Williams Vix Fix OHLC candles plot indicator (Tartigradia)OHLC candles plot of the Williams VixFix indicator, which allows to draw trend lines.
Williams VixFix is a realized volatility indicator developed by Larry Williams, and can help in finding market bottoms.
Indeed, as Williams describe in his paper, markets tend to find the lowest prices during times of highest volatility, which usually accompany times of highest fear. The VixFix is calculated as how much the current low price statistically deviates from the maximum within a given look-back period.
The Williams VixFix indicator is usually presented as a curve or histogram. The novelty of this indicator is to present the data as a OHLC candles plot: whereas the original Williams VixFix calculation only involves the close value, we here use the open, high and low values as well. This led to some mathematical challenges because some of these calculations led to absurd values, so workarounds had to be found, but in the end I think the result was worth it, it reproduces the VIX chart quite well.
A great additional value of the OHLC chart is that it shows not just the close value, but all the values during the session: open, high and low in addition to close. This allows to draw trend lines and can provide additional information on momentum and sentiment. In addition, other indicators can be used on it, as if it was a price chart, such as RSI indicators (see RSI+ (alt) indicator for example).
For more information on the Vix Fix, which is a strategy published under public domain:
The VIX Fix, Larry Williams, Active Trader magazine, December 2007, web.archive.org
Fixing the VIX: An Indicator to Beat Fear, Amber Hestla-Barnhart, Journal of Technical Analysis, March 13, 2015, ssrn.com
Replicating the CBOE VIX using a synthetic volatility index trading algorithm, Dayne Cary and Gary van Vuuren, Cogent Economics & Finance, Volume 7, 2019, Issue 1, doi.org
This indicator includes only the Williams VixFix as an OHLC candles or bars plot, and price / vixfix candles plot, as well as the typical vixfix histogram. Indeed, it is much more practical for unbounded range indicators to be plotted in their own separate panel, hence why this indicator is released separately, so that it can work and be scaled adequately out of the box.
Note that the there are however no bottom buy signals. For a more complete indicator, which also includes the OHLC candles plots present here, but also bottom signals and Inverse VixFix (top signals), see:
Set Index symbol to SPX, and index_current = false, and timeframe Weekly, to reproduce the original VIX as close as possible by the VIXFIX (use the Add Symbol option, because you want to plot CBOE:VIX on the same timeframe as the current chart, which may include extended session / weekends). With the Weekly timeframe, off days / extended session days should not change much, but with lower timeframes this is important, because nights and weekends can change how the graph appears and seemingly make them different because of timing misalignment when in reality they are not when properly aligned.
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Real Woodies CCIAs always, this is not financial advice and use at your own risk. Trading is risky and can cost you significant sums of money if you are not careful. Make sure you always have a proper entry and exit plan that includes defining your risk before you enter a trade.
Ken Wood is a semi-famous trader that grew in popularity in the 1990s and early 2000s due to the establishment of one of the earliest trading forums online. This forum grew into "Woodie's CCI Club" due to Wood's love of his modified Commodity Channel Index (CCI) that he used extensively. From what I can tell, the website is still active and still follows the same core principles it did in the early days, the CCI is used for entries, range bars are used to help trader's cut down on the noise, and the optional addition of Woodie's Pivot Points can be used as further confirmation of support and resistance. This is my take on his famous "Woodie's CCI" that has become standard on many charting packages through the years, including a TradingView sponsored version as one of the many stock indicators provided by TradingView. Woodie has updated his CCI through the years to include several very cool additions outside of the standard CCI. I will have to say, I am a bit biased, but I think this is hands down one of the best indicators I have ever used, and I am far too young to have been part of the original CCI Club. Being a daytrader primarily, this fits right in my timeframe wheel house. Woodie designed this indicator to work on a day-trading time scale and he frequently uses this to trade futures and commodity contracts on the 30 minute, often even down to the one minute timeframe. This makes it unique in that it is probably one of the only daytrading-designed indicators out there that I am aware of that was not a popular indicator, like the MACD or RSI, that was just adopted by daytraders.
