Cyber MondayGIẢM GIÁ lên tới 80%

Chỉ báo và chiến lược

Trendshift [CHE] StrategyTrendshift Strategy — First-Shift Structural Regime Trading Profitfactor 2,603 Summary Trendshift Strategy implements a structural regime-shift trading model built around the earliest confirmed change in directional structure. It identifies major swing highs and lows, validates breakouts through optional ATR-based conviction, and reacts only to the first confirmed shift in each direction. After a regime reversal, the strategy constructs a premium and discount band between the breakout candle and the previous opposite swing. This band is used as contextual bias and may optionally inform stop placement and position sizing. The strategy focuses on clear, interpretable structural events rather than continuous signal generation. By limiting entries to the first valid shift, it reduces false recycles and allows the structural state to stabilize before a new trade occurs. All signals operate on closed-bar logic, and the strategy avoids higher-timeframe calls to stabilize execution behavior. Motivation: Why this design? Many structure-based systems repeatedly trigger as price fluctuates around prior highs and lows. This often leads to multiple flips during volatile or choppy conditions. Trendshift Strategy addresses this problem by restricting execution to the first confirmed structural event in each direction. ATR-based filters help differentiate genuine structural breaks from noise, while the contextual band ensures that the breakout is meaningful in relation to recent volatility. The design aims to represent a minimalistic structural trading framework focused on regime turns rather than continuous trend signaling. This reduces chart noise and clarifies where the market transitions from one regime to another. What’s different vs. standard approaches? Baseline reference Typical swing-based structure indicators report every break above or below recent swing points. Architecture differences First-shift-only regime logic that blocks repeated signals until direction reverses ATR-filtered validation to avoid weak or momentum-less breaks Premium and discount bands derived from breakout structure Optional band-driven stop placement Optional band-dependent position-sizing factor Regime timeout system to neutralize structure after extended inactivity Persistent-state architecture to prevent re-triggering Practical effect Only the earliest actionable structure change is traded Fewer but higher-quality signals Premium/discount tint assists contextual evaluation Stops and sizing can be aligned with structural context rather than arbitrary volatility measures Improved chart interpretability due to reduced marker frequency How it works (technical) The algorithm evaluates symmetric swing points using a fixed bar window. When a swing forms, its value and bar index are stored as persistent state. A structural shift occurs when price closes beyond the most recent major swing on the opposite side. If ATR filtering is enabled, the breakout must exceed a volatility-scaled distance to prevent micro-breaks from firing. Once a valid shift is confirmed, the regime is updated to bullish or bearish. The script records the breakout level, the opposite swing, and derives a band between them. This band is checked for minimum size relative to ATR to avoid unrealistic contexts. The first shift in a new direction generates both the strategy entry and a visual marker. Additional shifts in the same direction are suppressed until a reversal occurs. If a timeout is enabled, the regime resets after a specified number of bars without structural change, optionally clearing the band. Stop placement, if enabled, uses either the opposite or same band edge depending on configuration. Position size is computed from account percentage and may optionally scale with the price-span-to-ATR relationship. Parameter Guide Market Structure Swing length (default 5): Controls swing sensitivity. Lower values increase responsiveness. Use ATR filter (default true): Requires breakouts to show momentum relative to ATR. Reduces false shifts. ATR length (default 14): Volatility estimation for breakout and band validation. Break ATR multiplier (default 1.0): Required breakout strength relative to ATR. Premium/Discount Framework Enable framework (default true): Activates premium/discount evaluation. Persist band on timeout (default true): Keeps structural band after timeout. Min band ATR mult (default 0.5): Rejects narrow bands. Regime timeout bars (default 500): Neutralizes regime after inactivity. Invert colors (default false): Color scheme toggle. Visuals Show zone tint (default true): Background shade in premium or discount region. Show shift markers (default true): Display first-shift markers. Execution and Risk Risk per trade percent (default 1.0): Determines position size as account percentage. Use band for size (default false): Scales size relative