Buscar en scripts para "Exponential"
Noro's MAs Cross Tests v1.01 = SMA = Simple Moving Average
2 = EMA = Exponential Moving Average
3 = VWMA = Volume-Weighted Moving Average
4 = DEMA = Double Exponential Moving Average
5 = TEMA = Triple Exponential Moving Average
6 = KAMA = Kaufman's Adaptive Moving Average
7 = Price Channel
Noro's MAs Tests v1.1Trade strategy from one moving average. To choose what sliding average it is more effective to use for this pair and this timeframe.
Types:
1 = SMA = Simple Moving Average
2 = EMA = Exponential Moving Average
3 = VWMA = Volume-Weighted Moving Average
4 = DEMA = Double Exponential Moving Average
5 = TEMA = Triple Exponential Moving Average
6 = KAMA = Kaufman's Adaptive Moving Average
7 = Price Channel
In new version 1.1:
+ "antipila"
+ longs
+ shorts
Noro's Trend MAs Strategy v1.7Trade strategy which uses only 2 MA.
The slow MA (blue) is used for definition of a trend
The fast MA (red) is used for an entrance to the transaction
For:
- For H1
- For crypto/fiat
Recomended:
Long = true (if it is profitable as a result of backtests)
Short = true (if it is profitable as a result of backtests)
Stops = false
Stop, % = any
Type of slow MA = 7 (only for Crypto/Fiat)
Source of slow MA = close or OHLC4
Use Fast MA = true
Fast MA Period = 5
Slow MA Period = 20
Bars Q = (2 for "BitCoin/Fiat" or 1 for "Fork/Fiat")
In the new version 1.7
+ stoporders
+ entry arrow (black)
Types of slow MA:
1 = SMA = Simple Moving Average
2 = EMA = Exponential Moving Average
3 = VWMA = Volume-Weighted Moving Average
4 = DEMA = Double Exponential Moving Average
5 = TEMA = Triple Exponential Moving Average
6 = KAMA = Kaufman's Adaptive Moving Average
7 = Price Channel
Noro's Trend MAs Strategy v1.6Trade strategy which uses only 2 MA.
The slow MA (blue) is used for definition of a trend
The fast MA (red) is used for an entrance to the transaction
For:
- For H1
- For crypto/fiat
Recomended:
Long = true (if it is profitable as a result of backtests)
Short = true (if it is profitable as a result of backtests)
Type of slow MA = 7 (only for Crypto/Fiat)
Source of slow MA = close or OHLC4
Use Fast MA = true
Fast MA Period = 5
Slow MA Period = 20
Bars Q = (2 for "BitCoin/Fiat" or 1 for "Fork/Fiat")
In the new version 1.5
+ Profit became more
+ Losses became less
+ Alerts
+ Background (lime = uptrend, red = downtrend)
Types of slow MA:
1 = SMA = Simple Moving Average
2 = EMA = Exponential Moving Average
3 = VWMA = Volume-Weighted Moving Average
4 = DEMA = Double Exponential Moving Average
5 = TEMA = Triple Exponential Moving Average
6 = KAMA = Kaufman's Adaptive Moving Average
7 = Price Channel
Noro's Trend MAs Strategy 1.5Trade strategy which uses only 2 MA .
The slow MA (blue) is used for definition of a trend
The fast MA (red) is used for an entrance to the transaction
For:
- For H1
- For crypto/fiat
Recomended:
Long = true (if it is profitable as a result of backtests)
Short = true (if it is profitable as a result of backtests)
Type of slow MA = 7 (only for Crypto/Fiat)
Source of slow MA = clole or OHLC4
Use Fast MA = true
Fast MA Period = 5
Slow MA Period = 20
Bars Q = (2 for "BitCoin/Fiat" or 1 for "Fork/Fiat")
In the new version 1.5
+ Source
+ Types of slow MA
Types of slow MA:
1 = SMA = Simple Moving Average
2 = EMA = Exponential Moving Average
3 = VWMA = Volume-Weighted Moving Average
4 = DEMA = Double Exponential Moving Average
5 = TEMA = Triple Exponential Moving Average
6 = KAMA = Kaufman's Adaptive Moving Average
7 = Price Channel
PS: 100000000%, because of use of a piramiding have turned out
Noro's MAs TestsTrade strategy from one moving average. To choose what sliding average it is more effective to use for this pair and this timeframe.
Types:
1 = SMA = Simple Moving Average
2 = EMA = Exponential Moving Average
3 = VWMA = Volume-Weighted Moving Average
4 = DEMA = Double Exponential Moving Average
5 = TEMA = Triple Exponential Moving Average
6 = KAMA = Kaufman's Adaptive Moving Average
7 = Price Channel
TEMA - Triple Moving Averages (50,100,200)Three Moving Averages in a single indicator, very useful if you are a free user and want to save some indicator slots.
Enjoy it :)
EMA Wave and GRaB Candles by JustUncleLThis is a specialised Price Action Channel (PAC) or Wave that mirrors the indicator used by Raghee Horner, the "34EMA Wave and GRaB Candles".