The CCI was originally created by Donald Lambert in 1980. Over time, it has become an extremely popular house-hold indicator, like the Stochastics, RSI, or MACD. However, like the RSI and Stochastics, there are extensive debates on how the CCI is actually meant to be used. Some trade it like a reversal indicator, where values greater than 100 or less than -100 are considered overbought or oversold, respectively. Others trade it like a typical zero-line cross indicator, where once the value goes above or below the zero-line, a trade should be considered in that direction. Lastly, some treat it as strictly a momentum indicator, where values greater than 100 or less than -100 are seen as strong momentum moves and when these values are reached, a new strong trend is establishing in the direction of the move. The CCI itself is nothing fancy, it just visualizes the distance of the closing price away from a user-defined SMA value and plots it as a line. However, Woodie's CCI takes this simple concept and adds to it with an indicator with 5 pieces to it designed to help the trader enter into the highest probability setups. Bear with me, it initially looks super complicated, but I promise it is pretty straight-forward and a fun indicator to use.
1) The CCI Histogram. This is your standard CCI value that you would find on the normal CCI. Woodie's CCI uses a value of 14 for most trades and a value of 20 when the timeframe is equal to or greater than 30minutes. I personally use this as a 20-period CCI on all time frames, simply for the fact that the 20 SMA is a very popular moving average and I want to know what the crowd is doing. This is your coloured histogram with 4 colours. A gray colouring is for any bars above or below the zero line for 1-4 bars. A yellow bar is a "trend bar", where the long period CCI has been above/below the zero line for 5 consecutive bars, indicating that a trend in the current direction has been established. Blue bars above and red bars below are simply 6+n number of bars above or below the zero line confirming trend. These are used for the Zero-Line Reject Trade (explained below). The CCI Histogram has a matching long-period CCI line that is painted the same colour as the histogram, it is the same thing but is used just to outline the Histogram a bit better.
2) The CCI Turbo line. This is a sped-up 6 period CCI. This is to be used for the Zero-Line Reject trades, trendline breaks, and to identify shorter term overbought/oversold conditions against the main trend. This is coloured as the white line.
3) The Least Squares Moving Average Baseline (LSMA) Zero Line. You will notice that the Zero Line of the indicator is either green or red. This is based on when price is above or below the 25-period LSMA on the chart. The LSMA is a 25 period linear regression moving average and is one of the best moving averages out there because it is more immune to noise than a typical MA. Statistically, an LSMA is designed to find the line of best fit across the lookback periods and identify whether price is advancing, declining, or flat, without the whipsaw that other MAs can be privy to. The zero line of the indicator will turn green when the close candle is over the LSMA or red when it is below the LSMA. This is meant to be a confirmation tool only and the CCI Histogram and Turbo Histogram can cross this zero line without any corresponding change in the colour of the zero line on that immediate candle.
4) The +100 and -100 lines are used in two ways. First, they can be used by the CCI Histogram and CCI Turbo as a sort of minor price resistance and if the CCI values cannot get through these, it is considered weakness in that trade direction until they do so. You will notice that both of these lines are multi-coloured. They have been plotted with the ChopZone Indicator, another TradingView built-in indicator. The ChopZone is a trend identification tool that uses the slope and the direction of a 34-period EMA to identify when price is trending or range bound. While there are ~10 different colours, the main two a trader needs to pay attention to are the turquoise/cyan blue, which indicates price is in an uptrend, and dark red, which indicates price is in a downtrend based on the slope and direction of the 34 EMA. All other colours indicate "chop". These colours are used solely for the Zero-Line Reject and pattern trades discussed below. They are plotted both above and below so you can easily see the colouring no matter what side of the zero line the CCI is on.
5) The +200 and -200 lines are also used in two ways. First, they are considered overbought/oversold levels where if price exceeds these lines then it has moved an extreme amount away from the average and is likely to experience a pullback shortly. This is more useful for the CCI Histogram than the Turbo CCI, in all honesty. You will also notice that these are coloured either red, green, or yellow. This is the Sidewinder indicator portion. The documentation on this is extremely sparse, only pointing to a "relationship between the LSMA and the 34 EMA" (see here: tlc.thinkorswim.com). Since I am not a member of Woodie's CCI Club and never intend to be I took some liberty here and decided that the most likely relationship here was the slope of both moving averages. Therefore, the Sidewinder will be green when both the LSMA and the 34 EMA are rising, red when both are falling, and yellow when they are not in agreement with one another (i.e. one rising/flat while the other is flat/falling). I am a big fan of Dr. Alexander Elder as those who follow me know, so consider this like Woodie's version of the Elder Impulse System. I will fully admit that this version of the Sidewinder is a guess and may not represent the real Sidewinder indicator, but it is next to impossible to find any information on this, so I apologize, but my version does do something useful anyways. This is also to be used only with the Zero-Line Reject trades. They are plotted both above and below so you can easily see the colouring no matter what side of the zero line the CCI is on.
