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Leverage

Global Debt-to-GDP DashboardI've created this macro indicator to monitor world debt. The Global Debt-to-GDP Dashboard helps you to track, compare, and analyze sovereign and private debt levels across 20 major economies. It provides institutional-grade debt analysis, allowing traders, long term investors and macro investors to identify systemic risks, divergences from global norms, and accelerating debt trends that could signal future economic instability or opportunities. What It Does This indicator transforms complex macroeconomic data into actionable insights to understand country leverage by: Tracking Dual Debt Metrics: Monitors both government debt-to-GDP and private debt-to-GDP ratios for 20 countries. This provides a complete picture of total leverage in each economy. Divergence Analysis: Compares each country's debt levels against the world average (calculated from selected countries), instantly highlighting which economies are over-leveraged or under-leveraged relative to global norms. Velocity Tracking: Calculates the rate of change in debt divergence, identifying which countries are rapidly increasing or decreasing their debt burden. Risk Assessment: Automatically categorizes countries by risk level using color-coded visual indicators Key Features 20 Major Economies: USA, China, Japan, Germany, UK, France, Italy, Canada, Spain, Australia, South Korea, Brazil, India, Mexico, Switzerland, Singapore, Norway, Saudi Arabia, Russia, and Turkey Simultaneous Visualization: Plot all selected countries on a single chart with unique color coding for easy identification Dynamic Country Selection: Enable/disable countries to focus on specific regions or economic blocs Three Divergence Analysis Modes Government Only: Analyze sovereign debt divergence from government debt average Private Only: Focus exclusively on private sector leverage (excludes countries without private debt data) Total (Gov + Private): Comprehensive view of total economy-wide debt burden Use Cases For Macro Traders Identify Sovereign Risk: Spot countries with unsustainable debt trajectories before credit events Currency Pairs: Correlate debt divergence with currency strength/weakness Bond Markets: Anticipate yield spread movements based on relative debt positions Risk-On/Risk-Off: Use global debt velocity as a systemic risk indicator For Long-Term Investors Country Allocation: Make informed decisions about geographic exposure Risk Management: Avoid over-exposure to high-debt economies Opportunity Identification: Find under-leveraged economies with growth potential Portfolio Rebalancing: Use debt trends as signals for portfolio adjustments How to Use Initial Setup: Select the countries you want to monitor from the Settings panel Choose Divergence Type: Select Government Only, Private Only, or Total debt analysis Configure Display: Adjust table position, text size, and sorting preferences Set Alerts (Optional): Define your alert thresholds and enable notifications Table Risk Scores: 🔴🔴🔴 Extreme Risk: Divergence > +100pp above average 🔴🔴 High Risk: Divergence > +50pp above average 🟠 Moderate Risk: Divergence > 0pp above average 🟢 Low Risk: Divergence > -50pp below average 🟢🟢 Very Low Risk: Divergence < -50pp below average NOTE: "Low Risk" and low-debt might also mean that a country is unable to finance through the issuance of government debt. This is not necessarily a good thing and might not correspond to low risk. Velocity Indicators: ⬆️⬆️⬆️ Rapid Acceleration: +5pp or more per period ⬆️⬆️ Strong Acceleration: +2 to +5pp per period ⬆️ Moderate Increase: +0.5 to +2pp per period ➡️ Stable: -0.5 to +0.5pp per period ⬇️ Moderate Decrease: -2 to -0.5pp per period ⬇️⬇️ Strong Deceleration: -5 to -2pp per period ⬇️⬇️⬇️ Rapid Deceleration: -5pp or less per period Important Notes Data Availability: Some countries lack private debt data (e.g., China, India, Brazil). World Average Calculation: The world average is dynamically calculated based on your selected countries, not a fixed global figure. This allows for custom peer group comparisons. Percentage Points vs Percentages: All divergence values are in percentage points (pp), representing the absolute difference from the average, not a relative percentage change. Let me know if you have comments or suggestions. Truly yours, Henrique Centieiro
อินดิเคเตอร์ Pine Script®
โดย HenriqueCentieiro
Liquidation Heatmap by RumiancevLiquidation Heatmap by Rumiancev Overview Liquidation Heatmap is an open-source visual map of estimated liquidation zones built from activity spikes . When the script detects an unusually large spike, it projects liquidation levels for multiple leverage tiers and aggregates them into horizontal price “bins”. Each bin accumulates weight over time and is displayed as a color gradient: • Brighter / hotter = higher accumulated weight • Darker / colder = lower accumulated weight Important: This is not an exchange liquidation feed and it does not display “real liquidation prices”. It is a proxy model designed to visualize where liquidation pressure could be clustering based on abnormal market activity. Why BTCUSDT.P is recommended For the most consistent and “liquidation-relevant” behavior, use a perpetual futures symbol such as BTCUSDT.P . Perpetual markets provide Open Interest , so the script can use OI Delta (change in OI) as the spike stream. OI delta typically reflects leveraged positioning changes (build-up / flushes) more directly than spot volume. • On perpetuals → OI data is available → spikes are usually cleaner for this model • On spot → OI is not available → the script may fall back to volume, which can be noisier If needed, set OI Symbol Override manually (examples are shown in the input tooltip). How it works (logic) 1) Select a spike stream • AUTO : uses OI Delta if available, otherwise Volume • OI : forces OI Delta • VOL : forces Volume only 2) Detect spike events The script measures abnormal activity using a Z-score style approach on the absolute stream: • Spike Lookback defines the baseline window • Sensitivity maps to a threshold (lower = more events, higher = fewer events) • Min bars between events optionally reduces clustering on lower timeframes 3) Project liquidation prices For each spike event, liquidation estimates are calculated for up to three leverage tiers: • Long liquidations are projected below the reference price • Short liquidations are projected above the reference price 4) Bin, accumulate, and colorize Projected levels are snapped into bins using Bin Scale (ticks) . Bin weight is accumulated and displayed as a gradient between Low density and High density . 5) Freeze on touch When price touches a bin (wick or close, depending on settings), the bin is frozen : • it stops updating • it becomes dotted / high transparency This keeps a lightweight history of zones that have been interacted with. How to read the map • Bins below price often represent potential long-liquidation pressure zones • Bins above price often represent potential short-liquidation pressure zones • Brighter bins = more accumulated spike weight → potentially more crowded zone • Frozen dotted bins = price already touched that zone (historical interaction) Timeframes (recommendations) This indicator runs on any timeframe, but density/noise changes significantly. Best balance (recommended): • 15m / 1H / 4H — good signal-to-noise and clean structure Higher timeframes (cleaner, fewer zones): • 12H / 1D — fewer events, more “macro” zones Lower timeframes (noisier by nature): • 1m / 3m / 5m — more spikes and more bins To reduce clutter on low TF, consider: • increasing Sensitivity (e.g., 14–18) • enabling Min bars between events (e.g., 10–30) • increasing Bin Scale (ticks) (thicker bins → fewer levels) • enabling Keep only local range bins Inputs (what each setting does) Source • Source Mode : AUTO / VOL / OI • OI Symbol Override : manual OI source if AUTO is not suitable Event (Spike) Filter • Spike Lookback : baseline window for mean/stdev • Sensitivity : lower = more events, higher = fewer events • Use high/low for touch test : wick-based touches • Min bars between events : reduces spike clustering Liquidation Levels (bins) • Reference Price : base price for projections (close/hl2/etc.) • Leverage 1/2/3 : leverage tiers (set to 0 to disable any tier) • Bin Scale (ticks) : bin thickness (bigger = fewer bins) • Extend (bars) : how long active bins extend • Max active bins per side : cap for active bins • Dispersion (%) : splits part of the weight to the opposite side • Keep only local range bins + Local range lookback : trims bins far from recent range Visual • Gradient colors, frozen transparency, legend, and debug marker Limitations / Disclaimer This script is provided for research and educational purposes only . It is not financial advice . The plotted zones are estimates derived from a simplified model (spike detection + leverage projections). Results depend on symbol, exchange data availability (OI), and timeframe.
