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THF Scalp & Trend + FVG [English]This indicator is a comprehensive "All-In-One" trading suite designed for Scalpers and Day Traders who look for confluence between Trend Following indicators and Price Action (Fair Value Gaps). It combines two powerful concepts into a single chart overlay: 1. Moving Average Crossovers & Trend Filtering (THF Logic). 2. Fair Value Gaps (FVG) detection for entry/exit targets. ### 🛠️ Key Features: **1. Trend & Scalp Signals:** - **Scalp Signals:** Based on fast EMA crossovers (default 7/21). These signals can be filtered by a long-term SMA (200) to ensure you are trading with the major trend. - **Trend Signals:** Identifies stronger trend shifts using EMA 21 crossing SMA 50. - **Major Crosses:** Automatically highlights Golden Cross (SMA 50 > 200) and Death Cross events. **2. Price Action (FVG - Fair Value Gaps):** - Integrated **LuxAlgo's Fair Value Gap** logic to identify imbalances in the market. - Displays Bullish/Bearish zones which act as magnets for price or support/resistance levels. - Includes a Dashboard to track mitigated vs. unmitigated zones. **3. Momentum & Volume Confluence:** - **Visual Volume:** Candles are colored based on volume relative to the average (Volume SMA). - **RSI & MACD Signals:** Optional overlays to spot overbought/oversold conditions or momentum shifts directly on the chart. ### 🎯 How to Use: - **For Scalping:** Wait for a "SCALP BUY" signal while the price is above the SMA 200 (Trend Filter). Use the FVG boxes as potential Take Profit targets. - **For Trend Trading:** Look for the "Trend BUY" label and confirm with the Golden Cross. - **Stop Loss:** Can be placed below the recent swing low or below the EMA 50. ---------------------------------------------------------------- **CREDITS & ATTRIBUTION:** This script is a mashup of custom trend logic and open-source community codes. - **Fair Value Gap:** Full credit goes to **LuxAlgo** for the FVG detection algorithm and dashboard logic. This script utilizes their open-source calculation methods to enhance the trend strategy. - **Trend Logic:** Based on classic Moving Average crossover strategies tailored for scalping. *Disclaimer: This tool is for educational purposes only. Always manage your risk.*
Индикатор Pine Script®
от THF84
SNP420/RSI_GOD_KOMPLEXRSI_GOD_KOMPLEX is a multi–timeframe RSI scanner for TradingView that displays a compact table in the top-right corner of the chart. For each timeframe (1m, 5m, 15m, 30m, 1h, 4h, 1d) it tracks the fast RSI line (not the smoothed/main one) and marks BUY in green when RSI crosses up through 30 (leaving oversold territory) and SELL in red when RSI crosses down through 70 (leaving overbought territory), always using only closed candles for reliable, non-repainting signals. The indicator remembers the last valid signal per timeframe, so the table always shows the most recent directional impulse from RSI across all selected timeframes on the same instrument. author: SNP420 + Jarvis project: FNXS ps: piece and love
Индикатор Pine Script®
от SNP_420
AlphaRank MA Lens – Multi-Timeframe Moving Average MapAlphaRank MA Lens – Multi-Timeframe Moving Average Map AlphaRank MA Lens is a clean, open-source moving-average overlay that turns price action into an easy-to-read trend map. It focuses on structure and context only — no signals, no backtest, no hype — just a clear view of where price sits relative to key moving averages. The script plots the 10 / 20 / 50 / 100 / 150 / 200 / 730 moving averages with full color control and a single “MA Type” switch, so you can flip the whole stack between SMA and EMA in one click. Instead of loading multiple separate MA indicators, this puts the full trend stack in one tool. An optional background highlight lets you choose a reference MA (for example the 200 MA) and softly shade the chart: Green when price is above that MA Red when price is below it This makes trend regime changes easy to see at a glance. How traders typically use it (education only): 10/20/50 MAs → short-term trend and momentum. 100/150/200/730 MAs → bigger structural trend and “where price lives” in the long-term range. Many traders consider conditions healthier when price and the short MAs are stacked above the longer MAs, and weaker when price trades below them. Follow my work: AlphaRank This script is for educational and analytical purposes only and does not provide trading advice or performance promises. Always combine it with your own judgment, testing, and risk management.