The Wave consist of:
34 period exponential moving average on the high
34 period exponential moving average on the close
34 period exponential moving average on the low
The GRaB candles colour scheme:
Lime = Bull candle closed above Wave
Green = Bear candle closed above Wave
Red = Bull candle closed below Wave
DarkRed = Bear candle closed below Wave
Aqua = Bull candle closed inside Wave
Blue = Bear candle closed inside Wave
Optionally display a trend direction indication along bottom of chart.
References:
For some details on how Raghee uses this indicator check out this:
www.forexfactory.com
Also her various training and webinar videos on Youtube
Note: This code is licensed under open source GPLv3 terms and conditions. Any modifications to it should be made public and linked to the original code.
EMARCOThis is the study of the ratio of the MACD exponential moving averages, 0.993 and 1.003 were used to define the overextended positions since this is the highest the oscillator usually goes, price tends to reverse when overextended. RE1 (ratio equation 1) = the fast Exponential Moving Average (12 points) divided by the slow Exponential Moving Average (26 points) and RE2 is reciprocal. Here we see that when the RE1 is greater than RE2 price tends to drop and so when the opposite is true
Heiken Ashi zero lag EMA v1.1 by JustUncleLI originally wrote this script earlier this year for my own use. This released version is an updated version of my original idea based on more recent script ideas. As always with my Alert scripts please do not trade the CALL/PUT indicators blindly, always analyse each position carefully. Always test indicator in DEMO mode first to see if it profitable for your trading style.
DESCRIPTION:
This Alert indicator utilizes the Heiken Ashi with non lag EMA was a scalping and intraday trading system
that has been adapted also for trading with binary options high/low. There is also included
filtering on MACD direction and trend direction as indicated by two MA: smoothed MA(11) and EMA(89).
The the Heiken Ashi candles are great as price action trending indicator, they shows smooth strong
and clear price fluctuations.
Financial Markets: any.
Optimsed settings for 1 min, 5 min and 15 min Time Frame;
Expiry time for Binary options High/Low 3-6 candles.
Indicators used in calculations:
- Exponential moving average, period 89
- Smoothed moving average, period 11
- Non lag EMA, period 20
- MACD 2 colour (13,26,9)
Generate Alerts use the following Trading Rules
Heiken Ashi with non lag dot
Trade only in direction of the trend.
UP trend moving average 11 period is above Exponential moving average 89 period,
Doun trend moving average 11 period is below Exponential moving average 89 period,
CALL Arrow appears when:
Trend UP SMA11>EMA89 (optionally disabled),
Non lag MA blue dot and blue background.
Heike ashi green color.
MACD 2 Colour histogram green bars (optional disabled).
PUT Arrow appears when:
Trend UP SMA11
GC Magic Overlay V2This script is based on Guppy method (www.guppytraders.com
) , it was introduced to me by fellow trader @nmike. I am using this script in conjunction to Clones ,Harmonic and other tools.
Script Function:
a. Script plots the fast and slow Exponential moving averages as ribbons.
EMA's used
EMA (close): 25,30,35,40,45,50,55 (Green)
EMA (close): 89,99,109,119,129,139,149 (Red)
b. It draws the Circle dots in Pink for Sell and Black for Buy.
Script Parameters:
a. EMA : 2 emas for cross
b. Signal Exponential moving average
c. which time frame to Plot the above Signal Exponential
d. Show Guppy Slow - Red - Toggle to show red emas on chart
e. Show Guppy Fast - Green- Toggle to show green emas on chart
How to Trade:
a. Wait for the Pink/Black Dot to appear on Chart
b. Do not take trade immediately after the dot appears. Wait for the price to retrace back and touch the ema ribbons.This will keep you away from fake breakouts.
c. Rentries : in examples below
Examples:
Body Close Continuity & failure Backtesting @MaxMaseratiThis indicator, is a highly advanced institutional-grade tool designed to track the "lifespan" of a trend based on Body Close (BC) sequences.
Unlike basic indicators that just show direction, this script analyzes the structural integrity of a trend by monitoring how many candles continue the move before a "Touch" (retest) or a "Break" (failure) occurs.
The Continuity & Failure Stats indicator tracks sequences of Bullish Body Closes (BuBC) and Bearish Body Closes (BeBC). It measures three critical phases: Building (pure momentum), Touching (price retesting the low/high of the sequence), and Resumption (price continuing the trend after a retest). It provides a statistical distribution of how long these "buildings" typically last before failing, allowing traders to know exactly when a trend is overextended.
This comprehensive analysis blends the statistical breakdown of the Continuity & Failure Stats indicator to provide a deep understanding of the structural momentum for the S&P 500 E-mini (ES1!) on a 4-hour timeframe.
1. Extensive Table Breakdown
A. Building Distribution (Left Table): The Fatigue Gauge
This table acts as a histogram of momentum, tracking the "Building Count"—the number of consecutive candles closing in a trend without price returning to its origin.