อินดิเคเตอร์ Pine Script®
โดย Rumiancev
Sigmoid Risk AllocatorThe Sigmoid Risk Allocator is a dynamic position sizing indicator that tells you how much of your capital to allocate based on current market conditions. Unlike simple "risk-on/risk-off" signals, this indicator gives you smooth, gradual transitions based on a sigmoid function. Why a Sigmoid Curve? Most position sizing approaches use fixed thresholds: "If drawdown > 20%, buy. Otherwise, don't." This creates all-or-nothing decisions. Using the sigmoid (S-curve) makes this decision different. It creates a smooth transition where: Small drawdowns → Stay near your baseline allocation Moderate drawdowns → Gradually increase exposure Large drawdowns → Approach maximum allocation The sigmoid curve naturally "saturates" at the extremes, preventing you from going all-in too early or panicking out too fast. This is very useful to meek traders psychology and risk management in check. What's a Sigmoid Function? The sigmoid function is a mathematical S-curve defined as: σ(x) = 1 / (1 + e^(-x)) This formula takes any input value and smoothly maps it to a number between 0 and 1. The curve has three key properties that make it ideal for position sizing in investing: Smooth transitions: No sudden jumps. Allocation changes gradually. Saturation at extremes: The curve flattens near 0 and 1, preventing overreaction and overexposure. Sensitive in the middle: Most of the action happens around the midpoint. To convert this into an allocation percentage, the indicator uses: Allocation = α_min + (α_max - α_min) × σ(k × (Risk - Midpoint)) Where: - `α_min` = Your minimum allocation (default 50%) - `α_max` = Your maximum allocation (default 100%) - `Risk` = Current risk metric (drawdown %, volatility, or Kelly %) - `Midpoint` = The risk level where allocation sits halfway between min and max (default 15%) - `k` = Steepness—how quickly allocation changes around the midpoint Example : With defaults, if drawdown hits 15% (the midpoint), your allocation will be 75% (halfway between 50% and 100%). As the drawdown increases beyond 15%, the allocation curves toward 100%. As it decreases toward 0%, allocation curves toward 50%. Cool, isn't it? Asymmetric Response: Fast In, Slow Out The indicator uses different steepness values for scaling in vs. scaling out. This is great to increase trend following. This is something I'm proud of too in this indicator. k_increase = 30 (steep curve): When drawdowns appear, allocation ramps up quickly to catch the opportunity k_decrease = 5 (slower curve): When conditions normalize, allocation decreases slowly to avoid selling the rebound This asymmetry reflects how markets behave—drawdowns often overshoot fundamentals (rewarding quick entries), while recoveries tend to be more orderly (rewarding patience on exits). Three Risk Metrics You can choose what drives your allocation: Drawdown (Default) Volatility - Scales your position inversely to current market volatility. Kelly Criterion - Automatically calculates optimal position size. The indicator applies a conservative "half Kelly" by default. Use Cases Position sizing for swing trading or trend following Risk management overlay for any existing strategy Drawdown-based DCA (dollar cost averaging) decisions Volatility-adjusted exposure management Feel free to provide feedback and share your thoughts! - Henrique Centieiro
อินดิเคเตอร์ Pine Script®
โดย HenriqueCentieiro
[Saga Trading] Liquidation Leverages ProThis indicator is designed to provide context on leverage-related market risk, not trading signals. Its purpose is to help traders visualize price areas where leveraged positions become vulnerable due to increased exposure, rather than to predict forced events. By mapping zones where leverage sensitivity increases, the script highlights areas of potential instability, where price reactions may accelerate due to risk management constraints, margin pressure, or position adjustment. These zones do not imply direction and should not be interpreted as targets. Instead, they offer risk awareness, helping traders assess where market movement may become less stable or more reactive. This tool is intended as a contextual risk-mapping indicator, allowing traders to better evaluate exposure when price approaches leverage-sensitive areas. It does not provide buy or sell signals and should be used in conjunction with price action and market structure analysis.
อินดิเคเตอร์ Pine Script®
โดย KevSagaT
Sigmoid Allocation Indicator & DashboardTL;DR This sigmoid-based allocation indicator tells you percentage of your portfolio to invest based on how much the market has dropped. Market at all-time high? → Stay defensive, invest less (e.g., 30%) Market crashed hard? → Get aggressive, invest more (e.g., 100%) The "sigmoid" part just means the transition between these two extremes follows a smooth S-shaped curve. Description This indicator is a sigmoid-based allocation system that dynamically adjusts a portfolio exposure based on market drawdown. It compares multiple steepness curves (K values) to find your optimal risk profile for leveraged ETF strategies, but it can also be used to scale in-out from stocks, crypto and to understand whether to use leverage or not. The Sigmoid Allocation Dashboard helps you to dynamically adjust a portfolio allocation based on how much a market has dropped from its all-time high. I've implemented it using a sigmoid (S-curve) function, that dynamically calculates the optimal allocation percentages. Depending on the market conditions, the S curves transition between defensive and aggressive allocations. The Math Behind It (if you are a geek like me) This indicator uses the sigmoid function to create smooth S-curve transitions: α(D) = α_min + (α_max - α_min) × σ(k × (D - D_mid)) Where: σ(x) = 1 / (1 + e^(-x)) ← Standard sigmoid function You can also check it here: // Sigmoid function: σ(x) = 1 / (1 + e^(-x)) sigmoid(float x) => 1.0 / (1.0 + math.exp(-x)) // Alpha calculation: α(D) = α_min + (α_max - α_min) × σ(k × (D - D_mid)) calcAlpha(float drawdown, float k, float a_min, float a_max, float d_midpoint) => sig_input = k * (drawdown - d_midpoint) / 100.0 a_min + (a_max - a_min) * sigmoid(sig_input) User parameters (you can tweak this): Allocation Min (%): Your baseline allocation when markets are at ATH (default: 30%) Allocation Max (%): Your maximum allocation during deep drawdowns (default: 100%) D_mid (%): The drawdown level where you want to be at the midpoint (default: 25%) Why do I like sigmoid and not a linear line? Unlike linear models, the sigmoid creates "floors" and "ceilings" for your allocation. It transitions smoothly, no sudden jumps, and you never exceed your defined min/max bounds. Understand the K Values (Steepness) The K parameter controls how quickly your allocation shifts from defensive to aggressive. Lower K (for example K=5) will give you a gradual transition, but at 0% drawdown you are already at a 46% allocation. A higher like (like K=40) will give you a sharp transition, but at 0% drawdown you are close to the minimum allocation. On the other hand, a higher K will give close to 100% allocation when the markets are at new lows. The example below illustrates this well, then the S&P 500 reached new lows in October 2022: Different K values will affect the sigmoid curves (and you allocations differently). The chart below illustrates well how K affects the sigmoid curves: Read the Dashboard The main dashboard shows: Current drawdown from ATH Allocation % for each K value Suggested action (Defensive → MAX LONG) Use the Reference Chart The static reference panel shows what your allocation would be at various drawdown levels (0%, 10%, 20%, 30%, 40%, 50%), helping you plan ahead. Identify Zones The color-coded chart background shows: - 🟢 Green Zone: Aggressive positioning - "Buy the Dip" - 🟡 Yellow Zone: Transition zone - Scaling in/out - 🔴 Red Zone: Defensive positioning - Protect ya gains Use Cases Use case 1: Leveraged ETF Portfolio Management (this is my main use case) When holding leveraged ETFs like TQQQ or UPRO, volatility makes it important to: - Reduce exposure near all-time highs (when crashes hurt most) - Increase exposure during drawdowns (when recovery potential is highest) Example Strategy: - At ATH: Hold 30% TQQQ, 70% cash/bonds or other uncorrelated assets - At 25% drawdown: Hold 65% TQQQ, 35% cash/bonds - At 40%+ drawdown: Hold 100% TQQQ Use case 2: Diversified Leveraged Portfolio Compare different K values for different assets: - Use K = 10 for broad market (QQQ/SPY exposure via TQQQ/UPRO) - Use K = 25 for sector bets (TECL, SOXL, TMF) that you want to scale into faster Use case 3: Systematic Rebalancing Signals Use the alerts to trigger rebalancing: - Alert when K3 allocation crosses above 90% (time to add) - Alert when drawdown exceeds your D_mid threshold - Alert when market returns to within 5% of ATH Tips for Best Results It works best in longer time frames Adjust the ATR lookback window Match your risk tolerance level I use this for index investing and stocks and haven't tried with crypto Thanks for using the indicator and let me know if you have any feedback :) - Henrique Centieiro
