Komut dosyalarını "Volatility" için ara
Upside Downside Volatility of ProfitTrailer FeederThis is to calculate UpsideVolatility, DownsideVolatility, UpsideDownsideVolatilityDifference, and AverageCandlesize in offset group of ProfitTrailer Feeder.
Volatilities are average changes between open and high or between open and low of 5 miniue candles in designated time frame by MinutesToMeasureTrend which can be inputable in format of this script.
In other words, volatilites are SMAspread between open and high or between open and low prices.
As described in wiki, use this script in 5 minute candle chart.
This script is to help myself understand the criteria of Feeder.
Any comments and suggestions are welcome.
Donchian Channel Width Strategy The Donchian Channel was developed by Richard Donchian and it could be compared
to the Bollinger Bands. When it comes to volatility analysis, the Donchian Channel
Width was created in the same way as the Bollinger Bandwidth technical indicator was.
You can change long to short in the Input Settings
WARNING:
- For purpose educate only
- This script to change bars colors.
Donchian Channel Width Strategy The Donchian Channel was developed by Richard Donchian and it could be compared
to the Bollinger Bands. When it comes to volatility analysis, the Donchian Channel
Width was created in the same way as the Bollinger Bandwidth technical indicator was.
WARNING:
- This script to change bars colors.
Moving Average Range Channels [DW]This study is an experiment based off the concept used in my Dynamic Range Channel indicator.
Rather than using a McGinley Dynamic, a moving average of your choice is used in this calculation.
There are eight different moving average types to choose from in this script:
- Kaufman's Adaptive Moving Average
- Geometric Moving Average
- Hull Moving Average
- Volume Weighted Moving Average
- Least Squares Moving Average
- Arnaud Legoux Moving Average
- Exponential Moving Average
- Simple Moving Average
For a more refined picture of volatility, I've added upper and lower extension channels. They are calculated by adding the upper half range to the channel high, and subtracting the lower half range from the channel low.
The new custom bar color scheme indicates trends, midline crosses, MA crosses, and overbought and oversold conditions.
Symmetrical Standard Deviation ChannelsChannels help with identifying buying and selling opportunities and avoid bad trades. This channel consists of two lines parallel to a moving average. The distance between the lines vary depending on the market's volatility (standard deviation channels). Channels mark the boundaries between normal and abnormal price action. The market is undervalued below its lower channel line and overvalued above its upper channel line.
WhenWasThePriceAction
Bars of largest range (volatility)
* see moments of strongest price action immediately
* colored & upDown by candle color
* amplifier: you see only the bull runs, and subsequent dumps
Very nice on the 5 years scale of BITSTAMP:BTCUSD - nothing comparable to 2013 has happened yet.
Internals:
squared_range = pow(high-low, 2)
That is essentially it already. The rest are details:
* gauge with (in case of Bitcoin exponentially rising) price
* show in red for negative candles
* take even higher polynomial (than 2) to show only the very largest values
* allow some user input (but there is not much more that can be chosen here.)
Sorry for such a simple formula - but sometimes the easiest things are powerful.
Please give feedback. www.tradingview.com and/or in the cryptocurrency chat. Thanks.
h chop filter v1.1
Chop Filter based on Chaikin's Volatility but faster with 0 lag.
Use it to filter out (in brown) when it is not worth trading as we are in chop zone.
Pip Foundry - BitMEX BVOL7D IndexMy premium indicators are available for monthly lease at www.tradingview.com
A quick indicator in response to the product released this month from BitMEX - a Weekly settled volatility index for bitcoin!
www.bitmex.com
uses BVOL calculation from www.bitmex.com
Indicator: STARC BandsSTARC (Stoller Average Range Channels) bands make use of ATR to form the bands, giving it a more in-depth snapshot of market volatility compared to Bollinger Bands.
When a price curve penetrates a Bollinger Band, it may indicate the continuation of a price move; in contrast, the STARC bands tend to define upper and lower limits for normal price action.
Directional ATROANDA:EURUSD
TLDR: A custom volatility indicator that combines Average True Range with candle direction.
The Directional ATR (DATR) is an indicator that enhances the traditional Average True Range (ATR) by incorporating the direction of the candle (bullish or bearish).
This indicator is designed to help traders identify trend strength, potential trend reversals, and market volatility.
Key Features:
Trend Confirmation: Positive and increasing DATR values suggest a bullish trend, while negative and decreasing values indicate a bearish trend. A higher absolute DATR value signifies a stronger trend.
Trend Reversal: A change in the direction of the DATR from positive to negative or vice versa may signal a potential trend reversal.
Volatility: Like the standard ATR, the DATR can be used to gauge market volatility, with larger absolute values indicating higher volatility and smaller values suggesting lower volatility.
Divergence: Divergence between the price and the DATR could signal a potential weakening of the trend and an upcoming reversal.
Overbought/Oversold Levels: Extreme DATR values can be used to identify overbought or oversold market conditions, signaling potential reversals or corrections.
Please note that the Directional ATR is just an indicator, and the interpretations provided are based on its underlying logic.
It is essential to combine the DATR with other technical analysis tools and test the indicator on historical data before using it in your trading strategy. Additionally, consider other factors such as risk management, and your own trading style.
TrendSphere (Zeiierman)█ Overview
TrendSphere is designed to capture and visualize market trends and volatility effectively. It combines various volatility measures and trend analysis techniques, producing dynamic bands and a central trend line on the price chart. Its essence is to offer a real-time, reliable estimate of the underlying linear trend in the price.
█ How It Works
Real-Time Trend Estimation
At its core, TrendSphere is designed to offer instantaneous and accurate insights into the inherent linear trend of asset prices. By continually updating its estimations, it ensures traders are equipped with the most current data. This allows the construction of support and resistance bands around the estimated trend, providing trading opportunities.
Dynamic Bands and Trend Line
TrendSphere plots a central trend line and dynamic bands around it on the price chart. Influenced by volatility, the distance between these elements offers a clear view of market conditions and the strength or weakness of trends. These bands not only depict potential turning points but also offer traders valuable opportunities to trade within the confines of the overarching trend.
Volatility Measures
Traders can select their preferred volatility measure and adjust settings to best fit their analysis needs. The bands and trend line dynamically respond to these selections, offering a tailored view of market conditions.
ATR (Average True Range): Reflects market volatility by evaluating the range between high and low prices.
Historical Volatility: Computes price variability using the standard deviation of log returns.
Bollinger Band Width: Measures the distance between Bollinger Bands, providing another angle on market volatility.
Eliminating Common Complications
One of the standout features of TrendSphere is its ability to determine linear price trends without falling prey to challenges like backpainting or repainting. In layman's terms, this means traders get a more trustworthy and unaltered view of price movements, leading to enhanced decision-making in line with the genuine trajectory of price trends.
█ How to Use
Trend Analysis
Observe the central trend line; its direction indicates the prevailing trend. When the price is above the trend line, it suggests an upward trend, and when it's below, it indicates a downward trend.
Volatility Analysis
Wider bands imply higher market volatility, suggesting larger price swings, while narrower bands indicate lower volatility. Traders can use the bands to identify potential reversal points and overbought/oversold conditions.
Potential Trading Signals (Using Bollinger bandwidth as volatility measure)
Consider buying when the price is above the trend line with narrowing bands, suggesting a strong upward trend.
Consider selling when the price is below the trend line with narrowing bands, indicating a strong downward trend.
█ Settings
Select Volatility Measure
Choose the desired volatility measure: ATR, Historical Volatility, or Bollinger Band Width.
Volatility Scaling Factor
Adjusts the scale of the volatility measure, influencing the width of the bands.
Volatility Strength
Modifies the influence of volatility on the bands, adjusting their responsiveness to volatility changes.
Length
Defines the number of periods used in calculating the selected volatility measure, impacting the stability and responsiveness of the bands.
Trend Sensitivity
Adjusts the sensitivity of the trend component, affecting how quickly it reacts to price changes.