Count Column: Represents the streak length (e.g., 1, 2, or 3 candles).
Touch Column: Shows how many times a streak was interrupted by a retest ("touch") but remained structurally intact.
Break Column: Counts total structural failures where price closed beyond the sequence's anchor.
Data Insight: For BuBC, 92 sequences reached Count 1, but only 28 remained by Count 4. This reveals a steep momentum decay after the 3rd candle, establishing a "Statistical Wall" where only 2 sequences in history reached a count of 9.
B. MMM Summary Stats (Top Right): The Mathematical DNA
This table provides the "Expected Value" and behavior of a trend over the lookback period.
Avg Building (2.39 for BuBC): On average, a bullish move lasts ~2.4 candles of pure momentum before a retest or reversal occurs.
Avg Touches (0.8): This low number indicates "clean" trends that rarely wobble back to retest levels multiple times before reaching a conclusion.
Avg R Cycles (0.55): This suggests that once a bullish trend is interrupted, it only successfully resumes its momentum about half the time.
Max R Count (1): Typically, once a trend is "touched," it only manages one more push before failing.
C. Multi-Timeframe (MTF) Quick Stats (Bottom Right): Trend Weight
This compares the 4H chart against other layers of the market to identify "global" alignment.
Sample Comparison: There are 3,594 tracked BuBC sequences on the 4H compared to only 142 on the Weekly chart.
Fractal Law: The Avg Building (2.4) is consistent across several timeframes, implying that the "Rule of Three" (momentum fading after 3 candles) is a fractal characteristic of this asset.
2. Table Comparison: Synthesizing the Data
To trade effectively, you must compare Distribution (timing) against Summary Stats (averages):
Continuity vs. Failure: The Summary Stats show an average building of 2.39. When checking the Distribution table at Count 2, the "Break" count (58) is already high relative to the "Total". This confirms that the risk of failure increases exponentially the moment you exceed the average.
Momentum vs. Mean Reversion: Distribution tells you when a trend is "tired". If the 4H is at a "Building Count 4" (statistically overextended) while the Weekly chart is at "Building Count 1" (fresh momentum), you may choose to prioritize the higher timeframe's strength despite the local overextension.
3. Strategic Summary & Application
This indicator proves that market momentum follows a predictable "Building" cycle rather than an infinite streak.
The "Rule of Three" for ES1! 4H:
The Entry Zone (Momentum Start): The most profitable entries occur at Building Count 1. Statistically, you have a high probability of reaching a count of 2 or 3.
The Exit Zone (Momentum Limit): Take profits or tighten stops at Count 3. The data shows the sample size drops by nearly 50% between Count 3 and Count 4.
The "Touch" Rule (Retest Reliability): If price returns to the sequence low (a "Touch"), do not expect a massive continuation. The Max R Count of 1 tells us that resumptions are usually short-lived.
Danger Zone: Entering at Building Count 4 or higher is statistically dangerous, as the "Break" probability significantly outweighs the "Touch" or continuation probability.
BTC - RHODL (Proxy Flow) b]Title: BTC - RHODL Ratio (Proxy Flow Edition) | RM
Overview & Philosophy
The RHODL Ratio is one of the most respected macro-on-chain metrics in the Bitcoin industry. Originally developed by Philip Swift, it identifies cycle tops by looking at the velocity of money moving between long-term HODLers and new speculators.
Why a "Proxy" instead of the "Original"? The original RHODL Ratio relies on Realized Value HODL Waves—where coins are weighted by the price at which they last moved. On TradingView, these specific "Realized" age-bands are often locked behind high-tier professional vendor subscriptions (e.g., Glassnode Pro), making the original indicator inaccessible to most retail investors.
To solve this, I present this Proxy Flow Edition. Instead of weighting by cost-basis, it utilizes more accessible Supply-Age data to simulate the "Speculative Fever" of a bull market. By mathematically isolating the "Flow" between young and old cohorts, we achieve a signal that captures ~95% of the original's historical accuracy while remaining fully functional for the broader community.
Methodology: The Proxy Flow Framework
Most indicators look at price; the RHODL Proxy looks at behavioral shift .
1. The Young vs. Old Battle:
The script tracks the percentage of supply held for at least one year ( Active 1Y+ ). It then derives the "Flow" of coins:
• Young Flow: Measures coins entering the <1-year cohort (speculative interest).
• Old Flow: Measures the baseline of coins remaining in the 1-year+ cohort (HODLer conviction).
2. The Ratio of Distribution:
When the Young Flow exponentially outpaces the Old Flow , it signifies that long-term holders are distributing their coins to a flood of new retail entrants. Historically, this "transfer of wealth" from smart money to retail marks the terminal phase of a bull cycle.
3. Age Normalization:
Bitcoin’s network naturally matures over time. This script includes an Age Normalization Divisor that adjusts the ratio based on Bitcoin's days since genesis, accounting for the secular growth in lost coins and deep-cold storage.