Индикатор Pine Script®
от AlphaRank
️Omega RatioThe Omega Ratio is a risk-return performance measure of an investment asset, portfolio, or strategy. It is defined as the probability-weighted ratio, of gains versus losses for some threshold return target. The ratio is an alternative for the widely used Sharpe ratio and is based on information the Sharpe ratio discards. █ OVERVIEW As we have mentioned many times, stock market returns are usually not normally distributed. Therefore the models that assume a normal distribution of returns may provide us with misleading information. The Omega Ratio improves upon the common normality assumption among other risk-return ratios by taking into account the distribution as a whole. █ CONCEPTS Two distributions with the same mean and variance, would according to the most commonly used Sharpe Ratio suggest that the underlying assets of the distribution offer the same risk-return ratio. But as we have mentioned in our Moments indicator, variance and standard deviation are not a sufficient measure of risk in the stock market since other shape features of a distribution like skewness and excess kurtosis come into play. Omega Ratio tackles this problem by employing all four Moments of the distribution and therefore taking into account the differences in the shape features of the distributions. Another important feature of the Omega Ratio is that it does not require any estimation but is rather calculated directly from the observed data. This gives it an advantage over standard statistical estimators that require estimation of parameters and are therefore sampling uncertainty in its calculations. █ WAYS TO USE THIS INDICATOR Omega calculates a probability-adjusted ratio of gains to losses, relative to the Minimum Acceptable Return (MAR). This means that at a given MAR using the simple rule of preferring more to less, an asset with a higher value of Omega is preferable to one with a lower value. The indicator displays the values of Omega at increasing levels of MARs and creating the so-called Omega Curve. Knowing this one can compare Omega Curves of different assets and decide which is preferable given the MAR of your strategy. The indicator plots two Omega Curves. One for the on chart symbol and another for the off chart symbol that u can use for comparison. When comparing curves of different assets make sure their trading days are the same in order to ensure the same period for the Omega calculations. Value interpretation: Omega<1 will indicate that the risk outweighs the reward and therefore there are more excess negative returns than positive. Omega>1 will indicate that the reward outweighs the risk and that there are more excess positive returns than negative. Omega=1 will indicate that the minimum acceptable return equals the mean return of an asset. And that the probability of gain is equal to the probability of loss. █ FEATURES • "Low-Risk security" lets you select the security that you want to use as a benchmark for Omega calculations. • "Omega Period" is the size of the sample that is used for the calculations. • “Increments” is the number of Minimal Acceptable Return levels the calculation is carried on. • “Other Symbol” lets you select the source of the second curve. • “Color Settings” you can set the color for each curve.
Индикатор Pine Script®
от Motgench
3
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.
Индикатор Pine Script®
от Motgench
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.
Индикатор Pine Script®
от Motgench
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.
Индикатор Pine Script®
от Menash100
HTF FVG + SessionsThis indicator combines multi-timeframe FVG A–C detection with intraday session boxes on a single chart. It automatically finds bullish and bearish Fair Value Gaps on 15m, 30m, 1H, 4H, 1D and 1W timeframes. Fresh FVGs are drawn in a transparent gold color, then dynamically shrink as price trades back into the gap. Once price fully fills the gap, the FVG box and its label are automatically removed from the chart. After the first touch, each FVG changes to a per-timeframe gray shade, making overlapping HTF gaps easy to see. You can toggle each timeframe on/off and also globally enable/disable all FVGs from the settings panel. Session boxes highlight Asia, London, NY AM, NY Lunch and NY PM using soft colored rectangles. Each session box is plotted from the high to the low of that session and labeled with its name in white text. A global “Show all session boxes” switch allows you to quickly hide or display the session structure. This tool is designed for traders who want to combine FVG liquidity maps with clear intraday session context.