█ Related scripts with the same calculation philosophy
TrendCylinder
Predictive Trend and Structure
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Curved Price Channels (Zeiierman)█ Overview
The Curved Price Channels (Zeiierman) is designed to plot dynamic channels around price movements, much like the traditional Donchian Channels, but with a key difference: the channels are curved instead of straight. This curvature allows the channels to adapt more fluidly to price action, providing a smoother representation of the highest high and lowest low levels.
Just like Donchian Channels, the Curved Price Channels help identify potential breakout points and areas of trend reversal. However, the curvature offers a more refined approach to visualizing price boundaries, making it potentially more effective in capturing price trends and reversals in markets that exhibit significant volatility or price swings.
The included trend strength calculation further enhances the indicator by offering insight into the strength of the current trend.
█ How It Works
The Curved Price Channels are calculated based on the asset's average true range (ATR), scaled by the chosen length and multiplier settings. This adaptive size allows the channels to expand and contract based on recent market volatility. The central trendline is calculated as the average of the upper and lower curved bands, providing a smoothed representation of the overall price trend.
Key Calculations:
Adaptive Size: The ATR is used to dynamically adjust the width of the channels, making them responsive to changes in market volatility.
Upper and Lower Bands: The upper band is calculated by taking the maximum close value and adjusting it downward by a factor proportional to the ATR and the multiplier. Similarly, the lower band is calculated by adjusting the minimum close value upward.
Trendline: The trendline is the average of the upper and lower bands, representing the central tendency of the price action.
Trend Strength
The Trend Strength feature in the Curved Price Channels is a powerful feature designed to help traders gauge the strength of the current trend. It calculates the strength of a trend by analyzing the relationship between the price's position within the curved channels and the overall range of the channels themselves.
Range Calculation:
The indicator first determines the distance between the upper and lower curved channels, known as the range. This range represents the overall volatility of the price within the given period.
Range = Upper Band - Lower Band
Relative Position:
The next step involves calculating the relative position of the closing price within this range. This value indicates where the current price sits in relation to the overall range.
RelativePosition = (Close - Trendline) / Range
Normalization:
To assess the trend strength over time, the current range is normalized against the maximum and minimum ranges observed over a specified look-back period.
NormalizedRange = (Range - Min Range) / (Max Range - Min Range)
Trend Strength Calculation:
The final Trend Strength is calculated by multiplying the relative position by the normalized range and then scaling it to a percentage.
TrendStrength = Relative Position * Normalized Range * 100
This approach ensures that the Trend Strength not only reflects the direction of the trend but also its intensity, providing a more comprehensive view of market conditions.
█ Comparison with Donchian Channels
Curved Price Channels offer several advantages over Donchian Channels, particularly in their ability to adapt to changing market conditions.
⚪ Adaptability vs. Fixed Structure
Donchian Channels: Use a fixed period to plot straight lines based on the highest high and lowest low. This can be limiting because the channels do not adjust to volatility; they remain the same width regardless of how much or how little the price is moving.
Curved Price Channels: Adapt dynamically to market conditions using the Average True Range (ATR) as a measure of volatility. The channels expand and contract based on recent price movements, providing a more accurate reflection of the market's current state. This adaptability allows traders to capture both large trends and smaller fluctuations more effectively.
⚪ Sensitivity to Market Movements
Donchian Channels: Are less sensitive to recent price action because they rely on a fixed look-back period. This can result in late signals during fast-moving markets, as the channels may not adjust quickly enough to capture new trends.
Curved Price Channels: Respond more quickly to changes in market volatility, making them more sensitive to recent price action. The multiplier setting further allows traders to adjust the channel's sensitivity, making it possible to capture smaller price movements during periods of low volatility or filter out noise during high volatility.
⚪ Enhanced Trend Strength Analysis
Donchian Channels: Do not provide direct insight into the strength of a trend. Traders must rely on additional indicators or their judgment to gauge whether a trend is strong or weak.
Curved Price Channels: Includes a built-in trend strength calculation that takes into account the distance between the upper and lower channels relative to the trendline. A broader range between the channels typically indicates a stronger trend, while a narrower range suggests a weaker trend. This feature helps traders not only identify the direction of the trend but also assess its potential longevity and strength.
⚪ Dynamic Support and Resistance
Donchian Channels: Offer static support and resistance levels that may not accurately reflect changing market dynamics. These levels can quickly become outdated in volatile markets.
Curved Price Channels: Offer dynamic support and resistance levels that adjust in real-time, providing more relevant and actionable trading signals. As the channels curve to reflect price movements, they can help identify areas where the price is likely to encounter support or resistance, making them more useful in volatile or trending markets.
█ How to Use
Traders can use the Curved Price Channels in similar ways to Donchian Channels but with the added benefits of the adaptive, curved structure:
Breakout Identification:
Just like Donchian Channels, when the price breaks above the upper curved band, it may signal the start of a bullish trend, while a break below the lower curved band could indicate a bearish trend. The curved nature of the channels helps in capturing these breakouts more precisely by adjusting to recent volatility.
Volatility:
The width of the price channels in the Curved Price Channels indicator serves as a clear indicator of current market volatility. A wider channel indicates that the market is experiencing higher volatility, as prices are fluctuating more dramatically within the period. Conversely, a narrower channel suggests that the market is in a lower volatility state, with price movements being more subdued.
Typically, higher volatility is observed during negative trends, where market uncertainty or fear drives larger price swings. In contrast, lower volatility is often associated with positive trends, where prices tend to move more steadily and predictably. The adaptive nature of the Curved Price Channels reflects these volatility conditions in real time, allowing traders to assess the market environment quickly and adjust their strategies accordingly.
Support and Resistance:
The trend line act as dynamic support and resistance levels. Due to it's adaptive nature, this level is more reflective of the current market environment than the fixed level of Donchian Channels.
Trend Direction and Strength:
The trend direction and strength are highlighted by the trendline and the directional candle within the Curved Price Channels indicator. If the price is above the trendline, it indicates a positive trend, while a price below the trendline signals a negative trend. This directional bias is visually represented by the color of the directional candle, making it easy for traders to quickly identify the current market trend.
In addition to the trendline, the indicator also displays Max and Min values. These represent the highest and lowest trend strength values within the lookback period, providing a reference point for understanding the current trend strength relative to historical levels.
Max Value: Indicates the highest recorded trend strength during the lookback period. If the Max value is greater than the Min value, it suggests that the market has generally experienced more positive (bullish) conditions during this time frame.
Min Value: Represents the lowest recorded trend strength within the same period. If the Min value is greater than the Max value, it indicates that the market has been predominantly negative (bearish) over the lookback period.
By assessing these Max and Min values, traders gain an immediate understanding of the underlying trend. If the current trend strength is close to the Max value, it indicates a strong bullish trend. Conversely, if the trend strength is near the Min value, it suggests a strong bearish trend.
█ Settings
Trend Length: Defines the number of bars used to calculate the core trendline and adaptive size. A length of 200 will create a smooth, long-term trendline that reacts slowly to price changes, while a length of 20 will create a more responsive trendline that tracks short-term movements.
Multiplier: Adjusts the width of the curved price channels. A higher value tightens the channels, making them more sensitive to price movements, while a lower value widens the channels. A multiplier of 10 will create tighter channels that are more sensitive to minor price fluctuations, which is useful in low-volatility markets. A multiplier of 2 will create wider channels that capture larger trends and are better suited for high-volatility markets.
Trend Strength Length: Defines the period over which the maximum and minimum ranges are calculated to normalize the trend strength. A length of 200 will smooth out the trend strength readings, providing a stable indication of trend health, whereas a length of 50 will make the readings more reactive to recent price changes.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Squeeze & Release [AlgoAlpha]Introduction:
💡The Squeeze & Release by AlgoAlpha is an innovative tool designed to capture price volatility dynamics using a combination of EMA-based calculations and ATR principles. This script aims to provide traders with clear visual cues to spot potential market squeezes and release scenarios. Hence it is important to note that this indicator shows information on volatility, not direction.