How to Trade It According to Woodie's CCI Club:
Now that I have all of my components and history out of the way, this is what you all care about. I will only provide a brief overview of the trades in this system, but there are quite a few more detailed descriptions listed in the Woodie's CCI Club pamphlet. I have had little success trading the "patterns" but they do exist and do work on occasion. I just prefer to trade with the flow of the markets rather than getting overly scalpy. If you are interested in these patterns, see the pamphlet here (www.trading-attitude.com), hop into the forums and see for yourself, or check out a couple of the YouTube videos.
1) Zero line cross. As simple as any other momentum oscillator out there. When the long period CCI crosses above or below the zero line open a trade in that direction. Extra confirmation can be had when the CCI Turbo has already broken the +100/-100 line "resistance or support". Trend traders may wish to wait until the yellow "trend confirmation bar" has been printed.
2) Zero Line Reject. This is when the CCI Turbo heads back down to the zero line and then bounces back in the same direction of the prevailing trend. These are fantastic continuation trades if you missed the initial entry either on the zero line cross or on the trend bar establishment. ZLR trades are only viable when you have the ChopZone indicator showing a trend (turquoise/cyan for uptrend, dark red for downtrend), the LSMA line is green for an uptrend or red for a downtrend, and the SideWinder is either green confirming the uptrend or red confirming the downtrend.
3) Hook From Extreme. This is the exact same as the Zero Line Reject trade, however, the CCI Turbo now goes to the +100/-100 line (whichever is opposite the currently established trend) and then hooks back into the established trend direction. Ideally the HFE trade needs to have the Long CCI Histogram above/below the corresponding 100 level and the CCI Turbo both breaks the 100 level on the trend side and when it does break it has increased ~20 points from the previous value (i.e. CCI Histogram = +150 with LSMA, CZ, and SW all matching up and trend bars printed on CCI Histogram, CCI Turbo went to -120 and bounced to +80 on last 2 bars, current bar closes with CCI Turbo closing at +110).
4) Trend Line Break. Either the CCI Turbo or CCI Histogram, whichever you prefer (I find the Turbo a bit more accurate since its a faster value) creates a series of higher highs/lows you can draw a trend line linking them. When the line breaks the trendline that is your signal to take a counter trade position. For example, if the CCI Turbo is making consistently higher lows and then breaks the trendline through the zero line, you can then go short. This is a good continuation trade.
5) The Tony Trade. Consider this like a combination zero line reject, trend line break, and weak zero line cross all in one. The idea is that the SW, CZ, and LSMA values are all established in one direction. The CCI Histogram should be in an established trend and then cross the zero line but never break the 100 level on the new side as long as it has not printed more than 9 bars on the new side. If the CCI Histogram prints 9 or less bars on the new side and then breaks the trendline and crosses back to the original trend side, that is your signal to take a reversal trade. This is best used in the Elder Triple Screen method (discussed in final section) as a failed dip or rip.
6) The GB100 Trade. This is a similar trade as the Tony Trade, however, the CCI Histogram can break the 100 level on the new side but has to have made less than 6 bars on the new side. A trendline break is not necessary here either, it is more of a "pop and drop" or "momentum failure" trade trying in the new direction.
7) The Famir Trade. This is a failed CCI Long Histogram ZLR trade and is quite complicated. I have never traded this but it is in the pamphlet. Essentially you have a typical ZLR reject (i.e. all components saying it is likely a long/short continuation trade), but the ZLR only stays around the 50 level, goes back to the trend side, fails there as well immediately after 1 bar and then rebreaks to the new side. This is important to be considered with the LSMA value matching the side of the trade, so if the Famir says to go long, you need the LSMA indicator to also say to go long.
8) The Vegas Trade. This is essentially a trend-reversal trade that takes into account the LSMA and a cup and handle formation on the CCI Long Histogram after it has reached an extreme value (+200/-200). You will see the CCI Histogram hit the extreme value, head towards the zero line, and then sort of round out back in the direction of the extreme price. The low point where it reversed back in the direction of the extreme can be considered support or resistance on the CCI and once the CCI Long Histogram breaks this level again, with LSMA confirmation, you can take a counter trend trade with a stop under/over the highest/lowest point of the last 2 bars as you want to be out quickly if you are wrong without much damage but can get a huge win if you are right and add later to the position once a new trade has formed.