อินดิเคเตอร์ Pine Script®
โดย HenriqueCentieiro
3
Crypto ATR Position Sizer + LeverageThis indicator is a "heads-up display" for crypto traders who need real time risk management without manually calculating position sizes. It uses Average True Range (ATR) to dynamically place Stop Losses based on current market volatility and automatically calculates the exact position size needed to respect your risk percentage. ​Key Features: ​Dynamic Risk Management: Stop Loss and Take Profit levels adjust automatically based on market volatility (ATR). ​Auto-Position Sizing: Calculates the exact Quantity (in coins) and Position Value (in $) to ensure you never risk more than your defined percentage (e.g., 1% or 2%). ​Leverage Calculator: Instantly sees the "Required Leverage" needed to execute the trade size relative to your account balance. ​Crypto Precision: Displays up to 8 decimal places, making it compatible with both Bitcoin and low-sat altcoins. ​Toggable Direction: Switch between Long and Short biases instantly via the settings menu. ​How to Use: ​Add the indicator to your chart. ​Open Settings and input your Account Balance and Risk %. ​Choose your direction (Long or Short) using the checkboxes. ​The label will display your Entry, SL, TP, Coin Quantity, and Required Leverage in real-time.
อินดิเคเตอร์ Pine Script®
โดย MonkeyPhone
DCA Ladder CalculatorThis script is a DCA (Dollar-Cost Averaging) Ladder Calculator with Risk & Leverage Management baked in. It’s designed for both LONG and SHORT positions, and helps you: 🎯 Strategically scale into positions across multiple entry points 🔐 Control risk exposure via defined capital allocation ⚖️ Utilize leverage responsibly — for efficiency, not destruction 🧮 Visualize risk, stop loss level, and entry distribution 🔁 Adapt to trend reversals or key zones, especially when combined with reversal indicators or higher timeframe signals 🧠 How It Works This tool takes a capital allocation approach to building a ladder of positions: 1. You define: - Portfolio value - Risk per trade (as %) - Leverage - Number of DCA levels - Entry multiplier (e.g. 1x, 2x, 4x...) 2. The script then: - Calculates total margin to risk = Portfolio × Risk % - Calculates total leveraged position size = Margin × Leverage - Distributes entries according to exponential weights (1x, 2x, 4x...), totaling 7 for 3 levels - Calculates per-entry: - Entry price (based on price zone spacing) - Multiplier - Exact margin per entry - Leverage per entry (margin × leverage) - Computes: - Average entry price (margin-weighted) - Approximate stop loss level based on recent ATR and price structure - % drawdown to SL - Total margin and position size 3. Displays all this in a clean on-chart table. 📈 How to Use It 1. Apply the indicator to a chart (default: 1D — ideal for clean zones). 2. Configure your: - Portfolio Value (total trading capital) - Risk per Trade (%) (your acceptable loss) - Leverage (exchange or strategy-based) - DCA Levels (e.g. 3 = anchor + 2 entries) - Multiplier (typically 2.0 for doubling) 3. Choose LONG or SHORT mode depending on direction. 4. The table will show: - Entry price ladder - Margin used per entry - Total position size - Approx. stop loss (where your full risk is defined) Use in conjunction with price action, S/R zones, trendline breaks, volume divergence, or reversal indicators. ✅ Best Practices for Using This Tool - Leverage is a tool, not a weapon. Use it to scale smartly — not recklessly. - Use fewer, higher-conviction entries. Don’t blindly ladder; combine with price structure and signals. - Stick to your risk percent. Never risk more than you can afford to lose. Let this calculator enforce discipline. - Combine with other confirmation tools, like RSI divergence, momentum shifts, OB zones, etc. - Avoid martingale-style over-exposure. This is not a gambling tool — it’s for capital efficiency. 🛡️ What This Tool Does NOT Do - This is not a trade signal indicator. - It does not place trades or auto-manage positions. - It does not replace personal responsibility or strategy — it's a tool to help apply structure. ⚠️ Disclaimer This script is for educational and informational purposes only. It does not constitute financial advice, nor is it a recommendation to buy or sell any financial instrument. Always consult a licensed financial advisor before making investment decisions. Use of leverage involves high risk and can lead to substantial losses. The author and publisher assume no liability for any trading losses resulting from use of this script.
อินดิเคเตอร์ Pine Script®
โดย RWCS_LTD
1
Position Size Calculator + Live R/R Panel — SMC/ICT (@PueblaATH)Position Size + Live R/R Panel — SMC/ICT (@PueblaATH) Position Size + Live R/R Panel — SMC/ICT (@PueblaATH) is a professional-grade risk management and execution module built for Smart Money Concepts (SMC) and ICT Traders who require accurate, repeatable, institution-style trade planning. This tool delivers precise position sizing, R:R modeling, leverage and margin projections, fee-adjusted PnL outcomes, and real-time execution metrics—all directly on the chart. Optimized for crypto, forex, and futures, it provides scalpers, day traders, and swing traders with the clarity needed to execute high-quality trades with confidence and consistency. What the Indicator Does Institutional Position Sizing Engine Calculates position size based on account balance, % risk, and SL distance. Supports custom minimum lot size rounding across crypto, FX, indices, and derivatives. Intelligent direction logic (Auto / Long / Short) based on SMC/ICT structure. Advanced Risk/Reward & Profit Modeling Real-time R:R ratio using actual rounded position size. Live PnL readout that updates with price movements. Gross & net profit projections with full fee deduction. Execution Planning with Draggable Levels Entry, SL, and TP levels fully draggable for fast scenario modeling. Automatic projected lines backward/forward with clean label alignment. TP and SL tags include % movement from Entry, ideal for SMC/ICT journaling. Precise modeling of real exchange fee structures Maker fee per side Taker fee per side Mixed fee modes (Maker entry, Taker exit, Average, etc.) Leverage & Margin Forecasting Margin requirements displayed for 3 customizable leverage settings. Helps traders understand capital commitment before executing the trade. Useful for futures, crypto perps, and CFD setups. Clean HUD Panel for Rapid Decision-Making A full professional trading panel displays: Target & actual risk Position size Entry / SL / TP TP/SL percentage distance Gross profit Net profit (after fees) Fees @ TP and @ SL Live PnL Margin requirements Optimized for SMC & ICT Workflows Perfect for traders using: Breakers, FVGs, OBs Liquidity sweeps Session models Precision entries (OTE, Displacement, Rebalancing) Leverage-based execution (crypto perps, futures) How to Use It Attach the indicator to your chart. Set account balance, risk %, fee model, and leverage presets. Drag Entry, SL, and TP to shape the setup. View instant calculations of: Position size; R:R; Net PnL after fees; Margin required Use it as your pre-trade checklist & execution model. Originality & Credits This script is an original creation by @PueblaATH, released under the MPL 2.0 license. It does not copy, modify, or repackage any existing TradingView code. All logic—including the fee engine, margin calculator, responsive HUD, dynamic risk model, and visual execution system—is authored specifically for this indicator.