Core Logic and Components:
🔶EMA Calculations: The script utilizes the Exponential Moving Average (EMA) in multiple ways to smooth out the data and provide indicator direction. There are specific lengths for the EMAs that users can modify as per their preference.
🔶ATR Dynamics: Average True Range (ATR) is a core component of the script. The differential between the smoothed ATR and its EMA is used to plot the main line. This differential, when represented as a percentage of the high-low range, provides insights into volatility.
🔶Squeeze and Release Detection: The script identifies and highlights squeeze and release scenarios based on the crossover and cross-under events between our main line and its smoothed version. Squeezes are potential setups where the market may be consolidating, and releases indicate a potential breakout or breakdown.
🔶Hyper Squeeze Detection: A unique feature that detects instances when the main line is rising consistently over a user-defined period. Hyper squeeze marks areas of extremely low volatility.
Visual Components:
The main line (ATR-based) changes color depending on its position relative to its EMA.
A middle line plotted at zero level which provides a quick visual cue about the main line's position. If the main line is above the zero level, it indicates that the price is squeezing on a longer time horizon, even if the indicator indicates a shorter-term release.
"𝓢" and "𝓡" characters are plotted to represent 'Squeeze' and 'Release' scenarios respectively.
Standard Deviation Bands are plotted to help users gauge the extremity and significance of the signal from the indicator, if the indicator is closer to either the upper or lower deviation bands, this means that statistically, the current value is considered to be more extreme and as it is further away from the mean where the indicator is oscillating at for the majority of the time. Thus indicating that the price has experienced an unusual amount or squeeze or release depending on the value of the indicator.
Usage Guidelines:
☝️Traders can use the script to:
Identify potential consolidation (squeeze) zones.
Gauge potential breakout or breakdown scenarios (release).
Fine-tune their entries and exits based on volatility.
Adjust the various lengths provided in the input for better customization based on individual trading styles and the asset being traded.
Williams Vix Fix [CC]The Vix Fix indicator was created by Larry Williams and is one of my giant backlog of unpublished scripts which I'm going to start publishing more of. This indicator is a great synthetic version of the classic Volatility Index and can be useful in combination with other indicators to determine when to enter or exit a trade due to the current volatility. The indicator creates this synthetic version of the Volatility Index by a fairly simple formula that subtracts the current low from the highest close over the last 22 days and then divides that result by the same highest close and multiplies by 100 to turn it into a percentage. The 22-day length is used by default since there is a max of 22 trading days in a month but this formula works well for any other timeframe. By itself, this indicator doesn't generate buy or sell signals but generally speaking, you will want to enter or exit a trade when the Vix fix indicator amount spikes and you get an entry or exit signal from another indicator of your choice. Keep in mind that the colors I'm using for this indicator are only a general idea of when volatility is high enough to enter or exit a trade so green colors mean higher volatility and red colors mean low volatility. This is one of the few indicators I have written that don't recommend to buy or sell when the colors change.
This was a custom request from one of my followers so please let me know if you guys have any other script requests you want to see!
+ Bollinger Bands WidthHere is my rendition of Bollinger Bands Width. If you are unfamiliar, Bollinger Bands Width is a measure of the distance between the top and bottom bands of Bollinger Bands. Bollinger Bands themselves being a measure of market volatility, BB Width is a simpler, cleaner way of determining the amount of volatility in the market. Myself, I found the original, basic version of BB Width a bit too basic, and I thought that by adding to it it might make for an improvement for traders over the original.
Simple things that I've done are adding a signal line; adding a 'baseline' using Donchian Channels (such as that which is in my Average Candle Bodies Range indicator); adding bar and background coloring; and adding alerts for increasing volatility, and baseline and signal line crosses. It really ends up making for a much improved version of the basic indicator.
A note on how I created the baseline:
First, what do I mean by 'baseline?' I think of it as an area of the indicator where if the BB Width is below you will not want to enter into any trades, and if the BB Width is above then you are free to enter trades based on your system. It's basically a volatility measure of the volatility indicator. Waddah Attar Explosion is a popular indicator that implements something similar. The baseline is calculated thus: make a Donchian Channel of the BB Width, and then use the basis as the baseline while not plotting the actual highs and lows of the Donchian Channel. Now, the basis of a Donchian Channel is the average of the highs and the lows. If we did that here we would have a baseline much too high, however, by making the basis adjustable with a divisor input it no longer must be plotted in the center of the channel, but may be moved much lower (unless you set the divisor to 2, but you wouldn't do that). This divisor is essentially a sensitivity adjustment for the indicator. Of course you don't have to use the baseline. You could ignore it and only use the signal line, or just use the rising and falling of the BB Width by itself as your volatility measure.
I should make note: the main image above at default settings is an 8 period lookback (so, yes, that is quite fast), and the signal line is a Hull MA set to 13. The background and bar coloring are simply set to the rising and falling of the BB Width. Images below will show some different settings, but definitely play with it yourself to determine if it might be a good fit for your system.
Above, settings are background and bar coloring tuned to BB Width being above the baseline, and also requiring that the BB Width be rising. Background coloring only highlights increasing volatility or volatility above a certain threshold. Grey candles are because the BB Width is above the baseline but falling. We'll see an example without the requirement of BB Width rising, below.
Here, we see that background highlights and aqua candles are more prevalent because I've checked off the requirement that BB Width be rising. The idea is that BB Width is above the baseline therefor there is sufficient volatility to enter trades if our indicators give us the go-ahead.
This here is set to BB Width being above the signal line and also requiring a rising BB Width. Keep in mind the signal line is a Hull MA.
And this fourth and final image uses a volume-weighted MA as the signal line. Bar coloring is turned off, and instead the checkboxes for volatility advancing and declining are turned on under the signal line options. BB Width crosses up the signal line is advancing volatility, while falling below it is declining volatility. Background highlights are set to baseline and not requiring a rising BB Width. This way, with a quick glance you can see if the rising volatility is legitimate, i.e., is the cross up of the signal line coupled with it being above the baseline.
Please enjoy.
+ Average Candle Bodies RangeACBR, or, Average Candle Bodies Range is a volatility and momentum indicator designed to indicate periods of increasing volatility and/or momentum. The genesis of the idea formed from my pondering what a trend trader is really looking for in terms of a volatility indicator. Most indicators I've come across haven't, in my opinion, done a satisfactory job of highlighting this. I kept thinking about the ATR (I use it for stops and targets) but I realized I didn't care about highs or lows in regards to a candle's volatility or momentum, nor do I care about their relation to a previous close. What really matters to me is candle body expansion. That is all. So, I created this.
ACBR is extremely simple at its heart. I made it more complicated of course, because why would I want anything for myself to be simple? Originally it was envisaged to be a simple volatility indicator highlighting areas of increasing and decreasing volatility. Then I decided some folks might want an indicator that could show this in a directional manner, i.e., an oscillator, so I spent some more hours tackling that
To start, the original version of the indicator simply subtracts opening price from closing price if the candle closes above the open, and subtracts the close from the open if the candle closes below the open. This way we get a positive number that simply measures candle expansion. We then apply a moving average to these values in order to smooth them (if you want). To get an oscillator we always subtract the close from the open, thus when a candle closes below its open we get a negative number.
I've naturally added an optional signal line as a helpful way of gauging volatility because obviously the values themselves may not tell you much. But I've also added something that I call a baseline. You can use this in a few ways, but first let me explain the two options for how the baseline can be calculated. And what do I mean by 'baseline?' I think of it as an area of the indicator where if the ACBR is below you will not want to enter into any trades, and if the ACBR is above then you are free to enter trades based on your system (or you might want to enter in areas of low volatility if your system calls for that). Waddah Attar Explosion is another indicator that implements something similar. The baseline is calculated in two different ways: one of which is making a Donchian Channel of the ACBR, and then using the basis as the baseline, while the other is applying an RMA to the cb_dif, which is the base unit that makes up the ACBR. Now, the basis of a Donchian Channel typically is the average of the highs and the lows. If we did that here we would have a baseline much too high (but maybe not...), however, I've made the divisor user adjustable. In this way you can adjust the height (or I guess you might say 'width' if it's an oscillator) however you like, thus making the indicator more or less sensitive. In the case of using the ACBR as the baseline we apply a multiplier to the values in order to adjust the height. Apologies if I'm being overly verbose. If you want to skip all of this I have tooltips in the settings for all of the inputs that I think need an explanation.