Индикатор Pine Script®
от Menash100
1
SYMBOL NOTES - UNCORRELATED TRADING GROUPSWrite symbol-specific notes that only appear on that chart. Organized into 6 uncorrelated groups for safe multi-pair trading. 📝 SYMBOL NOTES - UNCORRELATED TRADING GROUPS This indicator solves two problems every serious trader faces: 1. Keeping Track of Your Analysis Write notes for each trading pair and they'll only appear when you view that specific chart. No more forgetting your key levels, trade ideas, or analysis! 2. Avoiding Correlated Risk The symbols are organized into 6 groups where ALL pairs within each group are completely UNCORRELATED. Trade any combination from the same group without worrying about double exposure. ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🎯 THE PROBLEM THIS SOLVES Have you ever: - Opened XAUUSD and EURUSD at the same time, then Fed news hit and BOTH positions went against you? - Traded GBPUSD and GBPJPY together, then BOE announcement stopped out both trades? - Forgotten what levels you were watching on a pair? This indicator helps you avoid these costly mistakes! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 📁 THE 6 UNCORRELATED GROUPS Each group contains pairs that share NO common currency: ``` GRUP 1: XAUUSD • EURGBP • NZDJPY • AUDCHF • NATGAS GRUP 2: EURUSD • GBPJPY • AUDNZD • CADCHF GRUP 3: GBPUSD • EURJPY • AUDCAD • NZDCHF GRUP 4: USDJPY • EURCHF • GBPAUD • NZDCAD GRUP 5: USDCAD • EURAUD • GBPCHF GRUP 6: NAS100 • DAX40 • UK100 • JPN225 ``` **Example - GRUP 1:** - XAUUSD → Uses USD + Gold - EURGBP → Uses EUR + GBP - NZDJPY → Uses NZD + JPY - AUDCHF → Uses AUD + CHF - NATGAS → Commodity (independent) = 7 different currencies, ZERO overlap! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ **✅ HOW TO USE** 1. Add indicator to any chart 2. Open Settings (gear icon ⚙️) 3. Find your symbol's group and input field 4. Write your note (support levels, trade ideas, etc.) 5. Switch charts - your note appears only on that symbol! ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ ⚙️ SETTINGS - Note Position: Choose where the note box appears (6 positions) - Text Size: Tiny, Small, Normal, or Large - Show Group Name: Display which correlation group - Show Symbol Name: Display current symbol - Colors: Customize background, text, group label, and border colors ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 💡 TRADING STRATEGY TIPS Safe Multi-Pair Trading: 1. Pick ONE group for the day 2. Look for setups on ANY symbol in that group 3. Open positions freely - they won't correlate! 4. Even if major news hits, only ONE position is affected ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 🔧 COMPATIBLE WITH - All major forex brokers - Prop firms (FTMO, Alpha Capital, etc.) - Works on any timeframe - Futures symbols supported (MGC, M6E, etc.) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Индикатор Pine Script®
от Saeid21
