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Volatility Targeting: Single Asset [BackQuant]Volatility Targeting: Single Asset An educational example that demonstrates how volatility targeting can scale exposure up or down on one symbol, then applies a simple EMA cross for long or short direction and a higher timeframe style regime filter to gate risk. It builds a synthetic equity curve and compares it to buy and hold and a benchmark. Important disclaimer This script is a concept and education example only . It is not a complete trading system and it is not meant for live execution. It does not model many real world constraints, and its equity curve is only a simplified simulation. If you want to trade any idea like this, you need a proper strategy() implementation, realistic execution assumptions, and robust backtesting with out of sample validation. Single asset vs the full portfolio concept This indicator is the single asset, long short version of the broader volatility targeted momentum portfolio concept. The original multi asset concept and full portfolio implementation is here: That portfolio script is about allocating across multiple assets with a portfolio view. This script is intentionally simpler and focuses on one symbol so you can clearly see how volatility targeting behaves, how the scaling interacts with trend direction, and what an equity curve comparison looks like. What this indicator is trying to demonstrate Volatility targeting is a risk scaling framework. The core idea is simple: If realized volatility is low relative to a target, you can scale position size up so the strategy behaves like it has a stable risk budget. If realized volatility is high relative to a target, you scale down to avoid getting blown around by the market. Instead of always being 1x long or 1x short, exposure becomes dynamic. This is often used in risk parity style systems, trend following overlays, and volatility controlled products. This script combines that risk scaling with a simple trend direction model: Fast and slow EMA cross determines whether the strategy is long or short. A second, longer EMA cross acts as a regime filter that decides whether the system is ACTIVE or effectively in CASH. An equity curve is built from the scaled returns so you can visualize how the framework behaves across regimes. How the logic works step by step 1) Returns and simple momentum The script uses log returns for the base return stream: ret = log(price / price ) It also computes a simple momentum value: mom = price / price - 1 In this version, momentum is mainly informational since the directional signal is the EMA cross. The lookback input is shared with volatility estimation to keep the concept compact. 2) Realized volatility estimation Realized volatility is estimated as the standard deviation of returns over the lookback window, then annualized: vol = stdev(ret, lookback) * sqrt(tradingdays) The Trading Days/Year input controls annualization: 252 is typical for traditional markets. 365 is typical for crypto since it trades daily. 3) Volatility targeting multiplier Once realized vol is estimated, the script computes a scaling factor that tries to push realized volatility toward the target: volMult = targetVol / vol This is then clamped into a reasonable range: Minimum 0.1 so exposure never goes to zero just because vol spikes. Maximum 5.0 so exposure is not allowed to lever infinitely during ultra low volatility periods. This clamp is one of the most important “sanity rails” in any volatility targeted system. Without it, very low volatility regimes can create unrealistic leverage. 4) Scaled return stream The per bar return used for the equity curve is the raw return multiplied by the volatility multiplier: sr = ret * volMult Think of this as the return you would have earned if you scaled exposure to match the volatility budget. 5) Long short direction via EMA cross Direction is determined by a fast and slow EMA cross on price: If fast EMA is above slow EMA, direction is long. If fast EMA is below slow EMA, direction is short. This produces dir as either +1 or -1. The scaled return stream is then signed by direction: avgRet = dir * sr So the strategy return is volatility targeted and directionally flipped depending on trend. 6) Regime filter: ACTIVE vs CASH A second EMA pair acts as a top level regime filter: If fast regime EMA is above slow regime EMA, the system is ACTIVE. If fast regime EMA is below slow regime EMA, the system is considered CASH, meaning it does not compound equity. This is designed to reduce participation in long bear phases or low quality environments, depending on how you set the regime lengths. By default it is a classic 50 and 200 EMA cross structure. Important detail, the script applies regime_filter when compounding equity, meaning it uses the prior bar regime state to avoid ambiguous same bar updates. 7) Equity curve construction The script builds a synthetic equity curve starting from Initial Capital after Start Date . Each bar: If regime was ACTIVE on the previous bar, equity compounds by (1 + netRet). If regime was CASH, equity stays flat. Fees are modeled very simply as a per bar penalty on returns: netRet = avgRet - (fee_rate * avgRet) This is not realistic execution modeling, it is just a simple turnover penalty knob to show how friction can reduce compounded performance. Real backtesting should model trade based costs, spreads, funding, and slippage. Benchmark and buy and hold comparison The script pulls a benchmark symbol via request.security and builds a buy and hold equity curve starting from the same date and initial capital. The buy and hold curve is based on benchmark price appreciation, not the strategy’s asset price, so you can compare: Strategy equity on the chart symbol. Buy and hold equity for the selected benchmark instrument. By default the benchmark is TVC:SPX, but you can set it to anything, for crypto you might set it to BTC, or a sector index, or a dominance proxy depending on your study. What it plots If enabled, the indicator plots: Strategy Equity as a line, colored by recent direction of equity change, using Positive Equity Color and Negative Equity Color . Buy and Hold Equity for the chosen benchmark as a line. Optional labels that tag each curve on the right side of the chart. This makes it easy to visually see when volatility targeting and regime gating change the shape of the equity curve relative to a simple passive hold. Metrics table explained If Show Metrics Table is enabled, a table is built and populated with common performance statistics based on the simulated daily returns of the strategy equity curve after the start date. These include: Net Profit (%) total return relative to initial capital. Max DD (%) maximum drawdown computed from equity peaks, stored over time. Win Rate percent of positive return bars. Annual Mean Returns (% p/y) mean daily return annualized. Annual Stdev Returns (% p/y) volatility of daily returns annualized. Variance of annualized returns. Sortino Ratio annualized return divided by downside deviation, using negative return stdev. Sharpe Ratio risk adjusted return using the risk free rate input. Omega Ratio positive return sum divided by negative return sum. Gain to Pain total return sum divided by absolute loss sum. CAGR (% p/y) compounded annual growth rate based on time since start date. Portfolio Alpha (% p/y) alpha versus benchmark using beta and the benchmark mean. Portfolio Beta covariance of strategy returns with benchmark returns divided by benchmark variance. Skewness of Returns actually the script computes a conditional value based on the lower 5 percent tail of returns, so it behaves more like a simple CVaR style tail loss estimate than classic skewness. Important note, these are calculated from the synthetic equity stream in an indicator context. They are useful for concept exploration, but they are not a substitute for professional backtesting where trade timing, fills, funding, and leverage constraints are accurately represented. How to interpret the system conceptually Vol targeting effect When volatility rises, volMult falls, so the strategy de risks and the equity curve typically becomes smoother. When volatility compresses, volMult rises, so the system takes more exposure and tries to maintain a stable risk budget. This is why volatility targeting is often used as a “risk equalizer”, it can reduce the “biggest drawdowns happen only because vol expanded” problem, at the cost of potentially under participating in explosive upside if volatility rises during a trend. Long short directional effect Because direction is an EMA cross: In strong trends, the direction stays stable and the scaled return stream compounds in that trend direction. In choppy ranges, the EMA cross can flip and create whipsaws, which is where fees and regime filtering matter most. Regime filter effect The 50 and 200 style filter tries to: Keep the system active in sustained up regimes. Reduce exposure during long down regimes or extended weakness. It will always be late at turning points, by design. It is a slow filter meant to reduce deep participation, not to catch bottoms. Common applications This script is mainly for understanding and research, but conceptually, volatility targeting overlays are used for: Risk budgeting normalize risk so your exposure is not accidentally huge in high vol regimes. System comparison see how a simple trend model behaves with and without vol scaling. Parameter exploration test how target volatility, lookback length, and regime lengths change the shape of equity and drawdowns. Framework building as a reference blueprint before implementing a proper strategy() version with trade based execution logic. Tuning guidance Lookback lower values react faster to vol shifts but can create unstable scaling, higher values smooth scaling but react slower to regime changes. Target volatility higher targets increase exposure and drawdown potential, lower targets reduce exposure and usually lower drawdowns, but can under perform in strong trends. Signal EMAs tighter EMAs increase trade frequency, wider EMAs reduce churn but react slower. Regime EMAs slower regime filters reduce false toggles but will miss early trend transitions. Fees if you crank this up you will see how sensitive higher turnover parameter sets are to friction. Final note This is a compact educational demonstration of a volatility targeted, long short single asset framework with a regime gate and a synthetic equity curve. If you want a production ready implementation, the correct next step is to convert this concept into a strategy() script, add realistic execution and cost modeling, test across multiple timeframes and market regimes, and validate out of sample before making any decision based on the results.