When using the indicator as an oscillator there are baselines above and below the zero line. One funny thing: if using the ACBR as calculation type for the baselines in oscillator mode, the baselines themselves will oscillate around the zero line. There is no way to fix this due to the calculation. That isn't necessarily bad (based on my eyeball test), but I probably wouldn't use it in such a way. But experiment! They could actually be a very fine entry or confirmation indicator. And while I'm on the topic of confirmation indicators, using this indicator as an oscillator naturally makes it a confirmation indicator. It just happens to have a volatility measurement baked into it. It may also be used as an exit and continuation indicator. And speaking of these things, there are optional shapes for indicating when you might want to exit or take a continuation trade. I've added alerts for these things too.
Lastly, oscillator mode is good for identifying divergences.
Above we have the indicator set to directional, or oscillator, mode. Baselines are Donchian Channels. I changed the default EMA length from 4 to 24 in this case, otherwise all the settings are default, as in the main image for the indicator (which is clearly set to non-directional). The indicator is set to requiring an advancing signal line for background and bar colors. Background color is not on by default. Candle colors, as you can see are aqua when above the top baseline (and only when the signal line is advancing, as per the settings), magenta when below the bottom baseline, and grey for anything else. The red and blue X's are exit signals. There are two types: one, when the signal line weakens and, two, when the ACBR crosses above or below the signal line. There are also arrows. These are continuation signals (ACBR crossing signal line).
Same image as above, but the baselines are set to ACBR rather than Donchian Channels.
Again, the same image, but with everything but the ACBR Baseline turned off. You can see how this might make for an excellent confirmation indicator, but for the areas of chap. Maybe run a second instance of the indicator on your chart as a volatility indicator, as you would not be using it in that way in this instance.
Here I have bar coloring turned off except for signal line crosses NOT requiring the signal line to be advancing. Background coloring is also turned on. You can see that these all line up with continuation signals, or exits for purple candles.
Same image as above but requiring the signal line to be advancing. You can see that continuation signals are not contingent upon the signal line to be advancing. I had it setup that way at first, but of course it still gave false signals, so I thought more signals (not that there are many) is better than fewer. To be sure, just because the indicator shows a continuation signal does not mean you should always take it.
Position resetThe "Position Reset" indicator
The Position Reset indicator is a sophisticated technical analysis tool designed to identify possible entry points into short positions based on an analysis of market volatility and the behavior of various groups of bidders. The main purpose of this indicator is to provide traders with information about the current state of the market and help them decide whether to open short positions depending on the level of volatility and the mood of the main players.
The main components of the indicator:
1. Parameters for the RSI (Relative Strength Index):
The indicator uses two sets of parameters to calculate the RSI: one for bankers ("Banker"), the other for hot money ("Hot Money").
RSI for Bankers:
RSIBaseBanker: The baseline for calculating bankers' RSI. The default value is 50.
RSIPeriodBanker: The period for calculating the RSI for bankers. The default period is 14.
RSI for hot money:
RSIBaseHotMoney: The baseline for calculating the RSI of hot money. The default value is 30.
RSIPeriodHotMoney: The period for calculating the RSI for hot money. The default period is 21.
These parameters allow you to adjust the sensitivity of the indicator to the actions of different groups of market participants.
2. Sensitivity:
Sensitivity determines how strongly changes in the RSI will affect the final result of calculations. It is configured separately for bankers and hot money:
SensitivityBanker: Sensitivity for bankers' RSI. It is set to 2.0 by default.
SensitivityHotMoney: Sensitivity for hot money RSI. It is set to 1.0 by default.
Changing these parameters allows you to adapt the indicator to different market conditions and trader preferences.
3. Volatility Analysis:
Volatility is measured based on the length of the period, which is set by the volLength parameter. The default length is 30 candles. The indicator calculates the difference between the highest and lowest value for the specified period and divides this difference by the lowest value, thus obtaining the volatility coefficient.
Based on this coefficient, four levels of volatility are distinguished.:
Extreme volatility: The coefficient is greater than or equal to 0.25.
High volatility: The coefficient ranges from 0.125 to 0.2499.
Normal volatility: The coefficient ranges from 0.05 to 0.1249.
Low volatility: The coefficient is less than 0.0499.
Each level of volatility has its own significance for making decisions about entering a position.
4. Calculation functions:
The indicator uses several functions to process the RSI and volatility data.:
rsi_function: This function applies to every type of RSI (bankers and hot money). It adjusts the RSI value according to the set sensitivity and baseline, limiting the range of values from 0 to 20.
Moving Averages: Simple moving averages (SMA), exponential moving averages (EMA), and weighted moving averages (RMA) are used to smooth fluctuations. They are applied to different time intervals to obtain the average values of the RSI.
Thus, the indicator creates a comprehensive picture of market behavior, taking into account both short-term and long-term dynamics.
5. Bearish signals:
Bearish signals are considered situations when the RSI crosses certain levels simultaneously with a drop in indicators for both types of market participants (bankers and hot money).:
The bankers' RSI crossing is below the level of 8.5.
The current hot money RSI is less than 18.
The moving averages for banks and hot money are below their signal lines.
The RSI values for bankers are less than 5.
These conditions indicate a possible beginning of a downtrend.
6. Signal generation:
Depending on the current level of volatility and the presence of bearish signals, the indicator generates three types of signals:
Orange circle: Extremely high volatility and the presence of a bearish signal.
Yellow circle: High volatility and the presence of a bearish signal.
Green circle: Low volatility and the presence of a bearish signal.
These visual markers help the trader to quickly understand what level of risk accompanies each specific signal.
7. Notifications:
The indicator supports the function of sending notifications when one of the three types of signals occurs. The notification contains a brief description of the conditions under which the signal was generated, which allows the trader to respond promptly to a change in the market situation.
Advantages of using the "Position Reset" indicator:
Multi-level analysis: The indicator combines technical analysis (RSI) and volatility assessment, providing a comprehensive view of the current market situation.
Flexibility of settings: The ability to adjust the sensitivity parameters and the RSI baselines allows you to adapt the indicator to any market conditions and personal preferences of the trader.
Clear visualization: The use of colored labels on the chart simplifies the perception of information and helps to quickly identify key points for entering a trade.
Notification support: The notification sending feature makes it much easier to monitor the market, allowing you to respond to important events in time.
Regime Classifier Oscillator (AiBitcoinTrend)The Regime Classifier Oscillator (AiBitcoinTrend) is an advanced tool for understanding market structure and detecting dynamic price regimes. By combining filtered price trends, clustering algorithms, and an adaptive oscillator, it provides traders with detailed insights into market phases, including accumulation, distribution, advancement, and decline.
This innovative tool simplifies market regime classification, enabling traders to align their strategies with evolving market conditions effectively.
👽 What is a Regime Classifier, and Why is it Useful?
A Regime Classifier is a concept in financial analysis that identifies distinct market conditions or "regimes" based on price behavior and volatility. These regimes often correspond to specific phases of the market, such as trends, consolidations, or periods of high or low volatility. By classifying these regimes, traders and analysts can better understand the underlying market dynamics, allowing them to adapt their strategies to suit prevailing conditions.
👽 Common Uses in Finance
Risk Management: Identifying high-volatility regimes helps traders adjust position sizes or hedge risks.
Strategy Optimization: Traders tailor their approaches—trend-following strategies in trending regimes, mean-reversion strategies in consolidations.
Forecasting: Understanding the current regime aids in predicting potential transitions, such as a shift from accumulation to an upward breakout.
Portfolio Allocation: Investors allocate assets differently based on market regimes, such as increasing cash positions in high-volatility environments.
