously monitors price action and market conditions Updates probability calculations as new data becomes available Adjusts for level touches and changing market dynamics 💡 Note: This indicator works best on timeframes from 1-minute to 4-hour charts. For optimal results, combine with proper risk management and consider multiple timeframe analysis. The probability calculations are most accurate in trending markets with normal to high volatility conditions.
Pine Script® göstergesi
PhenLabs tarafından
Logarithmic Moving Average (LMA) [QuantAlgo]🟢 Overview The Logarithmic Moving Average (LMA) uses advanced logarithmic weighting to create a dynamic trend-following indicator that prioritizes recent price action while maintaining statistical significance. Unlike traditional moving averages that use linear or exponential weights, this indicator employs logarithmic decay functions to create a more sophisticated price averaging system that adapts to market volatility and momentum conditions. The indicator displays a smoothed signal line that oscillates around zero, with positive values indicating bullish momentum and negative values indicating bearish momentum. The signal incorporates trend quality assessment, momentum confirmation, and multiple filtering mechanisms to help traders and investors identify trend continuation and reversal opportunities across different timeframes and asset classes. 🟢 How It Works The indicator's core innovation lies in its logarithmic weighting system, where weights are calculated using the formula: w = 1.0 / math.pow(math.log(i + steepness), 2) The steepness parameter controls how aggressively recent data is prioritized over historical data, creating a dynamic weight decay that can be fine-tuned for different trading styles. This logarithmic approach provides more nuanced weight distribution compared to exponential moving averages, offering better responsiveness while maintaining stability. The LMA calculation combines multiple sophisticated components. First, it calculates the logarithmic weighted average of closing prices. Then it measures the slope of this average over a 10-period lookback: lmaSlope = (lma - lma ) / lma * 100 The system also incorporates trend quality assessment using R-squared correlation analysis of log-transformed prices, measuring how well the price data fits a linear trend model over the specified period. The final signal generation uses the formula: signal = lmaSlope * (0.5 + rSquared * 0.5) which combines the LMA slope with trend quality weighting. When momentum confirmation is enabled, the indicator calculates annualized log-return momentum and applies a multiplier when the momentum direction aligns with the signal direction, strengthening confirmed signals while filtering out weak or counter-trend movements. 🟢 How to Use 1. Signal Interpretation and Threshold Zones Positive Values (Above Zero): LMA slope indicating bullish momentum with upward price trajectory relative to logarithmic baseline Negative Values (Below Zero): LMA slope indicating bearish momentum with downward price trajectory relative to logarithmic baseline Zero Line Crosses: Signal transitions between bullish and bearish regimes, indicating potential trend changes Long Entry Threshold Zone: Area above positive threshold (default 0.5) indicating confirmed bullish signals suitable for long positions Short Entry Threshold Zone: Area below negative threshold (default -0.5) indicating confirmed bearish signals suitable for short positions Extreme Values: Signals exceeding ±1.0 represent strong momentum conditions with higher probability of continuation 2. Momentum Confirmation and Visual Analysis Signal Color Intensity: Gradient coloring shows signal strength, with brighter colors indicating stronger momentum Bar Coloring: Optional price bar coloring matches signal direction for quick visual trend identification Position Labels: Real-time position classification (Bullish/Bearish/Neutral) displayed on the latest bar Momentum Weight Factor: When short-term log-return momentum aligns with LMA signal direction, the signal receives additional weight confirmation Trend Quality Component: R-squared values weight the signal strength, with higher correlation indicating more reliable trend conditions 3. Examples: Preconfigured Settings Default: Universally applicable configuration balanced for medium-term investing and general trading across multiple timeframes and asset classes. Scalping: Highly responsive setup with shorter period and higher steepness for ultra-short-term trades on 1-15 minute charts, optimized for quick momentum shifts. Swing Trading: Extended period with moderate steepness and increased smoothing for multi-day positions, designed to filter noise while capturing larger price swings on 1-4 hour and daily charts. Trend Following: Maximum smoothing with lower steepness for established trend identification, generating fewer but more reliable signals optimal for daily and weekly timeframes. Mean Reversion: Shorter period with high steepness for counter-trend strategies, more sensitive to extreme moves and reversal opportunities in ranging market conditions.