Pine Script® indicator
by BackQuant
1
Index Construction Tool🙏🏻 The most natural mathematical way to construct an index || portfolio, based on contraharmonic mean || contraharmonic weighting. If you currently traded assets do not satisfy you, why not make your own ones? Contraharmonic mean is literally a weighted mean where each value is weighted by itself. ... Now let me explain to you why contraharmonic weighting is really so fundamental in two ways: observation how the industry (prolly unknowably) converged to this method, and the real mathematical explanation why things are this way. How it works in the industry. In indexes like TVC:SPX or TVC:DJI the individual components (stocks) are weighted by market capitalization. This market cap is made of two components: number of shares outstanding and the actual price of the stock. While the number of shares holds the same over really long periods of time and changes rarely by corporate actions , the prices change all the time, so market cap is in fact almost purely based on prices itself. So when they weight index legs by market cap, it really means they weight it by stock prices. That’s the observation: even tho I never dem saying they do contraharmonic weighting, that’s what happens in reality. Natural explanation Now the main part: how the universe works. If you build a logical sequence of how information ‘gradually’ combines, you have this: Suppose you have the one last datapoint of each of 4 different assets; The next logical step is to combine these datapoints somehow in pairs. Pairs are created only as ratios , this reveals relationships between components, this is the only step where these fundamental operations are meaningful, they lose meaning with 3+ components. This way we will have 16 pairs: 4 of them would be 1s, 6 real ratios, and 6 more inverted ratios of these; Then the next logical step is to combine all the pairs (not the initial single assets) all together. Naturally this is done via matrices, by constructing a 4x4 design matrix where each cell will be one of these 16 pairs. That matrix will have ones in the main diagonal (because these would be smth like ES/ES, NQ/NQ etc). Other cells will be actual ratios, like ES/NQ, RTY/YM etc; Then the native way to compress and summarize all this structure is to do eigendecomposition . The only eigenvector that would be meaningful in this case is the principal eigenvector, and its loadings would be what we were hunting for. We can multiply each asset datapoint by corresponding loading, sum them up and have one single index value, what we were aiming for; Now the main catch: turns out using these principal eigenvector loadings mathematically is Exactly the same as simply calculating contraharmonic weights of those 4 initial assets. We’re done here. For the sceptics, no other way of constructing the design matrix other than with ratios would result in another type of a defined mean. Filling that design matrix with ratios Is the only way to obtain a meaningful defined mean, that would also work with negative numbers. I’m skipping a couple of details there tbh, but they don’t really matter (we don’t need log-space, and anyways the idea holds even then). But the core idea is this: only contraharmonic mean emerges there, no other mean ever does. Finally, how to use the thing: Good news we don't use contraharmonic mean itself because we need an internals of it: actual weights of components that make this contraharmonic mean, (so we can follow it with our position sizes). This actually allows us to also use these weights but not for addition, but for subtraction. So, the script has 2 modes (examples would follow): Addition: the main one, allows you to make indexes, portfolios, baskets, groups, whatever you call it. The script will simply sum the weighted legs; Subtraction: allows you to make spreads, residual spreads etc. Important: the script will subtract all the symbols From the first one. So if the first we have 3 symbols: YM, ES, RTY, the script will do YM - ES - RTY, weights would be applied to each. At the top tight corner of the script you will see a lil table with symbols and corresponding weights you wanna trade: these are ‘already’ adjusted for point value of each leg, you don’t need to do anything, only scale them all together to meet your risk profile. Symbols have to be added the way the default ones are added, one line : one symbol. Pls explore the script’s Style setting: You can pick a visualization method you like ! including overlays on the main chart pane ! Script also outputs inferred volume delta, inferred volume and inferred tick count calculated with the same method. You can use them in further calculations. ... Examples of how you can use it ^^ Purple dotted line: overlay from ICT script, turned on in Style settings, the contraharmonic mean itself calculated from the same assets that are on the chart: CME_MINI:RTY1! , CME_MINI:ES1! , CME_MINI:NQ1! , CBOT_MINI:YM1! ^^ precious metals residual spread ( COMEX:GC1! COMEX:SI1! NYMEX:PL1! ) ^^ CBOT:ZC1! vs CBOT:ZW1! grain spread ^^ BDI (Bid Dope Index), constructed from: NYSE:MO , NYSE:TPB , NYSE:DGX , NASDAQ:JAZZ , NYSE:IIPR , NASDAQ:CRON , OTC:CURLF , OTC:TCNNF ^^ NYMEX:CL1! & ICEEUR:BRN1! basket ^^ resulting index price, inferred volume delta, inferred volume and inferred tick count of CME_MINI:NQ1! vs CME_MINI:ES1! spread ... Synthetic assets is the whole new Universe you can jump into and never look back, if this is your way ... ∞
Pine Script® indicator
by gorx1
Ichimoku Trading Checklist - 5 Rules🧠 Description This indicator implements a rule-based checklist built on Ichimoku Kinko Hyo, complemented with RSI and price structure, designed to help traders objectively evaluate whether a bullish setup is valid or not. ⚠️ This indicator does NOT generate buy or sell signals. ⚠️ It is NOT a trading system or financial advice. The core philosophy is discipline and consistency: If there is no setup, there is no trade. ________________________________________ ✅ The 5 Rules Evaluated 1. Chikou Span above price (26 bars back) Confirms that current price is above historical price, validating a bullish context. 2. Bullish TK Cross (Tenkan-sen > Kijun-sen) Measures bullish momentum within the Ichimoku framework. 3. Bullish divergence or convergence between RSI and price Evaluates relative strength using recent RSI pivots and price structure. 4. Kumo breakout followed by a valid pullback Requires a bullish cloud breakout and a pullback that respects the structure. 5. Bullish Kumo (green cloud / twist) Confirms that the Ichimoku cloud supports a bullish bias. ________________________________________ 🚦 Decision Traffic Light (Final Row) The last row of the table provides a traffic-light style summary: • 🟢 5/5 rules met → Valid setup • 🟡 1–4 rules met → Incomplete setup • 🔴 0 rules met → No trade Core message displayed: “No setup, No trade!” 🚫 ________________________________________ 🎨 Customization Through the Inputs panel, users can customize: • Header, body, and footer background colors • Traffic-light colors and icons (🟢 🟡 🔴) • Text alignment (left / center / right) • Optional rule counter (x/5) ⚠️ Tables do not use TradingView’s Style tab; all customization is handled via Inputs. ________________________________________ ⏱️ Timeframe The indicator is timeframe-agnostic, but it was designed and tested primarily on the 1H timeframe, where Ichimoku and RSI structure tend to be more consistent. ________________________________________ ⚠️ Disclaimer This script is provided for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. Trading involves risk, and all decisions remain the sole responsibility of the user. Remember that every strategy is based on probabilities and scenarios that you have already tested in hundreds of trades. ________________________________________ 👤 Author © Yesid Correa Cano Pine Script v6 License: Mozilla Public License 2.0 (MPL-2.0)
Pine Script® indicator
by Yesid_Correa_Cano
