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Forex Dynamic Lot Size CalculatorForex Dynamic Lot Size Calculator for Forex. Works on USD Base and USD Quote pairs. Provides real-time data based on stop-loss location. Allows you to know in real-time how the number of lots you need to purchase to match your risk %. Number of Lots is calculated based on total risk. Total risk is calculated based on Stop-Loss + Commission + Spread Fees + Slippage measured in pips. Also includes data such as break-even pips, net take profit, margin required, buying power used, and a few others. All are real-time and anchored to the current price. The intention of creating this indicator is to help with risk management. You know exactly how many lots you need to get this very moment to have your total risk at lets say $250, which includes commission fees, spread fees, and slippage. To put it simply, if I was to enter the trade right now and willing to risk exactly $250, how many lots will I need to get right this second? --- - To use adjust Account Settings along with other variables. - Stop Loss Mode can be Manual or Dynamic. If you select Dynamic, then you will have to adjust Stop Loss Level to where you can see the reference line on the screen. It is at 1.1 by default. Just enter current price and the line will appear. Adjust it by dragging it to where you want your stop loss to be. - Take Profit Mode can also be Manual or Dynamic. I just keep my TP at Manual and use Quick Access to set Quick RR levels. - Adjust Spreads and Slippage to your liking. I tried to have TV calculate current spread, but it seem like it doesn't have access to real-life data for me like MT5 does. I just use average instead. Both are optional, depending on your broker and type of account you use. - Pip Value for the current pair, Return on Margin, and Break-even line can be turned on and off, based on your needs. I just get the Break-even value in pips from the pannel and use that as reference where I need to relocate my stop loss to break-ever (commission + spreds + slippage). - Panel is fully customizable based on your liking. Important fields are highlighted along with reference lines.
Indicador Pine Script®
por qentis
Market Regime IndexThe Market Regime Index is a top-down macro regime nowcasting tool that offers a consolidated view of the market’s risk appetite. It tracks 32 of the world’s most influential markets across asset classes to determine investor sentiment by applying trend-following signals to each independent asset. It features adjustable parameters and a built-in alert system that notifies investors when conditions transition between Risk-On and Risk-Off regimes. The selected markets are grouped into equities (7), fixed income (9), currencies (7), commodities (5), and derivatives (4): Equities = S&P 500 E-mini Index Futures, Nasdaq-100 E-mini Index Futures, Russell 2000 E-mini Index Futures, STOXX Europe 600 Index Futures, Nikkei 225 Index Futures, MSCI Emerging Markets Index Futures, and S&P 500 High Beta (SPHB)/Low Beta (SPLV) Ratio. Fixed Income = US 10Y Treasury Yield, US 2Y Treasury Yield, US 10Y-02Y Yield Spread, German 10Y Bund Yield, UK 10Y Gilt Yield, US 10Y Breakeven Inflation Rate, US 10Y TIPS Yield, US High Yield Option-Adjusted Spread, and US Corporate Option-Adjusted Spread. Currencies = US Dollar Index (DXY), Australian Dollar/US Dollar, Euro/US Dollar, Chinese Yuan/US Dollar, Pound Sterling/US Dollar, Japanese Yen/US Dollar, and Bitcoin/US Dollar. Commodities = ICE Brent Crude Oil Futures, COMEX Gold Futures, COMEX Silver Futures, COMEX Copper Futures, and S&P Goldman Sachs Commodity Index (GSCI) Futures. Derivatives = CBOE S&P 500 Volatility Index (VIX), ICE US Bond Market Volatility Index (MOVE), CBOE 3M Implied Correlation Index, and CBOE VIX Volatility Index (VVIX)/VIX. All assets are directionally aligned with their historical correlation to the S&P 500. Each asset contributes equally based on its individual bullish or bearish signal. The overall market regime is calculated as the difference between the number of Risk-On and Risk-Off signals divided by the total number of assets, displayed as the percentage of markets confirming each regime. Green indicates Risk-On and occurs when the number of Risk-On signals exceeds Risk-Off signals, while red indicates Risk-Off and occurs when the number of Risk-Off signals exceeds Risk-On signals. Bullish Signal = (Fast MA – Slow MA) > (ATR × ATR Margin) Bearish Signal = (Fast MA – Slow MA) < –(ATR × ATR Margin) Market Regime = (Risk-On signals – Risk-Off signals) ÷ Total assets This indicator is designed with flexibility in mind, allowing users to include or exclude individual assets that contribute to the market regime and adjust the input parameters used for trend signal detection. These parameters apply to each independent asset, and the overall regime signal is smoothed by the signal length to reduce noise and enhance reliability. Investors can position according to the prevailing market regime by selecting factors that have historically outperformed under each regime environment to minimise downside risk and maximise upside potential: Risk-On Equity Factors = High Beta > Cyclicals > Low Volatility > Defensives. Risk-Off Equity Factors = Defensives > Low Volatility > Cyclicals > High Beta. Risk-On Fixed Income Factors = High Yield > Investment Grade > Treasuries. Risk-Off Fixed Income Factors = Treasuries > Investment Grade > High Yield. Risk-On Commodity Factors = Industrial Metals > Energy > Agriculture > Gold. Risk-Off Commodity Factors = Gold > Agriculture > Energy > Industrial Metals. Risk-On Currency Factors = Cryptocurrencies > Foreign Currencies > US Dollar. Risk-Off Currency Factors = US Dollar > Foreign Currencies > Cryptocurrencies. In summary, the Market Regime Index is a comprehensive macro risk-management tool that identifies the current market regime and helps investors align portfolio risk with the market’s underlying risk appetite. Its intuitive, color-coded design makes it an indispensable resource for investors seeking to navigate shifting market conditions and enhance risk-adjusted performance by selecting factors that have historically outperformed. While it has proven historically valuable, asset-specific characteristics and correlations evolve over time as market dynamics change.
Indicador Pine Script®
por QuantitativeAlpha
Thematic Portfolio: Quantum Computing & Core TechThis indicator tracks the aggregated performance of a curated thematic portfolio representing the Quantum Computing & Core Technology sector. It combines leading equities and ETFs with predefined weights to reflect a diversified exposure across quantum hardware, AI infrastructure, and semiconductor backbones. Composition: Stocks: Rigetti (RGTI), IonQ (IONQ), D-Wave (QBTS), Palantir (PLTR), Intel (INTC), Arqit (ARQQ) ETFs: BUG, QTUM, SOXX, IHAK Methodology: Each component’s normalized performance is weighted according to its strategic importance within the theme (R&D intensity, infrastructure leverage, and hardware dependence). The indicator dynamically aggregates the weighted series to visualize the cumulative return of the quantum computing ecosystem versus traditional benchmarks. Intended use: Compare thematic returns vs. S&P 500 or NASDAQ Identify macro inflection points in the quantum tech narrative Backtest thematic exposure strategies or structure twin-win / delta-one certificates Note: This script is for analytical and educational purposes only and does not constitute financial advice.