อินดิเคเตอร์ Pine Script®
โดย PueblaATH
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent. Table of contents What the script does at a glance Momentum, what it is, how to know if it is present Volatility targeting, why and how it is done here Portfolio construction modes: Long Only, Short Only, Delta Neutral Regime filter and when the strategy goes to cash Transaction cost modelling in this script Backtest metrics and definitions Performance attribution chart Monte Carlo simulation Scatter plot analysis modes Asset allocation pie chart Inputs, presets, and deployment checklist Suggested workflow 1) What the script does at a glance Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility. Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter. Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite. Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views. 2) Momentum: definition, detection, and validation Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints. Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization. How to know momentum is present . Leaders and laggards persist across adjacent windows rather than flipping every bar. Spread between average momentum of leaders and laggards is materially positive in sample. Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak. Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries. Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime. 3) Volatility targeting: purpose and implementation here Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk. Computation in this script (per asset, rolling): Return series ret = log(price/price ). Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays). Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0). This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero. Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations. Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure. 4) Portfolio construction modes Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties. Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult. Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs. Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3). Notes . The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage. If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta. 5) Regime filter A fast EMA(12) vs EMA(21) filter gates exposure. Long Only active when EMA12 > EMA21. Otherwise the book is set to cash. Short Only active when EMA12 < EMA21. Otherwise cash. Delta Neutral is always active. This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar. 6) Transaction cost modelling There are two cost touchpoints in the script. Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar. Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots. Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders. Practical advice . If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue. For crypto perp microcaps, stress higher cost assumptions and add slippage buffers. If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag. 7) Backtest metrics and definitions The script computes a standard set of portfolio statistics once the start date is reached. Net Profit percent over the full test. Max Drawdown percent, tracked from running peaks. Annualized Mean and Stdev using the chosen trading day count. Variance is the square of annualized stdev. Sharpe uses daily mean adjusted by risk-free rate and annualized. Sortino uses downside stdev only. Omega ratio of sum of gains to sum of losses. Gain-to-Pain total gains divided by total losses absolute. CAGR compounded annual growth from start date to now. Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM. Skewness of daily returns. VaR 95 linear-interpolated 5th percentile of daily returns. CVaR average of the worst 5 percent of daily returns. Benchmark Buy-and-Hold equity path for comparison. 8) Performance attribution Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels. 9) Monte Carlo simulation The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed. Normal uses Box-Muller with your seed. Good for quick, smooth envelopes. Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption. Bands show 10th, 25th, 75th, 90th percentiles and the path mean. 10) Scatter plot analysis Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels. Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite. Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations. Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta. Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk. 11) Asset allocation pie chart Builds a wheel of current allocations. Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent. Short Only, weights show the short book as positive slices that sum to 100 percent. Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional. Labels can show asset, percent, and current leverage. 12) Inputs and quick presets Core Portfolio Strategy . Long Only, Short Only, Delta Neutral. Initial Capital . For equity scaling in the panel. Trading Days/Year . 252 for stocks, 365 for crypto. Target Volatility . Annualized, drives volMult. Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above. Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier. Start Date . Ensure every symbol has data back to this date to avoid bias. Benchmark . Used for alpha, beta, and B&H line. Diagnostics Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill. Attribution panel. Toggle winners only to focus on what matters. Monte Carlo mode with Normal or Bootstrap and confidence bands. Scatter plot type and styling, labels, and portfolio star. Pie chart and labels for current allocation. Presets Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands. Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward. Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta. 13) Suggested workflow Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees. Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe. Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity. Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear. Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it. Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence. Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier. Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs. Important implementation details mapped to code Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting. Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult. Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively. Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate. Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison. Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures. Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds. Final notes Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call. Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it. Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
อินดิเคเตอร์ Pine Script®
โดย BackQuant
Margin Cost Calculator Screener - Taylor V1.2# Leverage Position Cost Calculator & Stop Lose Cost Screener # Designed to provide traders with crucial insights into their leveraged positions directly on the TradingView chart. Key Features: > Dynamic Display: Choose to view only the estimated entry cost, or a comprehensive overview including potential losses at specific stop-loss levels, and a custom remark. > Contract Size Input: Easily specify the contract size for your trades. > Leverage Level Input: Set your desired leverage level, with helpful tooltips explaining the margin requirements for various leverage ratios (e.g., 25x, 10x, 5x) and an included fee estimate. > Cost Calculation: Accurately calculates the estimated entry cost for your position based on the current market price, contract size, and leverage. > Stop-Loss Projections: It projects potential losses for stop-loss orders set at 3% and 5% below the entry price, helping you manage risk effectively. > Clear Table Visualization: All calculated data is presented in a clean, organized table anchored to the bottom-left of your chart, making it easy to reference at a glance. > Symbol Identification: Automatically displays the short ticker symbol for the asset you are analyzing. This tool is invaluable for traders who utilize leverage and need a quick, visual way to understand their financial exposure and potential outcomes before entering or managing a trade.