9) The Ghost Trade. This is nothing more than a(n) (inverse) head and shoulders pattern created on the CCI. Draw a trend line connecting the head and shoulders and trade a reversal trade once the CCI Long Histogram breaks the trend line. Same deal as the Vegas Trade, stop over/under the most recent 2 bar high/low and add later if it is a winner but cut quickly if it is a loser.
Like I said, this is a complicated system and could quite literally take years to master if you wanted to go into the patterns and master them. I prefer to trade it in a much simpler format, using the Elder Triple Screen System. First, since I am a day trader, I look to use the 20 period Woodie's on the hourly and look at the CZ, SW, and LSMA values to make sure they all match the direction of the CCI Long Histogram (a trend establishment is not necessary here). It shows you the hourly trend as your "tide". I then drill down to the 15 minute time frame and use the Turbo CCI break in the opposite direction of the trend as my "wave" and to indicate when there is a dip or rip against the main trend. Lastly, I drill down to a 3 minute time frame and enter when the CCI Long Histogram turns back to match the main trend ("ripple") as long as the CCI Turbo has broken the 100 level in the matched direction.
Enjoy, and please read the pamphlet if you have any questions about the patterns as they are not how I use these and will not be able to answer those questions.
Relative Strength Super Smoother by lastguruA better version of Apirine's RS EMA by using a superior MA: Ehlers Super Smoother.
In January 2022 edition of TASC Vitaly Apirine introduced his Relative Strength Exponential Moving Average. A concept not entirely new, as Tushar Chande used a similar calculation for his VIDYA moving average. Both are based on the idea to change EMA length depending on the absolute RSI value, so the moving average would speed up then RSI is going up or down from the center value (when there is a significant directional price movement), and slow down when RSI returns to the center value (when there is a neutral or sideways movement). That way EMA responsiveness would increase where it matters most, but decrease where there is a high probability of whipsaw.
There are only two main differences between VIDYA and RS EMA:
RSI internal smoothing - VIDYA uses SMA, as Chande's CMO is an RSI with SMA; RS EMA uses EMA
Change direction - VIDYA sets the fastest length; RS EMA sets the slowest length
Both algorithms use EMA as the base of their calculation. As John F. Ehlers has shown in his article "Predictive and Successful Indicators" (January 2014 issue of TASC), EMA is not a very efficient filter, as it introduces a significant lag if sufficient smoothing is required. He describes a new smoothing filter called SuperSmoother, "that sharply attenuates aliasing noise while minimizing filtering lag." In other words, it provides better smoothing with lower lag than EMA.
In this script, I try to get the best of all these approaches and present to you Relative Strength Super Smoother. It uses RS EMA algorithm to calculate the SuperSmoother length. Unlike the original RS EMA algorithm, that has an abstract "multiplier" setting to scale the period variance (without this parameter, RSI would only allow it to speed up twice; Vitaly Apirine sets the multiplier to 10 by default), my implementation has explicit lower bound setting, so you can specify the exact range of calculated length.
Settings:
Lower Bound - fastest SuperSmoother length (when RSI is +100 or -100)
Upper Bound - slowest SuperSmoother length (when RSI is 0)
RSI Length - underlying RSI length. Unlike the original RSI that uses RMA as an internal smoothing algorithm, Vitaly Apirine uses EMA, which is approximately twice as fast (that is needed because he uses a generally long RSI length and RMA would be too slow for this). It is the same as the Upper Bound by default (0), as in the original implementation
The original RS EMA is also shown on the chart for comparison. The default multiplier of 10 for RS EMA means that the fastest EMA period is around 4. I use the fastest period of 8 by default. It does not introduce too much of a lag in comparison, but the curve is much smoother.
This script is just an interface for my public libraries. Check them out for more information.
