Linear MomentsโโOVERVIEW
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.
โโCONCEPTS
โโFour 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, ฮปโ, is equivalent to the sample mean and represents the location of the distribution. The second L-moment, ฮปโ, is a measure of the dispersion of the distribution, similar to the sample standard deviation. The third and fourth L-moments, ฮปโ and ฮปโ, 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.
โโOrder 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.
โโBias 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.
โโWAYS 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.
โโFEATURES
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.
Indicators and strategies
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.
โโOVERVIEW 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.
โโCONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, HistoricalโโVolatilityโโRank, and HistoricalโโVolatilityโโPercentile. 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 โ 1 should be used here to get rid of the sampling bias.
Pros: โ
โข 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: โ
โข 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. โ
โข Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data. โ
โข 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 โ deviation makes the historical volatility more stable with extreme values.
Parkinson Hv Estimator: โ
โข Parkinson was one of the first to come up with improvements to historical volatility calculation. โข 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. โข 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: โ
โข Garman Klass expanded on Parkinsonโs Estimator. Instead of Parkinsonโs estimator using high and low, Garman Klassโs method uses open, close, high, and low to find the minimum variance method. โ
โข 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: โ
โข Rogers and Satchell found some drawbacks in Garman-Klassโs estimator. The Garman-Klass assumes price as Brownian motion with zero drift. โ
โข The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series. โ
โข Rogers-Satchell HV is more efficient than Garman-Klass HV when thereโs drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesnโt handle jumps, therefore it still underestimates volatility.
Garman-Klass Yang-Zhang extension: โ
โข 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. โ
โข The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when thereโs no gap in the data such as in cryptocurrencies.
Yang-Zhang Estimator: โ
โข 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. โ
โข 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. โ
โข 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:
โข EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted. โ
โข 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. โ
โข 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. โ
โข 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: โ
โข This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility. โ
โข Itโs a simple way to calculate volatility and itโs 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. โ
โข 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. โ
โข In Nassim Talebโs 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: โ
โข This is another estimator that does not use standard deviation to measure volatility. โ
โข Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution. โ
โข The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators.
โโFEATURES โ
โข You can select the volatility estimator models in the Volatility Model input โ
โข 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. โ
โข Alpha is used to adjust the Yang Zhang volatility estimator value. โ
โข Percentile Length is used to Adjust Percentile coloring lookbacks. โ
โข 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. โ
โข When percentile coloring is off, it wonโt show the gradient color. โ
โข 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. โ
โข When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon.
โข You can choose the plot style, such as lines, columns, areas in the plotstyle input. โ
โข When the show information panel is on, a small panel will display on the right. โ
โข 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.
โโHOW TO USE If you havenโt 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 itโs 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).
โโFINAL 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.
dr ram's banknifty fad%banknifty fad% calculation as per dr ram sir. based on 4 quadrant analysis . one of the criteria is calculating future asset difference for predicting market direction and entry plan.
FVG + Bollinger + Toggles + Swing H&L (Taken/Close modes)This indicator combines multiple advanced market-structure tools into one unified system.
It detects AโC 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 โclosesโ 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 elementsโFVGs, Bollinger Bands, and Swing Linesโcan 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.
Ultra Reversion DCA Strategy with Manual Leverage - V.1Ultra Reversion DCA Strategy with Manual Leverage - V.1
2025-10-27
MTF RSI + MACD Bullish Confluencethis based on rsi more then 50 and macd line bullish crossover or above '0' and time frame 15 min, 1 hour, 4 hour , 1 day and 1 week
HTF FVG + SessionsThis indicator combines multi-timeframe FVG AโC 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 โShow all session boxesโ 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.
SYMBOL NOTES - UNCORRELATED TRADING GROUPSWrite symbol-specific notes that only appear on that chart. Organized into 6 uncorrelated groups for safe multi-pair trading.
๐ 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.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ฏ 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!
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ THE 6 UNCORRELATED GROUPS
Each group contains pairs that share NO common currency:
```
GRUP 1: XAUUSD โข EURGBP โข NZDJPY โข AUDCHF โข NATGAS
GRUP 2: EURUSD โข GBPJPY โข AUDNZD โข CADCHF
GRUP 3: GBPUSD โข EURJPY โข AUDCAD โข NZDCHF
GRUP 4: USDJPY โข EURCHF โข GBPAUD โข NZDCAD
GRUP 5: USDCAD โข EURAUD โข GBPCHF
GRUP 6: NAS100 โข DAX40 โข UK100 โข JPN225
```
**Example - GRUP 1:**
- XAUUSD โ Uses USD + Gold
- EURGBP โ Uses EUR + GBP
- NZDJPY โ Uses NZD + JPY
- AUDCHF โ Uses AUD + CHF
- NATGAS โ Commodity (independent)
= 7 different currencies, ZERO overlap!
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
**โ
HOW TO USE**
1. Add indicator to any chart
2. Open Settings (gear icon โ๏ธ)
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!
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ๏ธ 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
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ก 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
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ง COMPATIBLE WITH
- All major forex brokers
- Prop firms (FTMO, Alpha Capital, etc.)
- Works on any timeframe
- Futures symbols supported (MGC, M6E, etc.)