X-Trend Macro Command CenterX-Trend Macro Command Center (MCC) | Institutional Grade Dashboard 📝 Description Body The Invisible Engine of the Market Revealed. Traders often focus solely on Price Action, ignoring the massive underwater currents that actually drive trends: Global Liquidity, Inflation, and Central Bank Policy. We created X-Trend Macro Command Center (MCC) to solve this problem. This is not just an indicator. It is a fundamental heads-up display that bridges the gap between technical charts and macroeconomic reality. 💡 The Idea & Philosophy Markets don't move in a vacuum. Bull runs are fueled by M2 Money Supply expansion and negative real yields. Crashes are triggered by liquidity crunches and aggressive rate hikes. X-Trend MCC was built to give retail traders the same "Macro Awareness" that institutional desks possess. It aggregates fragmented economic data from Federal Reserve databases (FRED) directly onto your chart in real-time. 🚀 Application & Logic This tool is designed for Trend Traders, Crypto Investors, and Macro Analysts. Identify the Regime: Instantly see if the environment is "RISK ON" (High Liquidity, Low Real Rates) or "RISK OFF" (Monetary Tightening). Validate the Trend: Don't buy the dip if Liquidity (M2) is crashing. Don't short the rally if Real Yields are negative. Multi-Region Analysis: Switch instantly between economic powerhouses (US, China, Japan) to see where the capital is flowing. 📊 Dashboard Metrics Explained Every row in the Command Center tells a specific story about the economy: Interest Rate: The "Gravity" of finance. Higher rates weigh down risk assets (Stocks/Crypto). Inflation (YoY): The erosion of purchasing power. We calculate this dynamically based on CPI data. Real Yield (The "Golden" Metric): Calculated as Interest Rate - Inflation. Green: Real Yield is low/negative. Cash is trash, assets fly. Red: Real Yield is high. Cash is King, assets struggle. US Debt & GDP: Fiscal health indicators formatted in Trillions ($T). Watch the Debt-to-GDP ratio—if it spikes >120%, expect currency debasement. M2 Money Supply: The fuel tank of the market. Tracks the total amount of money in circulation. ↗ Trend: Liquidity is entering the system (Bullish). ↘ Trend: Liquidity is drying up (Bearish). 🧩 The X-Trend Ecosystem X-Trend MCC is just the tip of the iceberg. This module is part of the larger X-Trend Project — a comprehensive suite of algorithmic tools being developed to quantify market chaos. While our Price Action algorithms (Lite/Pro/Ultra) handle the Micro, the MCC handles the Macro. Technical Note: Data Sources: Direct connection to FRED (Federal Reserve Economic Data). Zero Repainting: Historical data is requested strictly using closed bars to ensure accuracy. Open Source: We believe in transparency. The code is open for study under MPL 2.0. Build by Dev0880 | X-Trend © 2025
Pine Script® indicator
by Dev0880
Pair Creation🙏🏻 The one and only pair construction tech you need, unlike others: Applies one consistent operation to all the data features (not only prices). Then, the script outputs these, so you can apply other calculations on these outputs. calculates a very fast and native volatility based hedge ratio, that also takes into account point value (think SPY vs ES) so you can easily use it in position sizing Has built-in forward pricing aka cost of carry model , so you can de-drift pairs from cost of carry, discover spot price of oil based on futures, and ofc find arbitrage opportunities Also allows to make a pair as a product of 2 series, useful for triangular arbitrage This script can make a pair in 2 ways: Ratio, by dividing leg 1 by leg 2 Product, by multiplying leg 1 by leg 2 The real mathematically right way to construct a pair is a ratio/product (Spreads are in fact = 2 legged portfolio, but I ain't told ya that ok). Why? Because a pair of 2 entities has a mathematically unique beauty, it allows direct comparisons and relationship analysis, smth you can't do directly with 3 and more components. Multiplication (think inversions like (EURUSD -> USDEUR), and use cases for triangular arbitrage) is useful sometimes too. ... Quickguide: First, "Legs" are pair components: make a pair of related assets. Don’t be guided exclusively by clustering, cointegrations, mutual information etc. Common sense and exogenous info can easily made them all Forward pricing model: is useful when u work with spot vs futures pairs. Otherwise: put financing, storage and yield all on zeros, this way u will turn it off and have a pure ratio/product of 2 legs. Look at the 2 numbers on the script’s status line: the first one would always be 1), and the second one is a variable. First number (always 1) is multiplier for your position size on leg 1 The second number is the multiplier for your position size on leg 2 in the opposite direction. If both legs are related, trading your sizes with these multipliers makes you do statistical arbitrage -> trading ~ volatility in risk free mode, while the relationship between the assets is still in place. Also guys srsly, nobody ‘ever’ made a universal law that somewhy somehow for whatever secret conspiracy reason one shall only trade pairs in mean reverting style xd. You can do whatever you want: Tilt hedge ratio significantly based on relative strength of legs Trade the pair in momentum style Ignore hedge ratio all together And more and more, the limit is your imagination, e.g.: Anticipate hedge ratio changes based on exogenous info and act accordingly Scalp a pair just like any other asset Make a pair out of 2 pairs Like I mean it, whatever you desire About forward pricing model: It’s applied only to leg 2; Direct: takes spot price and finds out implied futures price Inverse: takes futures price and finds out implied spot price (try on oil) Pls read online how to choose parameters, it’s open access reliable info About the hedge ratio I use: You prolly noticed the way I prefer to use inferred volumes vs the “real” ones. In pairs it’s especially meaningful, because real volumes lose sense in pair creation. And while volumes are closely tied to volatility, the inferred volumes ‘Are’ volatility irl (and later can be converted to currency space by using point value, allowing direct comparisons symbol vs symbol). This hedge ratio is a good example of how discovering the real nature of entities beats making 100s of inventions, why domain knowledge and proper feature engineering beats difficult bulky models, neural networks etc. How simple data understanding & operations on it is all you need. This script simply does this: Takes inferred volume delta of both assets, makes a ratio, normalizes it by tick sizes and points values of both legs, calculates a typical value of this series. That’s it, no step 2, we’re done. No Kalman filters, no TLS regression, no vine copulas, or whatever new fancy keywords you can come up with etc. ... ^^ comparing real ES prices vs theoretical ones by forward-pricing model. Financing: 0.04, yield 0.0175 ^^ EURUSD, 6E futures with theoretical futures price calculated with interest rate differential 0.02 (4% USD - 2% EUR interest rates) ^^4 different pairs (RTY/ES, YM/ES, NQ/ES, ES/ZN) each with different plot style (pick one you like in script's Style settings) ^^ YM/RTY pair, each plot represents ratio of different features: ratio of prices, ratio of inferred volume deltas, ratio of inferred volumes, ratio of inferred tick counts (also can be turned on/off in Style settings) ... How can u upgrade it and make a step forward yourself: On tradingview missing values are automatically fixed by backfilling, and this never becomes a thing until you hit high frequency data. You can do better and use Kalman filter for filling missing values. Script contains the functions I use everywhere to calculate inferred volume delta, inferred volume, and inferred tick count. ... ∞
Pine Script® indicator
by gorx1
Realtime Position CalculatorRisk management is the single most important factor in trading success. This indicator automates the process of position sizing in real-time based on your account risk and a dynamic technical Stop Loss. It eliminates the need for manual calculations and helps you execute trades faster while adhering to strict risk management rules. How it Works The indicator visually places a Stop Loss line based on recent market structure (Highs/Lows) and instantly calculates the required position size (Contracts/Lots) to match your defined monetary risk. 1. Dynamic Stop Loss : It identifies the highest high (for Shorts) or lowest low (for Longs) over a user-defined lookback period. 2. Position Calculation : It calculates the distance between the current price and the Stop Loss level. 3. Formula : Contract Size = Risk Amount / (Distance * Point Value) 4. Actual vs. Target Risk : Because of the rounding, the script calculates and displays the Actual Risk (e.g., $95) alongside your Target Risk (e.g., $100), so you know exactly what is at stake. Key Features Real-time Calculation : Updates instantly as price moves. Copy Trading Support : Includes an "Account Multiplier" setting. If you trade 10 accounts via a copy trader, set the multiplier to 10. The indicator will show the total contract size needed across all accounts. Point Value Support : Works for Stocks/Crypto (Point Value = 1) and Futures (e.g., ES = 50, NQ = 20). Customizable UI : Toggle specific data on/off in the label (e.g., hide price, show only contracts). Adjustable label offset to keep the chart clean. Settings Guide Trade Direction : Toggle between Long and Short setups. Add the indicator two times and set another for Longs and another for Shorts so you can see both direction at the same time. Risk Amount : Your max risk in currency (e.g., $100). Lookback : How many bars back to look for the SL pivot (e.g., 10 bars). Point Value : Crucial for Futures. Use 1.0 for Crypto/Stocks. Use tick value/point value for futures (e.g., 50 for ES). Account Multiplier : Multiply the position size for multiple accounts. Label Offset : Move the information label to the right to avoid overlapping with price action. Disclaimer This tool is for informational and educational purposes only. Always verify calculations manually before executing trades. Past performance is not indicative of future results.