to band width behavior. Flat on opposite shift (default true): Forces reversal behavior. Use stop at band (default false): Stop anchored to band edges. Stop band side: Chooses which band edge is used for stop generation. Reading & Interpretation A green background indicates discount conditions within the structural band; red indicates premium conditions. A green triangle below price marks the first bullish structural shift after a bearish regime. A red triangle above price marks the first bearish structural shift after a bullish regime. When stops are active, the opposite band edge typically defines the protective level. Band width relative to ATR indicates how significant a structural change is: wider bands imply stronger volatility structure, while narrow bands may be suppressed by the minimum-size filter. Practical Workflows & Combinations Trend following: Use first-shift entries as initial regime confirmation. Add higher-timeframe trend filters for additional context. Swing trading: Combine with simple liquidity or fair-value-gap concepts to refine entries. Bias mapping: Use higher timeframes for structural regime and lower timeframes for execution within the premium/discount context. Exit management: When using stops, consider ATR-scaling or multi-stage profit targets. When not using stops, reversals become the primary exit. Behavior, Constraints & Performance The strategy uses only confirmed swings and closed-bar logic, avoiding intrabar repaint. Pivot-based swings inherently appear after the pivot window completes, which is standard behavior. No higher-timeframe calls are used, preventing HTF-related repaint issues. Persistent variables track regime and structural levels, minimizing recomputation. The maximum bars back setting is five-thousand. The design avoids loops and arrays, keeping performance stable. Known limitations include limited signal density during consolidations, delayed swing confirmation, and sensitivity to extreme gaps that stretch band logic. ATR filtering mitigates some of these effects but does not eliminate them entirely. Sensible Defaults & Quick Tuning Fewer but stronger entries: Increase swing length or ATR breakout multiplier. More responsive entries: Reduce swing length to capture earlier shifts. More active band behavior: Lower the minimum band ATR threshold. Stricter stop logic: Use the opposite band edge for stop placement. Volatile markets: Increase ATR length slightly to stabilize behavior. What this indicator is—and isn’t Trendshift Strategy is a structural-regime trading engine that evaluates major directional shifts. It is not a complete trading system and does not include take-profit logic or prediction features. It does not attempt to forecast future price movement and should be used alongside broader market structure, volatility context, and disciplined risk management. Disclaimer The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context. Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved. By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk. Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts. Best regards and happy trading Chervolino
Chiến lược Pine Script®
của chervolino
Weekly Open + Monday High/Low (After Monday Close)b]Description This indicator marks key weekly reference levels based on Monday’s price behavior. It automatically detects each trading week and tracks: • Weekly Open – the first traded price of the new week • Monday High – the highest price reached on Monday • Monday Low – the lowest price reached on Monday Logic The Monday range is fully captured only after Monday has closed . No levels are plotted during Monday. Starting from Tuesday, the indicator displays thin dots showing the completed Monday High, Monday Low, and Weekly Open for the remainder of the week. When a new week begins, the indicator resets automatically and begins tracking the new week’s Monday. Customization The user can choose colors for: • Monday High/Low • Weekly Open Purpose This indicator helps traders visualize weekly structure, monitor weekly opening levels, and quickly identify Monday’s range for weekly bias analysis or strategy development. It can also be used to manually backtest Monday range strategies .
Chỉ báo Pine Script®
của Random_TraderDude
LJ Parsons Adjustable expanding MRT Fibpapers.ssrn.com Market Resonance Theory (MRT) reinterprets financial markets as structured multiplicative, recursive systems rather than linear, dollar-based constructs. By mapping price growth as a logarithmic lattice of intervals, MRT identifies the deep structural cycles underlying long-term market behaviour. The model draws inspiration from the proportional relationships found in musical resonance, specifically the equal temperament system, revealing that markets expand through recurring octaves of compounded growth. This framework reframes volatility, not as noise, but as part of a larger self-organising structure.