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Abu Basel IQOption 2m Signals//@version=5
indicator("Abu Basel IQOption 2m Signals", overlay = true, timeframe = "", timeframe_gaps = true)
//========================
// ุงูุฅุนุฏุงุฏุงุช
//========================
emaFastLen = input.int(9, "EMA ุณุฑูุน (9)")
emaSlowLen = input.int(21, "EMA ุจุทูุก (21)")
rsiLen = input.int(14, "RSI Length", minval = 2)
rsiBuyLevel = input.float(50.0, "RSI ุญุฏ ุงูุดุฑุงุก (ุฃุนูู ู
ู)", minval = 0, maxval = 100)
rsiSellLevel= input.float(50.0, "RSI ุญุฏ ุงูุจูุน (ุฃูู ู
ู)", minval = 0, maxval = 100)
bbLen = input.int(20, "Bollinger Length")
bbMult = input.float(2.0, "Bollinger Deviation")
showSignals = input.bool(true, "ุฅุธูุงุฑ ุงูุฃุณูู
(CALL / PUT)")
showBg = input.bool(true, "ุชูููู ุงูุฎูููุฉ ุนูุฏ ุงูุฅุดุงุฑุงุช")
//========================
// ุงูู
ุคุดุฑุงุช ุงูุฃุณุงุณูุฉ
//========================
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)
// ุฑุณู
ุงูู
ุชูุณุทุงุช ูุงูุจููููุฌุฑ
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)
//========================
// ุฏูุงู ุฃุดูุงู ุงูุดู
ูุน ุงูุงูุนูุงุณูุฉ
//========================
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 ุตุงุนุฏุฉ (ุฐูู ุณููู ุทููู)
bullHammer() =>
lowerWick > bodySize * 2 and upperWick <= bodySize and close > open
// Shooting Star ูุงุจุทุฉ (ุฐูู ุนููู ุทููู)
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
// ุชุฌู
ูุน ุฃูู
ุงุท ุตุนูุฏ/ูุจูุท
bullPattern = bullHammer() or bullEngulfing()
bearPattern = bearShootingStar() or bearEngulfing()
//========================
// ุดุฑูุท ุงูุฏุฎูู
//========================
// ุชูุงุทุน ุงูู
ุชูุณุทุงุช
bullCross = ta.crossover(emaFast, emaSlow) // ุตุนูุฏ
bearCross = ta.crossunder(emaFast, emaSlow) // ูุจูุท
// ุดุฑูุท ุดุฑุงุก CALL:
// 1) ุชูุงุทุน EMA9 ููู EMA21
// 2) ุงูุณุนุฑ ููู ุฎุท ูุณุท ุงูุจูููุฌุฑ
// 3) RSI ุฃุนูู ู
ู 50
// 4) ุดู
ุนุฉ ุงูุนูุงุณูุฉ ุตุงุนุฏุฉ (Hammer ุฃู Engulfing)
callCond = bullCross and close > basis and rsi > rsiBuyLevel and bullPattern
// ุดุฑูุท ุจูุน PUT:
// 1) ุชูุงุทุน EMA9 ุชุญุช EMA21
// 2) ุงูุณุนุฑ ุชุญุช ุฎุท ูุณุท ุงูุจูููุฌุฑ
// 3) RSI ุฃูู ู
ู 50
// 4) ุดู
ุนุฉ ุงูุนูุงุณูุฉ ูุงุจุทุฉ (Shooting Star ุฃู Bearish Engulfing)
putCond = bearCross and close < basis and rsi < rsiSellLevel and bearPattern
//========================
// ุฑุณู
ุงูุฅุดุงุฑุงุช ุนูู ุงูุดุงุฑุช
//========================
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)
// ุชูููู ุงูุฎูููุฉ ุนูุฏ ุงูุฅุดุงุฑุงุช
bgcolor(showBg and callCond ? color.new(color.green, 85) :
showBg and putCond ? color.new(color.red, 85) : na)
//========================
// ุดุฑูุท ุงูุชูุจูู (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}}")
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.
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.
SPY โ ES 11 Levels (Hybrid RTH/Globex) [Tick Fixed]๐ Description for SPY โ 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.
๐ Features
โ
Converts SPY โ ES using custom or automatic ratio
Option to manually enter a ratio (recommended for accuracy)
Or automatically compute ES/SPY from live prices
โ
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
โGamma Level
Zero Gamma
Positive OI
Negative OI
Positive Volume
Negative Volume
All levels are drawn as clean horizontal lines using the converted ES value.
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.
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 โpressure gaugeโ 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 โfastโ versus โslowโ 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 symbolโs RSI ladder into a compact โmarket pressure gaugeโ 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 โterm structureโ 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 โ the base RSI length used for the shortest strip.
RSI Step โ the amount added to each successive RSI length.
RSI Multiplier โ a global scaling factor applied after the step.
Each of the 20 RSIs uses:
RSI length = round((base_length + step ร index) ร multiplier) , where the index goes from 0 to 19.
That means:
RSI 1 uses (len + step ร 0) ร mult.
RSI 2 uses (len + step ร 1) ร mult.
โฆ
RSI 20 uses (len + step ร 19) ร 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 โareaโ strip at a fixed vertical level using histbase to stack them:
RSI 1โ5 form Group 1.
RSI 6โ10 form Group 2.
RSI 11โ15 form Group 3.
RSI 16โ20 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 tileโs color is driven by the RSI value and your chosen palette. The script includes several palettes:
Viridis โ smooth green to yellow, good for subtle reading.
Jet โ strong blue to red sequence with high contrast.
Plasma โ purple through orange to yellow.
Custom Heat โ cool blues to neutral grey to hot reds.
Gray โ grayscale from white to black for minimalistic layouts.
Cividis, Inferno, Magma, Turbo, Rainbow โ additional scientific and rainbow-style maps.