Pine Script® göstergesi
QuantAlgo tarafından
7
Price Reaction Analysis by Day of WeekOverview The "Price Reaction Analysis by Day of Week" indicator is a tool that enables traders to analyze historical price reaction patterns to technical indicator signals on a selected day of the week. It examines price behavior on a chosen candle (from 1 to 30) in the next day or subsequent days after a signal, depending on the timeframe, and provides success rate statistics to support data-driven trading decisions. The indicator is optimized for timeframes up to 1 day (e.g., 1D, 12H, 8H, 6H, 4H, 1H, 15M), as the analysis relies on day-of-week comparisons. Lower timeframes generate more signals due to the higher number of candles per day. Key Features 1. Flexible Technical Indicator Selection Users can choose one of four technical indicators: RSI, SMI, MA, or Bollinger Bands. Each indicator has configurable parameters, such as: RSI length, oversold/overbought levels. SMI length, %K and %D smoothing, signal levels. MA length. Bollinger Bands length and multiplier. 2. Day-of-Week Analysis The indicator allows users to select a day of the week (Monday, Tuesday, Wednesday, Thursday, Friday) for generating signals. It analyzes price reactions on a selected candle (from 1 to 30) in the next day or subsequent days after the signal. Examples: On a daily timeframe, a signal on Monday can be analyzed for the first, fourth, or later candle (up to 30) in subsequent days (e.g., Tuesday, Wednesday). On timeframes lower than 1 day (e.g., 12H, 8H, 6H, 4H, 1H, 15M), the analysis targets the selected candle in the next day or subsequent days. For example, on a 4H timeframe, you can analyze the second Tuesday candle following a Monday signal. The maximum timeframe is 1 day to ensure consistent day-of-week analysis. 3. Visual Signals Signals for the analysis period are marked with background highlights in real-time when the indicator’s conditions are met. The last highlighted candle of the selected day is always analyzed. Arrows are displayed on the chart at the candle specified by the “Candles to Compare” setting (e.g., the first candle if set to 1): Green upward triangles (below the candle) for successful buy signals (the closing price of the selected candle is higher than the signal candle’s close). Red downward triangles (above the candle) for successful sell signals (the closing price of the selected candle is lower than the signal candle’s close). Gray “x” marks for unsuccessful signals (no price reversal in the expected direction). Arrow positions are intuitive: buy signals below the candle, sell signals above. Highlights and arrows do not require waiting for future signals but are essential for calculating statistics. Note: The first candle of the next day may appear shifted on the chart due to timezone differences, which can affect the timing of signal appearance. 4. Signal Conditions (Highlights) for Each Indicator RSI: The oscillator is in oversold (buy) or overbought (sell) zones. SMI: SMI returns from oversold (buy) or overbought (sell) zones. MA: Price crosses the MA (upward for buy, downward for sell). Bollinger Bands: Price returns inside the bands (from below for buy, from above for sell). 5. Success Rate Statistics A table in the top-right corner of the chart displays: The number of buy and sell signals for the selected day of the week. The percentage of cases where the price of the selected candle in the next day or subsequent days reversed as expected (e.g., rising after a buy signal). Statistics are based on comparing the closing price of the signal candle with the closing price of the selected candle (e.g., first, fourth) in the next day or subsequent days. Important: Statistics do not account for price movements within the candle or after its close. The price on the selected candle (e.g., fourth) may be lower than earlier candles but still higher than the signal candle, counting as a positive buy signal, though it does not guarantee profit. 6. Date Range Users can specify the analysis date range, enabling strategy testing on historical data from a chosen period. Ensure the start and end dates are set correctly. Applications The indicator is designed for traders who want to leverage historical patterns for position planning. Examples: On a 4-hour timeframe: If a sell signal highlight appears on Monday and statistics show an 80% chance that the fourth Tuesday candle is bearish, traders may consider playing a correction at the open of that candle. On a daily timeframe: If a highlight indicates market overheating, traders may consider entering a position at the open of the first candle after the signal (e.g., Tuesday), provided statistics suggest an edge. Users can analyze the signal on the first candle and check later candles to validate results, increasing confidence in consistent patterns. Key Settings Indicator Type: Choose between RSI, SMI, MA, or Bollinger Bands. Selected Day: Monday, Tuesday, Wednesday, Thursday, or Friday. Candles to Compare: The number of the candle in the next day or subsequent days (from 1 to 30). Indicator Parameters: Lengths, levels (e.g., oversold/overbought for RSI). Background Colors: Configurable highlights for buy and sell signals. Notes Timeframes: The indicator is optimized for timeframes up to 1 day (e.g., 1D, 12H, 8H, 6H, 4H, 1H, 15M), as the analysis relies on day-of-week patterns. Timeframes lower than 1 day generate more signals due to the higher number of candles per day. Candle Shift: The first candle of the next day may appear shifted on the chart due to timezone differences, affecting the timing of signals across markets or platforms. Statistical Limitations: Results are based on the closing prices of the selected candle, ignoring fluctuations in earlier candles, within the candle, or subsequent price movements. Traders must assess whether entering at the open or after the close of the selected candle is profitable. Testing: Effectiveness depends on historical data and parameter settings. Testing different configurations across markets and timeframes is recommended. Who Is It For? Swing and position traders who base decisions on technical analysis and historical patterns. Market analysts seeking patterns in price behavior by day of the week. TradingView users of all experience levels, thanks to an intuitive interface and flexible settings.
Pine Script® göstergesi
Uncle_the_shooter tarafından