อินดิเคเตอร์ Pine Script®
โดย TaylorTAL
TitanGrid L/S SuperEngineTitanGrid L/S SuperEngine Experimental Trend-Aligned Grid Signal Engine for Long & Short Execution 🔹 Overview TitanGrid is an advanced, real-time signal engine built around a tactical grid structure. It manages Long and Short trades using trend-aligned entries, layered scaling, and partial exits. Unlike traditional strategy() -based scripts, TitanGrid runs as an indicator() , but includes its own full internal simulation engine. This allows it to track capital, equity, PnL, risk exposure, and trade performance bar-by-bar — effectively simulating a custom backtest, while remaining compatible with real-time alert-based execution systems. The concept was born from the fusion of two prior systems: Assassin’s Grid (grid-based execution and structure) + Super 8 (trend-filtering, smart capital logic), both developed under the AssassinsGrid framework. 🔹 Disclaimer This is an experimental tool intended for research, testing, and educational use. It does not provide guaranteed outcomes and should not be interpreted as financial advice. Use with demo or simulated accounts before considering live deployment. 🔹 Execution Logic Trend direction is filtered through a custom SuperTrend engine. Once confirmed: • Long entries trigger on pullbacks, exiting progressively as price moves up • Short entries trigger on rallies, exiting as price declines Grid levels are spaced by configurable percentage width, and entries scale dynamically. 🔹 Stop Loss Mechanism TitanGrid uses a dual-layer stop system: • A static stop per entry, placed at a fixed percentage distance matching the grid width • A trend reversal exit that closes the entire position if price crosses the SuperTrend in the opposite direction Stops are triggered once per cycle, ensuring predictable and capital-aware behavior. 🔹 Key Features • Dual-side grid logic (Long-only, Short-only, or Both) • SuperTrend filtering to enforce directional bias • Adjustable grid spacing, scaling, and sizing • Static and dynamic stop-loss logic • Partial exits and reset conditions • Webhook-ready alerts (browser-based automation compatible) • Internal simulation of equity, PnL, fees, and liquidation levels • Real-time dashboard for full transparency 🔹 Best Use Cases TitanGrid performs best in structured or mean-reverting environments. It is especially well-suited to assets with the behavioral profile of ETH — reactive, trend-intraday, and prone to clean pullback formations. While adaptable to multiple timeframes, it shows strongest performance on the 15-minute chart , offering a balance of signal frequency and directional clarity. 🔹 License Published under the Mozilla Public License 2.0 . You are free to study, adapt, and extend this script. 🔹 Panel Reference The real-time dashboard displays performance metrics, capital state, and position behavior: • Asset Type – Automatically detects the instrument class (e.g., Crypto, Stock, Forex) from symbol metadata • Equity – Total simulated capital: realized PnL + floating PnL + remaining cash • Available Cash – Capital not currently allocated to any position • Used Margin – Capital locked in open trades, based on position size and leverage • Net Profit – Realized gain/loss after commissions and fees • Raw Net Profit – Gross result before trading costs • Floating PnL – Unrealized profit or loss from active positions • ROI – Return on initial capital, including realized and floating PnL. Leverage directly impacts this metric, amplifying both gains and losses relative to account size. • Long/Short Size & Avg Price – Open position sizes and volume-weighted average entry prices • Leverage & Liquidation – Simulated effective leverage and projected liquidation level • Hold – Best-performing hold side (Long or Short) over the session • Hold Efficiency – Performance efficiency during holding phases, relative to capital used • Profit Factor – Ratio of gross profits to gross losses (realized) • Payoff Ratio – Average profit per win / average loss per loss • Win Rate – Percent of profitable closes (including partial exits) • Expectancy – Net average result per closed trade • Max Drawdown – Largest recorded drop in equity during the session • Commission Paid – Simulated trading costs: maker, taker, funding • Long / Short Trades – Count of entry signals per side • Time Trading – Number of bars spent in active positions • Volume / Month – Extrapolated 30-day trading volume estimate • Min Capital – Lowest equity level recorded during the session 🔹 Reference Ranges by Strategy Type Use the following metrics as reference depending on the trading style: Grid / Mean Reversion • Profit Factor: 1.2 – 2.0 • Payoff Ratio: 0.5 – 1.2 • Win Rate: 50% – 70% (based on partial exits) • Expectancy: 0.05% – 0.25% • Drawdown: Moderate to high • Commission Impact: High Trend-Following • Profit Factor: 1.5 – 3.0 • Payoff Ratio: 1.5 – 3.5 • Win Rate: 30% – 50% • Expectancy: 0.3% – 1.0% • Drawdown: Low to moderate Scalping / High-Frequency • Profit Factor: 1.1 – 1.6 • Payoff Ratio: 0.3 – 0.8 • Win Rate: 80% – 95% • Expectancy: 0.01% – 0.05% • Volume / Month: Very high Breakout Strategies • Profit Factor: 1.4 – 2.2 • Payoff Ratio: 1.2 – 2.0 • Win Rate: 35% – 60% • Expectancy: 0.2% – 0.6% • Drawdown: Can be sharp after failed breakouts 🔹 Note on Performance Simulation TitanGrid includes internal accounting of fees, slippage, and funding costs. While its logic is designed for precision and capital efficiency, performance is naturally affected by exchange commissions. In frictionless environments (e.g., zero-fee simulation), its high-frequency logic could — in theory — extract substantial micro-edges from the market. However, real-world conditions introduce limits, and all results should be interpreted accordingly.
อินดิเคเตอร์ Pine Script®
โดย AssassinsGrid
5
Mxwll Hedge Suite [Mxwll]Hello Traders! The Mxwll Hedge Suite determines the best asset to hedge against the asset on your chart! By determining correlation between the asset on your chart and a group of internally listed assets, the Mxwll Hedge Suite determines which asset from the list exhibits the highest negative correlation, and then determines exactly how many coins/shares/contracts of the asset must be bought to achieve a perfect 1:1 hedge! The image above exemplifies the process! The purple box on the chart shows the eligible price action used to determine correlation between the asset on my chart (BTCUSDT.P) and the list of cryptocurrencies that can be used as a hedge! From this price action, the coin determined to have to greatest negative correlation to BTCUSDT.P is FTMUSD. The image above further outlines the hedge table located in the bottom-right corner of your chart! The hedge table shows exactly how many coins you’d need to purchase for the hedge asset at various leverages to achieve a perfect 1:1 hedge! Hedge Suite works on any asset on any timeframe! And that’s all! A short and sweet script that is hopefully helpful to traders looking to hedge their positions with a negatively correlated asset! Thank you, Traders!
อินดิเคเตอร์ Pine Script®
โดย MXWLL-Capital-Trading
4
LRS-Strategy: 200-EMA Buffer & Long/Short Signals LRS-Strategy: 200-EMA Buffer & Long/Short Signals This indicator is designed to help traders implement the Leveraged Return Strategy (LRS) using the 200-day Exponential Moving Average (EMA) as a key trend-following signal. The indicator offers clear long and short signals by analyzing the price movements relative to the 200-day EMA, enhanced by customizable buffer zones for increased precision. Key Features: 200-Day EMA: The main trend indicator. When the price is above the 200-day EMA, the market is considered in an uptrend, and when it is below, it indicates a downtrend. Customizable Buffer Zones: Users can define a percentage buffer around the 200-day EMA (default is 3%). The upper and lower buffer zones help filter out noise and prevent premature signals. Precise Long/Short Signals: Long Signal: Triggered when the price moves from below the lower buffer zone, crosses the 200-day EMA, and then breaks above the upper buffer zone. Short Signal: Triggered when the price moves from above the upper buffer zone, crosses the 200-day EMA, and then breaks below the lower buffer zone. Alternating Signals: Ensures that a new signal (long or short) is only generated after the opposite signal has been triggered, preventing multiple signals of the same type without a reversal. Clear Visual Aids: The indicator displays the 200-day EMA and buffer zones on the chart, along with buy (long) and sell (short) signals. This makes it easy to track trends and time entries/exits. How to Use: Long Entry: Look for the price to move below the lower buffer, cross the 200-day EMA from below, and then break out of the upper buffer to confirm a long signal. Short Entry: Look for the price to move above the upper buffer, cross below the 200-day EMA, and then break below the lower buffer to confirm a short signal. This indicator is perfect for traders who prefer a structured, trend-following approach, using clear rules to minimize noise and identify meaningful long or short opportunities.