NormalizedOscillatorsLibrary "NormalizedOscillators"
Collection of some common Oscillators. All are zero-mean and normalized to fit in the -1..1 range. Some are modified, so that the internal smoothing function could be configurable (for example, to enable Hann Windowing, that John F. Ehlers uses frequently). Some are modified for other reasons (see comments in the code), but never without a reason. This collection is neither encyclopaedic, nor reference, however I try to find the most correct implementation. Suggestions are welcome.
rsi2(upper, lower) RSI - second step
Parameters:
upper : Upwards momentum
lower : Downwards momentum
Returns: Oscillator value
Modified by Ehlers from Wilder's implementation to have a zero mean (oscillator from -1 to +1)
Originally: 100.0 - (100.0 / (1.0 + upper / lower))
Ignoring the 100 scale factor, we get: upper / (upper + lower)
Multiplying by two and subtracting 1, we get: (2 * upper) / (upper + lower) - 1 = (upper - lower) / (upper + lower)
rms(src, len) Root mean square (RMS)
Parameters:
src : Source series
len : Lookback period
Based on by John F. Ehlers implementation
ift(src) Inverse Fisher Transform
Parameters:
src : Source series
Returns: Normalized series
Based on by John F. Ehlers implementation
The input values have been multiplied by 2 (was "2*src", now "4*src") to force expansion - not compression
The inputs may be further modified, if needed
stoch(src, len) Stochastic
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
ssstoch(src, len) Super Smooth Stochastic (part of MESA Stochastic) by John F. Ehlers
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Introduced in the January 2014 issue of Stocks and Commodities
This is not an implementation of MESA Stochastic, as it is based on Highpass filter not present in the function (but you can construct it)
This implementation is scaled by 0.95, so that Super Smoother does not exceed 1/-1
I do not know, if this the right way to fix this issue, but it works for now
netKendall(src, len) Noise Elimination Technology by John F. Ehlers
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Introduced in the December 2020 issue of Stocks and Commodities
Uses simplified Kendall correlation algorithm
Implementation by @QuantTherapy:
rsi(src, len, smooth) RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
vrsi(src, len, smooth) Volume-scaled RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
This is my own version of RSI. It scales price movements by the proportion of RMS of volume
mrsi(src, len, smooth) Momentum RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018)
rrsi(src, len, smooth) Rocket RSI
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Inspired by RocketRSI by John F. Ehlers (Stocks and Commodities, May 2018)
Does not include Fisher Transform of the original implementation, as the output must be normalized
Does not include momentum smoothing length configuration, so always assumes half the lookback length
mfi(src, len, smooth) Money Flow Index
Parameters:
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
lrsi(src, in_gamma, len) Laguerre RSI by John F. Ehlers
Parameters:
src : Source series
in_gamma : Damping factor (default is -1 to generate from len)
len : Lookback period (alternatively, if gamma is not set)
Returns: Oscillator series
The original implementation is with gamma. As it is impossible to collect gamma in my system, where the only user input is length,
an alternative calculation is included, where gamma is set by dividing len by 30. Maybe different calculation would be better?
fe(len) Choppiness Index or Fractal Energy
Parameters:
len : Lookback period
Returns: Oscillator series
The Choppiness Index (CHOP) was created by E. W. Dreiss
This indicator is sometimes called Fractal Energy
er(src, len) Efficiency ratio
Parameters:
src : Source series
len : Lookback period
Returns: Oscillator series
Based on Kaufman Adaptive Moving Average calculation
This is the correct Efficiency ratio calculation, and most other implementations are wrong:
the number of bar differences is 1 less than the length, otherwise we are adding the change outside of the measured range!
For reference, see Stocks and Commodities June 1995
dmi(len, smooth) Directional Movement Index
Parameters:
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Based on the original Tradingview algorithm
Modified with inspiration from John F. Ehlers DMH (but not implementing the DMH algorithm!)
Only ADX is returned
Rescaled to fit -1 to +1
Unlike most oscillators, there is no src parameter as DMI works directly with high and low values
fdmi(len, smooth) Fast Directional Movement Index
Parameters:
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Same as DMI, but without secondary smoothing. Can be smoothed later. Instead, +DM and -DM smoothing can be configured
doOsc(type, src, len, smooth) Execute a particular Oscillator from the list
Parameters:
type : Oscillator type to use
src : Source series
len : Lookback period
smooth : Internal smoothing algorithm
Returns: Oscillator series
Chande Momentum Oscillator (CMO) is RSI without smoothing. No idea, why some authors use different calculations
LRSI with Fractal Energy is a combo oscillator that uses Fractal Energy to tune LRSI gamma, as seen here: www.prorealcode.com
doPostfilter(type, src, len) Execute a particular Oscillator Postfilter from the list
Parameters:
type : Oscillator type to use
src : Source series
len : Lookback period
Returns: Oscillator series
Bitcoin Power Law Bands (BTC Power Law) Indicator█ OVERVIEW
The 'Bitcoin Power Law Bands' indicator is a set of three US dollar price trendlines and two price bands for bitcoin , indicating overall long-term trend, support and resistance levels as well as oversold and overbought conditions. The magnitude and growth of the middle (Center) line is determined by double logarithmic (log-log) regression on the entire USD price history of bitcoin . The upper (Resistance) and lower (Support) lines follow the same trajectory but multiplied by respective (fixed) factors. These two lines indicate levels where the price of bitcoin is expected to meet strong long-term resistance or receive strong long-term support. The two bands between the three lines are price levels where bitcoin may be considered overbought or oversold.