Pine Script® indicator
by ViltsuFIN
Adaptive Risk Management [sgbpulse]1. Introduction: Adaptive Risk Management is an advanced indicator designed to provide traders with a comprehensive risk management tool directly on the chart. Instead of relying on complex manual calculations, the indicator automates all critical steps of trade planning. It dynamically calculates the estimated Entry Price , the Stop Loss location, the required Position Size (Quantity) based on your capital and risk limits, and the three Take Profit targets based on your defined Reward/Risk ratios. The indicator displays all these essential data points clearly and visually on the chart, ensuring you always know the potential risk-reward profile of every trade. ARM : The A daptive R isk M anagement every trader needs to ARM themselves with. 2. The Critical Importance of Risk Management Proper risk management is the cornerstone of successful trading. Consistent profitability in the market is impossible without rigorously defining risk limits. Risk Control: This starts by setting the maximum risk amount you are willing to lose in a single trade (Risk per Trade), and limiting the total capital allocated to the position (Max Capital per Trade). Defining Boundaries (Stop Loss & Take Profit): It is mandatory to define a technical Stop Loss and a Take Profit target. A fundamental rule of risk management is that the Reward/Risk Ratio (R/R) must be a minimum of 1:1. 3. Core Features, Adaptivity, and Customization The Adaptive Risk Management indicator is engineered for use across all major trading styles, including Swing Trading, Intraday Trading, and Scalping, providing consistent risk control regardless of the chosen timeframe. Real-Time Dynamic Adaptivity: The indicator calculates all risk management parameters (Entry, Stop Loss, Quantity) dynamically with every new bar, thus adapting instantly to changing market conditions. Trend Direction Adjustment: Define the analysis direction (Long/Uptrend or Short/Downtrend). Intraday Session Data Control: Full control over whether lookback calculations will include data from Extended Trading Hours (ETH), or if the daily calculations will start actively only from the first bar of Regular Trading Hours (RTH). Status Validation: The indicator performs critical status checks and displays clear Warning Messages if risk conditions are not met. 4. Intuitive Visualization and Real-Time Data Dynamic Tracking Lines: The Entry Price and Stop Loss lines are updated with every new bar. Crucially, the length of these lines dynamically reflects the calculation's lookback range (e.g., the extent of Lookback Bars or the location of the confirmed Pivot Point), providing a visual anchor for the calculated price. Risk and Reward Zones: The indicator creates a graphical background fill between Entry and Stop Loss (marked with the risk color) and between Entry and the Reward Targets (marked with the reward color). Essential Information Labels: Labels are placed at the end of each line, providing critical data: Estimated Entry Price, Stock/Contract Quantity (Quantity), Total Entry Amount, Estimated Stop Loss, Risk per Share, Total Financial Risk (Risk Amount), Exit Amount, Estimated Take Profit 1/2/3, Reward/Risk Ratio 1/2/3, Total Reward 1/2/3, TP Exit Amount 1/2/3. 4.1. Data Window Metrics (16 Full Series) The indicator displays 16 full data series in the TradingView Data Window, allowing precise tracking of every calculation parameter: Entry Data: Estimated Entry, Quantity, Entry Amount. Risk Data (Stop Loss): Estimated Stop Loss, Risk per Share, Risk Amount, Exit Amount. Reward Data (Take Profit): Estimated Take Profit 1/2/3, Reward/Risk Ratio 1/2/3, Total Reward 1/2/3, TP Exit Amount 1/2/3. 4.2. Instant Tracking in the Status Line The indicator displays 6 critical parameters continuously in the indicator's Status Line: Estimated Entry, Quantity, Estimated Stop Loss, Estimated Take Profit 1/2/3. 5. Detailed Indicator Inputs 5.1 General Focused Trend: Defines the analysis direction (Uptrend / Downtrend). Max Capital per Trade: The maximum amount allocated to purchasing stocks/contracts (in account currency). Risk per Trade: The maximum amount the user is willing to risk in this single trade (in account currency). ATR Length: The lookback period for the Average True Range (ATR) calculation. 5.2 Intraday Session Data Control Regular Hours Limitation : If enabled, all daily lookback calculations (for Entry/Stop Loss anchor points) will begin strictly from the first Regular Trading Hours (RTH) bar. This limits the lookback range to the current RTH session, excluding preceding Extended Trading Hours (ETH) data. Only relevant for Intraday charts. Default: False (Off) 5.3 Entry Inputs Entry Method: Selects the entry price calculation method: Current Price: Uses the closing price of the current bar as the estimated entry point (Market Entry). ATR Real Bodies Margin : - Uptrend: Calculates the Maximum Real Body over the lookback period + the calculated safety margin. - Downtrend: Calculates the Minimum Real Body over the lookback period - the calculated safety margin. ATR Bars Margin : - Uptrend: Calculates the Maximum High price over the lookback period + the calculated safety margin. - Downtrend: Calculates the Minimum Low price over the lookback period - the calculated safety margin. Lookback Bars: The number of bars used to calculate the extremes in the ATR-based entry methods (Relevant only for ATR Real Bodies Margin and ATR Bars Margin methods). ATR Multiplier (Entry): The multiplier applied to the ATR value. The result of the multiplication is the calculated safety margin used to determine the estimated Entry Price. 5.4 Risk Inputs (Stop Loss) Risk Method: Selects the Stop Loss price calculation method. ATR Current Price Margin : - Uptrend: Entry Price - the calculated safety margin. - Downtrend: Entry Price + the calculated safety margin. ATR Current Bar Margin : - Uptrend: Current Bar's Low price - the calculated safety margin. - Downtrend: Current Bar's High price + the calculated safety margin. ATR Bars Margin : - Uptrend: Lowest Low over lookback period - the calculated safety margin. - Downtrend: Highest High over lookback period + the calculated safety margin. ATR Pivot Margin : - Uptrend: The first confirmed Pivot Low point - the calculated safety margin. - Downtrend: The first confirmed Pivot High point + the calculated safety margin. Lookback Bars: The lookback period for finding the extreme price used in the 'ATR Bars Margin' calculation. ATR Multiplier (Risk): The multiplier applied to the ATR value. The result of the multiplication is the calculated safety margin used to place the estimated Stop Loss. Note: If set to 0, the Stop Loss will be placed exactly at the technical anchor point, provided the Minimum Margin Value is also 0. Minimum Margin Value: The minimum price value (e.g., $0.01) the Stop Loss margin buffer must be. Pivot (Left / Right): The number of bars required on either side of the pivot bar for confirmation (relevant only for the ATR Pivot Margin method). 5.5 Reward Inputs (Take Profit) Show Take Profit 1/2/3: ON/OFF switch to control the visibility of each Take Profit target. Reward/Risk Ratio 1/ 2/ 3: Defines the R/R ratio for the profit target. Must be ≥1.0. 6. Indicator Status/Warning Messages In situations where the Stop Loss location cannot be calculated logically and validly, often caused by a mismatch between the configured Focused Trend (Uptrend/Downtrend) and the actual price action, the indicator will display a warning message, explaining the reason and suggesting corrective action. Status Message 1: Pivot reference unavailable Condition: The Stop Loss is set to the "ATR Pivot Margin" method, but the anchor point (Pivot) is missing or inaccessible. Message Displayed: "Pivot reference unavailable. Wait for valid price action, or adjust the Regular Hours Limitation setting or Pivot Left/Right inputs." Status Message 2: Calculated Stop Loss is unsafe Condition: The calculated Stop Loss is placed illogically or unsafely relative to the trend direction and the Entry price. Message Displayed: "Calculated Stop Loss is unsafe for current trend. Wait for valid price action or adjust SL Lookback/Multiplier." 