Abu Basel IQOption 2m Signals//@version=5 indicator("Abu Basel IQOption 2m Signals", overlay = true, timeframe = "", timeframe_gaps = true) //======================== // الإعدادات //======================== emaFastLen = input.int(9, "EMA سريع (9)") emaSlowLen = input.int(21, "EMA بطيء (21)") rsiLen = input.int(14, "RSI Length", minval = 2) rsiBuyLevel = input.float(50.0, "RSI حد الشراء (أعلى من)", minval = 0, maxval = 100) rsiSellLevel= input.float(50.0, "RSI حد البيع (أقل من)", minval = 0, maxval = 100) bbLen = input.int(20, "Bollinger Length") bbMult = input.float(2.0, "Bollinger Deviation") showSignals = input.bool(true, "إظهار الأسهم (CALL / PUT)") showBg = input.bool(true, "تلوين الخلفية عند الإشارات") //======================== // المؤشرات الأساسية //======================== emaFast = ta.ema(close, emaFastLen) emaSlow = ta.ema(close, emaSlowLen) basis = ta.sma(close, bbLen) dev = bbMult * ta.stdev(close, bbLen) bbUpper = basis + dev bbLower = basis - dev rsi = ta.rsi(close, rsiLen) // رسم المتوسطات والبولينجر plot(emaFast, title = "EMA 9", linewidth = 2) plot(emaSlow, title = "EMA 21", linewidth = 2) plot(basis, title = "BB Basis", linewidth = 1) plot(bbUpper, title = "BB Upper", linewidth = 1, style = plot.style_line) plot(bbLower, title = "BB Lower", linewidth = 1, style = plot.style_line) //======================== // دوال أشكال الشموع الانعكاسية //======================== bodySize = math.abs(close - open) fullRange = high - low upperWick = high - math.max(open, close) lowerWick = math.min(open, close) - low isSmallBody = bodySize <= fullRange * 0.3 // Hammer صاعدة (ذيل سفلي طويل) bullHammer() => lowerWick > bodySize * 2 and upperWick <= bodySize and close > open // Shooting Star هابطة (ذيل علوي طويل) bearShootingStar() => upperWick > bodySize * 2 and lowerWick <= bodySize and close < open // Bullish Engulfing bullEngulfing() => close > open and close < open and close > open and open < close // Bearish Engulfing bearEngulfing() => close < open and close > open and close < open and open > close // تجميع أنماط صعود/هبوط bullPattern = bullHammer() or bullEngulfing() bearPattern = bearShootingStar() or bearEngulfing() //======================== // شروط الدخول //======================== // تقاطع المتوسطات bullCross = ta.crossover(emaFast, emaSlow) // صعود bearCross = ta.crossunder(emaFast, emaSlow) // هبوط // شروط شراء CALL: // 1) تقاطع EMA9 فوق EMA21 // 2) السعر فوق خط وسط البولنجر // 3) RSI أعلى من 50 // 4) شمعة انعكاسية صاعدة (Hammer أو Engulfing) callCond = bullCross and close > basis and rsi > rsiBuyLevel and bullPattern // شروط بيع PUT: // 1) تقاطع EMA9 تحت EMA21 // 2) السعر تحت خط وسط البولنجر // 3) RSI أقل من 50 // 4) شمعة انعكاسية هابطة (Shooting Star أو Bearish Engulfing) putCond = bearCross and close < basis and rsi < rsiSellLevel and bearPattern //======================== // رسم الإشارات على الشارت //======================== plotshape(showSignals and callCond, title="CALL 2m", style=shape.labelup, location=location.belowbar, text="CALL 2m", size=size.tiny) plotshape(showSignals and putCond, title="PUT 2m", style=shape.labeldown, location=location.abovebar, text="PUT 2m", size=size.tiny) // تلوين الخلفية عند الإشارات bgcolor(showBg and callCond ? color.new(color.green, 85) : showBg and putCond ? color.new(color.red, 85) : na) //======================== // شروط التنبيه (Alerts) //======================== alertcondition(callCond, title="CALL 2m Signal", message="Abu Basel Signal: CALL 2m on {{ticker}} at {{close}}") alertcondition(putCond, title="PUT 2m Signal", message="Abu Basel Signal: PUT 2m on {{ticker}} at {{close}}")
Индикатор Pine Script®
от abobasel5544
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.