👽 Why It’s Important
Markets behave differently under varying conditions. A regime classifier provides a structured way to analyze these changes, offering a systematic approach to decision-making. This improves both accuracy and confidence in navigating diverse market scenarios.
👽 How We Implemented the Regime Classifier in This Indicator
The Regime Classifier Oscillator takes the foundational concept of market regime classification and enhances it with advanced computational techniques, making it highly adaptive.
👾 Median Filtering: We smooth price data using a custom median filter to identify significant trends while eliminating noise. This establishes a baseline for price movement analysis.
👾 Clustering Model: Using clustering techniques, the indicator classifies volatility and price trends into distinct regimes:
Advance: Strong upward trends with low volatility.
Decline: Downward trends marked by high volatility.
Accumulation: Consolidation phases with subdued volatility.
Distribution: Topping or bottoming patterns with elevated volatility.
This classification leverages historical price data to refine cluster boundaries dynamically, ensuring adaptive and accurate detection of market states.
Volatility Classification: Price volatility is analyzed through rolling windows, separating data into high and low volatility clusters using distance-based assignments.
Price Trends: The interaction of price levels with the filtered trendline and volatility clusters determines whether the market is advancing, declining, accumulating, or distributing.
👽 Dynamic Cycle Oscillator (DCO):
Captures cyclic behavior and overlays it with smoothed oscillations, providing real-time feedback on price momentum and potential reversals.
Regime Visualization:
Regimes are displayed with intuitive labels and background colors, offering clear, actionable insights directly on the chart.
👽 Why This Implementation Stands Out
Dynamic and Adaptive: The clustering and refit mechanisms adapt to changing market conditions, ensuring relevance across different asset classes and timeframes.
Comprehensive Insights: By combining price trends, volatility, and cyclic behaviors, the indicator provides a holistic view of the market.
This implementation bridges the gap between theoretical regime classification and practical trading needs, making it a powerful tool for both novice and experienced traders.
👽 Applications
👾 Regime-Based Trading Strategies
Traders can use the regime classifications to adapt their strategies effectively:
Advance & Accumulation: Favorable for entering or holding long positions.
Decline & Distribution: Opportunities for short positions or risk management.
👾 Oscillator Insights for Trend Analysis
Overbought/oversold conditions: Early warning of potential reversals.
Dynamic trends: Highlights the strength of price momentum.
👽 Indicator Settings
👾 Filter and Classification Settings
Filter Window Size: Controls trend detection sensitivity.
ATR Lookback: Adjusts the threshold for regime classification.
Clustering Window & Refit Interval: Fine-tunes regime accuracy.
👾 Oscillator Settings
Dynamic Cycle Oscillator Lookback: Defines the sensitivity of cycle detection.
Smoothing Factor: Balances responsiveness and stability.
Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Cypto Oscillator with Sortino-like VolatilityEnhanced Inverted Ultimate Oscillator with Sortino-like Volatility
This indicator combines the power of the Ultimate Oscillator with a unique Sortino-like volatility calculation to provide a comprehensive view of market dynamics. It's designed to help traders identify potential turning points and assess the risk associated with price movements.
**Core Components:**
* **Ultimate Oscillator (UO):** The UO is a momentum indicator that incorporates short, medium, and long-term price action to identify overbought and oversold conditions. This indicator inverts and normalizes the UO to a 0-10 scale, providing a clear view of momentum shifts.
* **Sortino-like Volatility:** Instead of a standard deviation, this indicator uses a downside deviation calculation. This focuses specifically on *negative* price movements, offering a more relevant measure of risk for most traders. By not penalizing upside volatility, it avoids giving false signals during strong bull runs. The downside deviation is scaled as a percentage of the closing price for cross-asset comparability.
* **Volatility Signal:** The inverted UO is multiplied by the downside deviation to create a combined volatility signal. This signal reflects both momentum and downside risk, providing a more nuanced market perspective.
**Key Features and Uses:**
* **Identifying Potential Turning Points:** Divergences between the UO and price action can signal potential trend reversals. Look for the UO to make higher lows while price makes lower lows (bullish divergence) or the UO to make lower highs while price makes higher highs (bearish divergence).
* **Assessing Downside Risk:** The Sortino-like volatility component helps traders gauge the potential for downside price swings. Higher volatility suggests greater risk.
* **Dynamic Volatility Thresholds:** The indicator includes adjustable upper and lower volatility thresholds, based on a moving average of the volatility signal. These thresholds can be used to identify periods of unusually high or low volatility.
* **Customizable Lookback Periods:** Traders can adjust the lookback periods for the UO and the standard deviation calculation to fine-tune the indicator to their specific trading style and market conditions.
* **Visualizations:** The indicator provides several visual aids, including:
* A histogram of the volatility signal, colored dynamically based on its relationship to the moving average of volatility. Red indicates volatility above the upper bound, orange between the bounds and green below the lower bound.
* A line plot of the volatility signal.
* An optional moving average of the volatility signal.
* Optional upper and lower volatility threshold lines with a filled range for visual clarity.
* **Alerts:** The indicator includes alert conditions for when the volatility signal crosses above the upper threshold (high volatility) or below the lower threshold (low volatility).
**How to Use:**
1. **Inputs:** Adjust the input parameters to optimize the indicator for your chosen asset and timeframe.
2. **Divergences:** Look for divergences between the UO and price to identify potential trend reversals.
3. **Volatility:** Use the volatility signal and thresholds to assess downside risk.
4. **Alerts:** Enable alerts to be notified of high or low volatility events.
**Disclaimer:** This indicator is for informational purposes only and should not be considered financial advice. Always conduct your own thorough analysis before making any trading decisions.
Key improvements in this description:
Clear and concise language: Easy for traders to understand.
Focus on benefits: Highlights how the indicator can help traders.
Detailed explanation of features: Covers all the important aspects.
How-to-use section: Provides practical guidance.
Disclaimer: Includes a necessary disclaimer.
Emphasis on the Sortino-like approach: This is a unique selling point of your indicator.
Well-structured and formatted: Easy to read and digest.
This description should be a great starting point for sharing your indicator with the TradingView community. You can further customize it by adding screenshots of the indicator in action or linking to a chart where it's being used. Remember to respond to comments and questions from other users to build engagement and improve your indicator over time.
Mean Reversion Cloud (Ornstein-Uhlenbeck) // AlgoFyreThe Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator detects mean-reversion opportunities by applying the Ornstein-Uhlenbeck process. It calculates a dynamic mean using an Exponential Weighted Moving Average, surrounded by volatility bands, signaling potential buy/sell points when prices deviate.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Adaptive Mean Calculation
🔸Volatility-Based Cloud
🔸Speed of Reversion (θ)
🔶 FUNCTIONALITY
🔸Dynamic Mean and Volatility Bands
🞘 How it works
🞘 How to calculate
🞘 Code extract
🔸Visualization via Table and Plotshapes
🞘 Table Overview
🞘 Plotshapes Explanation
🞘 Code extract
🔶 INSTRUCTIONS
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
🞘 Understanding What to Look For on the Chart
🞘 Possible Entry Signals
🞘 Possible Take Profit Strategies
🞘 Possible Stop-Loss Levels
🞘 Additional Tips
🔸Customize settings
🔶 CONCLUSION
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🔶 ORIGINALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) is a unique indicator that applies the Ornstein-Uhlenbeck stochastic process to identify mean-reverting behavior in asset prices. Unlike traditional moving average-based indicators, this model uses an Exponentially Weighted Moving Average (EWMA) to calculate the long-term mean, dynamically adjusting to recent price movements while still considering all historical data. It also incorporates volatility bands, providing a "cloud" that visually highlights overbought or oversold conditions. By calculating the speed of mean reversion (θ) through the autocorrelation of log returns, this indicator offers traders a more nuanced and mathematically robust tool for identifying mean-reversion opportunities. These innovations make it especially useful for markets that exhibit range-bound characteristics, offering timely buy and sell signals based on statistical deviations from the mean.