Indicador Pine Script®
por vplanzo
J.P. Morgan Efficiente 5 IndexJ.P. MORGAN EFFICIENTE 5 INDEX REPLICATION Walk into any retail trading forum and you'll find the same scene playing out thousands of times a day: traders huddled over their screens, drawing trendlines on candlestick charts, hunting for the perfect entry signal, convinced that the next RSI crossover will unlock the path to financial freedom. Meanwhile, in the towers of lower Manhattan and the City of London, portfolio managers are doing something entirely different. They're not drawing lines. They're not hunting patterns. They're building fortresses of diversification, wielding mathematical frameworks that have survived decades of market chaos, and most importantly, they're thinking in portfolios while retail thinks in positions. This divide is not just philosophical. It's structural, mathematical, and ultimately, profitable. The uncomfortable truth that retail traders must confront is this: while you're obsessing over whether the 50-day moving average will cross the 200-day, institutional investors are solving quadratic optimization problems across thirteen asset classes, rebalancing monthly according to Markowitz's Nobel Prize-winning framework, and targeting precise volatility levels that allow them to sleep at night regardless of what the VIX does tomorrow. The game you're playing and the game they're playing share the same field, but the rules are entirely different. The question, then, is not whether retail traders can access institutional strategies. The question is whether they're willing to fundamentally change how they think about markets. Are you ready to stop painting lines and start building portfolios? THE INSTITUTIONAL FRAMEWORK: HOW THE PROFESSIONALS ACTUALLY THINK When Harry Markowitz published "Portfolio Selection" in The Journal of Finance in 1952, he fundamentally altered how sophisticated investors approach markets. His insight was deceptively simple: returns alone mean nothing. Risk-adjusted returns mean everything. For this revelation, he would eventually receive the Nobel Prize in Economics in 1990, and his framework would become the foundation upon which trillions of dollars are managed today (Markowitz, 1952). Modern Portfolio Theory, as it came to be known, introduced a revolutionary concept: through diversification across imperfectly correlated assets, an investor could reduce portfolio risk without sacrificing expected returns. This wasn't about finding the single best asset. It was about constructing the optimal combination of assets. The mathematics are elegant in their logic: if two assets don't move in perfect lockstep, combining them creates a portfolio whose volatility is lower than the weighted average of the individual volatilities. This "free lunch" of diversification became the bedrock of institutional investment management (Elton et al., 2014). But here's where retail traders miss the point entirely: this isn't about having ten different stocks instead of one. It's about systematic, mathematically rigorous allocation across asset classes with fundamentally different risk drivers. When equity markets crash, high-quality government bonds often rally. When inflation surges, commodities may provide protection even as stocks and bonds both suffer. When emerging markets are in vogue, developed markets may lag. The professional investor doesn't predict which scenario will unfold. Instead, they position for all of them simultaneously, with weights determined not by gut feeling but by quantitative optimization. This is what J.P. Morgan Asset Management embedded into their Efficiente Index series. These are not actively managed funds where a portfolio manager makes discretionary calls. They are rules-based, systematic strategies that execute the Markowitz framework in real-time, rebalancing monthly to maintain optimal risk-adjusted positioning across global equities, fixed income, commodities, and defensive assets (J.P. Morgan Asset Management, 2016). THE EFFICIENTE 5 STRATEGY: DECONSTRUCTING INSTITUTIONAL METHODOLOGY The Efficiente 5 Index, specifically, targets a 5% annualized volatility. Let that sink in for a moment. While retail traders routinely accept 20%, 30%, or even 50% annual volatility in pursuit of returns, institutional allocators have determined that 5% volatility provides an optimal balance between growth potential and capital preservation. This isn't timidity. It's mathematics. At higher volatility levels, the compounding drag from large drawdowns becomes mathematically punishing. A 50% loss requires a 100% gain just to break even. The institutional solution: constrain volatility at the portfolio level, allowing the power of compounding to work unimpeded (Damodaran, 2008). The strategy operates across thirteen exchange-traded funds spanning five distinct asset classes: developed equity markets (SPY, IWM, EFA), fixed income across the risk spectrum (TLT, LQD, HYG), emerging markets (EEM, EMB), alternatives (IYR, GSG, GLD), and defensive positioning (TIP, BIL). These aren't arbitrary choices. Each ETF represents a distinct factor exposure, and together they provide access to the primary drivers of global asset returns (Fama and French, 1993). The methodology, as detailed in replication research by Jungle Rock (2025), follows a precise monthly cadence. At the end of each month, the strategy recalculates expected returns and volatilities for all thirteen assets using a 126-day rolling window. This six-month lookback balances responsiveness to changing market conditions against the noise of short-term fluctuations. The optimization engine then solves for the portfolio weights that maximize expected return subject to the 5% volatility target, with additional constraints to prevent excessive concentration. These constraints are critical and reveal institutional wisdom that retail traders typically ignore. No single ETF can exceed 20% of the portfolio, except for TIP and BIL which can reach 50% given their defensive nature. At the asset class level, developed equities are capped at 50%, bonds at 50%, emerging markets at 25%, and alternatives at 25%. These aren't arbitrary limits. They're guardrails preventing the optimization from becoming too aggressive during periods when recent performance might suggest concentrating heavily in a single area that's been hot (Jorion, 1992). After optimization, there's one final step that appears almost trivial but carries profound implications: weights are rounded to the nearest 5%. In a world of fractional shares and algorithmic execution, why round to 5%? The answer reveals institutional practicality over mathematical purity. A portfolio weight of 13.7% and 15.0% are functionally similar in their risk contribution, but the latter is vastly easier to communicate, to monitor, and to execute at scale. When you're managing billions, parsimony matters. WHY THIS MATTERS FOR RETAIL: THE GAP BETWEEN APPROACH AND EXECUTION Here's the uncomfortable reality: most retail traders are playing a different game entirely, and they don't even realize it. When a retail trader says "I'm bullish on tech," they buy QQQ and that's their entire technology exposure. When they say "I need some diversification," they buy ten different stocks, often in correlated sectors. This isn't diversification in the Markowitzian sense. It's concentration with extra steps. The institutional approach represented by the Efficiente 5 is fundamentally different in several ways. First, it's systematic. Emotions don't drive the allocation. The mathematics do. When equities have rallied hard and now represent 55% of the portfolio despite a 50% cap, the system sells equities and buys bonds or alternatives, regardless of how bullish the headlines feel. This forced contrarianism is what retail traders know they should do but rarely execute (Kahneman and Tversky, 1979). Second, it's forward-looking in its inputs but backward-looking in its process. The strategy doesn't try to predict the next crisis or the next boom. It simply measures what volatility and returns have been recently, assumes the immediate future resembles the immediate