[Venturose] MACD x BB x STDEV x RVIDescription:
The MACD x BB x STDEV x RVI combines MACD, Bollinger Bands, Standard Deviation, and Relative Volatility Index into a single tool. This indicator is designed to provide insights into market trends, momentum, and volatility. It generates buy and sell signals, by analyzing the interactions between these components. These buy and sell signals are not literal, and should be used in combination with the current trend.
How It Works:
MACD: Tracks momentum and trend direction using customizable fast and slow EMA periods.
Bollinger Bands: Adds volatility bands to MACD to identify overextension zones.
Standard Deviation: Dynamically adjusts the Bollinger Band width based on MACD volatility.
RVI (Relative Volatility Index): Confirms momentum extremes with upper and lower threshold markers.
Custom Logic: Includes a trigger system ("inside" or "flipped") to adapt signals to various market conditions and an optional filter to reduce noise.
Key Features:
Combines MACD and Bollinger Bands with volatility and momentum confirmations from RVI.
Dynamic color-coded plots for identifying bullish, bearish, and neutral trends.
Customizable parameters for tailoring the indicator to different strategies.
Optional signal filtering to refine buy and sell triggers.
Alerts for buy and sell signals based on signal logic.
Why It’s Unique:
This indicator combines momentum (MACD), volatility (Bollinger Bands and Standard Deviation), and confirmation signals (RVI thresholds) into a unified system. It introduces custom "inside" and "flipped" triggers for adaptable signal generation and includes signal filtering to reduce noise. The addition of RVI-based hints helps identify early overbought or oversold conditions, providing an extra layer of insight for decision-making. The dynamic integration of these components ensures a comprehensive yet straightforward analysis tool for various market conditions.
Volatilität
Z-ScoresTLDR
Z-Scores ask "How many standard deviations is the current price, away from the moving average?"
Or put another way, it tells you how an asset is performing relative to its own moving average, centered about the zero-point.
INTRODUCTION
Z-Scores are a fundamental statistical concept which take any dataset (in this case price), and present the data in terms of standard deviation. In the case of price, we're using *moving* standard deviations, much like we use a moving average.
A useful aspect of z-scores is that data oscillates around a zero point. The chart then presents the "number_of_standard_deviations" that price is, away from the moving average. So for example, if we're looking at the 100-day z-score, and the price = 0, that means that the current price is right at the 100-day moving average. If price = 1, that means that the price is 1 standard deviation above the 100-day moving average.
HOW DO I USE THIS?
This particular script offers a ribbon of z-scores (much like how you might have a ribbon of moving averages). You can enter up to 5 z-scores in the options. Enter "0" to remove a ribbon from the chart.
If you're a math nerd, you can also select "Use Log Transform," which effectively uses the geometric mean for z-score calculation. For charts that are rangebound, you dont really need this option, but for charts that are highly exponential, you might want to select this.
I've noticed that z-scores tend to behave similar to RSI. I prefer z-scores because they're a non-arbitrary, fundamental statistical concept, whereas the RSI calculation is somewhat arbitrary. I offer the z-score as a ribbon, because it removes the arbitrary nature of selecting one particular moving average
PRACTICAL APPLICATION
Z-Score prices are akin to a momentum and/or trend indicator. They're useful for identifying a trending market, or trend reversals, before the actual price begins to reverse in earnest. The RSI concept of "divergence" can be applied. It can also be used to see how far out of trend a particularly violent movement up/down is, historically.
Another particularly useful aspect of z-scores, is comparing the performance of two different assets with very different price points. For example, maybe one chart is measured in cents, and another chart is measured in billions. Z-Scores normalize prices to a zero point, and normalize differing volatility by presenting it in terms of standard deviations. I use this for comparing things like in-asset-class performance, and also comparing various asset classes to others.
Remember, the z-score tells you how an asset is performing relative to its own moving average, centered about the zero-point.
CLOSING
I'm adding this to the public repository because there isnt a good z-score implementation here on TradingView, and especially not one that offers an adjustable ribbon like this. For the sharp eye, there is useful signal in z-scores, whether applied to a single asset, or to compare the performance of two assets against each other.
IU Price Density(Market Noise)This Price density Indicator will help you understand what and how market noise is calculated and treated.
Market noise = when the market is moving up and down without any clear direction
The Price Density Indicator is a technical analysis tool used to measure the concentration or "density" of price movements within a specific range. It helps traders differentiate between noisy, choppy markets and trending ones.
I’ve developed a custom Pine Script indicator, "IU Price Density," designed to help traders distinguish between noisy, indecisive markets and clear trading opportunities. It can be applied across multiple markets.
How this work:
Formula = (Σ (High𝑖 - Low𝑖)) / (Max(High) - Min(Low))
Where,
High𝑖 = the high price at the 𝑖 data point.
Low𝑖 = the low price at the 𝑖 data point.
Max(High) = highest price over the data set.
Max(Low) = Lowest price over the data set.
How to use it :
This indicator ranges from 0 to 10
Green(0-3) = Trending Market
Orange(3-6) = Market is normal
Red(6-10) = Noise market
💡 Key Features:
Dynamic Visuals: The indicator uses color-coded signals—green for trending markets and red for noisy, volatile conditions—making it easy to identify optimal trading periods at a glance.
Background Shading: With background colors highlighting significant market conditions, traders can quickly assess when to engage or avoid certain trades.
Customizable Parameters: The length and smoothing factors allow for flexibility in adapting the indicator to various assets and timeframes.
Whether you're a swing trader or an intraday strategist, this tool provides valuable insights to improve your market analysis. I’m excited to bring this indicator to the community!
ATR% Multiple from Key Moving AverageThis script gives signal when the ATR% multiple from any chosen moving average is beyond the configurable threshold value. This indicator quantifies how extended the stock is from a given key moving average.