Chỉ báo Pine Script®
của wallabimba
Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel. █ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values. █ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical  Volatility  Rank, and Historical  Volatility  Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models. Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias. Pros:   • Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator. Cons:   • The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.   • Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.   • Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute   deviation makes the historical volatility more stable with extreme values. Parkinson Hv Estimator:   • Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.) Garman-Klass Estimator:   • Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.   • The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility. Rogers-Satchell Estimator:   • Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.   • The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.   • Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility. Garman-Klass Yang-Zhang extension:   • Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.   • The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies. Yang-Zhang Estimator:   • The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.   • This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.   • 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV. EWMA Estimator: • EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.   • EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.   • RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.   • However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH. Adjusted Mean Absolute Deviation Estimator:   • This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.   • It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.   • The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.   • In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility. Adjusted Median Absolute Deviation Estimator:   • This is another estimator that does not use standard deviation to measure volatility.   • Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.   • The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators. █ FEATURES   • You can select the volatility estimator models in the Volatility Model input   • Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.   • Alpha is used to adjust the Yang Zhang volatility estimator value.   • Percentile Length is used to Adjust Percentile coloring lookbacks.   • The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.   • When percentile coloring is off, it won’t show the gradient color.   • You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.   • When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon. • You can choose the plot style, such as lines, columns, areas in the plotstyle input.   • When the show information panel is on, a small panel will display on the right.   • The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value. █ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move). █ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
Chỉ báo Pine Script®
của Motgench
Linear Moments█ OVERVIEW The Linear Moments indicator, also known as L-moments, is a statistical tool used to estimate the properties of a probability distribution. It is an alternative to conventional moments and is more robust to outliers and extreme values. █ CONCEPTS █ Four moments of a distribution We have mentioned the concept of the Moments of a distribution in one of our previous posts. The method of Linear Moments allows us to calculate more robust measures that describe the shape features of a distribution and are anallougous to those of conventional moments. L-moments therefore provide estimates of the location, scale, skewness, and kurtosis of a probability distribution. The first L-moment, λ₁, is equivalent to the sample mean and represents the location of the distribution. The second L-moment, λ₂, is a measure of the dispersion of the distribution, similar to the sample standard deviation. The third and fourth L-moments, λ₃ and λ₄, respectively, are the measures of skewness and kurtosis of the distribution. Higher order L-moments can also be calculated to provide more detailed information about the shape of the distribution. One advantage of using L-moments over conventional moments is that they are less affected by outliers and extreme values. This is because L-moments are based on order statistics, which are more resistant to the influence of outliers. By contrast, conventional moments are based on the deviations of each data point from the sample mean, and outliers can have a disproportionate effect on these deviations, leading to skewed or biased estimates of the distribution parameters. █ Order Statistics L-moments are statistical measures that are based on linear combinations of order statistics, which are the sorted values in a dataset. This approach makes L-moments more resistant to the influence of outliers and extreme values. However, the computation of L-moments requires sorting the order statistics, which can lead to a higher computational complexity. To address this issue, we have implemented an Online Sorting Algorithm that efficiently obtains the sorted dataset of order statistics, reducing the time complexity of the indicator. The Online Sorting Algorithm is an efficient method for sorting large datasets that can