อินดิเคเตอร์ Pine Script®
โดย k_j_witt
Liquidations [ChartPrime]Liquidations Indicator: The Liquidations indicator is a powerful tool designed to help traders identify significant liquidation levels in financial markets. By analyzing volume data over a specified lookback period, the indicator highlights potential areas where market participants with high leverage positions may face liquidation, providing valuable insights into market dynamics. Usage: Traders can use the Liquidations indicator to: ◈ Identify liquidity grab opportunities: Liquidation levels often attract price action as market participants with leveraged positions face the risk of forced liquidation. Traders can anticipate price movements as the market aims to trigger these stops, potentially leading to rapid price movements or reversals. ◈ Confirm trend strength: A cluster of liquidation levels in the same direction as the prevailing trend may confirm the strength of the trend, while divergences between liquidation levels and price movements may signal potential trend reversals. Settings: ◈ Previous Value Bars Back: Specifies the number of previous bars used in calculating the liquidation levels. ◈ Show Leverage: Allows users to selectively display liquidation levels for different leverage multiples, including 5x, 10x, 25x, 50x, and 100x. ◈ Liquidation Levels Width: Sets the width of the lines representing liquidation levels on the chart. ◈ Short Liquidations Color: Specifies the color of the lines representing short liquidation levels. ◈ Long Liquidations Color: Specifies the color of the lines representing long liquidation levels. ◈ Bar Color: Sets the color of the background bar when the indicator is active. Visual Representation: ◈ Liquidation levels are plotted as horizontal lines on the chart, with different colors representing short and long liquidation levels. ◈ Each liquidation level is labeled with the corresponding leverage multiple (e.g., 5x, 10x, etc.). A dashboard displays the active liquidation levels for each leverage multiple, allowing traders to quickly assess the current market conditions. ◈ Time Window allows users to cut off unnecessary part of the chart and concentrate on a current active part of the chart to make better trading decisions: Interpretation: Market participants tend to place stop-loss orders near liquidation levels , creating clusters of pending orders. As price approaches these levels, it may trigger a cascade of stop-loss orders, providing liquidity for market orders and potentially leading to rapid price movements in the opposite direction. Traders can anticipate price reversals or accelerations as price interacts with liquidation levels, using them as reference points for identifying potential entry or exit opportunities. Note: While the Liquidations indicator provides valuable insights into market dynamics, traders should use it in conjunction with other technical analysis tools and risk management strategies to make informed trading decisions.
อินดิเคเตอร์ Pine Script®
โดย ChartPrime
7
Optimal Leverage IndicatorThe goal of this indicator is to calculate and visualize the optimal leverage and average leverage for a given security based on its historical price data. The optimal leverage is determined by analyzing the relationship between the annualized return and annualized volatility of the security over a specified lookback period. The methodology can be broken down into the following steps: Data Input: The script takes two user inputs: the lookback period and the number of annual trading days. The lookback period determines the number of historical data points used in the calculations. The number of annual trading days is used to annualize the return and volatility metrics. Daily Returns Calculation: The script retrieves the closing prices of the security on a daily timeframe. It calculates the daily returns by comparing the current close price with the previous close price. Mean Return and Volatility Calculation: The script calculates the mean daily return by taking the simple moving average (SMA) of the daily returns over the specified lookback period. It also calculates the volatility by taking the standard deviation of the daily returns over the same lookback period. Annualized Return and Volatility Calculation: The mean daily return is annualized by compounding it over the number of annual trading days. The daily volatility is annualized by multiplying it by the square root of the number of annual trading days. Optimal Leverage Calculation: The optimal leverage is calculated using the formula: Optimal Leverage = Annualized Return / (Annualized Volatility)^2 This formula assumes that the optimal leverage is proportional to the ratio of the annualized return to the square of the annualized volatility. This is based in this paper: papers.ssrn.com Average Leverage Calculation: The script calculates the average leverage by taking the simple moving average (SMA) of the optimal leverage over the specified lookback period. This provides a smoothed representation of the optimal leverage over time. The script plots two lines on the chart: The optimal leverage line (blue) represents the calculated optimal leverage values over time. The average leverage line (green) represents the average of the optimal leverage values over the specified lookback period. The main idea behind this methodology is to determine the optimal leverage based on the historical risk-return characteristics of the security. By analyzing the relationship between the annualized return and volatility, the script aims to identify the leverage level that maximizes the return relative to the risk. The average leverage line provides a smoothed representation of the optimal leverage over time.
อินดิเคเตอร์ Pine Script®
โดย HenriqueCentieiro
4
Bitcoin Leverage Sentiment - Strategy [presentTrading]█ Introduction and How it is Different The "Bitcoin Leverage Sentiment - Strategy " represents a novel approach in the realm of cryptocurrency trading by focusing on sentiment analysis through leveraged positions in Bitcoin. Unlike traditional strategies that primarily rely on price action or technical indicators, this strategy leverages the power of Z-Score analysis to gauge market sentiment by examining the ratio of leveraged long to short positions. By assessing how far the current sentiment deviates from the historical norm, it provides a unique lens to spot potential reversals or continuation in market trends, making it an innovative tool for traders who wish to incorporate market psychology into their trading arsenal. BTC 4h L/S Performance local █ Strategy, How It Works: Detailed Explanation 🔶 Data Collection and Ratio Calculation Firstly, the strategy acquires data on leveraged long (**`priceLongs`**) and short positions (**`priceShorts`**) for Bitcoin. The primary metric of interest is the ratio of long positions relative to the total of both long and short positions: BTC Ratio=priceLongs / (priceLongs+priceShorts) This ratio reflects the prevailing market sentiment, where values closer to 1 indicate a bullish sentiment (dominance of long positions), and values closer to 0 suggest bearish sentiment (prevalence of short positions). 🔶 Z-Score Calculation The Z-Score is then calculated to standardize the BTC Ratio, allowing for comparison across different time periods. The Z-Score formula is: Z = (X - μ) / σ Where: - X is the current BTC Ratio. - μ is the mean of the BTC Ratio over a specified period (**`zScoreCalculationPeriod`**). - σ is the standard deviation of the BTC Ratio over the same period. The Z-Score helps quantify how far the current sentiment deviates from the historical norm, with high positive values indicating extreme bullish sentiment and high negative values signaling extreme bearish sentiment. 🔶 Signal Generation: Trading signals are derived from the Z-Score as follows: Long Entry Signal: Occurs when the BTC Ratio Z-Score crosses above the thresholdLongEntry, suggesting bullish sentiment. - Condition for Long Entry = BTC Ratio Z-Score > thresholdLongEntry Long Exit/Short Entry Signal: Triggered when the BTC Ratio Z-Score drops below thresholdLongExit for exiting longs or below thresholdShortEntry for entering shorts, indicating a shift to bearish sentiment. - Condition for Long Exit/Short Entry = BTC Ratio Z-Score < thresholdLongExit or BTC Ratio Z-Score < thresholdShortEntry Short Exit Signal: Happens when the BTC Ratio Z-Score exceeds the thresholdShortExit, hinting at reducing bearish sentiment and a potential switch to bullish conditions. - Condition for Short Exit = BTC Ratio Z-Score > thresholdShortExit 🔶Implementation and Visualization: The strategy applies these conditions for trade management, aligning with the selected trade direction. It visualizes the BTC Ratio Z-Score with horizontal lines at entry and exit thresholds, illustrating the current sentiment against historical norms. █ Trade Direction The strategy offers flexibility in trade direction, allowing users to choose between long, short, or both, depending on their market outlook and risk tolerance. This adaptability ensures that traders can align the strategy with their individual trading style and market conditions. █ Usage To employ this strategy effectively: 1. Customization: Begin by setting the trade direction and adjusting the Z-Score calculation period and entry/exit thresholds to match your trading preferences. 2. Observation: Monitor the Z-Score and its moving average for potential trading signals. Look for crossover events relative to the predefined thresholds to identify entry and exit points. 3. Confirmation: Consider using additional analysis or indicators for signal confirmation, ensuring a comprehensive approach to decision-making. █ Default Settings - Trade Direction: Determines if the strategy engages in long, short, or both types of trades, impacting its adaptability to market conditions. - Timeframe Input: Influences signal frequency and sensitivity, affecting the strategy's responsiveness to market dynamics. - Z-Score Calculation Period: Affects the strategy’s sensitivity to market changes, with longer periods smoothing data and shorter periods increasing responsiveness. - Entry and Exit Thresholds: Set the Z-Score levels for initiating or exiting trades, balancing between capturing opportunities and minimizing false signals. - Impact of Default Settings: Provides a balanced approach to leverage sentiment trading, with adjustments needed to optimize performance across various market conditions.