🔸Adaptive Mean Calculation Traditional MA indicators use fixed lengths, which can lead to lagging signals or over-sensitivity in volatile markets. The Mean Reversion Cloud uses an Exponentially Weighted Moving Average (EWMA), which adapts to price movements by dynamically adjusting its calculation, offering a more responsive mean.
🔸Volatility-Based Cloud Unlike simple moving averages that only plot a single line, the Mean Reversion Cloud surrounds the dynamic mean with volatility bands. These bands, based on standard deviations, provide traders with a visual cue of when prices are statistically likely to revert, highlighting potential reversal zones.
🔸Speed of Reversion (θ) The indicator goes beyond price averages by calculating the speed at which the price reverts to the mean (θ), using the autocorrelation of log returns. This gives traders an additional tool for estimating the likelihood and timing of mean reversion, making the signals more reliable in practice.
🔶 FUNCTIONALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator is designed to detect potential mean-reversion opportunities in asset prices by applying the Ornstein-Uhlenbeck stochastic process. It calculates a dynamic mean through the Exponentially Weighted Moving Average (EWMA) and plots volatility bands based on the standard deviation of the asset's price over a specified period. These bands create a "cloud" that represents expected price fluctuations, helping traders to identify overbought or oversold conditions. By calculating the speed of reversion (θ) from the autocorrelation of log returns, the indicator offers a more refined way of assessing how quickly prices may revert to the mean. Additionally, the inclusion of volatility provides a comprehensive view of market conditions, allowing for more accurate buy and sell signals.
Let's dive into the details:
🔸Dynamic Mean and Volatility Bands The dynamic mean (μ) is calculated using the EWMA, giving more weight to recent prices but considering all historical data. This process closely resembles the Ornstein-Uhlenbeck (OU) process, which models the tendency of a stochastic variable (such as price) to revert to its mean over time. Volatility bands are plotted around the mean using standard deviation, forming the "cloud" that signals overbought or oversold conditions. The cloud adapts dynamically to price fluctuations and market volatility, making it a versatile tool for mean-reversion strategies. 🞘 How it works Step one: Calculate the dynamic mean (μ) The Ornstein-Uhlenbeck process describes how a variable, such as an asset's price, tends to revert to a long-term mean while subject to random fluctuations. In this indicator, the EWMA is used to compute the dynamic mean (μ), mimicking the mean-reverting behavior of the OU process. Use the EWMA formula to compute a weighted mean that adjusts to recent price movements. Assign exponentially decreasing weights to older data while giving more emphasis to current prices. Step two: Plot volatility bands Calculate the standard deviation of the price over a user-defined period to determine market volatility. Position the upper and lower bands around the mean by adding and subtracting a multiple of the standard deviation. 🞘 How to calculate Exponential Weighted Moving Average (EWMA)
The EWMA dynamically adjusts to recent price movements:
mu_t = lambda * mu_{t-1} + (1 - lambda) * P_t
Where mu_t is the mean at time t, lambda is the decay factor, and P_t is the price at time t. The higher the decay factor, the more weight is given to recent data.
Autocorrelation (ρ) and Standard Deviation (σ)
To measure mean reversion speed and volatility: rho = correlation(log(close), log(close ), length) Where rho is the autocorrelation of log returns over a specified period.
To calculate volatility:
sigma = stdev(close, length)
Where sigma is the standard deviation of the asset's closing price over a specified length.
Upper and Lower Bands
The upper and lower bands are calculated as follows:
upper_band = mu + (threshold * sigma)
lower_band = mu - (threshold * sigma)
Where threshold is a multiplier for the standard deviation, usually set to 2. These bands represent the range within which the price is expected to fluctuate, based on current volatility and the mean.
🞘 Code extract // Calculate Returns
returns = math.log(close / close )
// Calculate Long-Term Mean (μ) using EWMA over the entire dataset
var float ewma_mu = na // Initialize ewma_mu as 'na'
ewma_mu := na(ewma_mu ) ? close : decay_factor * ewma_mu + (1 - decay_factor) * close
mu = ewma_mu
// Calculate Autocorrelation at Lag 1
rho1 = ta.correlation(returns, returns , corr_length)
// Ensure rho1 is within valid range to avoid errors
rho1 := na(rho1) or rho1 <= 0 ? 0.0001 : rho1
// Calculate Speed of Mean Reversion (θ)
theta = -math.log(rho1)
// Calculate Volatility (σ)
sigma = ta.stdev(close, corr_length)
// Calculate Upper and Lower Bands
upper_band = mu + threshold * sigma
lower_band = mu - threshold * sigma
🔸Visualization via Table and Plotshapes
The table shows key statistics such as the current value of the dynamic mean (μ), the number of times the price has crossed the upper or lower bands, and the consecutive number of bars that the price has remained in an overbought or oversold state.
Plotshapes (diamonds) are used to signal buy and sell opportunities. A green diamond below the price suggests a buy signal when the price crosses below the lower band, and a red diamond above the price indicates a sell signal when the price crosses above the upper band.
The table and plotshapes provide a comprehensive visualization, combining both statistical and actionable information to aid decision-making.
🞘 Code extract // Reset consecutive_bars when price crosses the mean
var consecutive_bars = 0
if (close < mu and close >= mu) or (close > mu and close <= mu)
consecutive_bars := 0
else if math.abs(deviation) > 0
consecutive_bars := math.min(consecutive_bars + 1, dev_length)
transparency = math.max(0, math.min(100, 100 - (consecutive_bars * 100 / dev_length)))
🔶 INSTRUCTIONS
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator can be set up by adding it to your TradingView chart and configuring parameters such as the decay factor, autocorrelation length, and volatility threshold to suit current market conditions. Look for price crossovers and deviations from the calculated mean for potential entry signals. Use the upper and lower bands as dynamic support/resistance levels for setting take profit and stop-loss orders. Combining this indicator with additional trend-following or momentum-based indicators can improve signal accuracy. Adjust settings for better mean-reversion detection and risk management.
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
Adding the Indicator to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Mean Reversion Cloud (Ornstein-Uhlenbeck)" in the indicators list.
Click on the indicator to add it to your chart.
Configuring the Indicator:
Open the indicator settings by clicking on the gear icon next to its name on the chart.
Decay Factor: Adjust the decay factor (λ) to control the responsiveness of the mean calculation. A higher value prioritizes recent data.
Autocorrelation Length: Set the autocorrelation length (θ) for calculating the speed of mean reversion. Longer lengths consider more historical data.
Threshold: Define the number of standard deviations for the upper and lower bands to determine how far price must deviate to trigger a signal.
Chart Setup:
Select the appropriate timeframe (e.g., 1-hour, daily) based on your trading strategy.
Consider using other indicators such as RSI or MACD to confirm buy and sell signals.
🞘 Understanding What to Look For on the Chart
Indicator Behavior:
Observe how the price interacts with the dynamic mean and volatility bands. The price staying within the bands suggests mean-reverting behavior, while crossing the bands signals potential entry points.
The indicator calculates overbought/oversold conditions based on deviation from the mean, highlighted by color-coded cloud areas on the chart.
Crossovers and Deviation:
Look for crossovers between the price and the mean (μ) or the bands. A bullish crossover occurs when the price crosses below the lower band, signaling a potential buying opportunity.
A bearish crossover occurs when the price crosses above the upper band, suggesting a potential sell signal.
Deviations from the mean indicate market extremes. A large deviation indicates that the price is far from the mean, suggesting a potential reversal.
Slope and Direction:
Pay attention to the slope of the mean (μ). A rising slope suggests bullish market conditions, while a declining slope signals a bearish market.
The steepness of the slope can indicate the strength of the mean-reversion trend.
🞘 Possible Entry Signals
Bullish Entry:
Crossover Entry: Enter a long position when the price crosses below the lower band with a positive deviation from the mean.
Confirmation Entry: Use additional indicators like RSI (above 50) or increasing volume to confirm the bullish signal.
Bearish Entry:
Crossover Entry: Enter a short position when the price crosses above the upper band with a negative deviation from the mean.