7. Summary The Adaptive Risk Management (ARM) indicator provides a seamless and systematic approach to trade execution and risk control. By dynamically automating all critical trade parameters—from Entry Price and Stop Loss placement to Position Sizing and Take Profit targets—ARM removes emotional bias and ensures every trade adheres strictly to your predefined risk profile. Key Benefits: Systematic Risk Control: Strict enforcement of maximum capital allocation and risk per trade limits. Adaptivity: Dynamic calculation of prices and quantities based on real-time market data (ATR and Lookback). Clarity and Trust: Clear on-chart visualization, precise data metrics (16 series), and unambiguous Status/Warning Messages ensure transparency and reliability. ARM allows traders to focus on strategy and analysis, confident that their execution complies with the core principles of professional risk management. Important Note: Trading Risk This indicator is intended for educational and informational purposes only and does not constitute investment advice or a recommendation for trading in any form whatsoever. Trading in financial markets involves significant risk of capital loss. It is important to remember that past performance is not indicative of future results. All trading decisions are your sole responsibility. Never trade with money you cannot afford to lose.
Pine Script® indicator
by sgbpulse
FAIR VALUE CEDEARSFair Value CEDEARS y ETFs Important: load together with the CEDEARdata library. Returns the “Fair Value” of CEDEAR and CEDEAR-based ETF prices traded on ByMA, using as a reference the price of the underlying ordinary share or ETF traded on the NYSE or NASDAQ. It multiplies the NYSE/NASDAQ price by the CEDEAR or ETF conversion ratio and converts the currency to ARS or Dólar MEP using the exchange rate implied by the AL30/AL30C ratio for tickers quoted in ARS (e.g., AAPL) and AL30D/AL30C for tickers quoted in Dólar MEP (e.g., AAPLD). If the CEDEAR or ETF quote is higher than Fair Value, it highlights the difference in red; if it is lower, it highlights it in green. If any of the markets is closed or in an auction period, it notifies the user and changes the background color. By default, the CEDEAR or ETF quote used is the last price, but the user may choose to use the BID or OFFER instead. This allows CEDEAR and ETF buyers to compare Fair Value against the OFFER, while sellers may prefer to measure Fair Value against the BID of the local instrument. BCBA:AAPL BCBA:AAPLD NASDAQ:AAPL BCBA:SPY BCBA:TSLA BCBA:TSLAD CEDEARS ETFs ByMA
Pine Script® indicator
by EcoValores
Absorption RatioThe Hidden Connections Between Markets Financial markets are not isolated islands. When panic spreads, seemingly unrelated assets suddenly begin moving in lockstep. Stocks, bonds, commodities, and currencies that normally provide diversification benefits start falling together. This phenomenon, where correlations spike during crises, has devastated portfolios throughout history. The Absorption Ratio provides a quantitative measure of this hidden fragility. The concept emerged from research at State Street Associates, where Mark Kritzman, Yuanzhen Li, Sebastien Page, and Roberto Rigobon developed a novel application of principal component analysis to measure systemic risk. Their 2011 paper in the Journal of Portfolio Management demonstrated that when markets become tightly coupled, the variance explained by the first few principal components increases dramatically. This concentration of variance signals elevated systemic risk. What the Absorption Ratio Measures Principal component analysis, or PCA, is a statistical technique that identifies the underlying factors driving a set of variables. When applied to asset returns, the first principal component typically captures broad market movements. The second might capture sector rotations or risk-on/risk-off dynamics. Additional components capture increasingly idiosyncratic patterns. The Absorption Ratio measures the fraction of total variance absorbed or explained by a fixed number of principal components. In the original research, Kritzman and colleagues used the first fifth of the eigenvectors. When this fraction is high, it means a small number of factors are driving most of the market movements. Assets are moving together, and diversification provides less protection than usual. Consider an analogy: imagine a room full of people having independent conversations. Each person speaks at different times about different topics. The total "variance" of sound in the room comes from many independent sources. Now imagine a fire alarm goes off. Suddenly everyone is talking about the same thing, moving in the same direction. The variance is now dominated by a single factor. The Absorption Ratio captures this transition from diverse, independent behavior to unified, correlated movement. The Implementation Approach TradingView does not support matrix algebra required for true principal component analysis. This implementation uses a closely related proxy: the average absolute correlation across a universe of major asset classes. This approach captures the same underlying phenomenon because when assets are highly correlated, the first principal component explains more variance by mathematical necessity. The asset universe includes eight ETFs representing major investable categories: SPY and QQQ for large cap US equities, IWM for small caps, EFA for developed international markets, EEM for emerging markets, TLT for long-term treasuries, GLD for gold, and USO for oil. This selection provides exposure to equities across geographies and market caps, plus traditional diversifying assets. From eight assets, there are twenty-eight unique pairwise correlations. The indicator calculates each using a rolling window, takes the absolute value to measure coupling strength regardless of direction, and averages across all pairs. This average correlation is then transformed to match the typical range of published Absorption Ratio values. The transformation maps zero average correlation to an AR of 0.50 and perfect correlation to an AR of 1.00. This scaling aligns with empirical observations that the AR typically fluctuates between 0.60 and 0.95 in practice. Interpreting the Regimes The indicator classifies systemic risk into four regimes based on AR levels. The Extreme regime occurs when the AR exceeds 0.90. At this level, nearly all asset classes are moving together. Diversification has largely failed. Historically, this regime has coincided with major market dislocations: the 2008 financial crisis, the 2020 COVID crash, and significant correction periods. Portfolios constructed under normal correlation assumptions will experience larger drawdowns than expected. The High regime, between 0.80 and 0.90, indicates elevated systemic risk. Correlations across asset classes are above normal. This often occurs during the build-up to stress events or during volatile periods where fear is spreading but has not reached panic levels. Risk management should be more conservative. The Normal regime covers AR values between 0.60 and 0.80. This represents typical market conditions where some correlation exists between assets but diversification still provides meaningful benefits. Standard portfolio construction assumptions are reasonable. The Low regime, below 0.60, indicates that assets are behaving relatively independently. Diversification is working well. Idiosyncratic factors dominate returns rather than systematic risk. This environment is favorable for active management and security selection strategies. The Relationship to Portfolio Construction The implications for portfolio management are significant. Modern portfolio theory assumes correlations are stable and uses historical estimates