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TCP | Money Management indicator | Crypto Version📌 TCP | Money Management Indicator | Crypto Version A robust, multi-target risk and capital management indicator tailored for crypto traders. Whether you're trading spot, perpetual futures, or leverage tokens, this tool empowers you with precise control over risk, reward, and position sizing—directly on your chart. Eliminate guesswork and trade with confidence. 🔰 Introduction: Master Your Capital, Master Your Trades Poor money management is the number one reason traders lose their accounts, even with solid strategies. The TCP Money Management Indicator, built by Trade City Pro (TCP), solves this problem by providing a structured, rule-based approach to capital allocation. Want to dive deeper into the concept of money management? Check out our comprehensive tutorial on TradingView, " TradeCityPro Academy: Money Management ", to understand the principles that power this indicator and transform your trading mindset. This indicator equips you to: • Calculate optimal position sizes based on your capital, risk percentage, and leverage • Set up to 5 customizable take-profit targets with partial close percentages • Access real-time metrics like Risk-to-Reward (R/R), USD profit, and margin usage • Trade with discipline, avoiding emotional or inconsistent decisions 💸 Money Management Formula The indicator uses a professional capital allocation model: Position Size = (Capital × Risk %) ÷ (Stop Loss % × Leverage) From this, it calculates: • Total risk amount in USD • Optimal position size for your trade • Margin required for each take-profit target • Adjusted R/R for each target, accounting for partial position closures 🛠 How to Use Enter Trade Parameters: Input your capital, risk %, leverage, entry price, and stop-loss price. Set Take-Profit Targets: Enable 1 to 5 take-profit levels and specify the percentage of the position to close at each. Real-Time Calculations: The indicator automatically computes: • R/R ratio for each target • Profit in USD for each partial close • Margin used per target (in % and USD) Visualize Your Trade: • Price levels for entry, stop-loss, and take-profits are plotted on the chart. • A dynamic info panel on the left side displays all key metrics. 🔄 Dynamic Adjustments: As each take-profit target is hit and a portion of the position is closed, the indicator recalculates the remaining position size, expected profit, R/R, and margin for subsequent targets. This ensures accuracy and reflects real-world trade behavior. 📊 Table Overview The left-side panel provides a clear snapshot: • Trade Setup: Capital, entry price, stop-loss, risk amount, and position size • Per Target: Percentage closed, R/R, profit in USD, and margin used • Summary: Total expected profit across all targets ⚙️ Settings Panel • Total Capital ($): Your account size for the trade • Risk per Trade (%): The percentage of capital you’re willing to risk • Leverage: The leverage applied to the trade • Entry/Stop-Loss Prices: Define your trade’s risk zone • Take-Profit Targets (1–5): Set price levels and percentage to close at each 🔍 Use Case Example Imagine you have $1,000 capital, risking 1%, using 10x leverage: • Entry: $100 | Stop-Loss: $95 • TP1: $110 (close 50%) | TP2: $115 (close 50%) The indicator calculates the exact position size, profit at each target, and margin allocation in real time, with all metrics displayed on the chart. ✅ Why Traders Love It • Precision: No more manual calculations or guesswork • Versatility: Works on all crypto pairs (BTC, ETH, altcoins, etc.) • Flexibility: Perfect for scalping, swing trading, or futures strategies • Universal: Compatible with all timeframes • Transparency: Fully manual, with clear and reliable outputs 🧩 Built by Trade City Pro (TCP) Developed by TCP, a trusted name in trading tools, used by over 150,000 traders worldwide. This indicator is coded in Pine Script v5, ensuring compatibility with TradingView’s platform. 🧾 Final Notes • No Auto-Trading: This is a manual tool for disciplined traders • No Repainting: All calculations are accurate and non-repainting • Tested: Rigorously validated across major crypto pairs • Publish-Ready: Built for seamless use on TradingView 🔗 Resources • Money Management Tutorial: Learn the fundamentals of capital management with our detailed guide: TradeCityPro Academy: Money Management • TradingView Profile: Explore more tools by TCP on TradingView
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tradecitypro tarafından
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KingJakesFx CRTThis TradingView indicator is a comprehensive tool that identifies and marks significant high and low points of Candle Range Type (CRT) candles. Its standout feature is the ability to visualize these key levels across multiple timeframes, allowing traders to maintain awareness of important price zones even when analyzing shorter timeframes. The indicator extends high and low lines into the future, creating dynamic support and resistance levels that help anticipate potential price reactions. With extensive customization options, users can tailor the visual appearance of lines, labels, and alerts to match their trading setup and preferences. Perfect for traders who analyze multiple timeframes and want to maintain awareness of significant price levels, this indicator combines powerful technical analysis with flexible visual customization to enhance any trading strategy.