past more than it resembles some forecast, and positions accordingly. This humility regarding prediction is perhaps the most institutional characteristic of all. Third, and most critically, it treats the portfolio as a single organism. Retail traders typically view their holdings as separate positions, each requiring individual management. The institutional approach recognizes that what matters is not whether Position A made money, but whether the portfolio as a whole achieved its risk-adjusted return target. A position can lose money and still be a valuable contributor if it reduced portfolio volatility or provided diversification during stress periods. THE MATHEMATICAL FOUNDATION: MEAN-VARIANCE OPTIMIZATION IN PRACTICE At its core, the Efficiente 5 strategy solves a constrained optimization problem each month. In technical terms, this is a quadratic programming problem: maximize expected portfolio return subject to a volatility constraint and position limits. The objective function is straightforward: maximize the weighted sum of expected returns. The constraint is that the weighted sum of variances and covariances must not exceed the volatility target squared (Markowitz, 1959). The challenge, and this is crucial for understanding the Pine Script implementation, is that solving this problem properly requires calculating a covariance matrix. This 13x13 matrix captures not just the volatility of each asset but the correlation between every pair of assets. Two assets might each have 15% volatility, but if they're negatively correlated, combining them reduces portfolio risk. If they're positively correlated, it doesn't. The covariance matrix encodes these relationships. True mean-variance optimization requires matrix algebra and quadratic programming solvers. Pine Script, by design, lacks these capabilities. The language doesn't support matrix operations, and certainly doesn't include a QP solver. This creates a fundamental challenge: how do you implement an institutional strategy in a language not designed for institutional mathematics? The solution implemented here uses a pragmatic approximation. Instead of solving the full covariance problem, the indicator calculates a Sharpe-like ratio for each asset (return divided by volatility) and uses these ratios to determine initial weights. It then applies the individual and asset-class constraints, renormalizes, and produces the final portfolio. This isn't mathematically equivalent to true mean-variance optimization, but it captures the essential spirit: weight assets according to their risk-adjusted return potential, subject to diversification constraints. For retail implementation, this approximation is likely sufficient. The difference between a theoretically optimal portfolio and a very good approximation is typically modest, and the discipline of systematic rebalancing across asset classes matters far more than the precise weights. Perfect is the enemy of good, and a good approximation executed consistently will outperform a perfect solution that never gets implemented (Arnott et al., 2013). RETURNS, RISKS, AND THE POWER OF COMPOUNDING The Efficiente 5 Index has, historically, delivered on its promise of 5% volatility with respectable returns. While past performance never guarantees future results, the framework reveals why low-volatility strategies can be surprisingly powerful. Consider two portfolios: Portfolio A averages 12% returns with 20% volatility, while Portfolio B averages 8% returns with 5% volatility. Which performs better over time? The arithmetic return favors Portfolio A, but compound returns tell a different story. Portfolio A will experience occasional 20-30% drawdowns. Portfolio B rarely draws down more than 10%. Over a twenty-year horizon, the geometric return (what you actually experience) for Portfolio B may match or exceed Portfolio A, simply because it never gives back massive gains. This is the power of volatility management that retail traders chronically underestimate (Bernstein, 1996). Moreover, low volatility enables behavioral advantages. When your portfolio draws down 35%, as it might with a high-volatility approach, the psychological pressure to sell at the worst possible time becomes overwhelming. When your maximum drawdown is 12%, as might occur with the Efficiente 5 approach, staying the course is far easier. Behavioral finance research has consistently shown that investor returns lag fund returns primarily due to poor timing decisions driven by emotional responses to volatility (Dalbar, 2020). The indicator displays not just target and actual portfolio weights, but also tracks total return, portfolio value, and realized volatility. This isn't just data. It's feedback. Retail traders can see, in real-time, whether their actual portfolio volatility matches their target, whether their risk-adjusted returns are improving, and whether their allocation discipline is holding. This transparency transforms abstract concepts into concrete metrics. WHAT RETAIL TRADERS MUST LEARN: THE MINDSET SHIFT The path from retail to institutional thinking requires three fundamental shifts. First, stop thinking in positions and start thinking in portfolios. Your question should never be "Should I buy this stock?" but rather "How does this position change my portfolio's expected return and volatility?" If you can't answer that question quantitatively, you're not ready to make the trade. Second, embrace systematic rebalancing even when it feels wrong. Perhaps especially when it feels wrong. The Efficiente 5 strategy rebalances monthly regardless of market conditions. If equities have surged and now exceed their target weight, the strategy sells equities and buys bonds or alternatives. Every retail trader knows this is what you "should" do, but almost none actually do it. The institutional edge isn't in having better information. It's in having better discipline (Swensen, 2009). Third, accept that volatility is not your friend. The retail mythology that "higher risk equals higher returns" is true on average across assets, but it's not true for implementation. A 15% return with 30% volatility will compound more slowly than a 12% return with 10% volatility due to the mathematics of return distributions. Institutions figured this out decades ago. Retail is still learning. The Efficiente 5 replication indicator provides a bridge. It won't solve the problem of prediction no indicator can. But it solves the problem of allocation, which is arguably more important. By implementing institutional methodology in an accessible format, it allows retail traders to see what professional portfolio construction actually looks like, not in theory but in executable code. The the colorful lines that retail traders love to draw, don't disappear. They simply become less central to the process. The portfolio becomes central instead. IMPLEMENTATION CONSIDERATIONS AND PRACTICAL REALITY Running this indicator on TradingView provides a dynamic view of how institutional allocation would evolve over time. The labels on each asset class line show current weights, updated continuously as prices change and rebalancing occurs. The dashboard displays the full allocation across all thirteen ETFs, showing both target weights (what the optimization suggests) and actual weights (what the portfolio currently holds after price movements). Several key insights emerge from watching this process unfold. First, the strategy is not static. Weights change monthly as the optimization recalibrates to recent volatility and returns. What worked last month may not be optimal this month. Second, the strategy is not market-timing. It doesn't try to predict whether stocks will rise or fall. It simply measures recent behavior and positions accordingly. If volatility has risen, the strategy shifts toward defensive assets. If correlations have changed, the diversification benefits adjust. Third, and perhaps most importantly for retail traders, the strategy demonstrates that sophistication and complexity are not synonyms. The Efficiente 5 methodology is sophisticated in its framework but simple in its execution. There are