to construct efficient portfolios. The Absorption Ratio reveals that this assumption is violated precisely when it matters most. When AR is elevated, the effective number of independent bets in a diversified portfolio shrinks. A portfolio holding stocks, bonds, commodities, and real estate might behave as if it holds only one or two positions during high AR periods. Position sizing based on normal correlation estimates will underestimate portfolio risk. Conversely, when AR is low, true diversification opportunities expand. The same nominal portfolio provides more independent return streams. Risk can be deployed more aggressively while maintaining the same effective exposure. Component Analysis The indicator separately tracks equity correlations and cross-asset correlations. These components tell different stories about market structure. Equity correlations measure coupling within the stock market. High equity correlation indicates broad risk-on or risk-off behavior where all stocks move together. This is common during both rallies and selloffs driven by macroeconomic factors. Stock pickers face headwinds when equity correlations are elevated because individual company fundamentals matter less than market beta. Cross-asset correlations measure coupling between different asset classes. When stocks, bonds, and commodities start moving together, traditional hedges fail. The classic 60/40 stock/bond portfolio, for example, assumes negative or low correlation between equities and treasuries. When cross-asset correlation spikes, this assumption breaks down. During the 2022 market environment, for instance, both stocks and bonds fell significantly as inflation and rate hikes affected all assets simultaneously. High cross-asset correlation warned that the usual defensive allocations would not provide their expected protection. Mean Reversion Characteristics Like most risk metrics, the Absorption Ratio tends to mean-revert over time. Extremely high AR readings eventually normalize as panic subsides and assets return to more independent behavior. Extremely low readings tend to rise as some level of systematic risk always reasserts itself. The indicator tracks AR in statistical terms by calculating its Z-score relative to the trailing distribution. When AR reaches extreme Z-scores, the probability of normalization increases. This creates potential opportunities for strategies that bet on mean reversion in systemic risk. A buy signal triggers when AR recovers from extremely elevated levels, suggesting the worst of the correlation spike may be over. A sell signal triggers when AR rises from unusually low levels, warning that complacency about diversification benefits may be excessive. Momentum and Trend The rate of change in AR carries information beyond the absolute level. Rapidly rising AR suggests correlations are increasing and systemic risk is building. Even if AR has not yet reached the high regime, acceleration in coupling should prompt increased vigilance. Falling AR momentum indicates normalizing conditions. Correlations are decreasing and assets are returning to more independent behavior. This often occurs in the recovery phase following stress events. Practical Application For asset allocators, the AR provides guidance on how much diversification benefit to expect from a given allocation. During high AR periods, reducing overall portfolio risk makes sense because the usual diversifiers provide less protection. During low AR periods, standard or even aggressive allocations are more appropriate. For risk managers, the AR serves as an early warning indicator. Rising AR often precedes large market moves and volatility spikes. Tightening risk limits before correlations reach extreme levels can protect capital. For systematic traders, the AR provides a regime filter. Mean reversion strategies may work better during high AR periods when panics create overshooting. Momentum strategies may work better during low AR periods when trends can develop independently across assets. Limitations and Considerations The proxy methodology introduces some approximation error relative to true PCA-based AR calculations. The asset universe, while representative, does not include all possible diversifiers. Correlation estimates are inherently backward-looking and can change rapidly. The transformation from average correlation to AR scale is calibrated to match typical published ranges but is not mathematically equivalent to the eigenvalue ratio. Users should interpret levels directionally rather than as precise measurements. Correlation regimes can persist longer than expected. Mean reversion signals indicate elevated probability of normalization but do not guarantee timing. High AR can remain elevated throughout extended crisis periods. References Kritzman, M., Li, Y., Page, S., and Rigobon, R. (2011). Principal Components as a Measure of Systemic Risk. Journal of Portfolio Management, 37(4), 112-126. Kritzman, M., and Li, Y. (2010). Skulls, Financial Turbulence, and Risk Management. Financial Analysts Journal, 66(5), 30-41. Billio, M., Getmansky, M., Lo, A., and Pelizzon, L. (2012). Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors. Journal of Financial Economics, 104(3), 535-559.
Pine Script® indicator
by EdgeTools
RiskCraft - Advanced Risk Management SystemRiskCraft – Risk Intelligence Dashboard Trade like you actually respect risk "I know the setup looks good… but how much am I actually risking right now?" RiskCraft is an open-source Pine Script v6 indicator that keeps risk transparent directly on the chart. It is not a signal generator; it is a risk desk that calculates size, frames volatility, and reminds you when your behaviour drifts away from the plan. Core utilities Calculates professional-style position sizing in real time. Reads volatility and market regime before position size is confirmed. Adjusts risk based on the trader’s emotional state and confidence inputs. Maps session risk across Asian, London, and New York hours. Draws exactly one stop line and one target line in the preferred direction. Provides rotating education tips plus contextual warnings when risk escalates. It is intentionally conservative and keeps you in the game long enough for any separate entry logic to matter. --- Chart layout checklist Use a clean chart on a liquid symbol (e.g., AMEX:SPY or major FX pairs). Main RiskCraft dashboard placed on the right edge. Session Risk box on the left with UTC time visible. Floating risk badge above price. Stop/target guide lines enabled. Education panel visible in the bottom-right corner. --- 1. On-chart components Right-side dashboard : account risk %, position size/value, stop, target, risk/reward, regime, trend strength, emotional state, behavioural score, correlation, and preferred trade direction. Session Risk box : highlights active session (Asian, London, NY), current UTC time, and risk label (High/Med/Low) per session. Floating risk badge : keeps actual account risk percent visible with colour-coded wording from Ultra Cautious to Very Aggressive. Stop/target lines : exactly one dashed stop and one dashed target aligned with the preferred bias. Education panel : rotates core principles and AI-style warnings tied to volatility, risk %, and behaviour flags. --- 2. Volatility engine – ATR with context 📈 atr = ta.atr(atrLength) atrPercent = (atr / close) * 100 atrSMA = ta.sma(atr, atrLength) volatilityRatio = atr / atrSMA isHighVol = volatilityRatio > volThreshold ATR vs ATR SMA shows how wild price is relative to recent history. Volatility ratio above the threshold flips isHighVol , which immediately trims risk. An ATR percentile rank over the last 100 bars indicates calm versus chaotic regimes. Daily ATR sampling via request.security() gives higher time-frame context for intraday sessions. When volatility spikes the script dials position size down automatically instead of cheering for maximum exposure. --- 3. Market regime radar – Danger or Drift 🌊 ema20 = ta.ema(close, 20) ema50 = ta.ema(close, 50) ema200 = ta.ema(close, 200) trendScore = (close > ema20 ? 