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gdsbillion tarafından
Goldman Sachs Risk Appetite ProxyRisk appetite indicators serve as barometers of market psychology, measuring investors' collective willingness to engage in risk-taking behavior. According to Mosley & Singer (2008), "cross-asset risk sentiment indicators provide valuable leading signals for market direction by capturing the underlying psychological state of market participants before it fully manifests in price action." The GSRAI methodology aligns with modern portfolio theory, which emphasizes the importance of cross-asset correlations during different market regimes. As noted by Ang & Bekaert (2002), "asset correlations tend to increase during market stress, exhibiting asymmetric patterns that can be captured through multi-asset sentiment indicators." Implementation Methodology Component Selection Our implementation follows the core framework outlined by Goldman Sachs research, focusing on four key components: Credit Spreads (High Yield Credit Spread) As noted by Duca et al. (2016), "credit spreads provide a market-based assessment of default risk and function as an effective barometer of economic uncertainty." Higher spreads generally indicate deteriorating risk appetite. Volatility Measures (VIX) Baker & Wurgler (2006) established that "implied volatility serves as a direct measure of market fear and uncertainty." The VIX, often called the "fear gauge," maintains an inverse relationship with risk appetite. Equity/Bond Performance Ratio (SPY/IEF) According to Connolly et al. (2005), "the relative performance of stocks versus bonds offers significant insight into market participants' risk preferences and flight-to-safety behavior." Commodity Ratio (Oil/Gold) Baur & McDermott (2010) demonstrated that "gold often functions as a safe haven during market turbulence, while oil typically performs better during risk-on environments, making their ratio an effective risk sentiment indicator." Standardization Process Each component undergoes z-score normalization to enable cross-asset comparisons, following the statistical approach advocated by Burdekin & Siklos (2012). The z-score transformation standardizes each variable by subtracting its mean and dividing by its standard deviation: Z = (X - μ) / σ This approach allows for meaningful aggregation of different market signals regardless of their native scales or volatility characteristics. Signal Integration The four standardized components are equally weighted and combined to form a composite score. This democratic weighting approach is supported by Rapach et al. (2010), who found that "simple averaging often outperforms more complex weighting schemes in financial applications due to estimation error in the optimization process." The final index is scaled to a 0-100 range, with: Values above 70 indicating "Risk-On" market conditions Values below 30 indicating "Risk-Off" market conditions Values between 30-70 representing neutral risk sentiment Limitations and Differences from Original Implementation Proprietary Components The original Goldman Sachs indicator incorporates additional proprietary elements not publicly disclosed. As Goldman Sachs Global Investment Research (2019) notes, "our comprehensive risk appetite framework incorporates proprietary positioning data and internal liquidity metrics that enhance predictive capability." Technical Limitations Pine Script v6 imposes certain constraints that prevent full replication: Structural Limitations: Functions like plot, hline, and bgcolor must be defined in the global scope rather than conditionally, requiring workarounds for dynamic visualization. Statistical Processing: Advanced statistical methods used in the original model, such as Kalman filtering or regime-switching models described by Ang & Timmermann (2012), cannot be fully implemented within Pine Script's constraints. Data Availability: As noted by Kilian & Park (2009), "the quality and frequency of market data significantly impacts the effectiveness of sentiment indicators." Our implementation relies on publicly available data sources that may differ from Goldman Sachs' institutional data feeds. Empirical Performance While a formal backtest comparison with the original GSRAI is beyond the scope of this implementation, research by Froot & Ramadorai (2005) suggests that "publicly accessible proxies of proprietary sentiment indicators can capture a significant portion of their predictive power, particularly during major market turning points." References Ang, A., & Bekaert, G. (2002). "International Asset Allocation with Regime Shifts." Review of Financial Studies, 15(4), 1137-1187. Ang, A., & Timmermann, A. (2012). "Regime Changes and Financial Markets." Annual Review of Financial Economics, 4(1), 313-337. Baker, M., & Wurgler, J. (2006). "Investor Sentiment and the Cross-Section of Stock Returns." Journal of Finance, 61(4), 1645-1680. Baur, D. G., & McDermott, T. K. (2010). "Is Gold a Safe Haven? International Evidence." Journal of Banking & Finance, 34(8), 1886-1898. Burdekin, R. C., & Siklos, P. L. (2012). "Enter the Dragon: Interactions between Chinese, US and Asia-Pacific Equity Markets, 1995-2010." Pacific-Basin Finance Journal, 