no exotic derivatives, no complex market-timing rules, no predictions of future scenarios. Just systematic optimization, monthly rebalancing, and discipline. This simplicity is a feature, not a bug. The indicator also highlights limitations that retail traders must understand. The Pine Script implementation uses an approximation of true mean-variance optimization, as discussed earlier. Transaction costs are not modeled. Slippage is ignored. Tax implications are not considered. These simplifications mean the indicator is educational and analytical, not a fully operational trading system. For actual implementation, traders would need to account for these real-world factors. Moreover, the strategy requires access to all thirteen ETFs and sufficient capital to hold meaningful positions in each. With 5% as the rounding increment, practical implementation probably requires at least $10,000 to avoid having positions that are too small to matter. The strategy is also explicitly designed for a 5% volatility target, which may be too conservative for younger investors with long time horizons or too aggressive for retirees living off their portfolio. The framework is adaptable, but adaptation requires understanding the trade-offs. CAN RETAIL TRULY COMPETE WITH INSTITUTIONS? The honest answer is nuanced. Retail traders will never have the same resources as institutions. They won't have Bloomberg terminals, proprietary research, or armies of analysts. But in portfolio construction, the resource gap matters less than the mindset gap. The mathematics of Markowitz are available to everyone. ETFs provide liquid, low-cost access to institutional-quality building blocks. Computing power is essentially free. The barriers are not technological or financial. They're conceptual. If a retail trader understands why portfolios matter more than positions, why systematic discipline beats discretionary emotion, and why volatility management enables compounding, they can build portfolios that rival institutional allocation in their elegance and effectiveness. Not in their scale, not in their execution costs, but in their conceptual soundness. The Efficiente 5 framework proves this is possible. What retail traders must recognize is that competing with institutions doesn't mean day-trading better than their algorithms. It means portfolio-building better than their average client. And that's achievable because most institutional clients, despite having access to the best managers, still make emotional decisions, chase performance, and abandon strategies at the worst possible times. The retail edge isn't in outsmarting professionals. It's in out-disciplining amateurs who happen to have more money. The J.P. Morgan Efficiente 5 Index Replication indicator serves as both a tool and a teacher. As a tool, it provides a systematic framework for multi-asset allocation based on proven institutional methodology. As a teacher, it demonstrates daily what portfolio thinking actually looks like in practice. The colorful lines remain on the chart, but they're no longer the focus. The portfolio is the focus. The risk-adjusted return is the focus. The systematic discipline is the focus. Stop painting lines. Start building portfolios. The institutions have been doing it for seventy years. It's time retail caught up. REFERENCES Arnott, R. D., Hsu, J., & Moore, P. (2013). Fundamental Indexation. Financial Analysts Journal, 61(2), 83-99. Bernstein, W. J. (1996). The Intelligent Asset Allocator. New York: McGraw-Hill. Dalbar, Inc. (2020). Quantitative Analysis of Investor Behavior. Boston: Dalbar. Damodaran, A. (2008). Strategic Risk Taking: A Framework for Risk Management. Upper Saddle River: Pearson Education. Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern Portfolio Theory and Investment Analysis (9th ed.). Hoboken: John Wiley & Sons. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. Jorion, P. (1992). Portfolio optimization in practice. Financial Analysts Journal, 48(1), 68-74. J.P. Morgan Asset Management. (2016). Guide to the Markets. New York: J.P. Morgan. Jungle Rock. (2025). Institutional Asset Allocation meets the Efficient Frontier: Replicating the JPMorgan Efficiente 5 Strategy. Working Paper. Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291. Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91. Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons. Swensen, D. F. (2009). Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment. New York: Free Press.
Indicador Pine Script®
por EdgeTools
1
Fixed Dollar Risk LinesFixed Dollar Risk Lines is a utility indicator that converts a user-defined dollar risk into price distance and plots risk lines above and below the current price for popular futures contracts. It helps you place stops or entries at a consistent dollar risk per trade, regardless of the market’s tick value or tick size. What it does: -You choose a dollar amount to risk (e.g., $100) and a futures contract (ES, NQ, GC, YM, RTY, PL, SI, CL, BTC). The script automatically: -Looks up the contract’s tick value and tick size -Converts your dollar risk into number of ticks -Converts ticks into price distance Plots: -Long Risk line below current price -Short Risk line above current price -Optional labels show exact price levels and an information table summarizes your settings. Key features -Consistent dollar risk across instruments -Supports major futures contracts with built‑in tick values and sizes -Toggle Long and Short risk lines independently -Customizable line width and colors (lines and labels) -Right‑axis price level display for quick reading -Compact info table with contract, risk, and computed prices Typical use -Long setups: use the green line as a stop level below entry to match your chosen dollar risk. -Short setups: use the red line as a stop level above entry to match your chosen dollar risk. -Quickly compare how the same dollar risk translates to distance on different contracts. Inputs -Risk Amount (USD) -Futures Contract (ES, NQ, GC, YM, RTY, PL, SI, CL, BTC) -Show Long/Short lines (toggles) -Line Width -Colors for lines and labels Notes -Designed for futures symbols that match the listed contracts’ tick specs. If your symbol has different tick value/size than the defaults, results will differ. -Intended for educational/informational use; not financial advice. -This tool streamlines risk placement so you can focus on execution while keeping dollar risk consistent across markets.
Indicador Pine Script®
por smg387
Trading Toolkit - Comprehensive AnalysisTrading Toolkit – Comprehensive Analysis A unified trading analysis toolkit with four sections: 📊 Company Info Fundamentals, market cap, sector, and earnings countdown. 📅 Performance Date‑range analysis with key metrics. 🎯 Market Sentiment CNN‑style Fear & Greed Index (7 components) + 150‑SMA positioning. 🛡️ Risk Levels ATR/MAD‑based stop‑loss and take‑profit calculations. Key Features CNN‑style Fear & Greed approximation using: Momentum: S&P 500 vs 125‑DMA Price Strength: NYSE 52‑week highs vs lows Market Breadth: McClellan Volume Summation (Up/Down volume) Put/Call Ratio: 5‑day average (inverted) Volatility: VIX vs 50‑DMA (inverted) Safe‑Haven Demand: 20‑day SPY–IEF return spread Junk‑Bond Demand: HY vs IG credit spread (inverted) Normalization: z‑score → percentile (0–100) with ±3 clipping. CNN‑aligned thresholds: Extreme Fear: 0–24 | Fear: 25–44 | Neutral: 45–54 | Greed: 55–74 | Extreme Greed: 75+. Risk tools: ATR & MAD volatility measures with configurable multipliers. Flexible layout: vertical or side‑by‑side columns. Data Sources S&P 500: CBOE:SPX or AMEX:SPY NYSE: INDEX:HIGN, INDEX:LOWN, USI:UVOL, USI:DVOL Options: USI:PCC (Total PCR), fallback INDEX:CPCS (Equity PCR) Volatility: CBOE:VIX Treasuries: NASDAQ:IEF Credit Spreads: FRED:BAMLH0A0HYM2, FRED:BAMLC0A0CM Risk Management ATR risk bands: 🟢 ≤3%, 🟡 3–6%, ⚪ 6–10%, 🟠 10–15%, 🔴 >15% MAD‑based stop‑loss and take‑profit calculations. Author: Daniel Dahan (AI Generated, Merged & enhanced version with CNN‑style Fear & Greed)
Indicador Pine Script®
por danny_dahan