A lot of traders use ATR% multiple from 10DMA, 21EMA, 50SMA or 200SMA to determine how extended a stock is and accordingly sell partials or exit. By default the indicator takes 50SMA and when the ATR% multiple is greater than 7 then it gives the signal to take partials. You can back test this indicator with previous trades and determine the ideal threshold for the signal. For small and midcaps a threshold of 7 to 10 ATR% multiples from 50SMA is where partials can be taken while large caps can revert to mean even earlier at 3 to 5 ATR% multiples from 50SMA.
You can modify this script and use it anyway you please as long as you make it opensource on TradingView.
Bollinger Bands Adjusted for VolatilityDescription:
The Bollinger Bands Adjusted for Volatility is an advanced technical indicator designed to combine the precision of smoothed Bollinger Bands with the adaptability of linear regression for volatility analysis. This tool offers traders a dynamic way to visualize market trends while accounting for recent price movements and fluctuations in volatility.
Core Functionality:
Exponential Moving Average (EMA):
The indicator begins by calculating an Exponential Moving Average (EMA) over a user-defined period. This serves as the foundational trendline, smoothing out short-term fluctuations to highlight the overall trend.
Linear Regression Smoothing:
To account for price trends with greater precision, a Linear Regression line is calculated over a specified period.
The linear regression output is further smoothed using an EMA, ensuring a responsive yet stable representation of the price trend.
Standard Deviation and Volatility:
The indicator computes the standard deviation of the closing prices over the EMA period, dynamically capturing market volatility.
This measure of volatility is then integrated into the calculation of the upper and lower bands.
Smoothed Bollinger Bands:
The upper and lower bands are constructed by adjusting the smoothed linear regression line with the standard deviation, scaled by a user-defined multiplier.
This approach adapts to changing market conditions, offering a more nuanced view compared to traditional Bollinger Bands.
Visual Components:
EMA Line (Blue): A stable trendline that reflects the underlying market direction.
Upper Band (Red): Represents the upper boundary, adjusted for volatility and smoothed by linear regression.
Lower Band (Green): Marks the lower boundary, providing a measure of support based on volatility.
Band Fill (Shaded Area): A dynamic fill between the upper and lower bands for enhanced visualization of the price range.
Advanced Concepts:
Volatility-Responsive Bands:
By integrating the standard deviation into the bands and smoothing with linear regression, the indicator reacts effectively to market dynamics, widening during high volatility and contracting during low volatility.
Trend Adaptation:
The smoothed linear regression ensures that the bands align closely with the prevailing market trend, reducing noise and improving accuracy.
Applications:
Trend Identification:
Use the EMA and the central smoothed linear regression to identify the primary trend.
Observe price interaction with the upper and lower bands for potential trend continuations or reversals.
Volatility-Based Strategies:
Monitor band expansions and contractions to gauge shifts in market volatility.
Trade breakouts or reversals when the price breaches the bands under extreme conditions.
Support and Resistance:
The upper and lower bands act as dynamic support and resistance levels, adapting to the current market environment.
Disclaimer:
This indicator is provided for informational and educational purposes only. It does not constitute financial advice. Users should exercise caution and perform their own analysis when making trading decisions.
Z The Good Stuff +I created this script to have a couple datapoints that I want to look at when going through charts to find trade ideas. Qullamaggie is one of my biggest inspirations and I built in a couple of his concepts with a touch to help me with sizing properly, all explained below:
Box 1: ADR %, Average Daily Range, gives and indication of how volatile the stock is. It uses the 20 day average % move of the current stock on the chart.
Box 2: LOD Distance, low of day distance is a quality of life element I created. It calculates the low for the current candle and color codes it red or green depending on if it's higher or lower than the daily ADR. The logic is that if a stock has an average speed, buying on a setup it is preferred if the stop distance (assuming a low of day stop) should be less than the ADR to improve the odds of more upside.
Box 3: Todays DV, this shows a rough estimate of how much money was traded on the particular day.
Box 4: ADV 20 days, similar to above this shows the 20 day $ traded average. The point to look at it is to have a better idea what position size is possible to not get stuck in something too illiquid.
Box 5: Market cap, just shows the market cap of the stock to know what size the company is.
Box 6: Number of shares, this is an additional quality of life aspect. If using low of day stops, this part calculates based on the users' inputted portfolio size and portfolio risk preference and then calculates how many stocks to buy to stay within the risk parameters. It is obviously not a sole decision making parameter nor does it guarantee any execution, but if a stock is showing an entry you want to take you can use the number of shares to help you know how many to buy. The preset is a portfolio of 10000 and a risk of 0.25%. This means that the number of shares to buy will be at the current price with lod stop that would result in a 0.25% portfolio loss. OF COURSE the actual loss depends on the execution and if the user places a stop loss order.
Hope you find it useful and feel free to give feedback! Cheers!
Position Sizing Calculator (Real-Time)█ SUMMARY
The following indicator is a Position Sizing Calculator based on Average True Range (ATR), originally developed by market technician J. Welles Wilder Jr., intended for real-time trading.
This script utilizes the user's account size, acceptable risk percentage, and a stop-loss distance based on ATR to dynamically calculate the appropriate position size for each trade in real time.
█ BACKGROUND
Developed for use on the 5-minute timeframe, this script provides traders with continuously updated, dynamic position sizes. It enables traders to instantly determine the exact number of shares and dollar amount to use for entering a trade within their acceptable risk tolerance whenever a trade opportunity arises.
This real-time position sizing tool helps traders make well-informed decisions when planning trade entries and calculating maximum stop-loss levels, ultimately enhancing risk management.
█ USER INPUTS
Trading Account Size: Total dollar value of the user's trading account.
Acceptable Risk (%): Maximum percentage of the trading account that the user is willing to risk per trade.
ATR Multiplier for Stop-Loss: Multiplier used to determine the distance of the stop-loss from the current price, based on the ATR value.
ATR Length: The length of the lookback period used to calculate the ATR value.
ImbalancesThis Pine Script is a trading indicator designed to identify imbalances in the market, specifically on candlestick charts. An imbalance refers to situations where there is a significant difference between buyers and sellers, which can create gaps or areas of inefficiency in the price. These imbalances often act as zones where price may return to "fill" or correct these inefficiencies.