be updated incrementally, making it well-suited for use in trading applications where data is often streamed in real-time. By using this algorithm to compute L-moments, we can obtain robust estimates of distribution parameters while minimizing the computational resources required. █ Bias and efficiency of an estimator One of the key advantages of L-moments over conventional moments is that they approach their asymptotic normal closer than conventional moments. This means that as the sample size increases, the L-moments provide more accurate estimates of the distribution parameters. Asymptotic normality is a statistical property that describes the behavior of an estimator as the sample size increases. As the sample size gets larger, the distribution of the estimator approaches a normal distribution, which is a bell-shaped curve. The mean and variance of the estimator are also related to the true mean and variance of the population, and these relationships become more accurate as the sample size increases. The concept of asymptotic normality is important because it allows us to make inferences about the population based on the properties of the sample. If an estimator is asymptotically normal, we can use the properties of the normal distribution to calculate the probability of observing a particular value of the estimator, given the sample size and other relevant parameters. In the case of L-moments, the fact that they approach their asymptotic normal more closely than conventional moments means that they provide more accurate estimates of the distribution parameters as the sample size increases. This is especially useful in situations where the sample size is small, such as when working with financial data. By using L-moments to estimate the properties of a distribution, traders can make more informed decisions about their investments and manage their risk more effectively. Below we can see the empirical dsitributions of the Variance and L-scale estimators. We ran 10000 simulations with a sample size of 100. Here we can clearly see how the L-moment estimator approaches the normal distribution more closely and how such an estimator can be more representative of the underlying population. █ WAYS TO USE THIS INDICATOR The Linear Moments indicator can be used to estimate the L-moments of a dataset and provide insights into the underlying probability distribution. By analyzing the L-moments, traders can make inferences about the shape of the distribution, such as whether it is symmetric or skewed, and the degree of its spread and peakedness. This information can be useful in predicting future market movements and developing trading strategies. One can also compare the L-moments of the dataset at hand with the L-moments of certain commonly used probability distributions. Finance is especially known for the use of certain fat tailed distributions such as Laplace or Student-t. We have built in the theoretical values of L-kurtosis for certain common distributions. In this way a person can compare our observed L-kurtosis with the one of the selected theoretical distribution. █ FEATURES Source Settings Source - Select the source you wish the indicator to calculate on Source Selection - Selec whether you wish to calculate on the source value or its log return Moments Settings Moments Selection - Select the L-moment you wish to be displayed Lookback - Determine the sample size you wish the L-moments to be calculated with Theoretical Distribution - This setting is only for investingating the kurtosis of our dataset. One can compare our observed kurtosis with the kurtosis of a selected theoretical distribution.
Chỉ báo Pine Script®
của Motgench
Sequential Exhaustion 9/13 [Crypto Filter] - PyraTimeConcept: The Exhaustion Meter This indicator is a customized version of the Sequential count, a powerful tool used by institutional traders to measure buyer and seller exhaustion. It looks for a sequence of 9 (Setup) or 13 (Countdown) consecutive candles that satisfy specific price criteria. The purpose is simple: To tell you when a trend has run out of fuel. Key Differentiators (The Value) Due to the high volatility of the crypto market, standard Sequential indicators print too many false signals ("13s") during a strong trend. This custom version solves that problem with two core filters: 1. Trend Filter (EMA 200): If enabled, the indicator will automatically hide all Sell signals when the price is above the 200 EMA, protecting the user from shorting an uptrend (and vice-versa). 2. Color Confirmation: It will not print a signal unless the closing candle color matches the direction (e.g., no Red 13 sell signals on Green Candles). This drastically cleans up the chart. Understanding the Numbers The numbers appearing above and below the candles are your exhaustion meter. * The "9" (Setup): Indicates a short-term trend is nearing exhaustion. * The "13" (Countdown): Indicates the trend is statistically complete and a reversal is highly probable. The Actionable Strategy (The PyraTime Rule) This indicator is designed to be your Exit Tool. Use it to determine when to take profit from an existing trade. * Example: You enter Long at the GPM Time Line. When the PyraTD prints a Red 9 or Red 13, you take profit immediately. Final Note Use the integrated visibility settings to turn off signals (e.g., hide 9s or Sells) to customize the view to your preferred trading style. Disclaimer: This tool measures mathematical exhaustion and is part of the PyraTime system. It is not financial advice.