20(3), 521-541. Connolly, R., Stivers, C., & Sun, L. (2005). "Stock Market Uncertainty and the Stock-Bond Return Relation." Journal of Financial and Quantitative Analysis, 40(1), 161-194. Duca, M. L., Nicoletti, G., & Martinez, A. V. (2016). "Global Corporate Bond Issuance: What Role for US Quantitative Easing?" Journal of International Money and Finance, 60, 114-150. Froot, K. A., & Ramadorai, T. (2005). "Currency Returns, Intrinsic Value, and Institutional-Investor Flows." Journal of Finance, 60(3), 1535-1566. Goldman Sachs Global Investment Research (2019). "Risk Appetite Framework: A Practitioner's Guide." Kilian, L., & Park, C. (2009). "The Impact of Oil Price Shocks on the U.S. Stock Market." International Economic Review, 50(4), 1267-1287. Mosley, L., & Singer, D. A. (2008). "Taking Stock Seriously: Equity Market Performance, Government Policy, and Financial Globalization." International Studies Quarterly, 52(2), 405-425. Oppenheimer, P. (2007). "A Framework for Financial Market Risk Appetite." Goldman Sachs Global Economics Paper. Rapach, D. E., Strauss, J. K., & Zhou, G. (2010). "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy." Review of Financial Studies, 23(2), 821-862.
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EdgeTools tarafından
Statistical Reliability Index (SRI)Statistical Reliability Index (SRI) The Statistical Reliability Index (SRI) is a professional financial analysis tool designed to assess the statistical stability and reliability of market conditions. It combines advanced statistical methods to gauge whether current market trends are statistically consistent or prone to erratic behavior. This allows traders to make more informed decisions when navigating trending and choppy markets. Key Concepts: 1. Extrapolation of Cumulative Distribution Functions (CDF) What is CDF? A Cumulative Distribution Function (CDF) is a statistical tool that models the probability of a random variable falling below a certain value. How it’s used in SRI: The SRI utilizes the 95th percentile CDF of recent returns to estimate the likelihood of extreme price movements. This helps identify when a market is experiencing statistically significant changes, crucial for forecasting potential breakouts or breakdowns. Weight in SRI: The weight of the CDF extrapolation can be adjusted to emphasize its impact on the overall reliability index, allowing customization based on the trader's preference for tail risk analysis. 2. Bias Factor (BF) What is the Bias Factor? The Bias Factor measures the ratio of the current market price to the expected mean price calculated over a defined period. It represents the deviation from the typical price level. How it’s used in SRI: A higher bias factor indicates that the current price significantly deviates from the historical average, suggesting a potential mean reversion or trend exhaustion. Weight in SRI: Adjusting the Bias Factor weight lets users control how much this deviation influences the SRI, balancing between momentum trading and mean reversion strategies. 3. Coefficient of Variation (CV) What is CV? The Coefficient of Variation (CV) is a statistical measure that expresses the ratio of the standard deviation to the mean. It indicates the relative variability of asset returns, helping gauge the risk-to-return consistency. How it’s used in SRI: A lower CV indicates more stable and predictable price behavior, while a higher CV signals increased volatility. The SRI incorporates the inverse of the normalized CV to reflect price stability positively. Weight in SRI: By adjusting the CV weight, users can prioritize consistent price movements over erratic volatility, aligning the indicator with risk tolerance and strategy preferences. Interpreting the SRI: 1. SRI Plot: The SRI plot dynamically changes color to reflect market conditions: Aqua Line: Indicates uptrend stability, signaling statistically consistent upward movements. Fuchsia Line: Indicates downtrend stability, where statistically reliable downward movements are present. The overlay background shifts between colors: Aqua Background: Signifies statistical stability, where trends are historically consistent. Fuchsia Background: Indicates statistical instability, often associated with trend uncertainty. Yellow Background: Marks choppy periods, where statistical data suggests that market conditions are not conducive to reliable trading. 2. SRI Volatility Plot: Displays the volatility of the SRI itself to detect when the indicator is stable or unstable: Blue Area Fill: Signifies that the SRI is stable, indicating trending conditions. Yellow Area Fill: Represents choppy or unstable SRI movements, suggesting sideways or unreliable market conditions. A Chop Threshold Line (dotted yellow) highlights the maximum acceptable SRI volatility before the market is considered too unpredictable. 