Индикатор Pine Script®
от TheHeikinAshitrader
Adaptive Trend Navigator [ATH Filter & Risk Engine]Description: This strategy implements a systematic Trend Following approach designed to capture major moves while actively protecting capital during severe bear markets. It combines a classic Moving Average "Fan" logic with two advanced risk management layers: a 4-Stage Dynamic Stop Loss and a macro-economic "Circuit Breaker" filter. Core Concepts: 1. Trend Identification (Entry Logic) The script uses a cascade of Simple Moving Averages (SMA 25, 50, 100, 200) to identify the maturity of a trend. Entries are triggered by specific crossovers (e.g., SMA 25 crossing SMA 50) or by breaking above the previous trade's high ("High-Water Mark" Re-Entry). 2. The "Circuit Breaker" (Crash Protection) To prevent trading during historical market collapses (like 2000 or 2008), the strategy monitors the Nasdaq 100 (QQQ) as a global benchmark: Normal Regime: If the market is within 20% of its All-Time High, the strategy operates normally. Crisis Regime: If the QQQ falls more than 20% from its ATH, the "Circuit Breaker" activates (Visualized by a Red Background). Recovery Rule: In a Crisis Regime, new long positions are blocked unless the QQQ reclaims its SMA 200. This filters out "bull traps" in secular bear markets. 3. 4-Stage Risk Engine (Exit Logic) Once in a trade, the risk management adapts to the position's performance: Stage 1: Fixed initial Stop Loss (default 10%) for breathing room. Stage 2: Moves to Break-Even area once the price rises 12%. Stage 3: Tightens to a trailing stop (8%) after 25% profit. Stage 4: Maximizes gains with a tight trailing stop (5%) during parabolic moves (>40% profit). Visual Guide: SMAs: 25/50/100/200 period lines for trend visualization. Red Background: Indicates the "Crisis Regime" where trading is halted due to broad market weakness. Blue Background: Indicates a "Recovery Phase" (Crisis is active, but market is above SMA 200). Red Line: Shows the dynamic Stop Loss level for active positions. Settings: All parameters (SMA lengths, Drawdown threshold, Risk Stages) are fully customizable. The QQQ benchmark ticker can also be changed to SPY or other indices depending on the asset class traded.
Стратегия Pine Script®
от oppliger79
SPY → ES 11 Levels (Hybrid RTH/Globex) [Tick Fixed]📌 Description for SPY → ES 11-Level Converter (with Labels) This script converts important SPY options-based levels into their equivalent ES futures prices and plots them directly on the ES chart. Because SPY trades at a different price scale than ES, each SPY level is multiplied by a customizable ES/SPY ratio to project accurate ES levels. It is designed for traders who use SpotGamma, GEXBot, MenthorQ, Vol-trigger levels, or their own gamma/oi/volume models. 🔍 Features ✅ Converts SPY → ES using custom or automatic ratio Option to manually enter a ratio (recommended for accuracy) Or automatically compute ES/SPY from live prices ✅ Plots 11 major levels on the ES chart Each level can be individually turned ON/OFF: Call Wall Put Wall Volume Trigger Spot Price +Gamma Level –Gamma Level Zero Gamma Positive OI Negative OI Positive Volume Negative Volume All levels are drawn as clean horizontal lines using the converted ES value.
Индикатор Pine Script®
от LardVaark
Alper-EMAAlper-EMA Description: This indicator allows you to display 5 customizable EMAs (Exponential Moving Averages) on a single chart. Each EMA can be configured independently with length, color, visibility, and calculation timeframe. Features: 5 fully customizable EMAs Set individual length and color for each EMA Toggle visibility for each EMA Multi-timeframe calculation: e.g., display EMA300 calculated on a 30-minute timeframe while viewing a 1-minute chart Labels display EMA period and timeframe for clarity Adjustable label size: tiny / small / normal / large Clear and readable plot lines Use Cases: Monitor multiple timeframe EMAs simultaneously Analyze trend and support/resistance levels Track EMA crossovers for strategy development Note: This indicator is suitable for both short-term (scalping) and medium-to-long term analysis. The multi-timeframe feature allows you to see different EMA perspectives on a single chart quickly.