1. Identifying Imbalances
The script analyzes candlestick patterns to detect imbalances based on the relationship between the highs, lows, and closes of consecutive candles. Specifically, it looks for:
Top Imbalances (Bearish): Areas where selling pressure has dominated, causing inefficiencies in the price. These are represented by patterns like multiple consecutive bearish candles or bearish gaps.
Bottom Imbalances (Bullish): Areas where buying pressure has dominated, leading to bullish gaps or inefficiencies.
When an imbalance is detected, the script highlights the area using visual boxes on the chart.
2. Visual Representation
The indicator uses colored boxes to show imbalances directly on the chart:
Top (Bearish) Imbalances: Highlighted using shades of red.
Bottom (Bullish) Imbalances: Highlighted using shades of green.
The boxes are further categorized into three states based on their level of mitigation:
Unmitigated: The imbalance has not been "filled" by price yet.
Partially Mitigated: Price has entered the imbalance zone but not completely filled it.
Fully Mitigated: Price has completely filled the imbalance zone.
3. Mitigation Logic
The concept of mitigation refers to the price revisiting an imbalance zone to correct the inefficiency:
If price fully or partially revisits an imbalance zone, the box's color changes to indicate the mitigation level (e.g., from unmitigated to partially/fully mitigated).
Fully mitigated boxes may be removed or recolored, depending on user preferences.
4. User Customization
The script provides several inputs to customize its behavior:
Enable or disable top and bottom imbalance detection.
Color settings: Users can define different colors for unmitigated, partially mitigated, and fully mitigated imbalances.
Mitigation display options: Users can choose whether to show fully mitigated imbalances on the chart or remove them.
5. Key Calculations
Imbalance Size: The size of the imbalance is calculated as the price difference between a candle's high and low across the relevant pattern.
Pattern Detection: The script checks for specific candlestick patterns (e.g., three consecutive bearish candles) to identify potential imbalances.
6. Practical Use Case
This indicator is useful for traders who:
Rely on supply and demand zones for their trading strategies.
Look for areas where price is likely to return (retesting unmitigated imbalances can signal potential trade setups).
Want to visually track market inefficiencies over time.
In Summary
The "Imbalances" indicator highlights and tracks price inefficiencies on candlestick charts. It marks zones where buying or selling pressure was dominant, and it dynamically updates these zones based on price action to indicate their mitigation status. This tool is particularly helpful for traders who use price action and market structure in their strategies.
Ultimate Volatility RateUltimate Volatility Rate
This indicator measures the volatility of price movements.
Support and Resistance Identification:
High volatility periods indicate larger price movements, which can be useful in assessing the potential for support and resistance levels to be broken.
Stop Loss (SL) and Take Profit (TP) Calculations:
The average volatility can be used to calculate dynamic Stop Loss (SL) and Take Profit (TP) levels:
SL: Placing it at a certain volatility multiplier below/above the entry price.
TP: Setting it at a certain volatility multiplier below/above the entry price.
For example:
SL: Entry price +/- (UVR × 1.5)
TP: Entry price +/- (UVR × 2)
Market Condition Analysis:
When the indicator value is high, it suggests that the market is volatile (active).
When the value is low, it indicates the market is in consolidation (sideways movement).
This information helps traders decide whether to take trend-following or consolidation-based positions.
Trend Reversal Monitoring:
A sudden increase in volatility often signals the start of a strong trend.
Conversely, a decrease in volatility can signal the slowing down or end of a trend.
BTCUSD Momentum After Abnormal DaysThis indicator identifies abnormal days in the Bitcoin market (BTCUSD) based on daily returns exceeding specific thresholds defined by a statistical approach. It is inspired by the findings of Caporale and Plastun (2020), who analyzed the cryptocurrency market's inefficiencies and identified exploitable patterns, particularly around abnormal returns.
Key Concept:
Abnormal Days:
Days where the daily return significantly deviates (positively or negatively) from the historical average.
Positive abnormal days: Returns exceed the mean return plus k times the standard deviation.
Negative abnormal days: Returns fall below the mean return minus k times the standard deviation.
Momentum Effect:
As described in the academic paper, on abnormal days, prices tend to move in the direction of the abnormal return until the end of the trading day, creating momentum effects. This can be leveraged by traders for profit opportunities.
How It Works:
Calculation:
The script calculates the daily return as the percentage difference between the open and close prices. It then derives the mean and standard deviation of returns over a configurable lookback period.
Thresholds:
The script dynamically computes upper and lower thresholds for abnormal days using the mean and standard deviation. Days exceeding these thresholds are flagged as abnormal.
Visualization:
The mean return and thresholds are plotted as dynamic lines.
Abnormal days are visually highlighted with transparent green (positive) or red (negative) backgrounds on the chart.
References:
This indicator is based on the methodology discussed in "Momentum Effects in the Cryptocurrency Market After One-Day Abnormal Returns" by Caporale and Plastun (2020). Their research demonstrates that hourly returns during abnormal days exhibit a strong momentum effect, moving in the same direction as the abnormal return. This behavior contradicts the efficient market hypothesis and suggests profitable trading opportunities.
"Prices tend to move in the direction of abnormal returns till the end of the day, which implies the existence of a momentum effect on that day giving rise to exploitable profit opportunities" (Caporale & Plastun, 2020).
PIVOTBOSS ADR The PivotBoss ADR Method offers a complete approach to analyzing the volatility for a
given market in multiple timeframes by simply using average daily range. The ADR Breakout
helps us identify markets that are extremely compressed and due for significant expansion.
The PivotBoss ADR Targets Indicator is a simple, yet powerful, tool that helps you forecast extremely accurate targets based on the volatility of a given instrument. This indicator self-adjusts to a market's current volatility in order to plot reliable targets in multiple timeframes, including daily, weekly, and monthly targets.