กลยุทธ์ Pine Script®
โดย PresentTrading
2
Open Interest OscillatorIn the middle of a bustling cryptocurrency market, with Bitcoin navigating a critical phase and the community hype over potential ETF approvals, current funding rates, and market leverage, the timing is optimal to harness the capabilities of sophisticated trading tools. Meet the Open Interest Oscillator – special indicator tailored for the volatile arena of cryptocurrency trading. This powerful instrument is adept at consolidating open interest data from a multitude of exchanges, delivering an in-depth snapshot of market sentiment across all timeframes, be it a 1-minute sprint or a weekly timeframe. This versatile indicator is compatible with nearly all cryptocurrency pairs, offering an expansive lens through which traders can gauge the market's pulse. Key Features: -- Multi-exchange Data Aggregation: This feature taps into the heart of the crypto market by aggregating open interest data from premier exchanges such as BINANCE, BITMEX, BITFINEX, and KRAKEN. It goes a step further by integrating data from various pairs and stablecoins, thus providing traders with a rich, multi-dimensional view of market activities. -- Open Interest Bars: Witness the flow of market dynamics through bars that depict the volume of positions being opened or closed, offering a clear visual cue of trading behavior. In this mode, If bars are going into negative zone, then traders are closing their positions. If they go into positive territory - leveraged positions are being opened. -- Bollinger Band Integration: Incorporate a layer of statistical analysis with standard deviation calculations, which frame the open interest changes, giving traders a quantified edge to evaluate the market's volatility and momentum. -- Oscillator with Customizable Thresholds: Personalize your trading signals by setting thresholds that resonate with your unique trading tactics. This customization brings the power of tailored analytics to your strategic arsenal. -- Max OI Ceiling Setting: In the fast-paced crypto environment where data can surge to overwhelming levels, the Max OI Ceiling ensures you maintain a clear view by capping the open interest data, thus preserving the readability and interpretability of information, even when market activity reaches feverish heights.
อินดิเคเตอร์ Pine Script®
โดย mezor13
3
Liquidations Meter [LuxAlgo]The Liquidation Meter aims to gauge the momentum of the bar, identify the strength of the bulls and bears, and more importantly identify probable exhaustion/reversals by measuring probable liquidations. 🔶 USAGE This tool includes many features related to the concept of liquidation. The two core ones are the liquidation meter and liquidation price calculator, highlighted below. 🔹 Liquidation Meter The liquidation meter presents liquidations on the price chart by measuring the highest leverage value of longs and shorts that have been potentially liquidated on the last chart bar, hence allowing traders to: gauge the momentum of the bar. identify the strength of the bulls and bears. identify probable reversal/exhaustion points. Liquidation of low-leveraged positions can be indicative of exhaustion. 🔹 Liquidation Price Calculator A liquidation price calculator might come in handy when you need to calculate at what price level your leveraged position in Crypto, Forex, Stocks, or any other asset class gets liquidated to add a protective stop to mitigate risk. Monitoring an open position gets easier if the trader can calculate the total risk in order for them to choose the right amount of margin and leverage. Liquidation price is the distance from the trader's entry price to the price where trader's leveraged position gets liquidated due to a loss. As the leverage is increased, the distance from trader's entry price to the liquidation price shrinks. While you have one or several trades open you can quickly check their liquidation levels and determine which one of the trades is closest to their liquidation price. If you are a day trader that uses leverage and you want to know which trade has the best outlook you can calculate the liquidation price to see which one of the trades looks best. 🔹 Dashboard The bar statistics option enables measuring and presenting trading activity, volatility, and probable liquidations for the last chart bar. 🔶 DETAILS It's important to note that liquidation price calculator tool uses a formula to calculate the liquidation price based on the entry price + leverage ratio. Other factors such as leveraged fees, position size, and other interest payments have been excluded since they are variables that don’t directly affect the level of liquidation of a leveraged position. The calculator also assumes that traders are using an isolated margin for one single position and does not take into consideration the additional margin they might have in their account. 🔹Liquidation price formula the liquidation distance in percentage = 100 / leverage ratio the liquidation distance in price = current asset price x the liquidation distance in percentage the liquidation price (longs) = current asset price – the liquidation distance in price the liquidation price (shorts) = current asset price + the liquidation distance in price or simply the liquidation price (longs) = entry price * (1 – 1 / leverage ratio) the liquidation price (shorts) = entry price * (1 + 1 / leverage ratio) Example: Let’s say that you are trading a leverage ratio of 1:20. The first step is to calculate the distance to your liquidation point in percentage. the liquidation distance in percentage = 100 / 20 = 5% Now you know that your liquidation price is 5% away from your entry price. Let's calculate 5% below and above the entry price of the asset you are currently trading. As an example, we assume that you are trading bitcoin which is currently priced at $35000. the liquidation distance in price = $35000 x 0.05 = $1750 Finally, calculate liquidation prices. the liquidation price (longs) = $35000 – $1750 = $33250 the liquidation price (short) = $35000 + $1750 = $36750 In this example, short liquidation price is $36750 and long liquidation price is $33250. 🔹How leverage ratio affects the liquidation price The entry price is the starting point of the calculation and it is from here that the liquidation price is calculated, where the leverage ratio has a direct impact on the liquidation price since the more you borrow the less “wiggle-room” your trade has. An increase in leverage will subsequently reduce the distance to full liquidation. On the contrary, choosing a lower leverage ratio will give the position more room to move on. 🔶 SETTINGS 🔹Liquidations Meter Base Price: The option where to set the reference/base price. 🔹Liquidation Price Calculator Liquidation Price Calculator: Toggles the visibility of the calculator. Details and assumptions made during the calculations are stated in the tooltip of the option. Entry Price: The option where to set the entry price, a value of 0 will use the current closing price. Details are given in the tooltip of the option. Leverage: The option where to set the leverage value. Show Calculated Liquidation Prices on the Chart: Toggles the visibility of the liquidation prices on the price chart. 🔹Dashboard Show Bar Statistics: Toggles the visibility of the last bar statistics. 🔹Others Liquidations Meter Text Size: Liquidations Meter text size. Liquidations Meter Offset: Liquidations Meter offset. Dashboard/Calculator Placement: Dashboard/calculator position on the chart. Dashboard/Calculator Text Size: Dashboard text size. 🔶 RELATED SCRIPTS Here are some of the scripts that are related to the liquidation and liquidity concept, for more and other conceptual scripts you are kindly invited to visit LuxAlgo-Scripts . Liquidation-Levels Liquidations-Real-Time Buyside-Sellside-Liquidity
อินดิเคเตอร์ Pine Script®
โดย LuxAlgo
8