Confirmation Entry: Look for RSI (below 50) or decreasing volume to confirm the bearish signal.
Deviation Confirmation:
Enter trades when the deviation from the mean is significant, indicating that the price has strayed far from its expected value and is likely to revert.
🞘 Possible Take Profit Strategies
Static Take Profit Levels:
Set predefined take profit levels based on historical volatility, using the upper and lower bands as guides.
Place take profit orders near recent support/resistance levels, ensuring you're capitalizing on the mean-reversion behavior.
Trailing Stop Loss:
Use a trailing stop based on a percentage of the price deviation from the mean to lock in profits as the trend progresses.
Adjust the trailing stop dynamically along the calculated bands to protect profits as the price returns to the mean.
Deviation-Based Exits:
Exit when the deviation from the mean starts to decrease, signaling that the price is returning to its equilibrium.
🞘 Possible Stop-Loss Levels
Initial Stop Loss:
Place an initial stop loss outside the lower band (for long positions) or above the upper band (for short positions) to protect against excessive deviations.
Use a volatility-based buffer to avoid getting stopped out during normal price fluctuations.
Dynamic Stop Loss:
Move the stop loss closer to the mean as the price converges back towards equilibrium, reducing risk.
Adjust the stop loss dynamically along the bands to account for sudden market movements.
🞘 Additional Tips
Combine with Other Indicators:
Enhance your strategy by combining the Mean Reversion Cloud with momentum indicators like MACD, RSI, or Bollinger Bands to confirm market conditions.
Backtesting and Practice:
Backtest the indicator on historical data to understand how it performs in various market environments.
Practice using the indicator on a demo account before implementing it in live trading.
Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The indicator reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Customize settings 🞘 Decay Factor (λ): Defines the weight assigned to recent price data in the calculation of the mean. A value closer to 1 places more emphasis on recent prices, while lower values create a smoother, more lagging mean.
🞘 Autocorrelation Length (θ): Sets the period for calculating the speed of mean reversion and volatility. Longer lengths capture more historical data, providing smoother calculations, while shorter lengths make the indicator more responsive.
🞘 Threshold (σ): Specifies the number of standard deviations used to create the upper and lower bands. Higher thresholds widen the bands, producing fewer signals, while lower thresholds tighten the bands for more frequent signals.
🞘 Max Gradient Length (γ): Determines the maximum number of consecutive bars for calculating the deviation gradient. This setting impacts the transparency of the plotted bands based on the length of deviation from the mean.
🔶 CONCLUSION
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator offers a sophisticated approach to identifying mean-reversion opportunities by applying the Ornstein-Uhlenbeck stochastic process. This dynamic indicator calculates a responsive mean using an Exponentially Weighted Moving Average (EWMA) and plots volatility-based bands to highlight overbought and oversold conditions. By incorporating advanced statistical measures like autocorrelation and standard deviation, traders can better assess market extremes and potential reversals. The indicator’s ability to adapt to price behavior makes it a versatile tool for traders focused on both short-term price deviations and longer-term mean-reversion strategies. With its unique blend of statistical rigor and visual clarity, the Mean Reversion Cloud provides an invaluable tool for understanding and capitalizing on market inefficiencies.
Generalized Black-Scholes-Merton Option Pricing Formula [Loxx]Generalized Black-Scholes-Merton Option Pricing Formula is an adaptation of the Black-Scholes-Merton Option Pricing Model including Numerical Greeks aka "Option Sensitivities" and implied volatility calculations. The following information is an excerpt from Espen Gaarder Haug's book "Option Pricing Formulas".
Black-Scholes-Merton Option Pricing
The BSM formula and its binomial counterpart may easily be the most used "probability model/tool" in everyday use — even if we con- sider all other scientific disciplines. Literally tens of thousands of people, including traders, market makers, and salespeople, use option formulas several times a day. Hardly any other area has seen such dramatic growth as the options and derivatives businesses. In this chapter we look at the various versions of the basic option formula. In 1997 Myron Scholes and Robert Merton were awarded the Nobel Prize (The Bank of Sweden Prize in Economic Sciences in Memory of Alfred Nobel). Unfortunately, Fischer Black died of cancer in 1995 before he also would have received the prize.
It is worth mentioning that it was not the option formula itself that Myron Scholes and Robert Merton were awarded the Nobel Prize for, the formula was actually already invented, but rather for the way they derived it — the replicating portfolio argument, continuous- time dynamic delta hedging, as well as making the formula consistent with the capital asset pricing model (CAPM). The continuous dynamic replication argument is unfortunately far from robust. The popularity among traders for using option formulas heavily relies on hedging options with options and on the top of this dynamic delta hedging, see Higgins (1902), Nelson (1904), Mello and Neuhaus (1998), Derman and Taleb (2005), as well as Haug (2006) for more details on this topic. In any case, this book is about option formulas and not so much about how to derive them.
Provided here are the various versions of the Black-Scholes-Merton formula presented in the literature. All formulas in this section are originally derived based on the underlying asset S follows a geometric Brownian motion
dS = mu * S * dt + v * S * dz
where t is the expected instantaneous rate of return on the underlying asset, a is the instantaneous volatility of the rate of return, and dz is a Wiener process.
The formula derived by Black and Scholes (1973) can be used to value a European option on a stock that does not pay dividends before the option's expiration date. Letting c and p denote the price of European call and put options, respectively, the formula states that
c = S * N(d1) - X * e^(-r * T) * N(d2)
p = X * e^(-r * T) * N(d2) - S * N(d1)
where
d1 = (log(S / X) + (r + v^2 / 2) * T) / (v * T^0.5)
d2 = (log(S / X) + (r - v^2 / 2) * T) / (v * T^0.5) = d1 - v * T^0.5
Inputs
S = Stock price.
X = Strike price of option.
T = Time to expiration in years.
r = Risk-free rate
b = Cost of carry
v = Volatility of the underlying asset price
cnd (x) = The cumulative normal distribution function
nd(x) = The standard normal density function
convertingToCCRate(r, cmp ) = Rate compounder
gImpliedVolatilityNR(string CallPutFlag, float S, float x, float T, float r, float b, float cm, float epsilon) = Implied volatility via Newton Raphson
gBlackScholesImpVolBisection(string CallPutFlag, float S, float x, float T, float r, float b, float cm) = implied volatility via bisection
Implied Volatility: The Bisection Method
The Newton-Raphson method requires knowledge of the partial derivative of the option pricing formula with respect to volatility (vega) when searching for the implied volatility. For some options (exotic and American options in particular), vega is not known analytically. The bisection method is an even simpler method to estimate implied volatility when vega is unknown. The bisection method requires two initial volatility estimates (seed values):
1. A "low" estimate of the implied volatility, al, corresponding to an option value, CL
2. A "high" volatility estimate, aH, corresponding to an option value, CH
The option market price, Cm, lies between CL and cH. The bisection estimate is given as the linear interpolation between the two estimates:
v(i + 1) = v(L) + (c(m) - c(L)) * (v(H) - v(L)) / (c(H) - c(L))
Replace v(L) with v(i + 1) if c(v(i + 1)) < c(m), or else replace v(H) with v(i + 1) if c(v(i + 1)) > c(m) until |c(m) - c(v(i + 1))| <= E, at which point v(i + 1) is the implied volatility and E is the desired degree of accuracy.
Implied Volatility: Newton-Raphson Method
The Newton-Raphson method is an efficient way to find the implied volatility of an option contract. It is nothing more than a simple iteration technique for solving one-dimensional nonlinear equations (any introductory textbook in calculus will offer an intuitive explanation). The method seldom uses more than two to three iterations before it converges to the implied volatility. Let
v(i + 1) = v(i) + (c(v(i)) - c(m)) / (dc / dv(i))
until |c(m) - c(v(i + 1))| <= E at which point v(i + 1) is the implied volatility, E is the desired degree of accuracy, c(m) is the market price of the option, and dc/dv(i) is the vega of the option evaluaated at v(i) (the sensitivity of the option value for a small change in volatility).