Chỉ báo Pine Script®
của PyraTime
Daily ATR Dashboard - NIRALADaily ATR Dashboard: Volatility at a Glance What is this? The "Daily ATR Dashboard" is a simple, non-intrusive utility tool designed for intraday traders. It places a clean information table in the top-right corner of your chart, displaying the Daily Average True Range (DATR) for the current session and the previous two days. Why is it useful? Understanding daily volatility is crucial for setting realistic targets and stop-losses. Know the Range: Instantly see how much the instrument typically moves in a day. Context: Compare today's volatility with yesterday's and the day before to gauge if the market is expanding (becoming more volatile) or contracting (consolidating). Clean Charts: Instead of plotting a messy ATR line indicator below your price action, this dashboard gives you the raw data you need without cluttering your workspace. Features: Real-Time Data: The "Today" row updates in real-time as the current daily candle develops. Historical Context: Automatically fetches and displays the final DATR values for the previous two sessions ("Yesterday" and "Day Before"). Highlighted Current Day: The current day's data is highlighted in yellow for immediate visibility. Customizable: You can adjust the ATR length (default is 14) and the text size to fit your screen perfectly. How to Read It: Today: The current volatility of the ongoing daily session. Yesterday / Day Before: The finalized volatility of past sessions. Tip: If "Today's" ATR is significantly lower than the previous days, expect potential expansion or a breakout soon. If it is significantly higher, the market may be overextended. Settings: DATR Length: The lookback period for the ATR calculation (Default: 14). Text Size: Adjust the size of the table text (Tiny, Small, Normal, Large).
Chỉ báo Pine Script®
của aknoshutters
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.
Chỉ báo Pine Script®
của smg387
Trend Flip Exhaustion SignalsThis Pine Script is designed to generate buy and short trading signals based on a combination of technical indicators. It calculates fast and slow EMAs, RSI, a linear regression channel, and a simplified TTM squeeze histogram to measure momentum. - Short signals trigger when price is above both EMAs, near the upper regression channel, momentum is weakening, volume is fading, and RSI is overbought. - Buy signals trigger when price is below both EMAs, near the lower regression channel, momentum is strengthening, volume is surging, and RSI is oversold. - Signals are displayed as labels anchored to price bars (with optional plotshape arrows for backup). - The script also plots the EMAs and regression channel for visual context. In short - it’s a trend‑following entry tool that highlights potential exhaustion points for shorts and potential reversals for buys, with clear on‑chart markers to guide decision‑making.
Chỉ báo Pine Script®
của alexh1166
FPT - DCA ModelFPT - DCA Model is a simple but powerful tool to backtest a weekly “buy the dip” DCA plan with dynamic position sizing and partial profit-taking. 🔹 Core Idea - Invest a fixed amount every week (on Friday closes) - Buy more aggressively when price trades at a discount from its 52-week high - Take partial profits when price stretches too far above the daily EMA50 - Track the performance of your DCA plan vs a simple buy-and-hold from the same start date ⚙ How it works 1. Weekly DCA (on Daily timeframe) - On each Friday after the Start Date: - Add the “Weekly contribution” to the cash pool. - If the close is below the “Discount from 52W high” level: → FULL DCA: use the full weekly contribution + an extra booster from your stash (up to “Max extra stash used on dip”). → Marked on the chart with a small green triangle under the bar. - Otherwise: → HALF DCA: invest only 50% of the weekly contribution and keep the other 50% as stash (uninvested cash). → Marked with a small blue triangle under the bar. 2. 52-Week High Discount Logic - The script computes the 52-week high as the highest daily high of the last 252 trading days. - The “discount level” is: 52W high × (1 – Discount%). - When price is at or below this level, dips are treated as buying opportunities and the model allocates more. 3. Selling Logic (Partial Take Profit) - When the close is above the daily EMA50 by the selected percentage: → Sell the given “Sell portion of qty (%)” of your current holdings. → Marked with a small red triangle above the bar. - This behaves like a gradual profit-taking system: if price stays extended above EMA50, multiple partial sells can occur over time. 📊 Panel (top-right) The panel summarizes the state of your DCA plan: - Weeks: number of DCA weeks since Start Date - Total deposit: total money contributed (sum of all weekly contributions) - Shares qty: total number of shares accumulated - Avg price: volume-weighted average entry price - Shares value: current market value of all shares (qty × close) - Cash: uninvested cash (including saved stash) - Total equity: Shares value + Cash - DCA % PnL: performance of the DCA plan vs total deposits - Stock % since start: performance of the underlying asset since the Start Date ✅ Recommended Use - Timeframe: Daily (the DCA engine is designed to run on daily bars and Friday closes). - Works best on stocks, ETFs or indices where a 52-week high is a meaningful reference. - You can tune: - Weekly contribution - Discount from 52W high - Booster amount - EMA50 extension threshold and sell portion ⚠ Notes & Disclaimer - This script is a backtesting and educational tool. It does not place real orders. - Past performance does not guarantee future results. - Always combine DCA and risk management with your own research and judgment. Built by FPT (Funded Pips Trading) for long-term, rules-based DCA planning.