1 : -1) + (ema20 > ema50 ? 1 : -1) + (ema50 > ema200 ? 1 : -1) = ta.dmi(14, 14) Regimes covered: Danger : high volatility with weak trend. Volatile : volatility elevated but structure still directional. Choppy : low ADX and noisy action. Trending : directional flows without extreme volatility. Mixed : anything between. Each regime maps to a 1–10 risk score and a multiplier that feeds the final position size. Danger and Choppy clamp size; Trending restores normal risk. --- 4. Behaviour engine – trader inputs matter 🧠 You provide: Emotional state : Confident, Neutral, FOMO, Revenge, Fearful. Confidence : slider from 1 to 10. Toggle for behavioural adjustment on/off. Behind the scenes: Each state triggers an emotional multiplier . Confidence produces a confidence multiplier . Combined they form behavioralFactor and a 0–100 Behavioural Score . High-risk emotions or low conviction clamp the final risk. Calm inputs allow normal size. The dashboard prints both fields to keep accountability on-screen. --- 5. Correlation guardrail – avoid stacking identical risk 📊 Optional correlation mode compares the active symbol to a reference (default AMEX:SPY ): corrClose = request.security(correlationSymbol, timeframe.period, close) priceReturn = ta.change(close) / close corrReturn = ta.change(corrClose) / corrClose correlation = calcCorrelation() Absolute correlation above the threshold applies a correlation multiplier (< 1) to reduce size. Dashboard row shows the live correlation and reference ticker. When disabled, the row simply echoes the current symbol, keeping the table readable. --- 6. Position sizing engine – heart of the script 💰 baseRiskAmount = accountSize * (baseRiskPercent / 100) adjustedRisk = baseRiskAmount * behavioralFactor * regimeAdjustment * volAdjustment * correlationAdjustment finalRiskAmount = math.min(adjustedRisk, accountSize * (maxRiskCap / 100)) stopDistance = atr * atrStopMultiplier takeProfit = atr * atrTargetMultiplier positionSize = stopDistance > 0 ? finalRiskAmount / stopDistance : 0 positionValue = positionSize * close Outputs shown on the dashboard: Position size in units and value in currency. Actual risk % back on account after adjustments. Risk/Reward derived from ATR-based stop and target. --- 7. Intelligent trade direction – bias without signals 🎯 Direction score ingredients: EMA stack alignment. Price versus EMA20. RSI momentum relative to 50. MACD line vs signal. Directional Movement (DI+/DI–). The resulting Trade Direction row prints LONG, SHORT, or NEUTRAL. No orders are generated—this is guidance so you only risk capital when the structure supports it. --- 8. Stop/target guide lines – two lines only ✂️ if showStopLines if preferLong // long stop below, target above else if preferShort // short stop above, target below Lines refresh each bar to keep clutter low. When the direction score is neutral, no lines appear. Use them as visual anchors, not auto-orders. --- 9. Session Risk map – global volatility clock 🌍 Tracks Asian, London, and New York windows via UTC. Computes average ATR per session versus global ATR SMA. Labels each session High/Med/Low and colours the cells accordingly. Top row shows the active session plus current UTC time so you always know the regime you are trading. One glance tells you whether you are trading quiet drift or the part of the day that hunts stops. --- 10. Floating risk badge – honesty above price 🪪 Text ranges from Ultra Cautious through Very Aggressive. Colour matches the risk palette inputs (High/Med/Low). Updates on the last bar only, keeping historical clutter off the chart. Account risk becomes impossible to ignore while you stare at price. --- 11. Education engine & warnings 📚 Rotates evergreen principles (risk 1–2%, journal trades, respect plan). Triggers contextual warnings when volatility and risk % conflict. Flags when emotional state = FOMO or Revenge. Highlights sub-standard risk/reward setups. When multiple danger flags stack, an AI-style warning overrides the tip text so you can course-correct before capital is exposed. --- 12. Alerts – hard guard rails 🚨 Excessive Risk Alert : actual risk % crosses custom threshold. High Volatility Alert : ATR behaviour signals danger regime. Emotional State Warning : FOMO or Revenge selected. Poor Risk/Reward Alert : risk/reward drops below your standard. All alerts reinforce discipline; none suggest entries or exits. --- 13. Multi-market behaviour 🕒 Intraday (1m–1h): session box and badge react quickly; ideal for scalpers needing constant risk context. Higher time frames (1D–1W): dashboard shifts slowly, supporting swing planning. Asset classes confirmed in validation: crypto majors, large-cap equities, indices, major FX pairs, and liquid commodities. Risk logic is price-based, so it adapts across markets without bespoke tuning. 15. Key inputs & recommended defaults Account Size : 10,000 (modify to match actual account; min 100). Base Risk % : 1.0 with a Maximum Risk Cap of 2.5%. ATR Period : 14, Stop Multiplier 2.0, Target Multiplier 3.0. High Vol Threshold : 1.5 for ATR ratio. Behavioural Adjustment : enabled by default; disable for fixed risk. Correlation Check : optional; default symbol AMEX:SPY , threshold 0.7. Display toggles : main dashboard, risk badge, session map, education panel, and stop lines can be individually disabled to reduce clutter. 16. Usage notes & limits Indicator mode only; no automated entries or exits. Trade history panel intentionally disabled (requires strategy context). Correlation analysis depends on additional data requests and may lag slightly on illiquid symbols. Session timing uses UTC; adjust expectations if you trade localized instruments. HTF ATR sampling uses daily data, so bar replay on lower charts may show brief data gaps while HTF loads. What does everyone think RISK really means?
Pine Script® indicator
by officialjackofalltrades
Daily Dollar Cost Averaging (DCA) Simulator & Yearly PerformanceThis indicator simulates a "Daily Dollar Cost Averaging" strategy directly on your chart. Unlike standard backtesters that trade based on signals, this script calculates the performance of a portfolio where a fixed dollar amount is invested every single day, regardless of price action. Key Features: Daily Accumulation: Simulates buying a specific dollar amount (e.g., $10) at the market close every day. Yearly Breakdown Table: A detailed dashboard displayed on the chart that breaks down performance by year. It tracks total invested, average entry price, total holdings, current value, and PnL percentage for each individual year. Global Stats: The bottom row of the table summarizes the total performance of the entire strategy since the start date. Breakeven Line: Plots a yellow line on the chart representing your "Global Average Price." When the current price is above this line, the total strategy is in profit. How to Use: Add to chart (Works best on the Daily (D) timeframe). Open settings to adjust your Daily Investment Amount and Start Year. The table will automatically update to show how a daily investment strategy would have performed over time.
Pine Script® indicator
by oojeson
Monthly DCA & Last 10 YearsThis Pine Script indicator simulates a Monthly Dollar Cost Averaging (DCA) strategy to help long-term investors visualize historical performance. Instead of complex timing, the script automatically executes a hypothetical fixed-dollar purchase (e.g., $100) on the first trading day of every month. It visually marks entry points with green "B" labels and plots a dynamic yellow line representing your Global Break-Even Price, allowing you to instantly see if the current price is above or below your average cost basis. To provide deep insight, it generates a detailed performance table in the bottom-right corner that breaks down metrics year-by-year—including total capital invested, shares/coins accumulated, and Profit/Loss percentage—along with a grand total summary of the entire investment period.