Индикатор Pine Script®
от alperdesdicioglu
Relative Strength Heatmap [BackQuant]Relative Strength Heatmap A multi-horizon RSI matrix that compresses 20 different lookbacks into a single panel, turning raw momentum into a visual “pressure gauge” for overbought and oversold clustering, trend exhaustion, and breadth of participation across time horizons. What this is This indicator builds a strip-style heatmap of 20 RSIs, each with a different length, and stacks them vertically as colored tiles in a single pane. Every tile is colored by its RSI value using your chosen palette, so you can see at a glance: How many “fast” versus “slow” RSIs are overbought or oversold. Whether momentum is concentrated in the short lookbacks or spread across the whole curve. When momentum extremes cluster, signalling strong market pressure or exhaustion. On top of the tiles, the script plots two simple breadth lines: A white line that counts how many RSIs are above 70 (overbought cluster). A black line that counts how many RSIs are below 30 (oversold cluster). This turns a single symbol’s RSI ladder into a compact “market pressure gauge” that shows not only whether RSI is overbought or oversold, but how many different horizons agree at the same time. Core idea A single RSI looks at one length and one timescale. Markets, however, are driven by flows that operate on multiple horizons at once. By computing RSI over a ladder of lengths, you approximate a “term structure” of strength: Short lengths react to immediate swings and very recent impulses. Medium lengths reflect swing behaviour and local trends. Long lengths reflect structural bias and higher timeframe regime. When many lengths agree, for example 10 or more RSIs all above 70, it suggests broad participation and strong directional pressure. When only a few fast lengths stretch to extremes while longer ones stay neutral, the move is more fragile and more likely to mean-revert. This script makes that structure visible as a heatmap instead of forcing you to run many separate RSI panes. How it works 1) Generating RSI lengths You control three parameters in the calculation settings: RS Period – the base RSI length used for the shortest strip. RSI Step – the amount added to each successive RSI length. RSI Multiplier – a global scaling factor applied after the step. Each of the 20 RSIs uses: RSI length = round((base_length + step × index) × multiplier) , where the index goes from 0 to 19. That means: RSI 1 uses (len + step × 0) × mult. RSI 2 uses (len + step × 1) × mult. … RSI 20 uses (len + step × 19) × mult. You can keep the ladder dense (small step and multiplier) or stretch it across much longer horizons. 2) Heatmap layout and grouping Each RSI is plotted as an “area” strip at a fixed vertical level using histbase to stack them: RSI 1–5 form Group 1. RSI 6–10 form Group 2. RSI 11–15 form Group 3. RSI 16–20 form Group 4. Each group has a toggle: Show only Group 1 and 2 if you care mainly about fast and medium horizons. Show all groups for a full spectrum from very short to very long. Hide any group that feels redundant for your workflow. The actual numeric RSI values are not plotted as lines. Instead, each strip is drawn as a horizontal band whose fill color represents the current RSI regime. 3) Palette-based coloring Each tile’s color is driven by the RSI value and your chosen palette. The script includes several palettes: Viridis – smooth green to yellow, good for subtle reading. Jet – strong blue to red sequence with high contrast. Plasma – purple through orange to yellow. Custom Heat – cool blues to neutral grey to hot reds. Gray – grayscale from white to black for minimalistic layouts. Cividis, Inferno, Magma, Turbo, Rainbow – additional scientific and rainbow-style maps. Internally, RSI values are bucketed into ranges (for example, below 10, 10–20, …, 90–100). Each bucket maps to a unique colour for that palette. In all schemes, low RSI values are mapped to the “cold” or darker side and high RSI values to the “hot” or brighter side. The result is a true momentum heatmap: Cold or dark tiles show low RSI and oversold or compressed conditions. Mid tones show neutral or mid-range RSI. Warm or bright tiles show high RSI and overbought or stretched conditions. 4) Bull and bear breadth counts All 20 RSI values are collected into an array each bar. Two counters are then calculated: Bull count – how many RSIs are above 70. Bear count – how many RSIs are below 30. These are plotted as: A white line (“RSI > 70 Count”) for the overbought cluster. A black line (“RSI < 30 Count”) for the oversold cluster. If you enable the “Show Bull and Bear Count” option, you get an immediate reading of how many of the 20 horizons are stretched at any moment. 