1. Compression/Expansion: The development of trading ranges (Compression) builds the energy that will lead to the next
phase of price discovery (Expansion). ADR helps us quantify when a range is significantly compressed and due for expansion.
2. Volatility: Measures the SPEED of a market in order to forecast future volatility and price behavior. Markets rotate between
LOW and HIGH volatility states. Low ADR readings (<65% ADR) suggest significant compression, implying expansion ahead.
3. The ADR Breakout (Expansion Day): A true breakout from a narrow ADR range includes an Expansion Day, which is a
Trend Day on Day 1, wherein the session’s midpoint exceeds the breakout point and sees a Close beyond the range.
4. The ADR Breakout (Rejection Day): A failed breakout from a narrow ADR range includes a Rejection Day, which may take
the form of a long tail on Day 1, wherein the market attempted range expansion, but failed and closes back within the range.
This signature oftentimes leads to major expansion on the OPPOSITE side of the range.
5. A Variety of Trade Opportunities: Once TRUE expansion occurs from a narrow ADR range, a variety of trade opportunities
present themselves over the course of the next several days, or even weeks. These opportunities include swing trades, day
trades, and even scalps. Understanding when and where to look for these opportunities is key
Custom ATR with Paranormal Bar FilterCustom ATR with Paranormal Bar Filter
Description:
This indicator calculates a custom ATR (Average True Range) by filtering out bars with unusually large or small price ranges. It helps provide a more accurate measure of market volatility by ignoring outliers.
How it works:
True Range Calculation:
The price range for each bar is calculated.
Bars with ranges much larger or smaller than typical are excluded.
Filtered ATR:
The ATR is calculated using only the bars that pass the filter.
Current Bar Progress:
Measures how much the current bar has moved compared to the filtered ATR, based on the difference between its opening and closing prices.
Display:
A line represents the filtered ATR.
A table shows the filtered ATR, the current bar's range, and its progress relative to the ATR.
Input Settings:
ATR Period: Number of bars used to calculate the ATR.
Filter Window: Number of recent bars used to determine the typical range.
Filter Threshold: Sensitivity of the filter. A higher value allows more bars to pass.
How to Use:
Monitor Volatility:
Use the filtered ATR to understand market volatility while ignoring unusual price movements.
Track Current Bar Progress:
See how much of the ATR the current bar has completed.
Adjust Filter Settings:
Fine-tune the filter to match your trading timeframe and strategy.
This indicator is designed for traders who want to track market volatility without being misled by extreme outlier bars.
Conditional Volatility Explosion/ContractionThis indicator identifies zones of potential volatility expansion by analyzing the contraction and expansion of volatility bands, which are conditioned by the relationship of the price to moving averages
Volatility Squeeze: When the bands contract, it indicates a potential buildup in market tension, often preceding a significant price movement.
Volatility Expansion: When the bands expand, it signals the release of built-up tension, often resulting in increased volatility.
Trend Confirmation: The bands are active only when the price aligns with the moving average condition, helping to filter out less relevant signals during non-trending markets.
Upper Band: Displays as a red band when the volatility condition is met.
Represents the upper boundary of potential price action during high volatility.
Lower Band: Displays as a green band when the volatility condition is met.
Represents the lower boundary of potential price action during high volatility.
Fill Areas: The areas between the EMA and the bands are filled with transparent colors:
Red for the upper fill.
Green for the lower fill.
These highlights help visualize zones of potential volatility explosion.
Quick scan for signal🙏🏻 Hey TV, this is QSFS, following:
^^ Quick scan for drift (QSFD)
^^ Quick scan for cycles (QSFC)
As mentioned before, ML trading is all about spotting any kind of non-randomness, and this metric (along with 2 previously posted) gonna help ya'll do it fast. This one will show you whether your time series possibly exhibits mean-reverting / consistent / noisy behavior, that can be later confirmed or denied by more sophisticated tools. This metric is O(n) in windowed mode and O(1) if calculated incrementally on each data update, so you can scan Ks of datasets w/o worrying about melting da ice.
^^ windowed mode
Now the post will be divided into several sections, and a couple of things I guess you’ve never seen or thought about in your life:
1) About Efficiency Ratios posted there on TV;
Some of you might say this is the Efficiency Ratio you’ve seen in Perry's book. Firstly, I can assure you that neither me nor Perry, just as X amount of quants all over the world and who knows who else, would say smth like, "I invented it," lol. This is just a thing you R&D when you need it. Secondly, I invite you (and mods & admin as well) to take a lil glimpse at the following screenshot:
^^ not cool...
So basically, all the Efficiency Ratios that were copypasted to our platform suffer the same bug: dudes don’t know how indexing works in Pine Script. I mean, it’s ok, I been doing the same mistakes as well, but loxx, cmon bro, you... If you guys ever read it, the lines 20 and 22 in da code are dedicated to you xD
2) About the metric;
This supports both moving window mode when Length > 0 and all-data expanding window mode when Length < 1, calculating incrementally from the very first data point in the series: O(n) on history, O(1) on live updates.
Now, why do I SQRT transform the result? This is a natural action since the metric (being a ratio in essence) is bounded between 0 and 1, so it can be modeled with a beta distribution. When you SQRT transform it, it still stays beta (think what happens when you apply a square root to 0.01 or 0.99), but it becomes symmetric around its typical value and starts to follow a bell-shaped curve. This can be easily checked with a normality test or by applying a set of percentiles and seeing the distances between them are almost equal.
Then I noticed that on different moving window sizes, the typical value of the metric seems to slide: higher window sizes lead to lower typical values across the moving windows. Turned out this can be modeled the same way confidence intervals are made. Lines 34 and 35 explain it all, I guess. You can see smth alike on an autocorrelogram. These two match the mean & mean + 1 stdev applied to the metric. This way, we’ve just magically received data to estimate alpha and beta parameters of the beta distribution using the method of moments. Having alpha and beta, we can now estimate everything further. Btw, there’s an alternative parameterization for beta distributions based on data length.
Now what you’ll see next is... u guys actually have no idea how deep and unrealistically minimalistic the underlying math principles are here.