All parameters and visuals may be customized by the user as needed.
█ CONCEPTS
Long-term models
Long-term price models have many challenges, the most significant of which is getting the growth curve right overall. No one can predict how a certain market, asset class, or financial instrument will unfold over several decades. In the case of bitcoin , price history is very limited and extremely volatile, and this further complicates the situation. Fortunately for us, a few smart people already had some bright ideas that seem to have stood the test of time.
Power law
The so-called power law is the only long-term bitcoin price model that has a chance of survival for the years ahead. The idea behind the power law is very simple: over time, the rapid (exponential) initial growth cannot possibly be sustained (see The seduction of the exponential curve for a fun take on this). Year-on-year returns, therefore, must decrease over time, which leads us to the concept of diminishing returns and the power law. In this context, the power law translates to linear growth on a chart with both its axes scaled logarithmically. This is called the log-log chart (as opposed to the semilog chart you see above, on which only one of the axes - price - is logarithmic).
Log-log regression
When both price and time are scaled logarithmically, the power law leads to a linear relationship between them. This in turn allows us to apply linear regression techniques, which will find the best-fitting straight line to the data points in question. The result of performing this log-log regression (i.e. linear regression on a log-log scaled dataset) is two parameters: slope (m) and intercept (b). These parameters fully describe the relationship between price and time as follows: log(P) = m * log(T) + b, where P is price and T is time. Price is measured in US dollars , and Time is counted as the number of days elapsed since bitcoin 's genesis block.
DPC model
The final piece of our puzzle is the Dynamic Power Cycle (DPC) price model of bitcoin . DPC is a long-term cyclic model that uses the power law as its foundation, to which a periodic component stemming from the block subsidy halving cycle is applied dynamically. The regression parameters of this model are re-calculated daily to ensure longevity. For the 'Bitcoin Power Law Bands' indicator, the slope and intercept parameters were calculated on publication date (March 6, 2022). The slope of the Resistance Line is the same as that of the Center Line; its intercept was determined by fitting the line onto the Nov 2021 cycle peak. The slope of the Support Line is the same as that of the Center Line; its intercept was determined by fitting the line onto the Dec 2018 trough of the previous cycle. Please see the Limitations section below on the implications of a static model.
█ FEATURES
Inputs
• Parameters
• Center Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the grey line in the middle
• Resistance Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the red line at the top
• Support Intercept (b) and Slope (m): These log-log regression parameters control the behavior of the green line at the bottom
• Controls
• Plot Line Fill: N/A
• Plot Opportunity Label: Controls the display of current price level relative to the Center, Resistance and Support Lines
Style
• Visuals
• Center: Control, color, opacity, thickness, price line control and line style of the Center Line
• Resistance: Control, color, opacity, thickness, price line control and line style of the Resistance Line
• Support: Control, color, opacity, thickness, price line control and line style of the Support Line
• Plots Background: Control, color and opacity of the Upper Band
• Plots Background: Control, color and opacity of the Lower Band
• Labels: N/A
• Output
• Labels on price scale: Controls the display of current Center, Resistance and Support Line values on the price scale
• Values in status line: Controls the display of current Center, Resistance and Support Line values in the indicator's status line
█ HOW TO USE
The indicator includes three price lines:
• The grey Center Line in the middle shows the overall long-term bitcoin USD price trend
• The red Resistance Line at the top is an indication of where the bitcoin USD price is expected to meet strong long-term resistance
• The green Support Line at the bottom is an indication of where the bitcoin USD price is expected to receive strong long-term support
These lines envelope two price bands:
• The red Upper Band between the Center and Resistance Lines is an area where bitcoin is considered overbought (i.e. too expensive)
• The green Lower Band between the Support and Center Lines is an area where bitcoin is considered oversold (i.e. too cheap)
The power law model assumes that the price of bitcoin will fluctuate around the Center Line, by meeting resistance at the Resistance Line and finding support at the Support Line. When the current price is well below the Center Line (i.e. well into the green Lower Band), bitcoin is considered too cheap (oversold). When the current price is well above the Center Line (i.e. well into the red Upper Band), bitcoin is considered too expensive (overbought). This idea alone is not sufficient for profitable trading, but, when combined with other factors, it could guide the user's decision-making process in the right direction.