5) Cluster alerts and background tagging Two alert conditions monitor “strong cluster” regimes: RSI Heatmap Strong Bull – triggers when at least 10 RSIs are above 70. RSI Heatmap Strong Bear – triggers when at least 10 RSIs are below 30. When one of these conditions is true, the indicator can tint the background of the chart using a soft version of the current palette. This visually marks stretches where momentum is extreme across many lengths at once, not just on a single RSI. What it plots In one oscillator window, the indicator provides: Up to 20 horizontal RSI strips, each representing a different RSI length. Color-coded tiles reflecting the current RSI value for each length. Group toggles to show or hide each block of five RSIs. An optional white line that counts how many RSIs are above 70. An optional black line that counts how many RSIs are below 30. Optional background highlights when the number of overbought or oversold RSIs passes the strong-cluster threshold. How it measures breadth and pressure Single-symbol breadth Breadth is usually defined across a basket of symbols, such as how many stocks advance versus decline. This indicator uses the same concept across time horizons for a single symbol. The question becomes: “How many different RSI lengths are stretched in the same direction at once?” Examples: If only 2 or 3 of the shortest RSIs are above 70, bull count stays low. The move is fast and local, but not yet broadly supported. If 12 or more RSIs across short, medium and long lengths are above 70, the bull count spikes. The move has broad momentum and strong upside pressure. If 10 or more RSIs are below 30, bear count spikes and you are in a broad oversold regime. This is breadth of momentum within one market. Market pressure gauge The combination of heatmap tiles and breadth lines acts as a pressure gauge: High bull count with warm colors across most strips indicates strong upside pressure and crowded long positioning. High bear count with cold colors across most strips indicates strong downside pressure and capitulation or forced selling. Low counts with a mixed heatmap indicate neutral pressure, fragmented flows, or range-bound conditions. You can treat the strong-cluster alerts as “extreme pressure” signals. When they fire, the market is heavily skewed in one direction across many horizons. How to read the heatmap Horizontal patterns (through time) Look along the time axis and watch how the colors evolve: Persistent hot tiles across many strips show sustained bullish pressure and trend strength. Persistent cold tiles across many strips show sustained bearish pressure and weak demand. Frequent flipping between hot and cold colours indicates a choppy or mean-reverting environment. Vertical structure (across lengths at one bar) Focus on a single bar and read the column of tiles from top to bottom: Short RSIs hot, long RSIs neutral or cool: early trend or short-term fomo. Price has moved fast, longer horizons have not caught up. Short and long RSIs all hot: mature, entrenched uptrend. Broad participation, high pressure, greater risk of blow-off or late-entry vulnerability. Short RSIs cold but long RSIs mid to high: pullback in a higher timeframe uptrend. Dip-buy and continuation setups are often found here. Short RSIs high but long RSIs low: countertrend rallies within a broader downtrend. Good hunting ground for fades and short entries after a bounce. Bull and bear breadth lines Use the two lines as simple, numeric breadth indicators: A rising white line shows more RSIs pushing above 70, so bullish pressure is expanding in breadth. A rising black line shows more RSIs pushing below 30, so bearish pressure is expanding in breadth. When both lines are low and flat, few horizons are extreme and the market is in mid-range territory. Cluster zones When either count crosses the strong threshold (for example 10 out of 20 RSIs in extreme territory): A strong bull cluster marks a broadly overbought regime. Trend followers may see this as confirmation. Mean-reversion traders may see it as a late-stage or blow-off context. A strong bear cluster marks a broadly oversold regime. Downtrend traders see strong pressure, but the risk of sharp short-covering bounces also increases. Trading applications Trend confirmation Use the heatmap and breadth lines as a trend filter: Prefer long setups when the heatmap shows mostly mid to high RSIs and the bull count is rising. Avoid fresh shorts when there is a strong bull cluster, unless