3. Stability Assessment: Stable Trend (No Chop): The SRI is smooth and consistent, often accompanied by aqua or fuchsia lines. Volatility remains below the chop threshold, indicating a low-risk, trend-following environment. Chop Mode: The SRI becomes erratic, and the volatility plot spikes above the threshold. Marked by a yellow shaded background, indicating uncertain and non-trending conditions. [Trend Identification: Use the color-coded SRI line and background to determine uptrend or downtrend reliability. Be cautious when the SRI volatility plot shows yellow, as this signals trading conditions may not be reliable. Practical Use Cases: Trend Confirmation: Utilize the SRI plot color and background to confirm whether a detected trend is statistically reliable. Chop Mode Filtering: During yellow chop periods, it is advisable to reduce trading activity or adopt range-bound strategies. Strategy Filter: Combine the SRI with trend-following indicators (like moving averages) to enhance entry and exit accuracy. Volatility Monitoring: Pay attention to the SRI volatility plot, as spikes often precede erratic price movements or trend reversals. Disclaimer: The Statistical Reliability Index (SRI) is a technical analysis tool designed to aid in market stability assessment and trend validation. It is not intended as a standalone trading signal generator. While the SRI can help identify statistically reliable trends, it is essential to incorporate additional technical and fundamental analysis to make well-informed trading decisions. Trading and investing involve substantial risk, and past performance does not guarantee future results. Always use risk management practices and consult with a financial advisor to tailor strategies to your individual risk profile and objectives.
Pine Script® göstergesi
RWCS_LTD tarafından
TOP_BOTTOM_Ver_1.1_How to Use : First use the Fii Trend Indicator Ver_1.0_ to confirm the trend. If CMP above the Suport Line (Green) then follow LONG Signals only and if CMP below the Resistance Line (Red) then follow SHORT Signals only.. Whatsapp Contact +91 7802978035 This TradingView indicators are explicitly designed for educational purposes, meaning they are not intended for real-time trading or as financial advice. They are provided for learning, understanding, and exploring different concepts, not as a guide for making trading decisions.
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10percentin tarafından
Supply and Demand, Order blocks, SMC & Sessions1. Order Block Detection: Identifies potential order blocks (demand/supply zones) Bullish order blocks (demand) are highlighted with yellow boxes extending right Bearish order blocks (supply) are similarly marked Customizable lookback period and strength parameters ----------------------------------------------------------- 2. Session Highlighting: Colored boxes for Asian (blue), London (purple), and New York (orange) sessions Fully customizable session times via inputs ----------------------------------------------------------- 3. Supply & Demand Zones: Customizable Identifies Supply & Demand zones ----------------------------------------------------------- 4. Smart Money Concepts Automatically identifies and marks out SMCs
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CanxStixTrader tarafından
ORB Multi-Session Professional System ORB Multi-Session Professional Trading Strategy (Open Beta) ## Strategy Overview The ORB Multi-Session Professional is an advanced Opening Range Breakout (ORB) trading strategy designed to capture high-probability trading opportunities during the European and New York trading sessions. This sophisticated strategy leverages multiple technical filters and dynamic risk management to provide a robust approach to intraday trading. ## Key Strategy Features ### Dual Session Trading - Trades both European (07:00 UTC) and New York (13:30 UTC) opening ranges - Flexible configuration allows trading: - Full sessions - European session only - New York session only ### Advanced Entry Conditions - Breakout trading based on 15-minute opening range - Long and short entries triggered by price breaking above/below the session's high/low - Optional reversal trading capabilities ### Intelligent Risk Management - Dynamic position sizing based on: - ATR (Average True Range) - Customizable risk percentage - Multiple stop-loss calculation methods: - Percentage of opening range - ATR-based - Fixed value - Flexible take-profit strategies: - Risk-to-reward ratio - ATR-based - Fixed value - Optional trailing stop ### Comprehensive Filters - Trend filter using EMA (configurable period) - Volume filter to ensure market activity - Range size filter to avoid low-volatility periods - Customizable minimum and maximum range sizes ## Risk Control Mechanisms - Maximum trades per day (1-5 configurable) - Dynamic position sizing - Multiple stop-loss and take-profit strategies - Optional trailing stop functionality ## Visualization Features - Highlights trading sessions - Displays opening range levels - Shows previous day's high and low - Visual signals for trade entries - Informative performance table ## Strategy Configuration Options ### General Settings - Strategy type selection - Opening range duration - Dynamic position sizing - Risk percentage per trade - Maximum daily trades ### Risk Management - Stop-loss types: - Percentage - ATR-based - Fixed value - Take-profit types: - Risk-to-reward ratio - ATR-based - Fixed value - No take-profit option ### Filters - Trend filter - Volume filter - Range size constraints ### Session Management - Time zone adjustments - Session-specific offsets - Individual session enabling/disabling ## Performance Tracking - Accumulated profit tracking - Winning and losing trade count - Real-time performance statistics display ## Best Suited For - Forex markets - Futures markets - High-liquidity instruments - Traders seeking structured, rule-based entry and exit strategies ## Important Considerations ⚠️ **Disclaimer:** - Backtesting and forward testing are crucial - Past performance does not guarantee future results - Always use proper risk management - Adjust parameters to match your specific trading instrument and risk tolerance ## Recommended Setup - Timeframe: 5-15-minute charts - Recommended markets: Major currency pairs, stock indices, commodities - Suggested initial settings can be customized based on specific trading goals ## Version **Current Version:** 0.1 **Strategy Type:** Multi-Session Opening Range Breakout Trading System **Developed:** 2025 by CarlosGV
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CarlosGV tarafından
Correlation Drift📈 Correlation Drift The Correlation Drift indicator is designed to detect shifts in market momentum by analyzing the relationship between correlation and price lag. It combines the principles of correlation analysis and lag factor measurement to provide a unique perspective on trend alignment and momentum shifts. 🔍 Core Concept: The indicator calculates the Correlation vs PLF Ratio, which measures the alignment between an asset’s price movement and a chosen benchmark (e.g., BTCUSD). This ratio reflects how well the asset’s momentum matches the market trend while accounting for price lag. 📊 How It Works: Correlation Calculation: The script calculates the correlation between the asset and the selected benchmark over a specified period. A higher correlation indicates that the asset’s price movements are in sync with the benchmark. Price Lag Factor (PLF) Calculation: The PLF measures the difference between long-term and short-term price momentum, dynamically scaled by recent volatility. It highlights potential overextensions or lags in the asset’s price movements. Combining Correlation and PLF: The Correlation vs PLF Ratio combines these metrics to detect momentum shifts relative to the trend. The result is a dynamic, smoothed histogram that visualizes whether the asset is leading or lagging behind the trend. 💡 How to Interpret: Positive Values (Green/Aqua Bars): Indicates bullish alignment with the trend. Aqua: Rising bullish momentum, suggesting continuation. Teal: Decreasing bullish momentum, signaling caution. Negative Values (Purple/Fuchsia Bars): Indicates bearish divergence from the trend. Fuchsia: Falling bearish momentum, indicating increasing pressure. Purple: Rising bearish momentum, suggesting potential reversal. Clipping for Readability: Values are clipped between -3 and +3 to prevent outliers from compressing the histogram. This ensures clear visualization of typical momentum shifts while still marking extreme cases. 🚀 Best Practices: Use Correlation Drift as a confirmation tool in conjunction with trend indicators (e.g., moving averages) to identify momentum alignment or divergence. Look for transitions from positive to negative (or vice versa) as signals of potential trend shifts. Combine with volume analysis to strengthen confidence in breakout or breakdown signals. ⚠️ Key Features: Customizable Settings: Adjust the correlation length, PLF length, and smoothing factor to fine-tune the indicator for different market conditions. Visual Gradient: The histogram changes color based on the strength and direction of the ratio, making it easy to identify shifts at a glance. Zero Line Reference: Clearly distinguishes between bullish and bearish momentum zones. 🔧 Recommended Settings: Correlation Length: 14 (for short to medium-term analysis) PLF Length: 50 (to smooth out noise while capturing trend shifts) Smoothing Factor: 3 (for enhanced clarity without excessive lag) Benchmark Symbol: BTCUSD (or another relevant market indicator) By providing a quantitative measure of trend alignment while accounting for price lag, the Correlation Drift indicator helps traders make more informed decisions during periods of momentum change. Whether you are trading crypto, forex, or equities, this tool can be a powerful addition to your momentum-based trading strategies. ⚠️ Disclaimer: The Correlation Drift indicator is a technical analysis tool designed to aid in identifying potential shifts in market momentum and trend alignment. It is intended for informational and educational purposes only and should not be considered as financial advice or a recommendation to buy, sell, or hold any financial instrument. Trading financial instruments, including cryptocurrencies, involves significant risk and may result in the loss of your capital. Past performance is not indicative of future results. Always conduct thorough research and seek advice from a certified financial professional before making any trading decisions. The developer (RWCS_LTD) is not responsible for any trading losses or adverse outcomes resulting from the use of this indicator. Users are encouraged to test and validate the indicator in a simulated environment before applying it to live trading. Use at your own risk.
