Cyber Mondayลดสูงสุดถึง 80%

อินดิเคเตอร์และกลยุทธ์

Sector Rotation - Risk Preference Indicator# Sector Rotation - Risk Preference Indicator ## Overview This indicator measures market risk appetite by comparing the relative strength between **Aggressive** and **Defensive** sectors. It provides a clean, single-line visualization to help traders identify market sentiment shifts and potential trend reversals. ## How It Works The indicator calculates a **Bullish/Bearish Ratio** by dividing the average price of aggressive sector ETFs by defensive sector ETFs, then normalizing to a baseline of 100. **Formula:** - Ratio = (Aggressive Sectors Average / Defensive Sectors Average) × 100 **Interpretation:** - **Ratio > 100**: Risk-on sentiment (Aggressive sectors outperforming Defensive) - **Ratio < 100**: Risk-off sentiment (Defensive sectors outperforming Aggressive) - **Ratio ≈ 100**: Neutral (Both sector groups performing equally) ## Default Sectors **Defensive Sectors** (Safe havens during uncertainty): - XLP - Consumer Staples Select Sector SPDR Fund - XLU - Utilities Select Sector SPDR Fund - XLV - Health Care Select Sector SPDR Fund **Aggressive Sectors** (Growth-oriented, higher risk): - XLK - Technology Select Sector SPDR Fund - XBI - SPDR S&P Biotech ETF - XRT - SPDR S&P Retail ETF ## Features ✅ **Fully Customizable Sectors** - Choose any ETFs/tickers for each sector group ✅ **Smoothing Control** - Adjustable SMA period to reduce noise (default: 2) ✅ **Clean Visualization** - Single blue line for easy interpretation ✅ **Multi-timeframe Support** - Works on any timeframe ✅ **Lightweight** - Minimal calculations for fast performance ## Settings ### Defensive Sectors Group - **Defensive Sector 1**: First defensive ETF ticker (default: XLP) - **Defensive Sector 2**: Second defensive ETF ticker (default: XLU) - **Defensive Sector 3**: Third defensive ETF ticker (default: XLV) ### Aggressive Sectors Group - **Aggressive Sector 1**: First aggressive ETF ticker (default: XLK) - **Aggressive Sector 2**: Second aggressive ETF ticker (default: XBI) - **Aggressive Sector 3**: Third aggressive ETF ticker (default: XRT) ### Display Settings - **Smoothing Length**: SMA period for ratio smoothing (default: 2, range: 1-50) - Lower values = More responsive but noisier - Higher values = Smoother but more lagging ## Use Cases ### 1. Market Regime Identification - **Rising Ratio (trending up)** → Bull market / Risk-on environment - Aggressive sectors leading, investors chasing growth - Favorable for long positions in tech, growth stocks - **Falling Ratio (trending down)** → Bear market / Risk-off environment - Defensive sectors leading, investors seeking safety - Consider defensive positioning or short opportunities ### 2. Divergence Analysis - **Bullish Divergence**: Price makes new lows but ratio rises - Suggests underlying strength returning - Potential market bottom forming - **Bearish Divergence**: Price makes new highs but ratio falls - Suggests weakening momentum - Potential market top forming ### 3. Trend Confirmation - **Strong uptrend + Rising ratio** → Confirmed bullish trend - **Strong downtrend + Falling ratio** → Confirmed bearish trend - **Uptrend + Falling ratio** → Weakening trend, watch for reversal - **Downtrend + Rising ratio** → Potential trend exhaustion ## Best Practices ⚠️ **Timeframe Selection** - Recommended: Daily, 4H, 1H for cleaner signals - Lower timeframes (15m, 5m) may produce noisy signals ⚠️ **Complementary Analysis** - Use alongside price action and volume analysis - Combine with support/resistance levels - Not designed as a standalone trading system ⚠️ **Market Conditions** - Most effective in trending markets - Less reliable during ranging/consolidation periods - Works best in liquid, well-traded sectors ⚠️ **Customization Tips** - Can substitute with international sectors (EWU, EWZ, etc.) - Can use crypto sectors (DeFi vs Layer1, etc.) - Adjust smoothing based on trading style (day trading = 2-5, swing = 10-20) ## Display Options ### Default View (overlay=false) - Shows in separate pane below chart - Dedicated scale for ratio values ### Alternative View - Can be moved to main chart pane (drag indicator) I typically overlay this indicator on the SPY daily chart to observe divergences. I don’t focus on specific values but rather on the direction of the trend. The author is not responsible for any trading losses incurred using this indicator. ## Support & Feedback For questions, feature requests, or bug reports: - Comment below - Send a private message - Check for updates regularly If you find this indicator useful, please: - ⭐ Leave a like/favorite - 💬 Share your experience in comments - 📊 Share charts showing interesting patterns
อินดิเคเตอร์ Pine Script®
โดย gary186
NQ-VIX Expected Move LevelsNQ -VIX Daily Price Bands This indicator plots dynamic intraday price bands for NQ futures based on real-time volatility levels measured by the VIX (CBOE Volatility Index). The bands evolve throughout the trading day, providing volatility-adjusted price targets. Formulas: Upper Band = Daily Open + (NQ Price × VIX ÷ √252 ÷ 100) Lower Band = Daily Open - (NQ Price × VIX ÷ √252 ÷ 100) The calculation uses the square root of 252 (trading days per year) to convert annualized VIX volatility into an expected daily move, then scales it as a percentage adjustment from the current day's open. Features: Real-time band calculation that updates throughout the trading session Upper band (green) extends from the current day's open Lower band (red) contracts from the current day's open Inner upper band (green) at 50% of expected move Inner lower band (red) at 50% of expected move Middle Inner upper band (green) at 80% of expected move Middle Inner lower band (red) at 80% of expected move Information table displaying: Current NQ price and VIX level Daily Open Expected move