you are specifically trading exhaustion. Prefer short setups when the heatmap is mostly low RSIs and the bear count is rising. Avoid aggressive longs when a strong bear cluster is active, unless you are trading reflexive bounces. Mean-reversion timing Treat cluster extremes as exhaustion zones: Look for reversal patterns, failed breakouts, or order flow shifts when bull count is very high and price starts to stall or diverge. Look for reflexive bounce potential when bear count is very high and price stops making new lows or shows absorption at the lows. Use the palette and counts together: hot tiles plus a peaking white line can mark blow-off conditions, cold tiles plus a peaking black line can mark capitulation. Regime detection and risk toggling Use the overall shape of the ladder over time: If upper strips stay warm and lower strips stay neutral or warm for extended periods, the market is in an uptrend regime. You can justify higher risk for long-biased strategies. If upper strips stay cold and lower strips stay neutral or cold, the market is in a downtrend regime. You can justify higher risk for short-biased strategies or defensive positioning. If colours and counts flip frequently, you are likely in a range or choppy regime. Consider reducing size or using more tactical, short-term strategies. Multi-horizon synchronization You can think of each RSI length as a proxy for a different “speed” of the same market: When only fast RSIs are stretched, the move is local and less robust. When fast, medium and slow RSIs align, the move has multi-horizon confirmation. You can require a minimum bull or bear count before allowing your main strategy to engage. Spotting hidden shifts Sometimes price appears flat or drifting, but the heatmap quietly cools or warms: If price is sideways while many hot tiles fade toward neutral, momentum is decaying under the surface and trend risk is increasing. If price is sideways while many cold tiles climb back toward neutral, selling pressure is decaying and the tape is repairing itself. Settings overview Calculation Settings RS Period – base RSI length for the shortest strip. RSI Step – the increment added to each successive RSI length. RSI Multiplier – scales all generated RSI lengths. Calculation Source – the input series, such as close, hlc3 or others. Plotting and Coloring Settings Heatmap Color Palette – choose between Viridis, Jet, Plasma, Custom Heat, Gray, Cividis, Inferno, Magma, Turbo or Rainbow. Show Group 1 – toggles RSI 1–5. Show Group 2 – toggles RSI 6–10. Show Group 3 – toggles RSI 11–15. Show Group 4 – toggles RSI 16–20. Show Bull and Bear Count – enables or disables the two breadth lines. Alerts RSI Heatmap Strong Bull – fires when the number of RSIs above 70 reaches or exceeds the configured threshold (default 10). RSI Heatmap Strong Bear – fires when the number of RSIs below 30 reaches or exceeds the configured threshold (default 10). Tuning guidance Fast, tactical configurations Use a small base RS Period, for example 2 to 5. Use a small RSI Step, for tight clustering around the fast horizon. Keep the multiplier near 1.0 to avoid extreme long lengths. Focus on Group 1 and Group 2 for intraday and short-term trading. Swing and position configurations Use a mid-range RS Period, for example 7 to 14. Use a moderate RSI Step to fan out into slower horizons. Optionally use a multiplier slightly above 1.0. Keep all four groups enabled for a full view from fast to slow. Macro or higher timeframe configurations Use a larger base RS Period. Use a larger RSI Step so the top of the ladder reaches very slow lengths. Focus on Group 3 and Group 4 to see structural momentum. Treat clusters as regime markers rather than frequent trading signals. Notes This indicator is a contextual tool, not a standalone trading system. It does not model execution, spreads, slippage or fundamental drivers. Use it to: Understand whether momentum is narrow or broad across horizons. Confirm or filter existing signals from your primary strategy. Identify environments where the market is crowded into one side. Distinguish between isolated spikes and truly broad pressure moves. The Relative Strength Heatmap is designed to answer a simple but powerful question: “How many versions of RSI agree with what I am seeing on the chart?” By compressing those answers into a single panel with clear colour coding and breadth lines, it becomes a practical, visual gauge of momentum breadth and market pressure that you can overlay on any trading framework.
Индикатор Pine Script®
от BackQuant
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