How to Read the Chart
🟧 The RHODL Proxy (Orange Line): A logarithmic representation of the flow ratio. A rising line indicates increasing speculative velocity; a falling line indicates HODLer re-accumulation.
🔴 The Overheated Zone (> 0.5): The danger zone. This area captures the "Speculative Fever" typical of cycle peaks. When the line sustains here, the market is historically overextended and vulnerable to a massive deleveraging event.
🟢 The Accumulation Zone (< -0.5): The maximum opportunity zone. This occurs when the market is "dead"—speculators have left, and only the most patient HODLers remain. Historically, these green valleys represent the most asymmetric entry points in Bitcoin's history.
Status Dashboard
The real-time monitor in the bottom-right identifies the current market regime:
• RHODL Score: The raw logarithmic intensity of current supply rotation.
• Regime: ACCUMULATION (Smart Money), NEUTRAL (Trend), or OVERHEATED (Retail Mania).
Credits
Philip Swift: For the original inspiration and the groundbreaking Realized HODL Ratio concept.
⚠️ Note: This indicator is mathematically optimized for the Daily (1D) Timeframe to maintain the integrity of supply-flow calculations.
Disclaimer
This script is for research and educational purposes only. On-chain metrics are probabilistic, not deterministic. Always manage your risk according to your investment horizon.
Tags
bitcoin, btc, rhodl, on-chain, hodl, cycles, speculation, rotation, macro, Rob Maths
Probability Cone█ Overview:
Probability Cone is based on the Expected Move . While Expected Move only shows the historical value band on every bar, probability panel extend the period in the future and plot a cone or curve shape of the probable range. It plots the range from bar 1 all the way to bar 31.
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.
The area of probability range is based on an inverse normal cumulative distribution function. The inverse cumulative distribution gives the range of price for given input probability. People can adjust the range by adjusting the standard deviation in the settings. The probability of the entered standard deviation will be shown at the edges of the probability cone.
The shown 68% and 95% probabilities correspond to the full range between the two blue lines of the cone (68%) and the two purple lines of the cone (95%). The probabilities suggest the % of outcomes or data that are expected to lie within this range. It does not suggest the probability of reaching those price levels.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
█ 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.
You can learn more about each of the volatility models in out Historical Volatility Estimators indicator.
█ How to use
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 larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
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.
The shape of the cone will be skewed and have a directional bias when the length of mean is short. It might be more adaptive to the current price or trend, but more accurate estimation should use a longer period for the mean.
Using a short look back for mean will make the cone having a directional bias.
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
Time back settings shift the estimation period back by the input number. It's the origin of when the probability cone start to estimation it's range.
E.g., When time back = 5, the probability cone start its prediction interval estimation from 5 bars ago. So for time back = 5 , it estimates the probability range from 5 bars ago to X number of bars in the future, specified by the Forecast Period (max 1000).
█ 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.
The uncertainty in future bars makes the range wider. The overestimate effect of the body is partly neutralized when it's extended to future bars. We encourage people who use this indicator to further investigate the Historical Volatility Estimators , Fast Autocorrelation Estimator , Expected Move and especially the Linear Moments Indicator .
The probability is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between.
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.
WaveTrend RBF What it does
WT-RBF extracts a “wave” of momentum by subtracting a fast Gaussian-weighted smoother from a slow one, then robust-normalizes that wave with a median/MAD proxy to produce a z-score (z). A short EMA of z forms the signal line. Optional dynamic thresholds use the MAD of z itself so overbought/oversold levels adapt to volatility regimes.
How it’s built:
Radial (Gaussian) smoothers
Causal, exponentially-decaying weights over the last radius bars using σ (sigma) to control spread.
fast = rbf_smooth(src, fastR, fastSig)
slow = rbf_smooth(src, slowR, slowSig)
wave = fast − slow (band-pass)
Robust normalization
A two-stage EMA approximates the median; MAD is estimated from EMA of absolute deviations and scaled by 1.4826 to be stdev-comparable.
z = (wave − center) / MAD
Thresholds
Dynamic OB/OS: ±2.5 × MAD(z) (or fixed levels when disabled)
Reading the indicator
Bull Cross: z crosses above sig → momentum turning up.
Bear Cross: z crosses below sig → momentum turning down.
Exits / Bias flips: zero-line crosses (below 0 → exit long bias; above 0 → exit short bias).
Overbought/Oversold: z > +thrOB or z < thrOS. With dynamics on, the bands widen/narrow with recent noise; with dynamics off, static guides at ±2 / ±2.5 are shown.
Core Inputs
Source: Price series to analyze.
Fast Radius / Fast Sigma (defaults 6 / 2.5): Shorter radius/smaller σ = snappier, higher-freq.
Slow Radius / Slow Sigma (defaults 14 / 5.0): Larger radius/σ = smoother, lower-freq baseline.
Normalization
Robust Z-Score Window (default 200): Lookback for median/MAD proxy (stability vs responsiveness).
Small ε for MAD: Floor to avoid division by zero.