Internally, RSI values are bucketed into ranges (for example, below 10, 10โ20, โฆ, 90โ100). Each bucket maps to a unique colour for that palette. In all schemes, low RSI values are mapped to the โcoldโ or darker side and high RSI values to the โhotโ 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 โ how many RSIs are above 70.
Bear count โ how many RSIs are below 30.
These are plotted as:
A white line (โRSI > 70 Countโ) for the overbought cluster.
A black line (โRSI < 30 Countโ) for the oversold cluster.
If you enable the โShow Bull and Bear Countโ 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 โstrong clusterโ regimes:
RSI Heatmap Strong Bull โ triggers when at least 10 RSIs are above 70.
RSI Heatmap Strong Bear โ 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:
โHow many different RSI lengths are stretched in the same direction at once?โ
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 โextreme pressureโ 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 โspeedโ 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 โ base RSI length for the shortest strip.
RSI Step โ the increment added to each successive RSI length.
RSI Multiplier โ scales all generated RSI lengths.
Calculation Source โ the input series, such as close, hlc3 or others.
Plotting and Coloring Settings
Heatmap Color Palette โ choose between Viridis, Jet, Plasma, Custom Heat, Gray, Cividis, Inferno, Magma, Turbo or Rainbow.
Show Group 1 โ toggles RSI 1โ5.
Show Group 2 โ toggles RSI 6โ10.
Show Group 3 โ toggles RSI 11โ15.
Show Group 4 โ toggles RSI 16โ20.
Show Bull and Bear Count โ enables or disables the two breadth lines.
Alerts
RSI Heatmap Strong Bull โ fires when the number of RSIs above 70 reaches or exceeds the configured threshold (default 10).
RSI Heatmap Strong Bear โ 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:
โHow many versions of RSI agree with what I am seeing on the chart?โ
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.
MFM โ Light Context HUD (Minimal)Overview
MFM Light Context HUD is the free version of the Market Framework Model. It gives you a fast and clean view of the current market regime and phase without signals or chart noise. The HUD shows whether the asset is in a bullish or bearish environment and whether it is in a volatile, compression, drift, or neutral phase. This helps you read structure at a glance.
Asset availability
The free version works only on a selected list of five assets.
Supported symbols are
SP:SPX
TVC:GOLD
BINANCE:BTCUSD
BINANCE:ETHUSDT
OANDA:EURUSD
All other assets show a context banner only.
How it works
The free version uses fixed settings based on the original MFM model. It calculates the regime using a higher timeframe RSI ratio and identifies the current phase using simplified momentum conditions. The chart stays clean. Only a small HUD appears in the top corner. Full visual phases, ratio logic, signals, and auto tune are part of the paid version.
The free version shows the phase name only. It does not display colored phase zones on the chart.
Phase meaning
The Market Framework Model uses four structural phases to describe how the market
behaves. These are not signals but context layers that show the underlying environment.
Volatile (Phase 1)
The market is in a fast, unstable or directional environment. Price can move aggressively with
stronger momentum swings.
Compression (Phase 2)
The market is in a contracting state. Momentum slows and volatility decreases. This phase
often appears before expansion, but it does not predict direction.
Drift (Phase 3)
The market moves in a more controlled, persistent manner. Trends are cleaner and volatility
is lower compared to volatile phases.
No phase
No clear structural condition is active.
These phases describe market structure, not trade entries. They help you understand the conditions you are trading in.
Cross asset context
The Market Framework Model reads markets as a multi layer system. The full version includes cross asset analysis to show whether the asset is acting as a leader or lagger relative to its benchmark. The free version uses the same internal benchmark logic for regime detection but does not display the cross asset layer on the chart.
Cross asset structure is a core part of the MFM model and is fully available in the paid version.
Included in this free version
Higher timeframe regime
Current phase name
Clean chart output
Context only
Works on a selected set of assets
Not included
No forecast signals
No ratio leader or lagger logic
No MRM zones
No MPF timing
No auto tune
The full version contains all features of the complete MFM model.
Full version
You can find the full indicator here:
payhip.com
More information
Model details and documentation:
mfm.inratios.com
Momentum Framework Model free HUD indicator User Guide: mfm.inratios.com
Disclaimer
The Market Framework Model (MFM) and all related materials are provided for educational and informational purposes only. Nothing in this publication, the indicator, or any associated charts should be interpreted as financial advice, investment recommendations, or trading signals. All examples, visualizations, and backtests are illustrative and based on historical data. They do not guarantee or imply any future performance. Financial markets involve risk, including the potential loss of capital, and users remain fully responsible for their own decisions. The author and Inratiosยฉ make no representations or warranties regarding the accuracy, completeness, or reliability of the information provided. MFM describes structural market context only and should not be used as the sole basis for trading or investment actions.
By using the MFM indicator or any related insights, you agree to these terms.
ยฉ 2025 Inratios. Market Framework Model (MFM) is protected via i-Depot (BOIP) โ Ref. 155670. No financial advice.
EMA Percent Angle & Slope VisualizerEMA Percent Angle & Slope Visualizer is a powerful trend-strength tool that measures the true geometric slope of an EMA using percent-normalized angle calculations.
Unlike raw angle or ATR-based angle methods, this indicator uses the formula:
angle = atan( (EMA_t - EMA_(t-1)) / EMA_(t-1) ) * (180 / pi)
This gives you a universal slope measurement that works across stocks, indices, currencies, and crypto โ regardless of price scale.