Trading Lot & Margin Calculator # 💹 Trading Lot & Margin Calculator - Professional Risk Management Tool ## 🎯 Overview A comprehensive, all-in-one calculator dashboard that helps traders determine optimal position sizes, calculate margin requirements, and manage risk effectively across multiple asset classes. This indicator displays directly on your chart as a customizable table, providing real-time calculations based on current market prices. ## ✨ Key Features ### 📊 Three Powerful Calculation Modes: **1. Calculate Lot Size (Risk-Based Position Sizing)** - Input your risk percentage and stop loss in pips - Automatically calculates the optimal lot size for your risk tolerance - Respects margin limitations (configurable margin % cap) - Ensures positions don't exceed minimum lot size (0.01) - Perfect for risk management and proper position sizing **2. Calculate Margin Cost** - Input desired lot size - See exactly how much margin is required - Shows percentage of deposit used - Displays free margin remaining - Warns when insufficient funds **3. Margin to Lots** - Specify a fixed margin amount you want to use - Calculator shows how many lots/contracts you can buy - Ideal for traders with fixed margin budgets ## 🤖 Auto-Detection of Instruments The calculator **automatically detects** what you're trading and adjusts calculations accordingly: ### ✅ Fully Supported: - **💱 Forex Pairs** - All majors, minors, exotics (EURUSD, GBPJPY, etc.) - Standard lot: 100,000 units - JPY pairs: 0.01 pip size, others: 0.0001 - **🛢️ Commodities** - Gold, Silver, Oil - XAUUSD (Gold): 100 oz per lot - XAGUSD (Silver): 5,000 oz per lot - Oil (WTI/Brent): 1,000 barrels per lot - **📈 Indices** - US500, NAS100, US30, DAX, etc. - Correct contract sizes per point - **📊 Stocks** - All individual stocks - 1 lot = 1 share - Direct share calculations ### ⚠️ Known Limitation: - **₿ Crypto calculations may not work properly** on all crypto pairs. Use manual contract size if needed. ## 📋 Dashboard Information Displayed: - 🎯 Optimal/Requested Lot Size - 💰 Margin Required - 📊 Margin % of Deposit - 💵 Free Margin Remaining - 💎 Position Value - 📈 Pip/Point Value - ⚠️ Safety Warnings (insufficient funds, high risk, etc.) - 🔍 Detected Instrument Type - 📦 Contract Size ## ⚙️ Customizable Settings: **Account Settings:** - Account Deposit - Leverage (1:1 to 1:1000) - Max Margin % of Deposit (default 5% for safety) **Risk Management:** - Risk Percentage (for lot size calculation) - Stop Loss in Pips - Lot Amount (for margin cost calculation) - Margin to Use (for margin-to-lots calculation) **Display Options:** - Show/Hide Dashboard - Position: Top/Middle/Bottom, Left/Right - Auto-detect instrument ON/OFF - Manual contract size override ## 🎨 Professional Design - Clean, modern table interface - Color-coded warnings (red = danger, yellow = caution, green = safe) - Large, readable text - Minimal screen space usage - Non-intrusive overlay ## 💡 Use Cases: 1. **Day Traders** - Quick position sizing based on account risk 2. **Swing Traders** - Calculate optimal positions for longer-term setups 3. **Risk Managers** - Ensure positions stay within margin limits 4. **Beginners** - Learn proper position sizing and risk management 5. **Multi-Asset Traders** - Seamlessly switch between forex, commodities, indices, and stocks ## ⚠️ Important Notes: - ✅ Works on all timeframes - ✅ Updates in real-time with price changes - ✅ Minimum lot size enforced (0.01) - ✅ Margin calculations use current chart price - ⚠️ **Crypto calculations may be inaccurate** - verify with your broker - 📌 Always verify calculations with your broker's specifications - 📌 Contract sizes may vary by broker ## 🚀 How to Use: 1. Add indicator to any chart 2. Click settings ⚙️ icon 3. Enter your account details (deposit, leverage) 4. Choose calculation mode 5. Input your parameters 6. View optimal lot size and margin requirements on dashboard ## 📈 Perfect For: - Forex traders managing multiple currency pairs - Commodity traders (Gold, Silver, Oil) - Index traders (S&P 500, NASDAQ, etc.) - Stock traders - Anyone who wants professional risk management ## 🛡️ Risk Management Features: - Configurable margin % cap prevents over-leveraging - Risk-based position sizing protects your account - Warnings for high risk, insufficient funds, margin limitations - Prevents positions below minimum lot size --- **Trade smarter, not harder. Calculate before you trade!** 📊💪 --- ## Version Notes: - Pine Script v6 - Overlay mode for chart display - No external dependencies - Lightweight and fast **Disclaimer:** This calculator is for educational and informational purposes only. Always verify calculations with your broker and trade at your own risk. Past performance does not guarantee future results. ---
Indicador Pine Script®
por Makshels
Crypto Index Price# Crypto Index Price - Indicator Description ## 📊 What is this indicator? **Crypto Index Price** is an indicator for creating your own cryptocurrency index based on an equal-weighted portfolio. It allows you to track the overall dynamics of the cryptocurrency market through a composite index of selected assets. ## 🎯 Key Features - **Up to 20 assets in the index** — create an index from any trading pairs - **Equal-weighted methodology** — each asset has the same weight in the index - **Moving average** — optional trend filter for the index - **Flexible visualization settings** — customizable colors and line thickness ## 📈 How to Use The indicator is displayed in a separate pane below the chart and shows: 1. **Blue line** — crypto index value 2. **Orange line** (optional) — moving average of the index ### Trading Applications: - **Identify overall market trend** — if the index is rising, most coins are in an uptrend - **Divergences** — divergence between your asset and the index may signal local opportunities - **Signal confirmation** — use the index to confirm trading decisions on individual coins - **Market condition filter** — trade longs when index is above MA, shorts when below ## ⚙️ Settings ### Assets (Symbols) - **Asset 1-10** — main cryptocurrencies (default: BTC, ETH, BNB, SOL, XRP, ADA, AVAX, LINK, DOGE, TRX) - **Asset 11-20** — additional slots for index expansion ### Visual Parameters - **Index line color** — main line color (default: blue) - **Line width** — from 1 to 5 pixels - **Show moving average** — enable/disable MA - **MA period** — moving average calculation period (default: 20) - **MA color** — moving average line color (default: orange) ## 💡 Recommendations - For a top coins index, use 5-10 largest cryptocurrencies by market cap - For an altcoin index, add medium and small coins from your sector - Use MA to filter false signals and identify the global trend - Compare individual asset behavior with the index to find anomalies ## ⚠️ Important The indicator uses equal-weighted methodology — each coin contributes equally regardless of price or market cap. This differs from cap-weighted indices and may provide a different market perspective. --- *This indicator is intended for analysis and is not trading advice. Always conduct your own analysis before making trading decisions.* ---
Indicador Pine Script®
por AlekMel