Chỉ báo Pine Script®
của erendev
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.
Chỉ báo Pine Script®
của Yazici-Investment-Management
Intraday Day-Trade Scanner//@version=5 indicator("Intraday Day-Trade Scanner", overlay=true) // ----- Inputs ----- minFloat = input.int(10000000, "Min Float") maxFloat = input.int(20000000, "Max Float") minPrice = input.float(3, "Min Price") maxPrice = input.float(50, "Max Price") minRVOL = input.float(1.5, "Min Relative Volume") minAtrPct = input.float(1.0, "Min ATR %") maxAtrPct = input.float(5.0, "Max ATR %") useLong = input.bool(true, "Long scan (above VWAP)") useShort = input.bool(false, "Short scan (below VWAP)") // ----- Data ----- float = request.financial(syminfo.tickerid, "FLOAT", "FQ") avgVol = ta.sma(volume, 20) rvol = volume / avgVol atr = ta.atr(14) atrPct = (atr / close) * 100 // VWAP vwap = ta.vwap(close) // ----- Conditions ----- floatOK = float >= minFloat and float <= maxFloat priceOK = close >= minPrice and close <= maxPrice rvolOK = rvol >= minRVOL atrOK = atrPct >= minAtrPct and atrPct <= maxAtrPct longOK = useLong and close > vwap shortOK = useShort and close < vwap qualified = floatOK and priceOK and rvolOK and atrOK and (longOK or shortOK) // ----- Plot label on chart ----- plotshape(qualified,title ="Qualified Stock", text="SCAN HIT", style=shape.labelup, size=size.small, color=color.new(color.green, 0)) // ----- Alerts ----- alertcondition(qualified, title="Trade Candidate Found", message="This stock meets your day-trade scan criteria!")
Chỉ báo Pine Script®
của Remy72326
FVG + Bollinger + Toggles + Swing H&L (Taken/Close modes)This indicator combines multiple advanced market-structure tools into one unified system. It detects A–C Fair Value Gaps (FVG) and plots them as dynamic boxes projected a fixed number of bars forward. Each bullish or bearish FVG updates in real time and “closes” once price breaks through the opposite boundary. The indicator also includes Bollinger Bands based on EMA-50 with adjustable deviation settings for volatility context. Swing Highs and Swing Lows are identified using pivot logic and are drawn as dynamic lines that change color once taken out. You can choose whether swings end on a close break or on any touch/violation of the level. All visual elements—FVGs, Bollinger Bands, and Swing Lines—can be individually toggled on or off from the settings panel. A time-window session box is included, allowing you to highlight a custom intraday window based on your selected timezone. The session box automatically tracks the high and low of the window and locks the final range once the window closes. Overall, the tool is designed for traders who want a structured, multi-layered view of liquidity, volatility, and intraday timing.
Chỉ báo Pine Script®
của Menash100
Volume Profile S/R + OB/OS + BreaksAs a support resistance trader I have created this indicator that shows SR lines. RSI over bought and over sold. I also added momentum candle. It's easy to use. The arrows show over bought and over sold, that's where I start to be interested. Confirmation is if we are near a support/resistance area. shown as a red/green line. Don't just trade the RSI, Be patient and only take the perfekt setups. I't clean, it's simple it works.
Chỉ báo Pine Script®
của TheHeikinAshitrader