อินดิเคเตอร์ Pine Script®
โดย alanjeremiebarbe
MTF Trading Helper & Multi AlertsHi dear fellows, I´m using this indicator for my trading, so every then and when I will publish updates on this one. This indicator should help to identify the right trading setup. I´m using it to trade index futures and stocks. MTF Trading Helper & Multi Alerts Overview This indicator provides a clear visual representation of trend direction across three timeframes. It helps traders identify trend alignment, potential reversals, and optimal entry/exit points by analyzing the relationship between different smoothed timeframes. You can set up multiple alerts (as one alert in Tradingview) How It Works The indicator displays three colored circles representing the smoothed candle direction on three different timeframes: Bottom plot represents the overall trend direction, the plot in the middle shows intermediate momentum, and the one on top captures short-term price action. When a color change occurs, the circle appears in a darker shade to highlight the transition. 🟢 Green = Bullish - 🔴 Red = Bearish This change can also trigger multiple alerts. Timeframe Settings - important Choose between two trading setups, either for: Intraday 1-minute candles or 1h for swing trading. Set up your chart accordingly to that timeframe. Intraday | 1Min chart candles Swing | 1 hour chart candles Plots TF3 represents the overall trend direction (bottom), TF2 shows intermediate momentum (middle), and TF1 captures short-term price action (top). Interpretation & Strategy Alerts 1. Trend Bullish (TF3 turns Green) The higher timeframe has shifted bullish - a potential new uptrend is forming. Example: You're watching ES-mini on the Intraday setting. TF3 turns green after being red for several days. This signals the broader trend may be shifting bullish - consider looking for long opportunities. 2. Trend Bearish (TF3 turns Red) The higher timeframe has shifted bearish - consider protecting profits or exiting long positions. Example: You hold a long position in Es-mini. TF3 turns red, indicating the macro trend is weakening. This is your signal to take profits or tighten stop-losses. 3. Possible Accumulation (TF3 Red + TF2 turns Green) While the overall trend is still bearish, the medium timeframe shows buying pressure. Smart money may be accumulating - watch closely for a potential trend reversal. Example: Es-mini has been in a downtrend (TF3 red). Suddenly TF2 turns green while TF3 remains red. This could indicate institutional buying before a reversal. Don't buy yet, but add it to your watchlist and wait for confirmation. 4. Trend Continuation (TF3 Green + TF2 turns Green) The medium timeframe realigns with the bullish macro trend - a potential buying opportunity as momentum returns to the uptrend. Example: Es-mini is in an uptrend (TF3 green). After a pullback, TF2 was red but now turns green again. The pullback appears to be over - this is a trend continuation signal and a potential entry point. 5. Buy the Dip (TF3 + TF2 Green + TF1 turns Green) All timeframes are now aligned bullish. The short-term pullback is complete and price is resuming the uptrend - optimal entry for short-term trades. Example: Es-mini is trending up (TF3 + TF2 green). A small dip caused TF1 to turn red briefly. When TF1 turns green again, all three timeframes are aligned - this is your "Buy the Dip" signal with strong confirmation. 6. Sell the Dip (TF3 + TF2 Green + TF1 turns Red) Short-term weakness within an uptrend. This can be used to take partial profits, wait for a better entry, or trail stops tighter. Example: You're long on ES-mini with TF3 and TF2 green. TF1 turns red, indicating short-term selling pressure. Consider taking partial profits here and wait for TF1 to turn green again (Buy the Dip) to add back to your position. How to Use Choose your scenario: Select "Intraday" 1min-chart for day trading or "Swing" 1h-chart for swingtrading Enable alerts: Turn on the strategy alerts you want to receive in the settings Wait for signals: Let the indicator notify you when conditions align Confirm with price action: Always use additional confirmation before entering trades Best Practices ✅ Use TF3 as your trend filter - only take longs when TF3 turns green and hold them :) ✅ Use TF2 for timing - wait for TF2 to align with TF3 for swings. ✅ Use TF2 for early entries (accumulation phase) when TF3 is still red. Watch out! ✅ Use TF1 for entries when TF3 and TF2 are green. Only buy if TF1 is red. Keep it short and sweet. ✅ Combine with support/resistance levels for better entries ✅ Use proper risk management - no indicator is 100% accurate Disclaimer This indicator is for educational purposes only. Past performance does not guarantee future results. Always do your own research and use proper risk management. Never risk more than you can afford to lose.