๐ Features
Percent-normalized EMA angle for accurate trend strength
Auto-detected slope segments
Dynamic EMA color
๐ข Bullish slope
๐ด Bearish slope
โช Neutral (angle below threshold)
Dashed slope lines drawn only during valid slope runs
Angle label displayed at slope end
Works on any timeframe
Designed for momentum traders, trend followers, breakout traders, and algo developers
๐ Why Percent-Normalized Angle?
Raw price angle is meaningless because angles depend on chart scaling.
Percent-normalized angle gives a true slope, equal across all instruments.
โ Tip
Slopes above +0.15ยฐ and below โ0.15ยฐ represent strong trend phases for Nifty.
Adjust threshold for your timeframe according to your script
Dynamic SMA Trend System [Multi-Stage Risk Engine]Description:
This script implements a robust Trend Following strategy based on a multiple Simple Moving Average (SMA) crossover logic (25, 50, 100, 200). What sets this strategy apart is its advanced "4-Stage Risk Engine" and a smart "High-Water Mark" Re-Entry system, designed to protect profits during parabolic moves while filtering out chop during sideways markets.
How it works:
The strategy operates on three core pillars: Trend Identification, Dynamic Risk Management, and Momentum Re-Entry.
1. Entry Logic (Trend Identification) The script looks for crossovers at different trend stages to capture early reversals as well as established trends:
Short-Term: SMA 25 crosses over SMA 50.
Mid-Term: SMA 50 crosses over SMA 100.
Macro-Trend: SMA 100 crosses over SMA 200.
2. The 4-Stage Risk Engine (Dynamic Stop Loss) Instead of a static Stop Loss, this strategy uses a progressive system that adapts as the price increases:
Stage 1 (Protection): Starts with a fixed Stop Loss (default -10%) to give the trade room to breathe.
Stage 2 (Break-Even): Once the price rises by 12%, the Stop is moved to trailing mode (10% distance), effectively securing a near break-even state.
Stage 3 (Profit Locking): At 25% profit, the trailing stop tightens to 8% to lock in gains.
Stage 4 (Parabolic Mode): At 40% profit, the trailing stop tightens further to 5% to capture the peak of parabolic moves.
3. Dual Exit Mechanism The strategy exits a position if EITHER of the following happens:
Stop Loss Hit: Price falls below the dynamic red line (Risk Engine).
Dead Cross: The trend structure breaks (e.g., SMA 25 crosses under SMA 50), signaling a momentum loss even if the Stop Loss wasn't hit.
4. "High-Water Mark" Re-Entry To avoid "whipsaws" in choppy markets, the script does not re-enter immediately after a stop-out.
It marks the highest price of the previous trade (Green Dotted Line).
A Re-Entry only occurs if the price breaks above this previous high (showing renewed strength) AND the long-term trend is bullish (Price > SMA 200).
Visuals:
SMAs: 25 (Yellow), 50 (Orange), 100 (Blue), 200 (White).
Red Line: Visualizes the dynamic Stop Loss level.
Green Dots: Visualizes the target price needed for a valid re-entry.
Settings: All parameters (SMA lengths, Stop Loss percentages, Staging triggers) are fully customizable in the settings menu to fit different assets (Crypto, Stocks, Forex) and timeframes.
Current Candle Vertical LineDescription
The Current Candle Vertical Line indicator draws a fully customizable vertical line on the most recent candle (live bar). This provides a clear visual anchor for active traders, especially during fast-moving markets or multi-chart setups.
The line extends from the top of the chart to the bottom, ensuring maximum visibilityโregardless of zoom level or price scale.
Features
โ Fully customizable line color
โ Adjustable opacity (0โ100%)
โ Custom line thickness
โ Three selectable line styles: Solid, Dashed, or Dotted
โ Automatically deletes old line and redraws on the newest bar
โ Works on any timeframe, chart type, and asset
Use Cases
Highlight the current candle during live trading
Keep visual focus when scalping or trading futures
Align entries with indicators on lower or higher timeframes
Improve visibility during high volatility
Support multi-monitor or multi-chart layouts
Notes
The indicator draws the line only on the last active bar.
Since overlay=true, the line appears in the main chart panel.
This script does not generate alerts (visual marker only).
VCP Base Detector
๐ VCP BASE DETECTOR - AUTO-DETECT CONSOLIDATION ZONES
๐ฏ WHAT IS THIS INDICATOR?
This indicator automatically detects and marks ALL consolidation bases (VCP bases) on your chart. It:
โ
Auto-detects when price enters consolidation
โ
Measures base tightness (volatility contraction)
โ
Tracks base duration (how long consolidating)
โ
Rates base quality (1-5 stars)
โ
Shows volume drying confirmation
โ
Detects base breakouts
โ
Shows progression of multiple bases (VCP pattern)
Use this WITH the "Mark Minervini SEPA Balanced" indicator for complete trading setups!
โ
Mark Minervini SEPA Balanced = Trend + RS + Stage
โ
VCP Base Detector = Base Quality + Progression
Combined = Complete professional trading system!