Signal & Thresholds
Dynamic Thresholds (MAD-based) (on by default): Adaptive OB/OS; toggle off to use fixed guides.
Visuals
Shade OB/OS Regions: Background highlights when z is beyond thresholds.
Show Zero Line: Midline reference.
(“Plot Cross Markers” input is present for future use.)
Breakout Score (0–100)Breakouts are often the trader's best setups. Often seen on the chart as wedges and flags, consolidation before a pop up or down. This script attempts to visualize breakout potential with gradients in the background. I built this to place on my side charts to quickly visualize that a setup was forming.
For this indicator, Breakouts have generally been assumed as:
Decrease in average volume over N candles
Proximity to VWAP
Convergence/cross of price to the 9, 20 and 50 EMAs
Range compression (formation of flag or consolidation)
each of these factors are scored and rated. Multiple signals exponentially increase the gradient. Depending on the score, the chart will display a visual gradient behind the chart. Color, opacity and filtering fully customizable.
Enjoy!
RVol+ Enhanced Relative Volume Indicator📊 RVol+ Enhanced Relative Volume Indicator
Overview
RVol+ (Relative Volume Plus) is an advanced time-based relative volume indicator designed specifically for swing traders and breakout detection. Unlike simple volume comparisons, RVol+ analyzes volume at the same time of day across multiple sessions, providing statistically significant insights into institutional activity and breakout potential.
🎯 Key Features
Core Volume Analysis
Time-Based RVol Calculation - Compares current cumulative volume to the average volume at this exact time over the past N days
Statistical Z-Score - Measures volume in standard deviations from the mean for true anomaly detection
Volume Percentile - Shows where current volume ranks historically (0-100%)
Sustained Volume Filter - 3-bar moving average prevents false signals from single-bar spikes
Breakout Detection
🚀 Confirmed Breakouts - Identifies price breakouts validated by high volume (RVol > 1.5x)
⚠️ False Breakout Warnings - Alerts when price breaks key levels on low volume (high failure risk)
Multi-Timeframe Context - Weekly volume overlay prevents chasing daily noise
Advanced Metrics
OBV Divergence Detection - Spots bullish/bearish accumulation/distribution patterns
Volume Profile Integration - Identifies institutional positioning
Money Flow Analysis - Tracks smart money vs retail activity
Extreme Volume Alerts - 🔥 Labels mark unusual spikes beyond the display cap
Visual Intelligence
Smart Color Coding:
🟢 Bright Teal = High activity (RVol ≥ 1.5x)
🟡 Medium Teal = Caution zone (RVol ≥ 1.2x)
⚪ Light Teal = Normal activity
🟠 Orange = Breakout confirmed
🔴 Red = False breakout risk
Comprehensive Stats Table:
Current Volume (formatted as M/K/B)
RVol ratio
Z-Score with significance
Volume percentile
Historical average and standard deviation
Sustained volume confirmation
📈 How to Use
For Swing Trading (1D - 3W Holds)
Perfect Setup:
✓ RVol > 1.5x (bright teal)
✓ Z-Score > 2.0 (⚡ alert)
✓ Percentile > 90%
✓ Sustained = ✓
✓ 🚀 Breakout label appears
Avoid:
✗ Red "Low Vol" warning during breakouts
✗ RVol < 1.0 at key levels
✗ Sustained volume not confirmed
Signal Interpretation
⚡ Z>2 Labels - Statistically significant volume (95th+ percentile) - highest probability moves
↗️ OBV+ Labels - Bullish accumulation (OBV rising while price consolidates)
↘️ OBV- Labels - Bearish distribution (OBV falling while price rises)
🔵 Blue Background - Weekly volume elevated (confirms daily strength)
⚙️ Customization
Basic Settings
N Day Average - Number of historical days for comparison (default: 5)
RVol Thresholds - Customize highlight levels (default: 1.2x, 1.5x)
Visual Display Cap - Prevent extreme spikes from compressing view (default: 4.0x)
Advanced Metrics (Toggle On/Off)
Z-Score analysis
Weekly RVol context
OBV divergence detection
Volume percentile ranking
Breakout signal generation
Table Customization
Position - 9 placement options to avoid chart overlap
Size - Tiny to Huge
Colors - Full customization of positive/negative/neutral values
Transparency - Adjustable background
Debug Mode
Enable Pine Logs for calculation transparency
Adjustable log frequency
Real-time calculation breakdown
🔬 Technical Details
Algorithm:
Binary search for historical lookups (O(log n) performance)
Time-zone aware session detection
DST-safe timestamp calculations
Exponentially weighted standard deviation
Anti-repainting architecture
Performance:
Optimized for max_bars_back = 5000
Efficient array management
Built-in function optimization
Memory-conscious data structures
📊 What Makes RVol+ Different?