อินดิเคเตอร์ Pine Script®
โดย xriswart
Hurst Exponent - Detrended Fluctuation AnalysisIn stochastic processes, chaos theory and time series analysis, detrended fluctuation analysis (DFA) is a method for determining the statistical self-affinity of a signal. It is useful for analyzing time series that appear to be long-memory processes and noise. █ OVERVIEW We have introduced the concept of Hurst Exponent in our previous open indicator Hurst Exponent (Simple). It is an indicator that measures market state from autocorrelation. However, we apply a more advanced and accurate way to calculate Hurst Exponent rather than simple approximation. Therefore, we recommend using this version of Hurst Exponent over our previous publication going forward. The method we used here is called detrended fluctuation analysis. (For folks that are not interested in the math behind the calculation, feel free to skip to "features" and "how to use" section. However, it is recommended that you read it all to gain a better understanding of the mathematical reasoning). █ Detrend Fluctuation Analysis Detrended Fluctuation Analysis was first introduced by by Peng, C.K. (Original Paper) in order to measure the long-range power-law correlations in DNA sequences . DFA measures the scaling-behavior of the second moment-fluctuations, the scaling exponent is a generalization of Hurst exponent. The traditional way of measuring Hurst exponent is the rescaled range method. However DFA provides the following benefits over the traditional rescaled range method (RS) method:  • Can be applied to non-stationary time series. While asset returns are generally stationary, DFA can measure Hurst more accurately in the instances where they are non-stationary.  • According the the asymptotic distribution value of DFA and RS, the latter usually overestimates Hurst exponent (even after Anis- Llyod correction) resulting in the expected value of RS Hurst being close to 0.54, instead of the 0.5 that it should be. Therefore it's harder to determine the autocorrelation based on the expected value. The expected value is significantly closer to 0.5 making that threshold much more useful, using the DFA method on the Hurst Exponent (HE).  • Lastly, DFA requires lower sample size relative to the RS method. While the RS method generally requires thousands of observations to reduce the variance of HE, DFA only needs a sample size greater than a hundred to accomplish the above mentioned. █ Calculation DFA is a modified root-mean-squares (RMS) analysis of a random walk. In short, DFA computes the RMS error of linear fits over progressively larger bins (non-overlapped “boxes” of similar size) of an integrated time series. Our signal time series is the log returns. First we subtract the mean from the log return to calculate the demeaned returns. Then, we calculate the cumulative sum of demeaned returns resulting in the cumulative sum being mean centered and we can use the DFA method on this. The subtraction of the mean eliminates the “global trend” of the signal. The advantage of applying scaling analysis to the signal profile instead of the signal, allows the original signal to be non-stationary when needed. (For example, this process converts an i.i.d. white noise process into a random walk.) We slice the cumulative sum into windows of equal space and run linear regression on each window to measure the linear trend. After we conduct each linear regression. We detrend the series by deducting the linear regression line from the cumulative sum in each windows. The fluctuation is the difference between cumulative sum and regression. We use different windows sizes on the same cumulative sum series. The window sizes scales are log spaced. Eg: powers of 2, 2,4,8,16... This is where the scale free measurements come in, how we measure the fractal nature and self similarity of the time series, as well as how the well smaller scale represent the larger scale. As the window size decreases, we uses more regression lines to measure the trend. Therefore, the fitness of regression should be better with smaller fluctuation. It allows one to zoom into the “picture” to see the details. The linear regression is like rulers. If you use more rulers to measure the smaller scale details you will get a more precise measurement. The exponent we are measuring here is to determine the relationship between the window size and fitness of regression (the rate of change). The more complex the time series are the more it will depend on decreasing window sizes (using more linear regression lines to measure). The less complex or the more trend in the time series, it will depend less. The fitness is calculated by the average of root mean square errors (RMS) of regression from each window. Root mean Square error is calculated by square root of the sum of the difference between cumulative sum and regression. The following chart displays average RMS of different window sizes. As the chart shows, values for smaller window sizes shows more details due to higher complexity of measurements. The last step is to measure the exponent. In order to measure the power law exponent. We measure the slope on the log-log plot chart. The x axis is the log of the size of windows, the y axis is the log of the average RMS. We run a linear regression through the plotted points. The slope of regression is the exponent. It's easy to see the relationship between RMS and window size on the chart. Larger RMS equals less fitness of the regression. We know the RMS will increase (fitness will decrease) as we increases window size (use less regressions to measure), we focus on the rate of RMS increasing (how fast) as window size increases. If the slope is < 0.5, It means the rate of of increase in RMS is small when window size increases. Therefore the fit is much better when it's measured by a large number of linear regression lines. So the series is more complex. (Mean reversion, negative autocorrelation). If the slope is > 0.5, It means the rate of increase in RMS is larger when window sizes increases. Therefore even when window size is large, the larger trend can be measured well by a small number of regression lines. Therefore the series has a trend with positive autocorrelation. If the slope = 0.5, It means the series follows a random walk. █ FEATURES  • Sample Size is the lookback period for calculation. Even though DFA requires a lower sample size than RS, a sample size larger > 50 is recommended for accurate measurement.  • When a larger sample size is used (for example = 1000 lookback length), the loading speed may be slower due to a longer calculation. Date Range is used to limit numbers of historical calculation bars. When loading speed is too slow, change the data range "all" into numbers of weeks/days/hours to reduce loading time. (Credit to allanster)  • “show filter” option applies a smoothing moving average to smooth the exponent.  • Log scale is my work around for dynamic log space scaling. Traditionally the smallest log space for bars is power of 2. It requires at least 10 points for an accurate regression, resulting in the minimum lookback to be 1024. I made some changes to round the fractional log space into integer bars requiring the said log space to be less than 2. • For a more accurate calculation a larger "Base Scale" and "Max Scale" should be selected. However, when the sample size is small, a larger value would cause issues. Therefore, a general rule to be followed is: A larger "Base Scale" and "Max Scale" should be selected for a larger the sample size. It is recommended for the user to try and choose a larger scale if increasing the value doesn't cause issues. The following chart shows the change in value using various scales. As shown, sometimes increasing the value makes the value itself messy and overshoot. When using the lowest scale (4,2), the value seems stable. When we increase the scale to (8,2), the value is still alright. However, when we increase it to (8,4), it begins to look messy. And when we increase it to (16,4), it starts overshooting. Therefore, (8,2) seems to be optimal for our use. █ How to Use Similar to Hurst Exponent (Simple). 0.5 is a level for determine long term memory.  • In the efficient market hypothesis, market follows a random walk and Hurst exponent should be 0.5. When Hurst Exponent is significantly different from 0.5, the market is inefficient.  • When Hurst Exponent is > 0.5. Positive Autocorrelation. Market is Trending. Positive returns tend to be followed by positive returns and vice versa.  • Hurst Exponent is < 0.5. Negative Autocorrelation. Market is Mean reverting. Positive returns trends to follow by negative return and vice versa. However, we can't really tell if the Hurst exponent value is generated by random chance by only looking at the 0.5 level. Even if we measure a pure random walk, the Hurst Exponent will never be exactly 0.5, it will be close like 0.506 but not equal to 0.5. That's why we need a level to tell us if Hurst Exponent is significant. So we also computed the 95% confidence interval according to Monte Carlo simulation. The confidence level adjusts itself by sample size. When Hurst Exponent is above the top or below the bottom confidence level, the value of Hurst exponent has statistical significance. The efficient market hypothesis is rejected and market has significant inefficiency. The state of market is painted in different color as the following chart shows. The users can also tell the state from the table displayed on the right. An important point is that Hurst Value only represents the market state according to the past value measurement. Which means it only tells you the market state now and in the past. If Hurst Exponent on sample size 100 shows significant trend, it means according to the past 100 bars, the market is trending significantly. It doesn't mean the market will continue to trend. It's not forecasting market state in the future. However, this is also another way to use it. The market is not always random and it is not always inefficient, the state switches around from time to time. But there's one pattern, when the market stays inefficient for too long, the market participants see this and will try to take advantage of it. Therefore, the inefficiency will be traded away. That's why Hurst exponent won't stay in significant trend or mean reversion too long. When it's significant the market participants see that as well and the market adjusts itself back to normal. The Hurst Exponent can be used as a mean reverting oscillator itself. In a liquid market, the value tends to return back inside the confidence interval after significant moves(In smaller markets, it could stay inefficient for a long time). So when Hurst Exponent shows significant values, the market has just entered significant trend or mean reversion state. However, when it stays outside of confidence interval for too long, it would suggest the market might be closer to the end of trend or mean reversion instead. Larger sample size makes the Hurst Exponent Statistics more reliable. Therefore, if the user want to know if long term memory exist in general on the selected ticker, they can use a large sample size and maximize the log scale. Eg: 1024 sample size, scale (16,4). Following Chart is Bitcoin on Daily timeframe with 1024 lookback. It suggests the market for bitcoin tends to have long term memory in general. It generally has significant trend and is more inefficient at it's early stage.