๐จ WHAT YOU SEE ON YOUR CHART
1๏ธโฃ COLORED BOXES (Base Zones):
๐ฆ Aqua Box = โญโญโญโญโญ Excellent base (tightest)
๐ต Blue Box = โญโญโญโญ Very good base
๐ฃ Purple Box = โญโญโญ Good base
๐ Orange Box = โญโญ Fair base
โฌ Gray Box = โญ Weak base
2๏ธโฃ BASE LABELS (With Metrics):
Shows above each base:
โข Duration: 20 days
โข Tightness: 0.9%
โข Quality: โญโญโญโญโญ
3๏ธโฃ BREAKOUT LABELS (When price exits base):
Green "BREAKOUT โ" label shows:
โข Price: โน800
โข Volume: 1.6x
4๏ธโฃ DASHBOARD (Top-Left Panel):
Real-time base metrics showing:
โข In Base: YES/NO
โข Tightness: 0.8%
โข Duration: 22 days
โข Range: 3.5%
โข Volume: Drying/Normal
โข Quality: โญโญโญโญ
๐ UNDERSTANDING BASE QUALITY (โญ Rating System)
โญโญโญโญโญ (EXCELLENT)
โโ Tightness: < 0.8% ATR
โโ Duration: 15-40 days
โโ Volume: Significantly drying
โโ Price Range: < 5%
โโ Result: Most explosive breakouts (best quality)
โญโญโญโญ (VERY GOOD)
โโ Tightness: 0.8-1.0% ATR
โโ Duration: 15-35 days
โโ Volume: Very dry
โโ Price Range: < 7%
โโ Result: High probability breakouts
โญโญโญ (GOOD)
โโ Tightness: 1.0-1.3% ATR
โโ Duration: 15-30 days
โโ Volume: Drying
โโ Price Range: < 8%
โโ Result: Decent breakout probability
โญโญ (FAIR)
โโ Tightness: 1.3-1.5% ATR
โโ Duration: 15-25 days
โโ Volume: Moderate drying
โโ Price Range: < 10%
โโ Result: Lower quality, riskier
โญ (WEAK)
โโ Tightness: > 1.5% ATR
โโ Duration: Varies
โโ Volume: Not drying enough
โโ Price Range: > 10%
โโ Result: Low quality, skip these
๐ HOW TO USE - STEP BY STEP
STEP 1: ADD INDICATOR TO CHART
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
1. Open any stock chart (use 1D timeframe for swing trading)
2. Click "Indicators"
3. Search "VCP Base Detector"
4. Click to add to chart
5. Wait a moment for boxes to appear
STEP 2: SCAN FOR BASES
โโโโโโโโโโโโโโโโโโโโโโโ
Look for:
โ Colored boxes appearing on chart (bases forming)
โ Dashboard showing "In Base: YES"
โ Tightness below 1.5%
โ Volume Dry: YES
STEP 3: MONITOR BASE QUALITY
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Dashboard shows stars:
โญโญโญโญโญ = Wait for breakout (best setup)
โญโญโญโญ = Good quality, watch for breakout
โญโญโญ = Decent, but not ideal
โญโญ or โญ = Skip (lower probability)
STEP 4: WAIT FOR BREAKOUT
โโโโโโโโโโโโโโโโโโโโโโโโโโ
When price breaks above the box:
โ Green "BREAKOUT โ" label appears
โ Shows breakout price and volume
โ If volume shows 1.3x+, breakout is confirmed
โ This is your entry signal!
STEP 5: CHECK MINERVINI CRITERIA (Use Both Indicators)
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Before entering:
โ VCP Base Detector shows โญโญโญโญ+ quality base
โ Mark Minervini indicator shows BUY SIGNAL
โ Dashboard shows 10+ criteria GREEN
โ Stage shows S2
Result: HIGH-PROBABILITY SETUP! ๐ฏ
๐ DASHBOARD INDICATORS - WHAT EACH MEANS
BASE METRICS SECTION:
โโโโโโโโโโโโโโโโโโโโโ
In Base = โ YES or โ NO
Show if price is currently consolidating
Tightness = 0-3% (lower = tighter = better)
< 0.8% = โญโญโญโญโญ (excellent)
0.8-1.0% = โญโญโญโญ (very good)
1.0-1.3% = โญโญโญ (good)
1.3-1.5% = โญโญ (fair)
> 1.5% = โญ (weak)
Duration = Number of days in consolidation
15 days = โญ (too short, weak)
20 days = โญโญโญ (ideal)
30 days = โญโญโญโญ (very long, strong)
> 40 days = โ ๏ธ (too long, may break down)
Range = % movement within the base
< 5% = โญโญโญโญโญ (excellent, very tight)
5-8% = โญโญโญ (good)
> 10% = โญ (loose, not ideal)
Vol Dry = Volume status during consolidation
โ YES = Volume contracting (good)
โ NO = Normal/high volume (weak setup)
QUALITY SECTION:
โโโโโโโโโโโโโโโโ
Stars = Overall base quality rating
โญโญโญโญโญ = Best quality bases (most explosive)
โญโญโญโญ = Excellent quality
โญโญโญ = Good quality
โญโญ = Fair quality
โญ = Weak quality (skip)
52W INFO SECTION:
โโโโโโโโโโโโโโโโโ
From 52W Hi = How far below 52-week high is price?
< 25% = In sweet zone โ
> 25% = Too far from highs โ
From 52W Lo = How far above 52-week low is price?