█ LIMITATIONS
The indicator is based on a static model, and for this reason it will gradually lose its usefulness. The Center Line is the most durable of the three lines since the long-term growth trend of bitcoin seems to deviate little from the power law. However, how far price extends above and below this line will change with every halving cycle (as can be seen for past cycles). Periodic updates will be needed to keep the indicator relevant. The user is invited to adjust the slope and intercept parameters manually between two updates of the indicator.
█ RAMBLINGS
The 'Bitcoin Power Law Bands' indicator is a useful tool for users wishing to place bitcoin in a macro context. As described above, the price level relative to the three lines is a rough indication of whether bitcoin is over- or undervalued. Users wishing to gain more insight into bitcoin price trends may follow the author's periodic updates of the DPC model (contact information below).
█ NOTES
The author regularly posts on Twitter using the @DeFi_initiate handle.
█ THANKS
Many thanks to the following individuals, who - one way or another - made the 'Bitcoin Power Law Bands' indicator possible:
• TradingView user 'capriole_charles', whose open-source 'Bitcoin Power Law Corridor' script was the basis for this indicator
• Harold Christopher Burger, whose Bitcoin’s natural long-term power-law corridor of growth article (2019) was the basis for the 'Bitcoin Power Law Corridor' script
• Bitcoin Forum user "Trololo", who posted the original power law model at Logarithmic (non-linear) regression - Bitcoin estimated value (2014)
Bitcoin Risk Metric IIThesis: Bitcoin's price movements can be (dubiously) characterized by functional relationships between moving averages and standard deviations. These movements can be normalized into a risk metric through normalization functions of time. This risk metric may be able to quantify a long term "buy low, sell high" strategy.
This risk metric is the average of three normalized metrics:
1. (btc - 4 yma)/ (std dev)
2. ln(btc / 20 wma)
3. (50 dma)/(50 wma)
* btc = btc price
* yma = yearly moving average of btc, wma = weekly moving average of btc, dma = daily moving average of btc
* std dev = std dev of btc
Important note:
Historical data for this metric is only shown back until 2014, because of the nature of the 1st mentioned metric. The other two metrics produce a value back until 2011. A previous, less robust, version of metric 2 is posted on my TradingView as well.
How Old Is this Bull Run Getting? Check MA Test Bars SinceThere are many price-based techniques for anticipating the end of a move. However, the simple passage of time can also help because bull markets don’t last forever. While old age doesn’t necessarily cause investors to sell, a reversal becomes more likely the longer a trend lasts.
So, how long have prices been going up? There are various ways to measure that. Our earlier script, MA streak , offered one solution by counting the number of bars that a given moving average has been rising or falling.
Today’s script takes a different approach by counting the number of candles since price touched or crossed a given moving average. It tracks the 50-day simple moving average (SMA) by default. It can be adjusted to other types like exponential and weighted with the AvgType input.
In the chart above, Bars Since MA Test was adjusted to use the 200-day SMA. Viewing the S&P 500 with this study helps put the current market into context.
We can see that prices last touched the 200-day SMA 386 sessions ago (June 29, 2020). That’s relatively long based on history, but not unprecedented. For example, the indicator was at 407 in February 2018 as the market pulled back. It also hit 475 in October 2014 (following the breakout above 2007 highs).
Additionally, the S&P 500 is nearing the record of the 1990s bull market (393 candles on July 12, 1996).
Before that, you have to look all the way back to the 1950s, when it twice peaked at 627.
The conclusion? The current run without a test of the 200-day SMA is above average, but not yet record-setting. It may be interesting to watch as earnings season approaches and the Federal Reserve looks to tighten monetary policy.
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Investing involves risks. Past performance, whether actual or indicated by historical tests of strategies, is no guarantee of future performance or success. There is a possibility that you may sustain a loss equal to or greater than your entire investment regardless of which asset class you trade (equities, options, futures, or digital assets); therefore, you should not invest or risk money that you cannot afford to lose. Before trading any asset class, first read the relevant risk disclosure statements on the Important Documents page, found here: www.tradestation.com .