Margin/Leverage CalculationMargin This library calculates margin liquidation prices and quantities for long and short positions in your strategies. Usage example // ############################################################ // # INVESTMENT SETTINGS / INPUT // ############################################################ // Get the investment capital from the properties tab of the strategy settings. investment_capital = strategy.initial_capital // Get the leverage from the properties tab of the strategy settings. // The leverage is calculated from the order size for example: (300% = x3 leverage) investment_leverage = margin.leverage() // The maintainance rate and amount. investment_leverage_maintenance_rate = input.float(title='Maintanance Rate (%)', defval=default_investment_leverage_maintenance_rate, minval=0, maxval=100, step=0.1, tooltip=tt_investment_leverage_maintenance_rate, group='MARGIN') / 100 investment_leverage_maintenance_amount = input.float(title='Maintanance Amount (%)', defval=default_investment_leverage_maintenance_amount, minval=0, maxval=100, step=0.1, tooltip=tt_investment_leverage_maintenance_amount, group='MARGIN') // ############################################################ // # LIQUIDATION PRICES // ############################################################ leverage_liquidation_price_long = 0.0 leverage_liquidation_price_long := na(leverage_liquidation_price_long ) ? na : leverage_liquidation_price_long leverage_liquidation_price_short = 0.0 leverage_liquidation_price_short := na(leverage_liquidation_price_short ) ? na : leverage_liquidation_price_short leverage_liquidation_price_long := margin.liquidation_price_long(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount) leverage_liquidation_price_short := margin.liquidation_price_short(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount) Get the qty for margin long or short position. margin.qty_long(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount) margin.qty_short(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount) Get the price and qty for margin long or short position. = margin.qty_long(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount) = margin.qty_short(investment_capital, strategy.position_avg_price, investment_leverage, investment_leverage_maintenance_rate, investment_leverage_maintenance_amount)
ไลบรารี Pine Script®
โดย DinGrogu
Risk Management GO8686: Stop Loss, Position Size & TargetFull Name: Risk Management GO8686: Stop Loss, Position Size & Target What this indicator provides: A dashboard to calculate Stop Loss, Position Size and Target, where users can customize Risk Management parameters in the setting. Position Size: calculated from "initialCapital", "Leverage", "Max Loss", "feeMaker", "feeTaker". Stop Loss Price: using pivots, default length is set to 3, with an extra ATR value controlled by "'Multiplier OF Extra ATR". Target: calculated from entry price, risk reward, distance between entry and stop loss, fees What the indicator does Not provides: entries of positions: The Long/Short entries displayed are just MACD signal crossing zero, users can apply their own entry logic, by modifying ready2L / ready2S variables. What the indicator does Not guarantee: the integrity, timeliness, accuracy, and comprehensiveness of the data, calculation method, calculation results, etc. Two types labels: 1. Automated labels: they are displayed when MACD signal crossing zero, use "Display History Labels" to toggle display or not. 2. Setup Manually label: located at the right side of the latest bar, to display results when users setup manually The settings of the indicator: "Toggle to Reload", "InitialCapital", "Leverage", "Max Loss % per trade", "feeMaker", "feeTaker", 4 length inputs for Pivot, "Multiplier of Extra ATR for stop loss", "Toggle To setup manually", "Toggle between Long / Short", "Entry Price, set manually", "Stop Loss Price, set manually", "Risk-Reward Ratio" "Display History Labels" ---------- Disclaimer ---------- Before using or requesting access to the indicator, customers/users acknowledge that they have read and accepted that the indicator, any associated contents on all social medias and any communication with the indicator author, including but not limited to: product and service details, signals, alerts, data, calculation methods, calculation results, user manual, tutorials, ideas, videos, chats, messages, emails, blogs, tweets, etc. are provided solely for educational purpose and Not as financial advice. Customers/users understand and agree to use the aforementioned indicator and information at their own risk. ---------- Updates ---------- The latest updates override the previous content. To activate a update, if it does not load as expected: close the indicator, save the chart, clear browser caches, restart the browser, reload the chart and apply the indicator to the chart.
อินดิเคเตอร์ Pine Script®
โดย go8686
TTP OI + LS signal filterThis oscillator helps filtering specific conditions in the market based on open interest (OI) and the ratio of longs and shorts (LS) for crypto assets. Currently it works with BINANCE:BTCUSDT.P but soon I'll be adding support for more assets. It flags areas of interest like: - Too many longs, too many shorts in the market - Open interest too high or too low It accepts an external signal as a source in which case filters can be applied to the original signal. For example the external signal might trigger and plot a 1 when RSI break below 70. By connecting such signal with this oscillator you'll be able to only pass-through the ones that occur when any of the areas of interest mentioned above are also valid. If both filter are applied it acts as an OR. For example, if too many longs and too many shorts are active, it will pass through the signal in either condition. The results of the original signal filtered is printed to be able to later use it in any external backtester strategy that accepts external sources too. If external source signal is disabled it will trigger any time the combined filters are returning true. Open interest and the ratio of longs/shorts is considered too high whenever the stochastic RSI calculation of the OI or ratio LS reaches a level above 80 and too low when below 20 The ratio of long/shorts is calculated by dividing the ratio of longs vs shorts from BITFINEX:BTCUSDLONGS and BITFINEX:BTCUSDSHORTS
อินดิเคเตอร์ Pine Script®
โดย TheTradingParrot
7
Crypto Leverage Ratio [Market Cap / Open Interest in %]This indicator calculates what percentage of market cap data corresponds to open interest data. Leverage Ratio = 1/(Market Cap / 100 * Open Interest) Market Cap data comes from TradingView -> CRYPTOCAP:YOURCOINSYMBOL Open Interest data comes from IntoTheBlock -> INTOTHEBLOCK:YOURCOINSYMBOL_PERPETUALOPENINTEREST IntoTheBlock refresh perpetual data at the end of the day. It means there is no intraday data. It can only be used in Daily or higher time intervals. This indicator and any other indicator can not precisely calculate real leverage ratio except exchanges itself. This calculation is just based on assumption. You can see the exact same result by just adding: 1/(CRYPTOCAP:BTC/100*INTOTHEBLOCK:BTC_PERPETUALOPENINTEREST) to your symbol search, if your chart is a BTC chart. " The Futures Open Interest Leverage Ratio is calculated by dividing the market open contract value, by the market cap of the asset (presented as %). This returns an estimate of the degree of leverage that exists relative to market size as a gauge for whether derivatives markets are a source of deleveraging risk. High Values indicate that futures market open interest is large relative to the market size. This increases the risk of a short/long squeeze, deleveraging event, or liquidation cascade. Low Values indicate that futures market open interest is small relative to the market size. This is generally coincident with a lower risk of derivative led forced buying/selling and volatility. Deleveraging Events such as short/long squeezes, or liquidation cascades can be identified by rapid declines in OI relative to market cap, and vertical drops in the metric. -glassnode " says glassnode. I think it is more than that. Especially with MAs.
อินดิเคเตอร์ Pine Script®
โดย LordOfTheBlockchains