อินดิเคเตอร์ Pine Script®
โดย Motgench
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility. In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution. Note: Normal distribution is just an assumption, it's not the real distribution of return Settings: "Estimation Period Selection" is for selecting the period we want to construct the prediction interval. For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours. For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead. There are also other source selections based on high low. Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar. Note: It's probably better to use "probability cone" for visual presentation when time > 1 Volatility Models : Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps. about 8 x efficient Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster. YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance. Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator. Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters. Standard deviations: Standard Deviation One shows the estimated range where the closing price will be about 68% of the time. Standard Deviation two shows the estimated range where the closing price will be about 95% of the time. Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time. Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability. Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel. People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1. When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation. Note: The significance of autocorrelation can be checked on an ACF indicator. ACF The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds. Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag. Note: The range here after smooth no long represent the probability Panel positions can be adjusted in the settings. X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left. Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down. Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands. Warnings: People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails. Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption). Expected move Occurrence Test The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
อินดิเคเตอร์ Pine Script®
โดย Motgench
Relative Volume EMA (RVOL)Relative Volume EMA (RVOL) measures the current bar’s volume relative to its typical volume over a selected lookback period. It helps traders identify whether a price move is supported by real participation or if it’s occurring on weak, low-quality volume. This version uses: RVOL = Current Volume ÷ Volume EMA Volume EMA Length: adjustable Signal Threshold: a customizable horizontal line (default = 1.2) How to Use 1. RVOL > 1.2 → High-Quality Momentum A value above 1.2 indicates that the current bar has at least 20% more volume than normal, suggesting: Strong conviction Algorithmic activity Momentum-backed breakout or breakdown Higher probability trend continuation These bars are ideal for confirming entries after a technical setup (e.g., pullback, engulfing pattern, Ichimoku trend confirmation, etc.). 2. RVOL < 1.0 → Weak or Low-Quality Move When RVOL is below 1.0: Volume is below average Moves are more likely to fail or reverse Breakouts are unreliable Triggers lack institutional participation These bars are best avoided for trade entries. Why This Indicator Is Useful In many strategies, price alone is not enough. RVOL acts as a filter to ensure that your signals occur during times when the market is actually active and committed. Typical use cases: Confirm trend-following entries Validate pullbacks and breakout candles Filter out low-volume chop Identify session-based volume surges Improve risk-to-reward quality by entering only during true momentum Recommended Settings EMA Length: 20 Threshold Line: 1.2 Works well on Forex, Crypto, and Indices Best used on 15m, 30m, 1H, and 4H charts
อินดิเคเตอร์ Pine Script®
โดย FXLoungeIN
SPY EMA + VWAP Day Trading Strategy (Market Hours Only)//@version=5 indicator("SPY EMA + VWAP Day Trading Strategy (Market Hours Only)", overlay=true) // === Market Hours Filter (EST / New York Time) === nySession = input.session("0930-1600", "Market Session (NY Time)") inSession = time(timeframe.period, "America/New_York") >= time(nySession, "America/New_York") // EMAs ema9 = ta.ema(close, 9) ema21 = ta.ema(close, 21) // VWAP vwap = ta.vwap(close) // Plot EMAs & VWAP plot(ema9, "EMA 9", color=color.green, linewidth=2) plot(ema21, "EMA 21", color=color.orange, linewidth=2) plot(vwap, "VWAP", color=color.blue, linewidth=2) // ----------- Signals ----------- long_raw = close > ema9 and ema9 > ema21 and close > vwap and ta.crossover(ema9, ema21) short_raw = close < ema9 and ema9 < ema21 and close < vwap and ta.crossunder(ema9, ema21) // Apply Market Hours Filter long_signal = long_raw and inSession short_signal = short_raw and inSession // Plot Signals plotshape(long_signal, title="BUY", style=shape.labelup, location=location.belowbar, color=color.green, size=size.small, text="BUY") plotshape(short_signal, title="SELL", style=shape.labeldown, location=location.abovebar, color=color.red, size=size.small, text="SELL") // Alerts alertcondition(long_signal, title="BUY Alert", message="BUY Signal (Market Hours Only)") alertcondition(short_signal, title="SELL Alert", message="SELL Signal (Market Hours Only)")
อินดิเคเตอร์ Pine Script®
โดย hungryPiex3L
V Stop MTF → STRATEGY Why this strategy works so well (your backtest proves it): FeatureBenefitMulti-timeframe Volatility StopSmarter trend detection than single TFRepainting controlYou can choose safe non-repainting modeLimbo/breach detectionAvoids whipsaws during HTF conflictsReversing systemAlways in the market → captures all trendsCandle coloring on reversalInstant visual confirmation Recommended settings that match your +17.33% result: Symbol: SP:SPX or ES1! Timeframe: 9min or 15min Heikin-Ashi HTF: "Multiple Of Current TF" × 3 → gives ~45min on 15min chart ATR Length: 20 ATR Factor: **2.0
กลยุทธ์ Pine Script®
โดย PawelMr