> 30% = In sweet zone โ
< 30% = Too close to lows โ
โ๏ธ CUSTOMIZATION GUIDE
Click โ๏ธ gear icon next to indicator to adjust:
MINIMUM BASE DAYS (Default: 15)
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Current: 15 = Include shorter bases
Change to 20 = Longer bases only (higher quality)
Change to 10 = Include very short bases (more frequent)
Why: Longer bases = better breakouts, but fewer opportunities
ATR% TIGHTNESS THRESHOLD (Default: 1.5)
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Current: 1.5 = BALANCED for Indian stocks
Change to 1.0 = ONLY very tight bases (โญโญโญโญโญ)
Change to 2.0 = Looser bases included (more frequent)
Why: Lower = tighter bases = better quality, fewer signals
VOLUME DRYING THRESHOLD (Default: 0.7)
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
Current: 0.7 = Volume at 70% of average (good drying)
Change to 0.6 = Stricter (more volume drying required)
Change to 0.8 = Looser (less volume drying required)
Why: Volume drying = consolidation confirmation
52W PERIOD (Default: 252)
โโโโโโโโโโโโโโโโโโโโโโโโโ
Current: 252 = Full year lookback
Don't change unless you know what you're doing
๐ REAL TRADING EXAMPLE
SCENARIO: Trading MARUTI over 6 weeks
WEEK 1: Nothing happening
โโโโโโโโโโโโโโโโโโโโโโโโโ
- No boxes on chart
- Dashboard: "In Base: NO"
- Action: SKIP (not consolidating)
WEEK 2: Base Starting to Form
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
- Purple box appears (โญโญโญ quality)
- Dashboard: "In Base: YES"
- Tightness: 1.2%
- Duration: 3 days (too new)
- Action: MONITOR (let it develop)
WEEK 3-4: Base Tightening
โโโโโโโโโโโโโโโโโโโโโโโโโโ
- Box color changes from Purple โ Blue (โญโญโญโญ quality)
- Dashboard: Duration: 12 days
- Tightness: 0.9%
- Vol Dry: YES
- Action: GET READY (high-quality base forming)
WEEK 4-5: Perfect Base Formed
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
- Box changes to Aqua (โญโญโญโญโญ EXCELLENT!)
- Dashboard: Duration: 22 days โ
- Tightness: 0.8% โ
- Vol Dry: YES โ
- Range: 4.2% โ
- Action: WATCH FOR BREAKOUT
WEEK 5: BREAKOUT HAPPENS!
โโโโโโโโโโโโโโโโโโโโโโโโโโ
- Price closes above box
- Green "BREAKOUT โ" label appears
- Shows: Price โน850, Volume 1.6x
- Mark Minervini indicator: BUY SIGNAL โ
- Dashboard all GREEN โ
- Action: ENTER TRADE
Entry: โน850
Stop: Box low (โน820)
Target: โน980 (20% move)
RESULT: +15.3% profit in 2 weeks! โ
๐ก PRO TIPS FOR BEST RESULTS
1. COMBINE WITH MINERVINI INDICATOR
Use BOTH indicators together:
โ VCP Detector = Base quality
โ Minervini = Trend + RS + Volume
Result = Best high-probability setups
2. PREFER โญโญโญโญ+ QUALITY BASES
Don't trade โญโญ or โญ quality bases
Only trade โญโญโญ+ (ideally โญโญโญโญ+)
Higher quality = Higher win rate
3. WAIT FOR VOLUME CONFIRMATION
Base must show "Vol Dry: YES"
Breakout must have 1.3x+ volume
Low volume breakouts fail often
4. USE 1D TIMEFRAME ONLY
This indicator optimized for daily charts
Intraday = Too many false signals
Weekly = Misses good setups
5. MONITOR MULTIPLE BASES (VCP PATTERN)
Multiple bases getting tighter = VCP pattern
Each base should be better quality than last
Tightest base = Biggest breakout
6. COMBINE WITH 52W CONTEXT
Dashboard shows "From 52W Hi" and "From 52W Lo"
Price should be in sweet zone:
< 25% from 52W high (uptrend territory)
> 30% above 52W low (not oversold)
7. BACKTEST FIRST
Use TradingView Replay
Go back 6-12 months
See how many bases appeared
See which were profitable
โ BASES TO SKIP (Lower Probability)
Skip if:
โ Quality rating < โญโญโญ (only 1-2 stars)
โ Tightness > 1.5% (too loose)
โ Duration < 10 days (too short, weak)
โ Duration > 50 days (too long, may break down)
โ Vol Dry: NO (volume not contracting)
โ Range > 10% (not tight consolidation)
โ Price < 30% from 52W low (too weak)
โ Price > 30% from 52W high (too far up, late entry)
โ ๏ธ IMPORTANT DISCLAIMERS
โ This indicator is for educational purposes only
โ Past performance does not guarantee future results
โ Always use proper risk management (position sizing, stop loss)
โ Never risk more than 2% of your account on one trade
โ Base detection is technical analysis, not investment advice
โ Losses can occur - trade at your own risk
โ Combine with other indicators for best results
๐ LEARNING RESOURCES
To understand VCP bases better:
โ Study "Trade Like a Stock Market Wizard" by Mark Minervini
โ Watch: "VCP Pattern" videos on YouTube
โ Practice: Backtest on 1-2 years of historical data
โ Learn: How consolidation precedes breakouts
๐ YOU'RE READY!
Happy trading! ๐๐ฏ
Viprasol Elite Flow Pro - Premium Order Flow & Trend Systemโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ฅ VIPRASOL ELITE FLOW PRO
Professional Order Flow & Trend Detection System
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ WHAT IS THIS INDICATOR?
Viprasol Elite Flow Pro is a comprehensive trading system that combines institutional order flow analysis with adaptive trend detection. Unlike basic indicators, this tool identifies high-probability setups by analyzing where smart money is likely positioning, while filtering signals through multiple confirmation layers.