vs. Standard Volume:
Context-aware (time-of-day matters)
Statistical significance testing
False breakout filtering
vs. Basic RVol:
Z-Score normalization (2-3 sigma detection)
Multi-timeframe confirmation
OBV divergence integration
Sustained volume filtering
Smart visual scaling
vs. Professional Tools:
Free and open-source
Fully customizable
No black-box algorithms
Educational debug logs
💡 Best Practices
Wait for Confirmation - Don't enter on first bar; wait for sustained volume ✓
Combine with Price Action - RVol validates, price structure determines entry
Weekly Context Matters - Blue background = institutional interest
Z-Score is King - Focus on ⚡ alerts for highest probability
Avoid Low Volume Breakouts - Red ⚠️ labels = high failure risk
🎓 Trading Psychology
Volume precedes price. When RVol+ shows:
High RVol + Rising OBV = Accumulation before breakout
High RVol at Resistance = Test of conviction
Low RVol on Breakout = Retail-driven (fade candidate)
Z-Score > 3 = Potential "whale" positioning
📝 Credits
Based on the time-based RVol concept from /u/HurlTeaInTheSea, enhanced with:
Statistical analysis (z-scores, percentiles)
Multi-timeframe integration
OBV divergence detection
Professional-grade visualization
Swing trading optimization
🔧 Version History
v2.0 - Enhanced Edition
Added Z-Score analysis
Multi-timeframe volume context
OBV divergence detection
Breakout confirmation system
Smart color coding
Customizable stats table
Debug logging mode
Performance optimizations
📚 Learn More
For optimal use with swing trading:
Combine with support/resistance levels
Watch for volume clusters in consolidation
Use weekly timeframe for trend confirmation
Monitor OBV divergence for early warnings
⚠️ Disclaimer
This indicator is for educational purposes. Volume analysis is one component of trading decisions. Always use proper risk management, consider multiple timeframes, and validate signals with price structure. Past performance does not guarantee future results.
🚀 Getting Started
Add indicator to chart
Adjust "N Day Average" to your preference (5-10 days typical)
Position stats table to avoid overlap
Enable features you want to monitor
Watch for 🚀 breakout confirmations!
Happy Trading! 📈
Gamma Exposure Levels by OMG (Oh My Gamma)OMG (Oh My Gamma) - Daily GEX Levels
An operational framework for Gamma analysis with daily data.
Indicator's Purpose & Demo Data
This indicator plots key strategic levels derived from Gamma Exposure (GEX) analysis. It showcases the operational logic of OhMyGamma analytical engine.
IMPORTANT: The levels plotted by this public script are based on a past date's snapshot for demonstration purposes. They are not valid for live trading and will not update automatically.
The real edge comes from using the fresh data structure provided daily.
How to Read the Levels
This indicator is designed to provide actionable intelligence, not just data. Here's how to read it:
The Levels: Each line represents a key strategic zone (Zero Gamma, Call/Put Walls, etc.) where a market reaction is statistically probable due to dealer hedging flows.
Line Thickness = Strategic Importance: The thickness of each line directly corresponds to its strategic rating. Thicker, solid lines represent higher-conviction zones.
Labels & Tooltips: Hover over a level's label on your chart to see its full description, confluences, and strategic rating.
Pro Tip: The Power of Confluence
This indicator is not a standalone "system". It's an institutional-grade intelligence layer. Its predictive power increases exponentially when used to find confluence with your own analysis.
The highest-probability trades occur when a key Gamma level aligns with:
Price Action: Key support/resistance zones, order blocks, or liquidity pools.
Volumetric Indicators: High/Low Volume Nodes (HVN/LVN) from Volume Profile, VWAP, and Anchored VWAP.
Use these levels to confirm your setups and gain the conviction to act.
How to Get the Daily Updated Script
This indicator requires a new Pine Script code each day to load the current session's data.
To get the daily updated code feel free to visit www.ohmygamma.com
Feedback & Suggestions
This tool is built for the community. Suggestions for improvements and new features are highly welcome and help the project evolve. Feel free to get in touch via the contact form on the website.
Disclaimer: This tool is for informational and educational purposes only. Trading involves significant risk. The authors assume no responsibility for any trading decisions.
Markov 3D Trend AnalyzerMarkov 3D Trend Analyzer
🔹 What Is a Markov State?
A Markov chain models systems as states with probabilities of transitioning from one state to another. The key property is memorylessness: the next state depends only on the current state, not the full past history. In financial markets, this allows us to study how conditions tend to persist or flip — for example, whether a green candle is more likely to be followed by another green or by a red.
🔹 How This Indicator Uses It
The Markov 3D Trend Analyzer tracks three independent Markov chains:
Direction Chain (short-term): Probability that a green/red candle continues or reverses.
Volatility Chain (mid-term): Probability of volatility staying Low/Medium/High or transitioning between them.
Momentum Chain (structural): Probability of momentum (Bullish, Neutral, Bearish) persisting or flipping.
Each chain is updated dynamically using exponentially weighted probabilities (EMA), which balance the law of large numbers (stability) with adaptivity to new market conditions.
The indicator then classifies each chain’s dominant state and combines them into an actionable summary at the bottom of the table (e.g. “📈 Bullish breakout,” “⚠️ Choppy bearish fakeouts,” “⏳ Trend squeeze / possible reversal”).