MA200 Deviation Percentile200-Day MA Deviation with Dynamic Thresholds OVERVIEW This indicator measures price deviation from the 200-day moving average as a percentage, with dynamically calculated overbought/oversold thresholds based on historical percentiles. Best suited for broad market indices (SPY, QQQ, IWM, etc.) where the 200-day MA serves as a reliable long-term trend indicator. Individual stocks may exhibit more erratic behavior around this level. CALCULATION Deviation (%) = (Close - 200MA) / 200MA x 100 Dynamic thresholds are derived from actual historical distribution rather than assuming normal distribution: - Overbought threshold = 97.5th percentile of historical deviations - Oversold threshold = 2.5th percentile of historical deviations SETTINGS MA Length (default: 200) Moving average period. Lookback Period (default: 1260) Historical window for threshold calculation. 1260 bars approximates 5 years of daily data. Threshold Percentile (default: 5%) Two-tailed threshold. 5% places overbought/oversold boundaries at the 97.5th and 2.5th percentiles respectively. INTERPRETATION Deviation Value - Positive: Price trading above 200MA - Negative: Price trading below 200MA - Magnitude indicates extent of deviation Percentile Ranking (0-100%) - Shows where current deviation ranks historically - Above 90%: Historically elevated - Below 10%: Historically depressed Dynamic Threshold Lines - Red line: Upper boundary based on historical distribution - Green line: Lower boundary based on historical distribution - These adapt automatically to each asset's volatility characteristics APPLICATION Mean Reversion Extreme deviations tend to normalize over time. When deviation exceeds dynamic thresholds, probability of mean reversion increases. Trend Assessment Sustained positive/negative deviation confirms trend direction. Zero-line crossovers may signal trend changes. NOTES - Optimized for daily timeframe on market indices - Requires sufficient historical data (minimum equal to lookback period) - Extreme readings do not guarantee immediate reversals - Use in conjunction with other analysis methods
อินดิเคเตอร์ Pine Script®
โดย Go3x3
Mebane Faber GTAA 5In 2007, Mebane Faber published research that challenged the conventional wisdom of buy-and-hold investing. His paper, titled "A Quantitative Approach to Tactical Asset Allocation" and published in the Journal of Wealth Management, demonstrated that a simple timing mechanism could reduce portfolio volatility and drawdowns while maintaining competitive returns (Faber, 2007). This indicator implements his Global Tactical Asset Allocation strategy, known as GTAA5, following the original methodology. The core insight of Faber's research stems from a century of market data. By analyzing asset class performance from 1901 onwards, Faber found that a ten-month simple moving average served as an effective trend filter across major asset classes. When an asset trades above its ten-month moving average, it tends to continue its upward trajectory; when it falls below, significant drawdowns often follow (Faber, 2007, pp. 12-16). This observation aligns with momentum research by Jegadeesh and Titman (1993), who documented that intermediate-term momentum persists across equity markets. The GTAA5 strategy allocates capital equally across five diversified asset classes: domestic equities (SPY), international developed markets (EFA), aggregate bonds (AGG), commodities (DBC), and real estate investment trusts (VNQ). Each asset receives a twenty percent allocation when trading above its ten-month moving average. When an asset falls below this threshold, its allocation moves to short-term treasury bills (SHY), creating a dynamic cash position that scales with market risk (Cambria Investment Management, 2013). The strategy's historical performance during market crises illustrates its function. During the 2008 financial crisis, traditional sixty-forty portfolios experienced drawdowns exceeding forty percent. The GTAA5 strategy limited losses to approximately twelve percent by reducing equity exposure as prices declined below their moving averages (Faber, 2013). This asymmetric return profile represents the strategy's primary characteristic. This implementation uses monthly closing prices retrieved via request.security() to calculate the ten-month simple moving average. This distinction matters, as approximations using daily data (such as a 200-day moving average) can generate different signals during volatile periods. Monthly data ensures the indicator produces signals consistent with published academic research. The indicator provides position monitoring, automatic rebalancing detection on either the first or last trading day of each month, and share calculations based on user-defined capital. A dashboard displays current trend status for each asset class, target versus actual weightings, and trade instructions for rebalancing. Performance metrics including annualized volatility and Sharpe ratio provide ongoing risk assessment. Several limitations warrant acknowledgment. First, the strategy rebalances monthly, meaning it cannot respond to intra-month market crashes. Second, transaction costs and taxes from monthly rebalancing may reduce net returns for taxable accounts. Third, the ten-month lookback period, while historically robust, offers no guarantee of future effectiveness. As Ilmanen (2011) notes in "Expected Returns", all timing strategies face the risk of regime change, where historical relationships break down. This indicator serves educational purposes and portfolio monitoring. It does not constitute financial advice. References: Cambria Investment Management (2013). Global Tactical Asset Allocation: An Introduction to the Approach. Research Report, Los Angeles. Faber, M.T. (2007). A Quantitative Approach to Tactical Asset Allocation. Journal of Wealth Management, Spring 2007, pp. 9-79. Faber, M.T. (2013). Global Asset Allocation: A Survey of the World's Top Asset Allocation Strategies. Cambria Investment Management, Los Angeles. Ilmanen, A. (2011). Expected Returns: An Investor's Guide to Harvesting Market Rewards. John Wiley and Sons, Chichester. Jegadeesh, N. and Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), pp. 65-91.