Stock Fundamental Overlay [DarwinDarma]Stock Fundamental Overlay Stock Fundamental Overlay is a comprehensive valuation indicator that displays multiple fundamental analysis metrics directly on your price chart. Key Features: • Graham Number - Benjamin Graham's intrinsic value formula • Book Value Per Share (BVPS) - Net asset value baseline • DCF Valuation - Discounted Cash Flow analysis (non-financial stocks) • DDM Valuation - Dividend Discount Model (dividend-paying stocks) • Visual Value Zones - Color-coded undervalued/overvalued regions • Real-time Fundamental Table - Live metrics and valuations • Price vs Graham Comparison - Quick valuation assessment • Built-in Alerts - Notification when price crosses key levels Valuation Models: • Graham Number: √(22.5 × EPS × BVPS) • DCF: Customizable discount rate, growth rate, and forecast period • DDM: Gordon Growth Model for dividend analysis Visual Elements: • Plot lines for BVPS, Graham Number, and DCF values • Shaded value zone between BVPS and Graham Number • Background coloring: Deep value (below BVPS), Undervalued (below Graham), Overvalued (>1.5x Graham) • Dynamic table showing all metrics with theme-aware text colors Special Handling: • Financial sector detection - DCF disabled for banks/financials where FCF metrics are distorted • Automatic light/dark theme adaptation • TTM (Trailing Twelve Months) data for current metrics How to Use - Value Investing Approach: 1. Identifying Undervalued Stocks: • Look for price trading BELOW the Graham Number (green zone) - potential value opportunity • Deep value: Price below BVPS indicates trading below net asset value • Check "Price vs Graham" % in table - negative values suggest undervaluation • Compare multiple models: When price is below Graham, DCF, and BVPS simultaneously, stronger buy signal 2. Margin of Safety: • Benjamin Graham recommended buying at 2/3 of intrinsic value (33% margin of safety) • Monitor the gap between current price and valuation lines • Larger gaps = greater margin of safety = lower downside risk • Use the shaded "Value Zone" as your target buying range 3. Setting Alerts: • "Price Below Graham Number" - Notifies when stock enters value territory • "Price Below Book Value" - Extreme value alert for deep value hunters • "Price Below DCF Value" - Cash flow-based value signal • Set alerts on watchlist stocks to catch value opportunities 4. Customizing for Your Strategy: • Conservative investors: Use lower growth rates (3-4%) and higher discount rates (12-15%) • Growth-value investors: Adjust growth rate (6-8%) for quality compounders • Dividend investors: Focus on DDM value and Div/Share metrics • Adjust forecast years based on business predictability (stable = 10 years, cyclical = 5 years) 5. Red Flags to Avoid: • Negative EPS or FCF (red values in table) - proceed with caution • Financial sector stocks - Use DDM and Graham, ignore DCF • Price far above Graham (>1.5x) with red background = overvalued territory • No fundamental data = "N/A" in table - stock may lack reporting or be too small • Stock persistently below BVPS for extended periods - potential value trap or business in distress • Price significantly above ALL models (BVPS, Graham, DCF) - sentiment-driven, lacks intrinsic value foundation (fragile) ⚠️ Important Value Investing Warnings: • Value Trap Alert: A stock staying below BVPS for months/years may signal fundamental deterioration, asset impairments, or dying industry - not just "cheap." Investigate WHY it's cheap before buying • Sentiment Bubble Risk: When price trades far above BVPS, Graham Number, AND DCF simultaneously, the stock has no intrinsic value basis. Examples: commodity stocks during boom cycles (gold miners in gold rallies), meme stocks, hype-driven sectors. These are highly fragile and vulnerable to mean reversion • Cyclical Trap: Commodity/cyclical stocks can appear "cheap" at peak earnings (low P/E, high FCF) but are actually expensive. Normalize earnings across the cycle before valuing • Quality Matters: Some excellent businesses (asset-light, high ROIC) naturally trade above book value. Don't avoid quality - adjust expectations for business model 6. Monitoring Positions: • Watch for price approaching or exceeding Graham Number - consider taking profits • Track EPS and FCF trends quarter-to-quarter in the table • If fundamentals deteriorate (falling BVPS, negative FCF), reassess thesis • Use background colors for quick visual check: green = hold/buy, red = overvalued Perfect for: Value investors seeking multi-model fundamental analysis, long-term investors comparing intrinsic value to market price, dividend investors evaluating yield stocks, and fundamental traders looking for entry/exit signals. Note: Only works with stocks that have financial data available. Not applicable to crypto, forex, or futures. This indicator provides analysis tools; always conduct thorough research and due diligence before investing.
Indicador Pine Script®
por DarwinDarma
Risk-On / Risk-Off CompositeReal-time Risk-On / Risk-Off Composite from your four ratios: SPY / TLT (equities vs long bonds) HYG / LQD (high-yield vs IG credit) HG / GOLD (copper vs gold) BTC / GOLD (speculative vs defensive) It: normalizes each ratio with a z-score (so they’re comparable), lets you weight them, plots a composite line + histogram (up = risk-on, down = risk-off), shows a small heat-table for each sub-signal, and includes alert conditions for Risk-On / Risk-Off flips.
Indicador Pine Script®
por elliotallan20
Portfolio Strategy TesterThe Portfolio Strategy Tester is an institutional-grade backtesting framework that evaluates the performance of trend-following strategies on multi-asset portfolios. It enables users to construct custom portfolios of up to 30 assets and apply moving average crossover strategies across individual holdings. The model features a clear, color-coded table that provides a side-by-side comparison between the buy-and-hold portfolio and the portfolio using the risk management strategy, offering a comprehensive assessment of both approaches relative to the benchmark. Portfolios are constructed by entering each ticker symbol in the menu, assigning its respective weight, and reviewing the total sum of individual weights displayed at the top left of the table. For strategy selection, users can choose between Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), Moving Average Convergence Divergence (MACD), and Volume-Weighted Moving Average (VWMA). Moving average lengths are defined in the menu and apply only to strategy-enabled assets. To accurately replicate real-world portfolio conditions, users can choose between daily, weekly, monthly, or quarterly rebalancing frequencies and decide whether cash is held or redistributed. Daily rebalancing maintains constant portfolio weights, while longer intervals allow natural drift. When cash positions are not allowed, capital from bearish assets is automatically redistributed proportionally among bullish assets, ensuring the portfolio remains fully invested at all times. The table displays a comprehensive set of widely used institutional-grade performance metrics: CAGR = Compounded annual growth rate of returns. Volatility = Annualized standard deviation of returns. Sharpe = CAGR per unit of annualized standard deviation. Sortino = CAGR per unit of annualized downside deviation. Calmar = CAGR relative to maximum drawdown. Max DD = Largest peak-to-trough decline in value. Beta (β) = Sensitivity of returns relative to benchmark returns. Alpha (α) = Excess annualized risk-adjusted returns relative to benchmark. Upside = Ratio of average return to benchmark return on up days. Downside = Ratio of average return to benchmark return on down days. Tracking = Annualized standard deviation of returns versus benchmark. Turnover = Average sum of absolute changes in weights per year. Cumulative returns are displayed on each label as the total percentage gain from the selected start date, with green indicating positive returns and red indicating negative returns. In the table, baseline metrics serve as the benchmark reference and are always gray. For portfolio metrics, green indicates outperformance relative to the baseline, while red indicates underperformance relative to the baseline. For strategy metrics, green indicates outperformance relative to both the baseline and the portfolio, red indicates underperformance relative to both, and gray indicates underperformance relative to either the baseline or portfolio. Metrics such as Volatility, Tracking Error, and Turnover ratio are always displayed in gray as they serve as descriptive measures. In summary, the Portfolio Strategy Tester is a comprehensive backtesting tool designed to help investors evaluate different trend-following strategies on custom portfolios. It enables real-world simulation of both active and passive investment approaches and provides a full set of standard institutional-grade performance metrics to support data-driven comparisons. While results are based on historical performance, the model serves as a powerful portfolio management and research framework for developing, validating, and refining systematic investment strategies.