This indicator is designed for traders who want to:
โ Identify premium (supply) and discount (demand) zones automatically
โ Detect trend direction with adaptive cloud technology
โ Spot high-volume rejection points before major moves
โ Filter low-quality signals with intelligent confirmation logic
โ Track market strength in real-time via elite dashboard
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ฏ CORE FEATURES
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
1๏ธโฃ ELITE TREND ENGINE
โข Adaptive Moving Average system (Fast/Adaptive/Smooth modes)
โข Dynamic trend cloud that expands/contracts with volatility
โข Real-time trend state tracking (Bullish/Bearish/Ranging)
โข Trend strength meter (0-10 scale)
โข ATR-based volatility adjustments
2๏ธโฃ ORDER FLOW DETECTION
โข Automatic Premium Zone (Supply) identification
โข Automatic Discount Zone (Demand) identification
โข Smart zone extension - zones remain valid until broken
โข Zone rejection detection with price action confirmation
โข Customizable zone strength (5-30 bars lookback)
3๏ธโฃ VOLUME INTELLIGENCE
โข Volume spike detection (configurable threshold)
โข Climax bar identification (exhaustion signals)
โข Volume filter for signal validation
โข Institutional activity detection
4๏ธโฃ SMART SIGNAL SYSTEM
โข 3 Signal Modes: Aggressive, Balanced, Conservative
โข Multi-layer confirmation logic
โข Automatic profit targets (2:1 risk-reward)
โข Stop loss suggestions based on ATR
โข Prevents overtrading with bars-since-signal filter
5๏ธโฃ ELITE DASHBOARD (HUD)
โข Real-time trend direction and strength
โข Volume status monitoring
โข Active zones counter
โข Market volatility gauge
โข Current signal status
โข 4 positioning options, compact mode available
6๏ธโฃ PREMIUM STYLING
โข 4 Professional color themes (Cyber/Gold/Ocean/Fire)
โข Adjustable transparency and label sizes
โข Clean, institutional-grade visuals
โข Optimized for all chart types
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ HOW TO USE THIS INDICATOR
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
STEP 1: TREND IDENTIFICATION
โ Green Cloud = Bullish trend - look for LONG opportunities
โ Red Cloud = Bearish trend - look for SHORT opportunities
โ Purple Cloud = Ranging - wait for breakout or fade extremes
STEP 2: ZONE ANALYSIS
โ PREMIUM (Red) zones = Potential resistance/supply areas
โ DISCOUNT (Green) zones = Potential support/demand areas
โ Price rejecting from zones = high-probability setups
STEP 3: SIGNAL CONFIRMATION
โ Wait for "LONG" or "SHORT" labels to appear
โ Check dashboard for trend strength (Moderate/Strong preferred)
โ Confirm volume status is "HIGH" or "CLIMAX"
โ Entry: Enter when label appears
โ Stop Loss: Use dotted line (1 ATR away)
โ Take Profit: Use dashed line (2 ATR away)
STEP 4: RISK MANAGEMENT
โ Never risk more than 1-2% per trade
โ Use the provided stop loss levels
โ Trail stops as price moves in your favor
โ Avoid trading during low volatility periods
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ๏ธ RECOMMENDED SETTINGS
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
FOR SCALPING (1M - 5M):
- Trend Type: Fast
- Sensitivity: 15
- Signal Mode: Aggressive
- Zone Strength: 8
FOR DAY TRADING (15M - 1H):
- Trend Type: Adaptive
- Sensitivity: 21 (default)
- Signal Mode: Balanced
- Zone Strength: 12 (default)
FOR SWING TRADING (4H - Daily):
- Trend Type: Smooth
- Sensitivity: 34
- Signal Mode: Conservative
- Zone Strength: 20
BEST MARKETS:
โ Crypto (BTC, ETH, major altcoins)
โ Forex (Major pairs: EUR/USD, GBP/USD)
โ Indices (S&P 500, NASDAQ, DAX)
โ High-liquidity stocks
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ UNDERSTANDING THE METHODOLOGY
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
This indicator is built on three core concepts:
1. ORDER FLOW THEORY
Markets move between premium (expensive) and discount (cheap) zones. Smart money accumulates in discount zones and distributes in premium zones. This indicator identifies these zones automatically.
2. ADAPTIVE TREND FOLLOWING
Unlike fixed-period moving averages, the Elite Trend Engine adjusts to current market volatility, providing more accurate trend signals in both trending and ranging conditions.
3. CONFLUENCE-BASED ENTRIES
Signals only trigger when multiple conditions align:
- Price in correct zone (premium for shorts, discount for longs)
- Trend confirmation (cloud color matches direction)
- Volume validation (spike or climax present)
- Price action strength (strong rejection candles)
This multi-layer approach dramatically reduces false signals.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
๐ ALERT SETUP
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
This indicator includes 5 alert types:
1. Long Signal โ Triggers when buy conditions met
2. Short Signal โ Triggers when sell conditions met
3. Volume Climax โ Warns of pot
Trendshift [CHE] StrategyTrendshift Strategy โ First-Shift Structural Regime Trading
Profitfactor 2,603
Summary
Trendshift Strategy implements a structural regime-shift trading model built around the earliest confirmed change in directional structure. It identifies major swing highs and lows, validates breakouts through optional ATR-based conviction, and reacts only to the first confirmed shift in each direction. After a regime reversal, the strategy constructs a premium and discount band between the breakout candle and the previous opposite swing. This band is used as contextual bias and may optionally inform stop placement and position sizing.
The strategy focuses on clear, interpretable structural events rather than continuous signal generation. By limiting entries to the first valid shift, it reduces false recycles and allows the structural state to stabilize before a new trade occurs. All signals operate on closed-bar logic, and the strategy avoids higher-timeframe calls to stabilize execution behavior.