I’m sure I’m not the only one in the universe who figured it out, but the thing is, it’s nowhere online or offline. By calculating higher-order moments & combining them, you can find natural adaptive thresholds that can later be used for anomaly detection/control applications for any data. No hardcoded thresholds, purely data-driven. Imma come back to this in one of the next drops, but the truest ones can already see it in this code. This way we get dem thresholds.
Your main thresholds are: basis, upper, and lower deviations. You can follow the common logic I’ve described in my previous scripts on how to use them. You just register an event when the metric goes higher/lower than a certain threshold based on what you’re looking for. Then you take the time series and confirm a certain behavior you were looking for by using an appropriate stat test. Or just run a certain strategy.
To avoid numerous triggers when the metric jitters around a threshold, you can follow this logic: forget about one threshold if touched, until another threshold is touched.
In general, when the metric gets higher than certain thresholds, like upper deviation, it means the signal is stronger than noise. You confirm it with a more sophisticated tool & run momentum strategies if drift is in place, or volatility strategies if there’s no drift in place. Otherwise, you confirm & run ~ mean-reverting strategies, regardless of whether there’s drift or not. Just don’t operate against the trend—hedge otherwise.
3) Flex;
Extension and limit thresholds based on distribution moments gonna be discussed properly later, but now you can see this:
^^ magic
Look at the thresholds—adaptive and dynamic. Do you see any optimizations? No ML, no DL, closed-form solution, but how? Just a formula based on a couple of variables? Maybe it’s just how the Universe works, but how can you know if you don’t understand how fundamentally numbers 3 and 15 are related to the normal distribution? Hm, why do they always say 3 sigmas but can’t say why? Maybe you can be different and say why?
This is the primordial power of statistical modeling.
4) Thanks;
I really wanna dedicate this to Charlotte de Witte & Marion Di Napoli, and their new track "Sanctum." It really gets you connected to the Source—I had it in my soul when I was doing all this ∞
RS Cycles [QuantVue]The RS Cycles indicator is a technical analysis tool that expands upon traditional relative strength (RS) by incorporating Beta-based adjustments to provide deeper insights into a stock's performance relative to a benchmark index. It identifies and visualizes positive and negative performance cycles, helping traders analyze trends and make informed decisions.
Key Concepts:
Traditional Relative Strength (RS):
Definition: A popular method to compare the performance of a stock against a benchmark index (e.g., S&P 500).
Calculation: The traditional RS line is derived as the ratio of the stock's closing price to the benchmark's closing price.
RS=Stock Price/Benchmark Price
Usage: This straightforward comparison helps traders spot periods of outperformance or underperformance relative to the market or a specific sector.
Beta-Adjusted Relative Strength (Beta RS):
Concept: Traditional RS assumes equal volatility between the stock and benchmark, but Beta RS accounts for the stock's sensitivity to market movements.
Calculation:
Beta measures the stock's return relative to the benchmark's return, adjusted by their respective volatilities.
Alpha is then computed to reflect the stock's performance above or below what Beta predicts:
Alpha=Stock Return−(Benchmark Return×β)
Significance: Beta RS highlights whether a stock outperforms the benchmark beyond what its Beta would suggest, providing a more nuanced view of relative strength.
RS Cycles:
The indicator identifies positive cycles when conditions suggest sustained outperformance:
Short-term EMA (3) > Mid-term EMA (10) > Long-term EMA (50).
The EMAs are rising, indicating positive momentum.
RS line shows upward movement over a 3-period window.
EMA(21) > 0 confirms a broader uptrend.
Negative cycles are marked when the opposite conditions are met:
Short-term EMA (3) < Mid-term EMA (10) < Long-term EMA (50).
The EMAs are falling, indicating negative momentum.
RS line shows downward movement over a 3-period window.
EMA(21) < 0 confirms a broader downtrend.
This indicator combines the simplicity of traditional RS with the analytical depth of Beta RS, making highlighting true relative strength and weakness cycles.
Adaptive bollinger bands cloud v1 trend & trade signalsadaptive bollinger bands cloud:
the script extends the concept of bollinger bands by creating a "cloud" between the upper and lower bands. this cloud visually represents market conditions, with its color dynamically adjusting based on trend strength and volatility.
the gradient fill between the bands changes according to the deviation of the price from its basis, offering a visual cue for trend momentum.
trend detection logic:
a trend variable determines whether the price is in a bullish, bearish, or neutral state. if the price is above the upper band and the basis, the trend is marked bullish. if it's below the lower band and the basis, the trend is bearish. otherwise, it's neutral.
this trend logic is further enhanced with visual markers like arrows to indicate potential trend reversals.
extended take-profit bands:
additional upper and lower bands are calculated using a higher multiplier. these extended bands help identify potential take-profit levels, signaling when the price may have reached an overextended state.
gradient calculation:
the script computes a gradient based on the deviation of the price from its basis and normalizes it over a lookback period. this normalized gradient is smoothed to reflect volatility intensity and used to color the cloud dynamically.
signal generation:
buy and sell signals are generated based on crossovers of the trend variable. for instance, when the trend shifts from negative to positive, it signals a bullish opportunity. conversely, a shift from positive to negative indicates bearish conditions.
take-profit markers ("x") are plotted when the price crosses the extended bands, suggesting potential exit points.
trade entry tracking:
the script includes a table to display real-time entry signals and prices for long (buy) or short (sell) trades. this feature helps traders keep track of signals without needing to reference the chart visually.
customizable inputs:
users can adjust the bb period, multiplier, and colors to suit their trading preferences. this flexibility allows for tuning the indicator based on different market conditions or asset classes.
overall, the indicator blends traditional bollinger bands with innovative visualization, trend identification, and trading signals to enhance decision-making.
how to use this indicator
trend detection:
watch for arrows indicating trend shifts:
an upward arrow (green) signals a bullish trend; consider buying or entering a long position.
a downward arrow (red) signals a bearish trend; consider selling or entering a short position.