อินดิเคเตอร์ Pine Script®
โดย EdgeTools
4H EMA 21/30 Cloud on 15mThis indicator displays the 4-hour EMA 21 and EMA 30 as a dynamic cloud directly on the 15-minute chart, providing a clean and reliable higher-timeframe trend filter for intraday and scalping setups. The cloud turns: Green when EMA21 > EMA30 → bullish HTF trend Red when EMA21 < EMA30 → bearish HTF trend Because the 4H EMA 21/30 combination tracks mid-term momentum and trend structure extremely well, this indicator helps traders avoid counter-trend trades, time pullbacks more effectively, and align entries with dominant higher-timeframe flow. Perfect for traders using: Price Action FVG / Imbalance concepts CHOCH/BOS structure Liquidity-based models ICT-style intraday execution Use the 4H cloud as your HTF bias anchor, and execute trades using your own entry model on the 15m timeframe.
อินดิเคเตอร์ Pine Script®
โดย CryptoWitch_NL
1
🗓️ FTD Cycle Lite Tracker🗓️ FTD Cycle Lite Tracker (Open Source)This is the simplified, open-source companion to the premium FTD SPIKE PREDICTOR - ML Model.This Lite version focuses purely on time-based cyclic analysis, highlighting the periods when the market is approaching the most well-known FTD-related time windows, based on historical, cyclic patterns.It's the perfect tool for traders who want clean, visual confirmation of anticipated cyclic dates without the complexity or predictive power of a multi-factor model.Key Features of the Lite Version:T+35 Cycle Tracking: Highlights the approximate 49-day calendar cycle (representing 35 trading days) often associated with mandatory Failures-to-Deliver clearing.147-Day Major Cycle: Highlights the long-term institutional cycle commonly observed in assets with complex contract deadlines, anchored from the January 28, 2021 date.Custom Anchor Points: Both cycles allow you to adjust the anchor date to suit different ticker-specific patterns.Visual Windows: Provides clear background shading and shape markers to indicate when the critical 5-day cycle windows are active.👑 Upgrade to the Full Prediction Engine!The open-source Lite version only gives you the calendar dates. The full, proprietary indicator goes far beyond simple calendar counting by telling you how probable a spike is on those dates, and which other factors are confirming the risk.Why Upgrade?FeatureFTD Cycle Lite (Free)FTD SPIKE PREDICTOR (Premium)OutputCalendar Dates0-100% Probability ScoreLogic2 Time Cycles Only7 Weighted Features (ML Model)ConfirmationNoneVolume, Price, Volatility, OPEX, Swap RollConfidenceNone95% Confidence IntervalsSignalsDate MarkersCritical Alerts & Feature BreakdownUnlock the Full PowerYou can get the FTD SPIKE PREDICTOR - ML Model for a one-time fee of $50.00.Since TradingView's invite-only feature is not available, you can contact me directly to gain access:TradingView: Timmy741X.com (Twitter): TimmyCrypto78
อินดิเคเตอร์ Pine Script®
โดย Timmy741
TQQQ Ultra Clean Trend Strategy⭐ TradingView Script Description (Layman Friendly, Polished, Professional) TQQQ Ultra Clean Trend Strategy This strategy is designed to make trend-following simple and easy to understand, even for beginners. It looks at three basic conditions to decide when to buy and when to sell, using only price action and two moving averages. 🔵 Buy Logic (in simple English) The strategy generates a Buy when: Price is moving upward (above the 50-day average) The overall trend is healthy (50-day average above the 250-day average) Strength is increasing (momentum is positive) In plain words: 👉 “Price is climbing strongly, buyers are in control, and the trend is pointing upward.” Only when all three conditions agree do we buy. 🔴 Sell Logic (in simple English) A Sell happens when any of these warning signs appear: Price starts to fall below the short-term trend The trend begins to weaken Momentum turns negative In plain words: 👉 “Price is starting to drop, the up-move is losing strength, and the trend may be ending.” This helps lock in gains when the market starts showing weakness. 🟢 Why this strategy is clean and easy to read Only small text labels appear on the chart (“Buy: Price climbing strongly” / “Sell: Price starting to drop”) No clutter, no shapes, no background boxes Makes it easy to visually understand why a trade happened Uses only reliable long-term signals to avoid noise Perfect for trending instruments like TQQQ
กลยุทธ์ Pine Script®
โดย basantd
SHAMAZZ = Smoothed Heikin Ashi + MA + ZigZagSHAMAZZ: Smoothed Heikin Ashi + Moving Averages + ZigZag Structure This script is a visual analysis tool that combines three components in one place: Smoothed Heikin Ashi candles • Candles are generated using a two-stage exponential smoothing process applied to open, high, low, and close • Helps visualize general price direction and candle transitions • Supports optional multi-timeframe views using TradingView’s request.security() Moving Averages • Includes two standard moving averages (SMA 50 and SMA 200 by default) • These are plotted on the same timeframe as the main chart or a selected higher timeframe • No trading signals or strategies are generated from the averages ZigZag Pivot Mapping • Identifies swing highs and lows based on user-selected pivot length • Classifies pivots into simple categories such as higher high, lower high, higher low, or lower low • Draws connecting lines between detected pivots • Can optionally display small labels showing the pivot type • The ZigZag is not predictive and only reflects swings already formed by the chosen pivot settings Purpose The script is meant as a charting helper for traders who want to visualize smoothed candles, major moving averages, and swing structure without switching indicators. It does not generate signals, alerts, or trading advice. It does not imply future outcomes, accuracy, or profitability. Note on Higher Timeframes When higher-timeframe values are requested, the script only displays confirmed higher-timeframe candle closes. No lookahead behavior is intended. Users who want the safest and strictest mode should keep all additional timeframe options disabled and use the indicator on one timeframe only. How to Use • Turn components on or off depending on your workflow • Adjust pivot length to make the ZigZag more or less sensitive • Use smoothed candles and moving averages as visual references • Use ZigZag swings only for structure mapping, not for trade signals or forecasts This tool is provided for visual analysis only and does not promise performance or predictive value.