Numerical Greeks or Greeks by Finite Difference
Analytical Greeks are the standard approach to estimating Delta, Gamma etc... That is what we typically use when we can derive from closed form solutions. Normally, these are well-defined and available in text books. Previously, we relied on closed form solutions for the call or put formulae differentiated with respect to the Black Scholes parameters. When Greeks formulae are difficult to develop or tease out, we can alternatively employ numerical Greeks - sometimes referred to finite difference approximations. A key advantage of numerical Greeks relates to their estimation independent of deriving mathematical Greeks. This could be important when we examine American options where there may not technically exist an exact closed form solution that is straightforward to work with. (via VinegarHill FinanceLabs)
Things to know
Only works on the daily timeframe and for the current source price.
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Black Scholes Option Pricing Model w/ Greeks [Loxx]The Black Scholes Merton model
If you are new to options I strongly advise you to profit from Robert Shiller's lecture on same . It combines practical market insights with a strong authoritative grasp of key models in option theory. He explains many of the areas covered below and in the following pages with a lot intuition and relatable anecdotage. We start here with Black Scholes Merton which is probably the most popular option pricing framework, due largely to its simplicity and ease in terms of implementation. The closed-form solution is efficient in terms of speed and always compares favorably relative to any numerical technique. The Black–Scholes–Merton model is a mathematical go-to model for estimating the value of European calls and puts. In the early 1970’s, Myron Scholes, and Fisher Black made an important breakthrough in the pricing of complex financial instruments. Robert Merton simultaneously was working on the same problem and applied the term Black-Scholes model to describe new generation of pricing. The Black Scholes (1973) contribution developed insights originally proposed by Bachelier 70 years before. In 1997, Myron Scholes and Robert Merton received the Nobel Prize for Economics. Tragically, Fisher Black died in 1995. The Black–Scholes formula presents a theoretical estimate (or model estimate) of the price of European-style options independently of the risk of the underlying security. Future payoffs from options can be discounted using the risk-neutral rate. Earlier academic work on options (e.g., Malkiel and Quandt 1968, 1969) had contemplated using either empirical, econometric analyses or elaborate theoretical models that possessed parameters whose values could not be calibrated directly. In contrast, Black, Scholes, and Merton’s parameters were at their core simple and did not involve references to utility or to the shifting risk appetite of investors. Below, we present a standard type formula, where: c = Call option value, p = Put option value, S=Current stock (or other underlying) price, K or X=Strike price, r=Risk-free interest rate, q = dividend yield, T=Time to maturity and N denotes taking the normal cumulative probability. b = (r - q) = cost of carry. (via VinegarHill-Financelab )
Things to know
This can only be used on the daily timeframe
You must select the option type and the greeks you wish to show
This indicator is a work in process, functions may be updated in the future. I will also be adding additional greeks as I code them or they become available in finance literature. This indictor contains 18 greeks. Many more will be added later.
Inputs
Spot price: select from 33 different types of price inputs
Calculation Steps: how many iterations to be used in the BS model. In practice, this number would be anywhere from 5000 to 15000, for our purposes here, this is limited to 300
Strike Price: the strike price of the option you're wishing to model
% Implied Volatility: here you can manually enter implied volatility
Historical Volatility Period: the input period for historical volatility ; historical volatility isn't used in the BS process, this is to serve as a sort of benchmark for the implied volatility ,
Historical Volatility Type: choose from various types of implied volatility , search my indicators for details on each of these
Option Base Currency: this is to calculate the risk-free rate, this is used if you wish to automatically calculate the risk-free rate instead of using the manual input. this uses the 10 year bold yield of the corresponding country
% Manual Risk-free Rate: here you can manually enter the risk-free rate
Use manual input for Risk-free Rate? : choose manual or automatic for risk-free rate
% Manual Yearly Dividend Yield: here you can manually enter the yearly dividend yield
Adjust for Dividends?: choose if you even want to use use dividends
Automatically Calculate Yearly Dividend Yield? choose if you want to use automatic vs manual dividend yield calculation
Time Now Type: choose how you want to calculate time right now, see the tool tip
Days in Year: choose how many days in the year, 365 for all days, 252 for trading days, etc
Hours Per Day: how many hours per day? 24, 8 working hours, or 6.5 trading hours
Expiry date settings: here you can specify the exact time the option expires
The Black Scholes Greeks
The Option Greek formulae express the change in the option price with respect to a parameter change taking as fixed all the other inputs. ( Haug explores multiple parameter changes at once .) One significant use of Greek measures is to calibrate risk exposure. A market-making financial institution with a portfolio of options, for instance, would want a snap shot of its exposure to asset price, interest rates, dividend fluctuations. It would try to establish impacts of volatility and time decay. In the formulae below, the Greeks merely evaluate change to only one input at a time. In reality, we might expect a conflagration of changes in interest rates and stock prices etc. (via VigengarHill-Financelab )
First-order Greeks
Delta: Delta measures the rate of change of the theoretical option value with respect to changes in the underlying asset's price. Delta is the first derivative of the value
Vega: Vegameasures sensitivity to volatility. Vega is the derivative of the option value with respect to the volatility of the underlying asset.
Theta: Theta measures the sensitivity of the value of the derivative to the passage of time (see Option time value): the "time decay."
Rho: Rho measures sensitivity to the interest rate: it is the derivative of the option value with respect to the risk free interest rate (for the relevant outstanding term).
Lambda: Lambda, Omega, or elasticity is the percentage change in option value per percentage change in the underlying price, a measure of leverage, sometimes called gearing.
Epsilon: Epsilon, also known as psi, is the percentage change in option value per percentage change in the underlying dividend yield, a measure of the dividend risk. The dividend yield impact is in practice determined using a 10% increase in those yields. Obviously, this sensitivity can only be applied to derivative instruments of equity products.
Second-order Greeks
Gamma: Measures the rate of change in the delta with respect to changes in the underlying price. Gamma is the second derivative of the value function with respect to the underlying price.
Vanna: Vanna, also referred to as DvegaDspot and DdeltaDvol, is a second order derivative of the option value, once to the underlying spot price and once to volatility. It is mathematically equivalent to DdeltaDvol, the sensitivity of the option delta with respect to change in volatility; or alternatively, the partial of vega with respect to the underlying instrument's price. Vanna can be a useful sensitivity to monitor when maintaining a delta- or vega-hedged portfolio as vanna will help the trader to anticipate changes to the effectiveness of a delta-hedge as volatility changes or the effectiveness of a vega-hedge against change in the underlying spot price.
Charm: Charm or delta decay measures the instantaneous rate of change of delta over the passage of time.
Vomma: Vomma, volga, vega convexity, or DvegaDvol measures second order sensitivity to volatility. Vomma is the second derivative of the option value with respect to the volatility, or, stated another way, vomma measures the rate of change to vega as volatility changes.
Veta: Veta or DvegaDtime measures the rate of change in the vega with respect to the passage of time. Veta is the second derivative of the value function; once to volatility and once to time.
Vera: Vera (sometimes rhova) measures the rate of change in rho with respect to volatility. Vera is the second derivative of the value function; once to volatility and once to interest rate.
Third-order Greeks
Speed: Speed measures the rate of change in Gamma with respect to changes in the underlying price.
Zomma: Zomma measures the rate of change of gamma with respect to changes in volatility.
Color: Color, gamma decay or DgammaDtime measures the rate of change of gamma over the passage of time.
Ultima: Ultima measures the sensitivity of the option vomma with respect to change in volatility.
Dual Delta: Dual Delta determines how the option price changes in relation to the change in the option strike price; it is the first derivative of the option price relative to the option strike price
Dual Gamma: Dual Gamma determines by how much the coefficient will changedual delta when the option strike price changes; it is the second derivative of the option price relative to the option strike price.
Related Indicators
Cox-Ross-Rubinstein Binomial Tree Options Pricing Model
Implied Volatility Estimator using Black Scholes
Boyle Trinomial Options Pricing Model