🔹 Settings
Direction Lookback / Volatility Lookback / Momentum Lookback
Control the rolling window length (sample size) for each chain. Larger = smoother but slower to adapt.
EMA Weight
Adjusts how much weight is given to recent transitions vs. older history. Lower values adapt faster, higher values stabilize.
Table Position
Choose where the table is displayed on your chart.
Table Size
Adjust the font size for readability.
🔹 How To Consider Using
Contextual tool: Use the summary row to understand the current market condition (trending, mean-reverting, expanding, compressing, continuation, fakeout risk).
Complementary filter: Combine with your existing strategies to confirm or filter signals. For example:
📈 If your breakout strategy fires and the summary says Bullish breakout, that’s confirmation.
⚠️ If it says Choppy fakeouts, be cautious of traps.
Visualization aid: The table lets you see how probabilities shift across direction, volatility, and momentum simultaneously.
⚠️ This indicator is not a signal generator. It is designed to help interpret market states probabilistically. Always use in conjunction with broader analysis and risk management.
🔹 Disclaimer
This script is for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any security, cryptocurrency, or instrument. Trading involves risk, and past probabilities or behaviors do not guarantee future outcomes. Always conduct your own research and use proper risk management.
Volatility Forecast/*==============================================================================
Volatility Forecast — Publishable Documentation
Author: @BB_9791
License: Mozilla Public License 2.0
WHAT THIS INDICATOR SHOWS
- A daily volatility estimate in percent points, called sigma_day.
- A slow volatility anchor, the 10-year EMA of sigma_day.
- A blended volatility series in percent points:
sigma_blend = (1 − p) * sigma_day + p * EMA_10y(sigma_day)
where p is the Slow weight %, default 30.
- Optional annualization by multiplying by 16, this is a daily-to-annual
conversion used by Robert Carver in his writings.
METHODOLOGY, CREDIT
The estimator follows the approach popularized by Robert Carver
("Systematic Trading", "Advanced Futures Trading Strategies", blog qoppac).
Current daily volatility is computed as an exponentially weighted standard
deviation of daily percent returns, with alpha = 2 / (span + 1).
The slow leg is a long EMA of that volatility series, about 10 years.
The blend uses fixed weights. This keeps the slow leg meaningful through
large price level changes, since the blend is done in percent space first.
MATH DETAILS
Let r_t be daily percent return:
r_t = 100 * (Close_t / Close_{t−1} − 1)
EWMA mean and variance:
m_t = α * r_t + (1 − α) * m_{t−1}
v_t = α * (r_t − m_t)^2 + (1 − α) * v_{t−1}
where α = 2 / (span_current + 1)
Current daily sigma in percent points:
sigma_day = sqrt(v_t)
Slow leg:
sigma_10y = EMA(sigma_day, span_long)
Blend:
sigma_blend = (1 − p) * sigma_day + p * sigma_10y
Annualized option:
sigma_ann = 16 * sigma_blend
INPUTS
- Threshold (percent points): horizontal guide level on the chart.
- Short term span (days): EW stdev span for sigma_day.
- Long term span (days): EMA span for the slow leg, choose about 2500 for 10 years.
- Slow weight %: p in the blend.
- Annualize (x16): plot daily or annualized values.
- Show components: toggles Current and 10y EMA lines.
- The script uses the chart symbol by default.
PLOTS
- Blended σ% as the main line.
- Optional Current σ% and 10y EMA σ%.
- Editable horizontal threshold line in the same units as the plot
(percent points per day or per year).
- Optional EMA 9 and EMA 20 cloud on the blended series, green for uptrend
when EMA 9 is above EMA 20, red otherwise. Opacity is configurable.
HOW TO READ
- Values are percent points of movement per day when not annualized,
for example 1.2 means about 1.2% typical daily move.
- With annualize checked, values are percent points per year, for example 18
means about 18% annualized volatility.
- Use the threshold and the EMA cloud to mark high or low volatility regimes.
NOTES
- All calculations use daily data via request.security at the chart symbol.
- The blend is done in percent space, then optionally annualized, which avoids
bias from the price level.
- This script does not produce trading signals by itself, it is a risk and
regime indicator.
CREDITS
Volatility forecasting method and scaling convention credited to Robert Carver.
See his books and blog for background and parameter choices.
VERSION
v1.0 Initial public release.
==============================================================================*/
ForecastForecast (FC), indicator documentation
Type: Study, not a strategy
Primary timeframe: 1D chart, most plots and the on-chart table only render on daily bars
Inspiration: Robert Carver’s “forecast” concept from Advanced Futures Trading Strategies, using normalized, capped signals for comparability across markets
⸻
What the indicator does
FC builds a volatility-normalized momentum forecast for a chosen symbol, optionally versus a benchmark. It combines an EWMAC composite with a channel breakout composite, then caps the result to a common scale. You can run it in three data modes:
• Absolute: Forecast of the selected symbol
• Relative: Forecast of the ratio symbol / benchmark
• Combined: Average of Absolute and Relative
A compact table can summarize the current forecast, short-term direction on the forecast EMAs, correlation versus the benchmark, and ATR-scaled distances to common price EMAs.