อินดิเคเตอร์ Pine Script®
โดย V_R_S
Levels S/R Boxes + Gaps + SL/TPWhat It Does: Automatically identifies and displays: 🟦 Support/Resistance zones (horizontal boxes) 🟨 Price gaps (unfilled gaps from market open/close) 🎯 Stop Loss levels (where to protect trades) 💰 Take Profit levels (where to exit trades) Purpose: Shows you exactly where price is likely to bounce, reverse, or break through. Best Practices: ✅ Trade at the boxes - Don't chase price ✅ Use SL/TP lines - Automatic risk management ✅ Wait for confirmation - Candle pattern + S/R level ✅ Gaps get filled - Trade towards yellow boxes ✅ Solid lines = stronger - Prefer 3+ touch levels ❌ Don't ignore SL - Always protect yourself ❌ Don't trade middle - Wait for S/R zones ❌ Don't fight strong levels - Respect solid boxes Settings (Quick Reference): S/R Strength: 10 (default) - Lower = more levels, Higher = fewer stronger levels Max Levels: 5 (default) - Number of S/R boxes to show Show Gaps: ON - Display yellow gap boxes Show SL/TP: ON - Display entry/exit suggestions
อินดิเคเตอร์ Pine Script®
โดย ognenmitev
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.) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
อินดิเคเตอร์ Pine Script®
โดย Saeid21
ADX Breakout Enhanced Signal🥋 Trading Dojo – ADX Breakout Enhanced Signal This indicator combines the trend-strength power of the ADX with dynamic breakout-based signals, designed for traders who want more frequent and higher-probability entries on timeframes like 1 hour. The core logic focuses on: 📌 1. Trend Strength Detection with ADX The indicator evaluates whether the market is showing a strong directional trend using an optimized ADX. When ADX rises above the configured threshold, the system interprets that price has enough momentum to validate an entry. 📌 2. Breakout Entry Logic It identifies points where price breaks recent highs or lows, confirming the start or continuation of movement. This breakout-based approach produces more entries than traditional ADX strategies alone. 📌 3. Clear and Simple Signals 🟩 Long when price breaks a recent high with strong trend confirmation. 🟥 Short when price breaks a recent low with strong trend confirmation. 📌 4. Built-In Automated Alerts The indicator automatically generates JSON alerts ready for use with automation tools such as trading bots, webhooks, BingX, 3Commas, Discord bots, and more. 🎯 Purpose of the Indicator To provide more frequent, well-distributed, and momentum-validated entries, while maintaining simplicity and speed — perfect for real-time decision-making. Perfect For: Intraday trading 1h, 30m, and 15m timeframes Breakout-based strategies Automated trading systems
อินดิเคเตอร์ Pine Script®
โดย Mike-Caves
DANCE WITH WOLVES VN ALL TO 1DANCE WITH WOLVES VN is a smart-money volume indicator designed for stocks and crypto. Main features: • logic to detect Distribution, No Demand, Absorption and Exhaustion. • Automatically builds smart Support/Resistance zones from high-volume price leaders. • Regression trend channel to see the short-term trend and trading range. • Dashboard table that shows the top high/low price bars with buy/sell volume and group labels. • Alert conditions for Breakout above resistance and At Support Area so you don’t need to watch the chart all the time. You can use it on any symbol and timeframe. Just add the script to your chart and follow the zones (red = resistance, green = support) together with the P/L labels and the status line. Vietnamese note: Indicator dùng volume + để vẽ vùng hỗ trợ/kháng cự thông minh, label phân phối / hấp thụ / cạn lực bán và kênh xu hướng. Dùng được cho cả stock và crypto. tot nhat dung khung 5 den 15 phut
อินดิเคเตอร์ Pine Script®
โดย tungtran2006