Motivation: Why this design?
Many structure-based systems repeatedly trigger as price fluctuates around prior highs and lows. This often leads to multiple flips during volatile or choppy conditions. Trendshift Strategy addresses this problem by restricting execution to the first confirmed structural event in each direction. ATR-based filters help differentiate genuine structural breaks from noise, while the contextual band ensures that the breakout is meaningful in relation to recent volatility.
The design aims to represent a minimalistic structural trading framework focused on regime turns rather than continuous trend signaling. This reduces chart noise and clarifies where the market transitions from one regime to another.
Whatโs different vs. standard approaches?
Baseline reference
Typical swing-based structure indicators report every break above or below recent swing points.
Architecture differences
First-shift-only regime logic that blocks repeated signals until direction reverses
ATR-filtered validation to avoid weak or momentum-less breaks
Premium and discount bands derived from breakout structure
Optional band-driven stop placement
Optional band-dependent position-sizing factor
Regime timeout system to neutralize structure after extended inactivity
Persistent-state architecture to prevent re-triggering
Practical effect
Only the earliest actionable structure change is traded
Fewer but higher-quality signals
Premium/discount tint assists contextual evaluation
Stops and sizing can be aligned with structural context rather than arbitrary volatility measures
Improved chart interpretability due to reduced marker frequency
How it works (technical)
The algorithm evaluates symmetric swing points using a fixed bar window. When a swing forms, its value and bar index are stored as persistent state. A structural shift occurs when price closes beyond the most recent major swing on the opposite side. If ATR filtering is enabled, the breakout must exceed a volatility-scaled distance to prevent micro-breaks from firing.
Once a valid shift is confirmed, the regime is updated to bullish or bearish. The script records the breakout level, the opposite swing, and derives a band between them. This band is checked for minimum size relative to ATR to avoid unrealistic contexts.
The first shift in a new direction generates both the strategy entry and a visual marker. Additional shifts in the same direction are suppressed until a reversal occurs. If a timeout is enabled, the regime resets after a specified number of bars without structural change, optionally clearing the band.
Stop placement, if enabled, uses either the opposite or same band edge depending on configuration. Position size is computed from account percentage and may optionally scale with the price-span-to-ATR relationship.
Parameter Guide
Market Structure
Swing length (default 5): Controls swing sensitivity. Lower values increase responsiveness.
Use ATR filter (default true): Requires breakouts to show momentum relative to ATR. Reduces false shifts.
ATR length (default 14): Volatility estimation for breakout and band validation.
Break ATR multiplier (default 1.0): Required breakout strength relative to ATR.
Premium/Discount Framework
Enable framework (default true): Activates premium/discount evaluation.
Persist band on timeout (default true): Keeps structural band after timeout.
Min band ATR mult (default 0.5): Rejects narrow bands.
Regime timeout bars (default 500): Neutralizes regime after inactivity.
Invert colors (default false): Color scheme toggle.
Visuals
Show zone tint (default true): Background shade in premium or discount region.
Show shift markers (default true): Display first-shift markers.
Execution and Risk
Risk per trade percent (default 1.0): Determines position size as account percentage.
Use band for size (default false): Scales size relative to band width behavior.
Flat on opposite shift (default true): Forces reversal behavior.
Use stop at band (default false): Stop anchored to band edges.
Stop band side: Chooses which band edge is used for stop generation.
Reading & Interpretation
A green background indicates discount conditions within the structural band; red indicates premium conditions. A green triangle below price marks the first bullish structural shift after a bearish regime. A red triangle above price marks the first bearish structural shift after a bullish regime.
When stops are active, the opposite band edge typically defines the protective level. Band width relative to ATR indicates how significant a structural change is: wider bands imply stronger volatility structure, while narrow bands may be suppressed by the minimum-size filter.
Practical Workflows & Combinations
Trend following: Use first-shift entries as initial regime confirmation. Add higher-timeframe trend filters for additional context.
Swing trading: Combine with simple liquidity or fair-value-gap concepts to refine entries.
Bias mapping: Use higher timeframes for structural regime and lower timeframes for execution within the premium/discount context.
Exit management: When using stops, consider ATR-scaling or multi-stage profit targets. When not using stops, reversals become the primary exit.
Behavior, Constraints & Performance
The strategy uses only confirmed swings and closed-bar logic, avoiding intrabar repaint. Pivot-based swings inherently appear after the pivot window completes, which is standard behavior. No higher-timeframe calls are used, preventing HTF-related repaint issues.
Persistent variables track regime and structural levels, minimizing recomputation. The maximum bars back setting is five-thousand. The design avoids loops and arrays, keeping performance stable.
Known limitations include limited signal density during consolidations, delayed swing confirmation, and sensitivity to extreme gaps that stretch band logic. ATR filtering mitigates some of these effects but does not eliminate them entirely.
Sensible Defaults & Quick Tuning
Fewer but stronger entries: Increase swing length or ATR breakout multiplier.
More responsive entries: Reduce swing length to capture earlier shifts.
More active band behavior: Lower the minimum band ATR threshold.
Stricter stop logic: Use the opposite band edge for stop placement.
Volatile markets: Increase ATR length slightly to stabilize behavior.
What this indicator isโand isnโt
Trendshift Strategy is a structural-regime trading engine that evaluates major directional shifts. It is not a complete trading system and does not include take-profit logic or prediction features. It does not attempt to forecast future price movement and should be used alongside broader market structure, volatility context, and disciplined risk management.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