use the gradient-colored cloud to assess trend strength:
bright and strong colors indicate significant momentum.
fading colors suggest weakening trends or consolidation.
entry signals:
refer to the table in the top-right corner of the chart for real-time buy or sell entry signals.
when a "buy" signal is displayed with the price, it suggests a potential entry point for a long trade.
when a "sell" signal is displayed, consider shorting or exiting long positions.
take-profit signals:
look for the "x" markers near the extended bands (upper1 and lower1):
an "x" above the price suggests taking profit on long positions.
an "x" below the price suggests taking profit on short positions.
background gradient analysis:
observe the dynamic background color:
a strong purple gradient indicates significant price movement or volatility.
a lighter gradient suggests reduced momentum, signaling caution or a potential reversal.
alerts for automation:
set alerts using the predefined conditions:
bullish trend start, bearish trend start, and take-profit levels can be used to automate notifications for trade actions.
why to use this indicator
enhanced decision-making:
the adaptive cloud and gradient provide visual insights into trend strength and volatility, allowing traders to assess market conditions at a glance.
precise signals:
the indicator uses crossover logic and extended bollinger bands to generate clear buy, sell, and take-profit signals, reducing guesswork.
trend confirmation:
combining the bollinger bands with the trend variable ensures that traders only act on confirmed market trends rather than noise.
dynamic volatility assessment:
the normalized gradient calculation highlights periods of high or low volatility, helping traders adjust their strategies accordingly.
customizable settings:
adjustable parameters (period, multiplier, colors) allow the indicator to fit various markets, timeframes, and trading styles.
all-in-one tool:
integrates trend detection, entry signals, and take-profit levels into a single indicator, minimizing the need for multiple tools.
this indicator is especially useful for traders seeking a balance between simplicity and precision, whether scalping, day trading, or swing trading. it not only identifies trends but also highlights actionable entry and exit points, making it a versatile addition to any trading strategy.
IV Rank/Percentile with Williams VIX FixDisplay IV Rank / IV Percentile
This indicator is based on William's VixFix, which replicates the VIX—a measure of the implied volatility of the S&P 500 Index (SPX). The key advantage of the VixFix is that it can be applied to any security, not just the SPX.
IV Rank is calculated by identifying the highest and lowest implied volatility (IV) values over a selected number of past periods. It then determines where the current IV lies as a percentage between these two extremes. For example, if over the past five periods the highest IV was 30%, the lowest was 10%, and the current IV is 20%, the IV Rank would be 50%, since 20% is halfway between 10% and 30%.
IV Percentile, on the other hand, considers all past IV values—not just the highest and lowest—and calculates the percentage of these values that are below the current IV. For instance, if the past five IV values were 30%, 10%, 11%, 15%, and 17%, and the current IV is 20%, the IV Rank remains at 50%. However, the IV Percentile is 80% because 4 out of the 5 past values (80%) are below the current IV of 20%.
Real-Time Custom Candle Range Color Indicator
The script allows the user to input a custom range value (default set to 100 points) through the userDefinedRange variable. This value determines the minimum range required for a candle to change color.
Calculating Candle Range:
The script calculates the range of each candle by subtracting the low from the high price.
Determining Bullish or Bearish Candles:
It checks whether the close price is higher than the open price to determine if a candle is bullish (isBullish variable).
Coloring Candles:
Based on the custom range input, the script changes the color of the candles:
If the candle's range is greater than or equal to the custom range and it is bullish, the candle color is set to blue (bullishColor).
If the range condition is met and the candle is bearish, the color is set to orange (bearishColor).
If the range condition is not met, the color is set to na (not applicable).
Plotting Colored Candles:
The plotcandle function is used to plot candles with colors based on the custom range and bullish/bearish conditions. The candles will have a higher z-order to be displayed in front of default candles.
Displaying High and Low Price Points:
Triangular shapes are plotted at the high and low price levels using the plotshape function, with colors representing bullish (blue) and bearish (orange) conditions.
In trading, this indicator can help traders visually identify candles that meet a specific range criteria, potentially signaling strength or weakness in price movements. By customizing the range parameter, traders can adapt the indicator to different market conditions and trading strategies. It can be used in conjunction with other technical analysis tools to make informed trading decisions based on candlestick patterns and price movements.
Adaptive AI Predictor (v2) by OberlunarAdaptive AI Predictor by Oberlunar
This script is designed to dynamically adapt to market changes, leveraging a neural network-inspired model to identify reliable trading signals. It analyzes price variations, processes patterns in the market, and provides clear buy and sell signals based on dynamic force calculations.
The script goes beyond simple indicators by incorporating adaptive learning principles. It tracks the success of its signals over time, calculating both the average and median forces behind winning trades. These insights allow the script to continuously refine its performance, ensuring it remains responsive to evolving market conditions.
Clear signals are displayed on the chart, showing the strength of the signal and its median historical success.
Configuration Parameters
Number of Nodes: : This parameter controls the number of nodes through which the data is processed. A higher number of nodes can improve the model’s ability to represent complex dynamics, but may also increase bias and a low capacity of generalization.
Input Scaling: Determines how much the input signal (percentage price change) is amplified before being processed. If the value is too low, the system may not react sufficiently to price changes; if too high, it might become too sensitive to market noise.
Scaling: Controls the strength of interactions between internal nodes. A higher value makes interactions between the neurons (nodes) stronger, but might also lead to instability in the signals.
Leak Rate: This parameter determines how fast information is "forgotten" within the system. A higher value means the model "forgets" previous information more quickly, making it more responsive to recent changes.
Sparsity: Controls the density of connections between internal nodes. A higher value increases the likelihood that a connection between nodes is "active." This affects the system’s ability to model complex dynamics and can also influence computational speed.
Signal Threshold: Sets the limit beyond which the predicted signal is considered significant. A value too low could generate too frequent and noisy signals, while a value too high might reduce useful signals.
History Length: Determines how much historical data is considered for training the system. A higher value uses more historical data but could slow down computations.