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RWCS_LTD tarafından
DAILY CANDLE PROFIT TARGET BIAS @MaxMaserati Max Maserati Method for Candle Bias and effective price action Analysis The MMM CANDLE BIAS 2.0 indicator, built on the proprietary Max Maserati Method, classifies candles to deliver clear, real-time market bias insights. It decodes price action, revealing institutional trading patterns often missed by retail traders. The Six Core Candle Classifications: The Foundation of MMM Analysis Master these six closing patterns, and you'll unlock the true language of price action. These are the building blocks of institutional trading behavior: Bullish Body Close Identification: Candle closes above the previous candle’s high. Psychology: Strong buying pressure overcomes prior resistance. Implication: Signals bullish trend continuation or reversal. Bearish Body Close Identification: Candle closes below the previous candle’s low. Psychology: Intense selling pressure breaks past support. Implication: Indicates bearish trend continuation or reversal. Bullish Affinity Identification: High tests or breaches previous low, but close stays within previous candle’s range. Psychology: Buyers defend lower levels, rejecting downside. Implication: Hidden bullish strength in consolidation. Bearish Affinity Identification: Low tests or breaches previous high, but close remains within previous candle’s range. Psychology: Sellers cap upside attempts, gaining control. Implication: Subtle bearish pressure despite failed breakout. Seek & Destroy Identification: Candle breaks both previous high and low, closing inside previous range. Psychology: Institutions test liquidity on both sides before committing. Implication: Direction depends on close—upper half (bullish affinity) or lower half (bearish affinity). Close Inside Identification: High and low stay within previous candle’s range. Psychology: Consolidation with underlying directional bias. Implication: Bias determined by close position relative to range. Plus/Minus Strength System Bullish Strength: Measures distance from low to close (buying pressure). Bearish Strength: Measures distance from high to close (selling pressure). Plus (+): Dominant strength significantly outweighs the other, indicating strong directional conviction. Minus (-): Balanced strengths suggest a contested market, requiring caution. Key Features Automated Pattern Recognition: Instantly detects candle formations. Color-Coded Bars: Green for bullish, red for bearish bias. Dynamic Profit Targets: Projects targets based on higher timeframe high/low. Real-Time Metrics: Displays bullish/bearish strength percentages and volume delta. Customizable Table: Shows timeframe, symbol, bias, volume, and special note (“Analyze | Wait | Repeat”). Bias Lines: Plots high/low lines on higher timeframe, with optional extension. Labels: Customizable bias and profit target labels (Tiny, Small, Normal sizes). Trading Advantages Reveals institutional moves before retail traders react. Detects reversals ahead of conventional indicators. Enables precise entry timing with smart money. Enhances risk management with clear strength signals. Simplifies complex price action into actionable insights. Profit Target Framework Bullish Patterns: Target higher timeframe high. Bearish Patterns: Target higher timeframe low. Plus Strength: Expects direct move to target. Minus Strength: Anticipates measured advance with potential pullbacks and/or violations. Visual Implementation Lines and Labels: High/low bias lines and profit target markers adapt to timeframe. Table Display: Configurable position (top/middle/bottom, left/center/right) with key metrics. Bar Coloring: Optional coloring based on bias or plus/minus strength. Trader’s Mantra "Analyze | Wait | Repeat" - Discipline turns market reading into consistent profits. Elevate your trading with MMM CANDLE BIAS 2.0, where professional-grade analysis meets intuitive design. Note: Based on the proprietary Max Maserati Method for educational and analytical use.
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MaxMaserati tarafından