Indicador Pine Script®
por QuantitativeAlpha
Position Size ToolPosition Size Tool What it does: Shows a small on-chart table that converts per-ticker dollar amounts into share counts (shares = amount ÷ current price) for up to 4 configurable tickers. Inputs (indicator settings) Ticker 1–4 — select the symbol (TradingView will show the exchange-qualified form like BATS:TQQQ in the settings). Ticker N $ Amount — dollar amount to convert into shares for that ticker. Show Ticker N — toggle each row on/off. Table Text Color — color of the table text. Table Position — screen location (Top/ Middle/ Bottom × Left/Center/Right). Font Size — Small / Medium / Large. Show Empty Top Row — optional spacer row. What the table displays Left column: the ticker symbol only (the script strips the exchange prefix for display, so BATS:TQQQ appears as TQQQ in the table). Right column: the calculated share count, formatted to two decimal places (or "—" if price is not available or zero). Table updates on the chart’s timeframe using live/last bar prices. How to use Add the indicator to a chart. Open the indicator’s settings panel. In Ticker 1–4, type/select the symbols you want (you may see the exchange prefix there; that’s TradingView’s UI). Enter the dollar amounts for each ticker. Use Show Ticker N to hide/show rows. Adjust text color, font size, and table position as desired. Notes The settings field will always show the exchange-qualified symbol (TradingView behavior); the script strips the exchange only for the on-chart display. If the selected symbol has no price data on the chart/timeframe, the table shows "—". Shares are computed as amt ÷ current close from the requested symbol and timeframe. Example of how to use this tool: Monitor an index and execute trades on leveraged derivative products. This tool will determine the quantity of shares that can be purchased with a pre-determined dollar amount. Ex: Monitor SPX for entry/exit signals and execute trades on UPRO/SPXU/SPXL/SPXS. Input a ticker and a dollar amount for position size, shares that can be purchased will be calculated based on the current asset price. This tool can be helpful for those that use multiple platforms simultaneously to monitor and execute trades.
Indicador Pine Script®
por abbadon9
Jensen Alpha RS🧠 Jensen Alpha RS (J-Alpha RS) Jensen Alpha RS is a quantitative performance evaluation tool designed to compare multiple assets against a benchmark using Jensen’s Alpha — a classic risk-adjusted return metric from modern portfolio theory. It helps identify which assets have outperformed their benchmark on a risk-adjusted basis and ranks them in real time, with optional gating and visual tools. 📊 ✨ Key Features • 🧩 Multi-Asset Comparison: Evaluate up to four assets simultaneously. • 🔀 Adaptive Benchmarking: TOTALES mode uses CRYPTOCAP:TOTALES (total crypto market cap ex-stablecoins). Dynamic mode automatically selects the strongest benchmark among BTC, ETH, and TOTALES based on rolling momentum. • 📐 Jensen’s Alpha Calculation: Uses rolling covariance, variance, and beta to estimate α, showing how much each asset outperformed its benchmark. • 📈 Z-Score & Consistency Metrics: Z-Score highlights statistical deviations in alpha; Consistency % shows how often α has been positive over a chosen window. • 🚦 Trend & Zero Gates: Optional filters that require assets to be above EMA (trend) and/or have α > 0 for confirmation. • 🏆 Leaders Board Table: Displays α, Z, Rank, Consistency %, and Gate ✓/✗ for all assets in a clear visual layout. • 🔔 Dynamic Alerts: Get notified whenever the top alpha leader changes on confirmed (non-repainting) data. • 🎨 Visual Enhancements: Smooth α with an SMA or color bars by the current top-performing asset. 🧭 Typical Use Cases • 🔄 Portfolio Rotation & Relative Strength: Identify which assets consistently outperform their benchmark to optimize capital allocation. • 🧮 Alpha Persistence Analysis: Gauge whether a trend’s performance advantage is statistically sustainable. • 🌐 Market Regime Insight: Observe how asset leadership rotates as benchmarks shift across market cycles. ⚙️ Inputs Overview • 📝 Assets (1–4): Select up to four tickers for evaluation. • 🧭 Benchmark Mode: Choose between static TOTALES or Dynamic auto-selection. • 📏 Alpha Settings: Adjustable lookback, smoothing, and consistency windows. • 🚦 Gates: Optional trend and alpha filters to refine results. • 🖥️ Display: Enable/disable table and customize colors. • 🔔 Alerts: Toggle notifications on leadership changes. 🔎 Formula Basis Jensen’s Alpha (α) is estimated as: α = E − β × E where β = Cov(Ra, Rb) / Var(Rb), and Ra/Rb represent asset and benchmark returns, respectively. A positive α indicates outperformance relative to the risk-adjusted benchmark expectation. ✅ ⚠️ Disclaimer This script is for educational and analytical purposes only. It is NOT a signal. 🚫📉 It does not constitute financial advice, trading signals, or investment recommendations. 💬 The author is not responsible for any financial losses or trading decisions made based on this indicator. 🙏 Always perform your own analysis and use proper risk management. 🛡️
Indicador Pine Script®
por AriSai_TRW
Risk ModuleThis indicator provides a visual reference for position sizing and approximate stop and target placement. It supports trade planning by calculating equalized risk per trade and maintaining consistent exposure across different markets. For more information about the concept, see the post Position Sizing and Risk Management . Fixed Fractional Risk The indicator calculates the number of shares that can be traded to maintain consistent monetary risk. The formula is based on the distance between the current price and stop reference, adjusting position size proportionally. A closer stop results in a larger position size, while a wider stop results in a smaller one. Position Size = (Account Size × Risk %) ÷ (Entry Price – Stop Price) Stop and Target Stop placement is derived from volatility using the Average True Range (ATR). The target is plotted as a multiple of the stop distance, defining the risk-to-reward relationship in R units. Stop = Price ± ATR × Multiplier Target = Price ± (R × Risk Distance) Chart Elements The stop and target levels are plotted above and below the current price, with the stop marked by a red dot and the target by a green dot. The information table displayed on the chart shows the number of shares to trade, stop level, and target level. Setup and Configuration This configuration only needs to be set once, but can be adjusted later if preferred. 1. Start by setting the account size and risk percentage per trade to define the monetary amount risked on each trade. These values form the basis for position size calculation. 2. Set the ATR multiplier to determine stop distance, common values range between 1 and 3 ATR. Lower values place stops closer to price, increasing sensitivity but risking short-term noise. Higher values widen the stop, which reduces noise impact but extends time in risk. 3. Set the R-multiple to determine target distance relative to the stop. A value of 1 represents a 1:1 risk-to-reward relationship. Lower values reduce potential reward but tend to increase win rate, whereas higher values increase potential reward but tend to reduce win rate. The selection depends on system characteristics and trade expectancy. When the parameters are defined, the indicator displays the stop, target, and calculated position size on the chart. All that remains is to enter the trade with the number of shares shown in the table and place bracket orders at the plotted stop and target levels. Settings Overview Account Size / Risk %: Defines account capital and per-trade exposure. ATR Multiplier: Adjusts stop distance relative to volatility. R Multiple: Sets target distance relative to stop (risk-reward ratio). Position: Choose Long or Short direction. Table Position: Controls information table placement and scale.