Learning Rate: Controls the speed at which the system updates its internal weights. A value too high might cause oscillations in the results, while one too low might slow down the adaptation process.
Exponential Decay Factor: Defines how quickly the weights adapt based on errors. A higher value reduces the impact of older weights, allowing the model to adapt faster to recent changes.
How It Works
Input Signal: The system observes the percentage price change between two consecutive bars (current close vs. previous close).
State Update: The states of the nodes are updated based on the input signal and internal interactions between the neurons. The update is influenced by the leak rate, which determines how fast nodes "forget" previous information.
Weight Training: Weights are trained to minimize the error between the system’s prediction and the observed price change. The system uses exponential regression to update the weights efficiently.
Signal Generation: Buy (BUY) and sell (SELL) signals are generated based on an analysis of the overall values of the nodes' states. If the overall strength (average of the nodes' states) exceeds a certain threshold, a buy signal is generated. If it's lower than a negative threshold, a sell signal is triggered.
Visualization and Signals
Signals on the Chart: Buy and sell signals are displayed on the chart with specific labels, indicating the signal's strength and median successful strength previously adopted . The strength is based on the distance from the threshold. The stronger the signal, the more intense the label color.
Debug Table: A debug table shows details about the input weights, node states, and the success of buy/sell signals, allowing you to monitor the system's behavior in real-time.
Simple Capital Management: The system calculates the position size based on available capital and updates the current capital after each trade. The profit or loss is displayed as a percentage of the initial capital.
How to Use It
Initial Configuration: Customize the configuration parameters based on your trading strategy and style. If you’re a more conservative trader, you might prefer higher thresholds and lower scaling.
Monitor Signals: Follow the buy and sell signals generated on the chart. Each signal is accompanied by its strength (percentage), which will help you decide how aggressively to position.
Very simple Position Management: When a buy signal is emitted, you can open a buy position, and when a sell signal is emitted, you can close the position. The system automatically calculates the profit or loss for each trade.
Adapting to Market Conditions: Adjust the parameters based on market volatility and your risk tolerance. If the market is highly volatile, you might want to increase sensitivity to signals or reduce the number of nodes for faster responsiveness.
With this system, you can leverage dynamic predictive signals based on a combination of historical data and continuous adaptation, improving your trading decisions.
To obtain good results remember to fine-tune by a model reparametrization.
Simplified Momentum ScoreIndicator Name: Simplified Momentum Score
Description:
The Simplified Momentum Score indicator calculates the normalized price momentum of an asset over a user-defined period (e.g., 30 days). It provides a single actionable score between 0 and 1, making it easy to compare the relative strength of different tokens or assets:
1: Strongest momentum (best performer).
0: Weakest momentum (worst performer).
How to Use:
Apply this indicator to any chart in TradingView.
Use the normalized score to rank tokens or assets:
Closer to 1: Indicates strong recent price performance.
Closer to 0: Indicates weak recent price performance.
Customize the momentum period to match your trading strategy.
This tool is ideal for quick comparative analysis of multiple tokens to identify top-performing assets. Keep it simple, actionable, and effective! 🚀
Relative Momentum StrengthThe Relative Momentum Strength (RMS) indicator is designed to help traders and investors identify tokens with the strongest momentum over two customizable timeframes. It calculates and plots the percentage price change over 30-day and 90-day periods (or user-defined periods) to evaluate a token's relative performance.
30-Day Momentum (Green Line): Short-term price momentum, highlighting recent trends and movements.
90-Day Momentum (Blue Line): Medium-term price momentum, providing insights into broader trends.
This tool is ideal for comparing multiple tokens or assets to identify those showing consistent strength or weakness. Use it to spot outperformers and potential reversals in a competitive universe of assets.
How to Use:
Apply this indicator to your TradingView chart for any token or asset.
Look for tokens with consistently high positive momentum for potential strength.
Use the plotted values to compare relative performance across your watchlist.
Customization:
Adjust the momentum periods to suit your trading strategy.
Overlay it with other indicators like RSI or volume for deeper analysis.
Candlestick Strength and Volatility ReadoutDisplays a readout on the top right corner of the screen displaying a two basic calculations (volatility and strength; i.e. candlestick size and how close to the highs or lows it closed) for more convenient candlestick (price action) analysis.
Due to restrictions with Pine Script (or my knowledge thereof) only the current and previous candlestick data is shown, rather than the one currently hovered over.
The data is derived via two simple calculations; volatility being division between the range of the candlestick's high and low by the ATR; 'strength' (what I like to call it) being the range of the body by the range of the open to high or low, depending on the facing direction (positive or negative candlestick). These are expressed as percentages and will turn green depending on the set threshold.
Using this, one can effectively automate calculations you'd have to do by hand otherwise. I personally use these as entry filters in my trading, so it helps to not have to measure, remeasure, and divide before each potential entry.
Settings are implemented to change certain variables to your liking.
Contraction & Expansion Multi-Screener █ Overview:
The Contraction & Expansion Multi-Screener analyzes market volatility across many symbols. It provides insights into whether a market is contracting or expanding in volatility. With using a range of statistical models for modeling realized volatility, the script calculates, ranks, and monitors the degree of contraction or expansions in market volatility. The objective is to provide actionable insights into the current market phases by using historical data to model current volatility conditions.
This indicator accomplishes this by aggregating a variety of volatility measures, computing ranks, and applying threshold-based methods to identify transitions in market behavior. Volatility itself helps you understand if the market is moving a lot. High volatility or volatility that is increasing over time, means that the price is moving a lot. Volatility also mean reverts so if its extremely low, you can eventually expect it to return to its expected value, meaning there will be bigger price moves, and vice versa.
█ Features of the Indicator
This indicator allows the user to select up to 14 different symbols and retrieve their price data. There is five different types of volatility models that you can choose from in the settings of this indicator for how to use the screener.