Pine Script® indicator
by oojeson
BTC Fear & Greed Incremental StrategyIMPORTANT: READ SETUP GUIDE BELOW OR IT WON'T WORK # BTC Fear & Greed Incremental Strategy — TradeMaster AI (Pure BTC Stack) ## Strategy Overview This advanced Bitcoin accumulation strategy is designed for long-term hodlers who want to systematically take profits during greed cycles and accumulate during fear periods, while preserving their core BTC position. Unlike traditional strategies that start with cash, this approach begins with a specified BTC allocation, making it perfect for existing Bitcoin holders who want to optimize their stack management. ## Key Features ### 🎯 **Pure BTC Stack Mode** - Start with any amount of BTC (configurable) - Strategy manages your existing stack, not new purchases - Perfect for hodlers who want to optimize without timing markets ### 📊 **Fear & Greed Integration** - Uses market sentiment data to drive buy/sell decisions - Configurable thresholds for greed (selling) and fear (buying) triggers - Automatic validation to ensure proper 0-100 scale data source ### 🐂 **Bull Year Optimization** - Smart quarterly selling during bull market years (2017, 2021, 2025) - Q1: 1% sells, Q2: 2% sells, Q3/Q4: 5% sells (configurable) - **NO SELLING** during non-bull years - pure accumulation mode - Preserves BTC during early bull phases, maximizes profits at peaks ### 🐻 **Bear Market Intelligence** - Multi-regime detection: Bull, Early Bear, Deep Bear, Early Bull - Different buying strategies based on market conditions - Enhanced buying during deep bear markets with configurable multipliers - Visual regime backgrounds for easy market condition identification ### 🛡️ **Risk Management** - Minimum BTC allocation floor (prevents selling entire stack) - Configurable position sizing for all trades - Multiple safety checks and validation ### 📈 **Advanced Visualization** - Clean 0-100 scale with 2 decimal precision - Three main indicators: BTC Allocation %, Fear & Greed Index, BTC Holdings - Real-time portfolio tracking with cash position display - Enhanced info table showing all key metrics ## How to Use ### **Step 1: Setup** 1. Add the strategy to your BTC/USD chart (daily timeframe recommended) 2. **CRITICAL**: In settings, change the "Fear & Greed Source" from "close" to a proper 0-100 Fear & Greed indicator --------------- I recommend Crypto Fear & Greed Index by TIA_Technology indicator When selecting source with this indicator, look for "Crypto Fear and Greed Index:Index" --------------- 3. Set your "Starting BTC Quantity" to match your actual holdings 4. Configure your preferred "Start Date" (when you want the strategy to begin) ### **Step 2: Configure Bull Year Logic** - Enable "Bull Year Logic" (default: enabled) - Adjust quarterly sell percentages: - Q1 (Jan-Mar): 1% (conservative early bull) - Q2 (Apr-Jun): 2% (moderate mid bull) - Q3/Q4 (Jul-Dec): 5% (aggressive peak targeting) - Add future bull years to the list as needed ### **Step 3: Fine-tune Thresholds** - **Greed Threshold**: 80 (sell when F&G > 80) - **Fear Threshold**: 20 (buy when F&G < 20 in bull markets) - **Deep Bear Fear Threshold**: 25 (enhanced buying in bear markets) - Adjust based on your risk tolerance ### **Step 4: Risk Management** - Set "Minimum BTC Allocation %" (default 20%) - prevents selling entire stack - Configure sell/buy percentages based on your position size - Enable bear market filters for enhanced timing ### **Step 5: Monitor Performance** - **Orange Line**: Your BTC allocation percentage (target: fluctuate between 20-100%) - **Blue Line**: Actual BTC holdings (should preserve core position) - **Pink Line**: Fear & Greed Index (drives all decisions) - **Table**: Real-time portfolio metrics including cash position ## Reading the Indicators ### **BTC Allocation Percentage (Orange Line)** - **100%**: All portfolio in BTC, no cash available for buying - **80%**: 80% BTC, 20% cash ready for fear buying - **20%**: Minimum allocation, maximum cash position ### **Trading Signals** - **Green Buy Signals**: Appear during fear periods with available cash - **Red Sell Signals**: Appear during greed periods in bull years only - **No Signals**: Either allocation limits reached or non-bull year ## Strategy Logic ### **Bull Years (2017, 2021, 2025)** - Q1: Conservative 1% sells (preserve stack for later) - Q2: Moderate 2% sells (gradual profit taking) - Q3/Q4: Aggressive 5% sells (peak targeting) - Fear buying active (accumulate on dips) ### **Non-Bull Years** - **Zero selling** - pure accumulation mode - Enhanced fear buying during bear markets - Focus on rebuilding stack for next bull cycle ## Important Notes - **This is not financial advice** - backtest thoroughly before use - Designed for **long-term holders** (4+ year cycles) - **Requires proper Fear & Greed data source** - validate in settings - Best used on **daily timeframe** for major trend following - **Cash calculations**: Use allocation % and BTC holdings to calculate available cash: `Cash = (Total Portfolio × (1 - Allocation%/100))` ## Risk Disclaimer This strategy involves active trading and position management. Past performance does not guarantee future results. Always do your own research and never invest more than you can afford to lose. The strategy is designed for educational purposes and long-term Bitcoin accumulation thesis. --- *Developed by Sol_Crypto for the Bitcoin community. Happy stacking! 🚀*
Pine Script® strategy
by Sol_Crypto
90% Buying Power Position Size Helper90% Buying Power Position Size Helper — Script Description This tool calculates a recommended share size based on your available buying power and the current market price. TradingView does not provide access to live broker balances, so this script allows you to manually enter your current buying power and instantly see how many shares you can buy using a chosen percentage of it (default: 90%). How It Works • Enter your Buying Power ($) • Choose the Percent to Use (e.g., 90%). • The script divides the selected portion of your buying power by the current price of the symbol. • A small display in the chart corner shows the recommended number of shares to buy. Formula shares = floor((buying_power * percent_to_use / 100) / price) What It’s For • Day traders who size positions based on account buying power • Traders who want a quick way to calculate share size per trade • Anyone who sizes entries using a fixed percentage of their account What It Doesn’t Do Due to TradingView limitations, the script cannot: • Read your live buying power or broker balance • Auto-fill orders or submit trades • Retrieve real account data from your broker You simply update the buying power input whenever your account changes, and the script does the rest. Why It’s Useful • Keeps you consistent with position sizing • Reduces manual math during fast trading • Prevents oversizing or undersizing trades • Helps maintain discipline and risk control
Pine Script® indicator
by TraderBecks
Fixed Dollar Risk Lines V2*This is a small update to the original concept that adds greater customization of the visual elements of the script. Since some folks have liked the original I figured I'd put this out there.* Fixed 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.
Pine Script® indicator
by smg387
3-Daumen-RegelThis indicator evaluates three key market conditions and summarizes them in a compact table using simple thumbs-up / thumbs-down signals. It’s designed specifically for daily timeframes and helps you quickly assess whether a market is showing technical strength or weakness. The Three Checks Price Above the 200-Day SMA Indicates the long-term trend direction. A thumbs-up means the price is trading above the 200-day moving average. Positive Performance During the First 5 Trading Days of the Year (YTD Start) Measures early-year strength. If not enough bars are available, a warning is shown. Price Above the YTD Level Compares the current price to the first trading day’s close of the year. Color Coding for Instant Clarity Green: Condition met Red: Condition not met This creates a compact “thumbs check” that gives you a quick read on the market’s technical health. Note The indicator is intended for daily charts. A message appears if a different timeframe is used.
Pine Script® indicator
by Yazici-Investment-Management