Indicador Pine Script®
por StockLeave
MNQ TopStep 50K | Ultra Quality v3.0MNQ TopStep 50K | Ultra Quality v3.0 - Publish Summary 📊 Overview A professional-grade trading indicator designed specifically for MNQ futures traders using TopStep funded accounts. Combines 7 technical confirmations with 5 advanced safety filters to deliver high-quality trade signals while managing drawdown risk. 🎯 Key Features Core Signal System 7-Point Confirmation: VWAP, EMA crossovers, 15-min HTF trend, MACD, RSI, ADX, and Volume Signal Grading: Each signal is rated A+ through D based on 7 quality factors Quality Threshold: Adjustable minimum grade requirement (A+, A, B, C, D) Advanced Safety Filters (Customizable) Mean Reversion Filter - Prevents chasing extended moves beyond VWAP bands ATR Spike Filter - Avoids trading during extreme volatility events EMA Spacing Filter - Ensures proper trend separation (optional) Momentum Filter - Requires consecutive directional bars (optional) Multi-Timeframe Confirmation - Aligns with 15-min trend (optional) TopStep Risk Management Real-time drawdown tracking Position sizing calculator based on remaining cushion Daily loss limit monitoring Consecutive loss protection Max trades per day limiter Visual Components VWAP with 1σ, 2σ, 3σ bands EMA 9/21 with cloud fill 15-min EMA 50 for HTF trend Comprehensive metrics dashboard Risk management panel Filter status panel Detailed trade labels with entry, stops, and targets ⚙️ Default Settings (Balanced for Regular Signals) Technical Indicators Fast EMA: 9 | Slow EMA: 21 | HTF EMA: 50 (15-min) MACD: 10/22/9 RSI: 14 period | Thresholds: 52 (buy) / 48 (sell) ADX: 14 period | Minimum: 20 ATR: 14 period | Stop: 2x | TP1: 2x | TP2: 3x Volume: 1.2x average required Session Settings Default: 9:30 AM - 11:30 AM ET (adjustable) Avoids first 15 minutes after market open Customizable trading hours Safety Filters (Default Configuration) ✅ Mean Reversion: Enabled (2.5σ max from VWAP) ✅ ATR Spike: Enabled (2.0x threshold) ❌ EMA Spacing: Disabled (can enable for quality) ❌ Momentum: Disabled (can enable for quality) ❌ MTF Confirmation: Disabled (can enable for quality) Risk Controls Minimum Signal Quality: C (adjustable to A+ for fewer/better signals) Min Bars Between Signals: 10 Max Trades Per Day: 5 Stop After Consecutive Losses: 2 📈 Expected Performance With Default Settings: Signals per week: 10-15 trades Estimated win rate: 55-60% Risk-Reward: 1:2 (TP1) and 1:3 (TP2) With Aggressive Settings (Min Quality = D, All Filters Off): Signals per week: 20-25 trades Estimated win rate: 50-55% With Conservative Settings (Min Quality = A, All Filters On): Signals per week: 3-5 trades Estimated win rate: 65-70% 🚀 How to Use Basic Setup: Add indicator to MNQ 5-minute chart Adjust TopStep account settings in inputs Set your risk per trade percentage (default: 0.5%) Configure trading session hours Set minimum signal quality (Start with C for balanced results) Signal Interpretation: Green Triangle (BUY): Long signal - all confirmations aligned Red Triangle (SELL): Short signal - all confirmations aligned Label Details: Shows entry, stop loss, take profit levels, position size, and signal grade Signal Grade: A+ = Elite (6-7 points) | A = Strong (5) | B = Good (4) | C = Fair (3) Dashboard Monitoring: Top Right: Technical metrics and market conditions Top Left: Filter status (which filters are passing/blocking) Bottom Right: TopStep risk metrics and position sizing ⚡ Customization Tips For More Signals: Lower "Minimum Signal Quality" to D Decrease ADX threshold to 18-20 Lower RSI thresholds to 50/50 Reduce Volume multiplier to 1.1x Disable additional filters For Higher Quality (Fewer Signals): Raise "Minimum Signal Quality" to A or A+ Increase ADX threshold to 25-30 Enable all 5 advanced filters Tighten VWAP distance to 2.0σ Increase momentum requirement to 3-4 bars For TopStep Compliance: Adjust "Max Total Drawdown" and "Daily Loss Limit" to match your account Update "Already Used Drawdown" daily Monitor the Risk Panel for cushion remaining Use recommended contract sizing 🛡️ Risk Disclaimer IMPORTANT: This indicator is for educational and informational purposes only. Past performance does not guarantee future results All trading involves substantial risk of loss Use proper risk management and position sizing Test thoroughly in paper trading before live use The indicator does not guarantee profitable trades Adjust settings based on your risk tolerance and trading style Always comply with your broker's and TopStep's rules
Indicador Pine Script®
por elivgorelick
3
Risk-Reward Position SizerRisk-Reward Position Sizer – Features Checklist Purpose: A visual calculator and position sizing tool for day traders, providing realistic risk, stop-loss, take-profit, and reward-to-risk information based on account size and position constraints. Features: Flexible Risk Settings Set risk as a percentage of your account or a fixed dollar amount per trade. Automatically calculates position size based on desired risk and stop distance. Stop Loss Options Stop distance can be defined as a percent of entry price or a fixed price. Automatically adjusts stop distance when position is cash-limited to achieve your target risk. Take Profit Options TP can be defined as a fixed R multiple (e.g., 2R) or fixed absolute price. Cash-Limited Position Handling Optional “Cap Position to Account Size” prevents buying more shares than your cash allows. Shows actual achievable risk if your cash limits position size. Realistic Risk / Reward Calculations Calculates Actual Risk $ based on position size and stop distance. Calculates Projected Win $ based on take profit and position size. Calculates Actual Reward-to-Risk (R:R) ratio using actual stop and TP. Position Metrics Estimated quantity of shares/contracts to buy. Estimated position value. Estimated leverage used relative to account size. Top-Right Table Display Clear, compact table showing: Account size Target risk $ Actual risk $ Stop distance Quantity Position value Take profit and stop-loss prices Projected win $ and % Projected loss % Actual R:R Leverage Trading Decision Aid Gives traders a realistic snapshot of achievable risk and reward before entering a trade. Helps avoid the common trap of setting tight stops that don’t actually match desired account risk. Why It’s Useful: This indicator turns abstract risk/reward concepts into concrete, actionable numbers, helping day traders size positions safely, plan stops and targets realistically, and maintain consistent risk management across trades.
Indicador Pine Script®
por rovangosen