Volatility Settings:
Standard Deviation
Relative Standard Deviation
Mean Absolute Deviation
Exponentially Weighted Moving Average (EWMA)
Average True Range (ATR)
Standard Deviation, Mean Absolute Deviation, and EWMA use returns to model the volatility, meanwhile Relative Standard Deviation uses price instead due to its geometric properties, and Average True Range for capturing the absolute movement in price. In this indicator the volatility is ranked, so if the volatility is at 0 or near 0 then it is contracting and the volatility is low. If the volatility is near 100 or at 100 then the volatility is at its maximum.
For traders that use the Forex Master Pattern Indicator 2 and want to use this indicator for that indicator, it is recommended to set your volatility type to Relative Standard Deviation.
Users can also modify the location of the screener to be on the top left, top right, bottom left, or bottom right. You also can disable sections of the screener and show a smaller list if you want to.
The Contraction & Expansion Screener shows you the following information:
Confirmation of whether or not there is a contraction or expansion
Percentage Rank of the volatility
Volatility MA direction: This screener uses moving averages on the volatility to determine if its increasing over time or decreasing over time.
MadTrend [InvestorUnknown]The MadTrend indicator is an experimental tool that combines the Median and Median Absolute Deviation (MAD) to generate signals, much like the popular Supertrend indicator. In addition to identifying Long and Short positions, MadTrend introduces RISK-ON and RISK-OFF states for each trade direction, providing traders with nuanced insights into market conditions.
Core Concepts
Median and Median Absolute Deviation (MAD)
Median: The middle value in a sorted list of numbers, offering a robust measure of central tendency less affected by outliers.
Median Absolute Deviation (MAD): Measures the average distance between each data point and the median, providing a robust estimation of volatility.
Supertrend-like Functionality
MadTrend utilizes the median and MAD in a manner similar to how Supertrend uses averages and volatility measures to determine trend direction and potential reversal points.
RISK-ON and RISK-OFF States
RISK-ON: Indicates favorable conditions for entering or holding a position in the current trend direction.
RISK-OFF: Suggests caution, signaling RISK-ON end and potential trend weakening or reversal.
Calculating MAD
The mad function calculates the median of the absolute deviations from the median, providing a robust measure of volatility.
// Function to calculate the Median Absolute Deviation (MAD)
mad(series float src, simple int length) =>
med = ta.median(src, length) // Calculate median
abs_deviations = math.abs(src - med) // Calculate absolute deviations from median
ta.median(abs_deviations, length) // Return the median of the absolute deviations
MADTrend Function
The MADTrend function calculates the median and MAD-based upper (med_p) and lower (med_m) bands. It determines the trend direction based on price crossing these bands.
MADTrend(series float src, simple int length, simple float mad_mult) =>
// Calculate MAD (volatility measure)
mad_value = mad(close, length)
// Calculate the MAD-based moving average by scaling the price data with MAD
median = ta.median(close, length)
med_p = median + (mad_value * mad_mult)
med_m = median - (mad_value * mad_mult)
var direction = 0
if ta.crossover(src, med_p)
direction := 1
else if ta.crossunder(src, med_m)
direction := -1
Trend Direction and Signals
Long Position (direction = 1): When the price crosses above the upper MAD band (med_p).
Short Position (direction = -1): When the price crosses below the lower MAD band (med_m).
RISK-ON: When the price moves further in the direction of the trend (beyond median +- MAD) after the initial signal.
RISK-OFF: When the price retraces towards the median, signaling potential weakening of the trend.
RISK-ON and RISK-OFF States
RISK-ON LONG: Price moves above the upper band after a Long signal, indicating strengthening bullish momentum.
RISK-OFF LONG: Price falls back below the upper band, suggesting potential weakness in the bullish trend.
RISK-ON SHORT: Price moves below the lower band after a Short signal, indicating strengthening bearish momentum.
RISK-OFF SHORT: Price rises back above the lower band, suggesting potential weakness in the bearish trend.
Picture below show example RISK-ON periods which can be identified by “cloud”
Note: Highlighted areas on the chart indicating RISK-ON and RISK-OFF periods for both Long and Short positions.
Implementation Details
Inputs and Parameters:
Source (input_src): The price data used for calculations (e.g., close, open, high, low).
Median Length (length): The number of periods over which the median and MAD are calculated.
MAD Multiplier (mad_mult): Determines the distance of the upper and lower bands from the median.
Calculations:
Median and MAD are recalculated each period based on the specified length.
Upper (med_p) and Lower (med_m) Bands are computed by adding and subtracting the scaled MAD from the median.
Visual representation of the indicator on a price chart:
Backtesting and Performance Metrics
The MadTrend indicator includes a Backtesting Mode with a performance metrics table to evaluate its effectiveness compared to a simple buy-and-hold strategy.
Equity Calculation:
Calculates the equity curve based on the signals generated by the indicator.
Performance Metrics:
Metrics such as Mean Returns, Standard Deviation, Sharpe Ratio, Sortino Ratio, and Omega Ratio are computed.
The metrics are displayed in a table for both the strategy and the buy-and-hold approach.
Note: Due to the use of labels and plot shapes, automatic chart scaling may not function ideally in Backtest Mode.
Alerts and Notifications
MadTrend provides alert conditions to notify traders of significant events:
Trend Change Alerts
RISK-ON and RISK-OFF Alerts - Provides real-time notifications about the RISK-ON and RISK-OFF states for proactive trade management.
Customization and Calibration
Default Settings: The provided default settings are experimental and not optimized. They serve as a starting point for users.
Parameter Adjustment: Traders are encouraged to calibrate the indicator's parameters (e.g., length, mad_mult) to suit their specific trading style and the characteristics of the asset being analyzed.
Source Input: The indicator allows for different price inputs (open, high, low, close, etc.), offering flexibility in how the median and MAD are calculated.
Important Notes
Market Conditions: The effectiveness of the MadTrend indicator can vary across different market conditions. Regular calibration is recommended.
Backtest Limitations: Backtesting results are historical and do not guarantee future performance.
Risk Management: Always apply sound risk management practices when using any trading indicator.