⸻
PineScreener, relative-strength screening
This indicator is excellent for screening on relative strength in PineScreener, since the forecast is volatility-normalized and capped on a common scale.
Available PineScreener columns
PineScreener reads the plotted series. You will see at least these columns:
• FC, the capped forecast
• from EMA20, (price − EMA20) / ATR in ATR multiples
• from EMA50, (price − EMA50) / ATR in ATR multiples
• ATR, ATR as a percent of price
• Corr, weekly correlation with the chosen benchmark
Relative mode and Combined mode are recommended for cross-sectional screens. In Relative mode the calculation uses symbol / benchmark, so ensure the ratio ticker exists for your data source.
⸻
How it works, step by step
1. Volatility model
Compute exponentially weighted mean and variance of daily percent returns on D, annualize, optionally blend with a long lookback using 10y %, then convert to a price-scaled sigma.
2. EWMAC momentum, three legs
Daily legs: EMA(8) − EMA(32), EMA(16) − EMA(64), EMA(32) − EMA(128).
Divide by price-scaled sigma, multiply by leg scalars, cap to Cap = 20, average, then apply a small FDM factor.
3. Breakout momentum, three channels
Smoothed position inside 40, 80, and 160 day channels, each scaled, then averaged.
4. Composite forecast
Average the EWMAC composite and the breakout composite, then cap to ±20.
Relative mode runs the same logic on symbol / benchmark.
Combined mode averages Absolute and Relative composites.
5. Weekly correlation
Pearson correlation between weekly closes of the asset and the benchmark over a user-set length.
6. Direction overlay
Two EMAs on the forecast series plus optional green or red background by sign, and optional horizontal level shading around 0, ±5, ±10, ±15, ±20.
⸻
Plots
• FC, capped forecast on the daily chart
• 8-32 Abs, 8-32 Rel, single-leg EWMAC plus breakout view
• 8-32-128 Abs, 8-32-128 Rel, three-leg composite views
• from EMA20, from EMA50, (price − EMA) / ATR
• ATR, ATR as a percent of price
• Corr, weekly correlation with the benchmark
• Forecast EMA1 and EMA2, EMAs of the forecast with an optional fill
• Backgrounds and guide lines, optional sign-based background, optional 0, ±5, ±10, ±15, ±20 guides
Most plots and the table are gated by timeframe.isdaily. Set the chart to 1D to see them.
⸻
Inputs
Symbol selection
• Absolute, Relative, Combined
• Vs. benchmark for Relative mode and correlation, choices: SPY, QQQ, XLE, GLD
• Ticker or Freeform, for Freeform use full TradingView notation, for example NASDAQ:AAPL
Engine selection
• Include:
• 8-32-128, three EWMAC legs plus three breakouts
• 8-32, simplified view based on the 8-32 leg plus a 40-day breakout
EMA, applied to the forecast
• EMA1, EMA2, with line-width controls, plus color and opacity
Volatility
• Span, EW volatility span for daily returns
• 10y %, blend of long-run volatility
• Thresh, Too volatile, placeholders in this version
Background
• Horizontal bg, level shading, enabled by default
• Long BG, Hedge BG, colors and opacities
Show
• Table, Header, Direction, Gain, Extension
• Corr, Length for correlation row
Table settings
• Position, background, opacity, text size, text color
Lines
• 0-lines, 10-lines, 5-lines, level guides
⸻
Reading the outputs
• Forecast > 0, bullish tilt; Forecast < 0, bearish or hedge tilt
• ±10 and ±20 indicate strength on a uniform scale
• EMA1 vs EMA2 on the forecast, EMA1 above EMA2 suggests improving momentum
• Table rows, label colored by sign, current forecast value plus a green or red dot for the forecast EMA cross, optional daily return percent, weekly correlation, and ATR-scaled EMA9, EMA20, EMA50 distances
⸻
Data handling, repainting, and performance
• Daily and weekly series are fetched with request.security().
• Calculations use closed bars, values can update until the bar closes.
• No lookahead, historical values do not repaint.
• Weekly correlation updates during the week, it finalizes on weekly close.
• On intraday charts most visuals are hidden by design.
⸻
Good practice and limitations
• This is a research indicator, not a trading system.
• The fixed Cap = 20 keeps a common scale, extreme moves will be clipped.
• Relative mode depends on the ratio symbol / benchmark, ensure both legs have data for your feed.
⸻
Credits
Concept inspired by Robert Carver’s forecast methodology in Advanced Futures Trading Strategies. Implementation details, parameters, and visuals are specific to this script.
⸻
Changelog
• First version
⸻
Disclaimer
For education and research only, not financial advice. Always test on your market and data feed, consider costs and slippage before using any indicator in live decisions.