Ripple (XRP) Model PriceAn article titled Bitcoin Stock-to-Flow Model was published in March 2019 by "PlanB" with mathematical model used to calculate Bitcoin model price during the time. We know that Ripple has a strong correlation with Bitcoin. But does this correlation have a definite rule?
In this study, we examine the relationship between bitcoin's stock-to-flow ratio and the ripple(XRP) price.
The Halving and the stock-to-flow ratio
Stock-to-flow is defined as a relationship between production and current stock that is out there.
SF = stock / flow
The term "halving" as it relates to Bitcoin has to do with how many Bitcoin tokens are found in a newly created block. Back in 2009, when Bitcoin launched, each block contained 50 BTC, but this amount was set to be reduced by 50% every 210,000 blocks (about 4 years). Today, there have been three halving events, and a block now only contains 6.25 BTC. When the next halving occurs, a block will only contain 3.125 BTC. Halving events will continue until the reward for minors reaches 0 BTC.
With each halving, the stock-to-flow ratio increased and Bitcoin experienced a huge bull market that absolutely crushed its previous all-time high. But what exactly does this affect the price of Ripple?
Price Model
I have used Bitcoin's stock-to-flow ratio and Ripple's price data from April 1, 2014 to November 3, 2021 (Daily Close-Price) as the statistical population.
Then I used linear regression to determine the relationship between the natural logarithm of the Ripple price and the natural logarithm of the Bitcoin's stock-to-flow (BSF).
You can see the results in the image below:
Basic Equation : ln(Model Price) = 3.2977 * ln(BSF) - 12.13
The high R-Squared value (R2 = 0.83) indicates a large positive linear association.
Then I "winsorized" the statistical data to limit extreme values to reduce the effect of possibly spurious outliers (This process affected less than 4.5% of the total price data).
ln(Model Price) = 3.3297 * ln(BSF) - 12.214
If we raise the both sides of the equation to the power of e, we will have:
============================================
Final Equation:
■ Model Price = Exp(- 12.214) * BSF ^ 3.3297
Where BSF is Bitcoin's stock-to-flow
============================================
If we put current Bitcoin's stock-to-flow value (54.2) into this equation we get value of 2.95USD. This is the price which is indicated by the model.
There is a power law relationship between the market price and Bitcoin's stock-to-flow (BSF). Power laws are interesting because they reveal an underlying regularity in the properties of seemingly random complex systems.
I plotted XRP model price (black) over time on the chart.
Estimating the range of price movements
I also used several bands to estimate the range of price movements and used the residual standard deviation to determine the equation for those bands.
Residual STDEV = 0.82188
ln(First-Upper-Band) = 3.3297 * ln(BSF) - 12.214 + Residual STDEV =>
ln(First-Upper-Band) = 3.3297 * ln(BSF) – 11.392 =>
■ First-Upper-Band = Exp(-11.392) * BSF ^ 3.3297
In the same way:
■ First-Lower-Band = Exp(-13.036) * BSF ^ 3.3297
I also used twice the residual standard deviation to define two extra bands:
■ Second-Upper-Band = Exp(-10.570) * BSF ^ 3.3297
■ Second-Lower-Band = Exp(-13.858) * BSF ^ 3.3297
These bands can be used to determine overbought and oversold levels.
Estimating of the future price movements
Because we know that every four years the stock-to-flow ratio, or current circulation relative to new supply, doubles, this metric can be plotted into the future.
At the time of the next halving event, Bitcoins will be produced at a rate of 450 BTC / day. There will be around 19,900,000 coins in circulation by August 2025
It is estimated that during first year of Bitcoin (2009) Satoshi Nakamoto (Bitcoin creator) mined around 1 million Bitcoins and did not move them until today. It can be debated if those coins might be lost or Satoshi is just waiting still to sell them but the fact is that they are not moving at all ever since. We simply decrease stock amount for 1 million BTC so stock to flow value would be:
BSF = (19,900,000 – 1.000.000) / (450 * 365) =115.07
Thus, Bitcoin's stock-to-flow will increase to around 115 until AUG 2025. If we put this number in the equation:
Model Price = Exp(- 12.214) * 114 ^ 3.3297 = 36.06$
Ripple has a fixed supply rate. In AUG 2025, the total number of coins in circulation will be about 56,000,000,000. According to the equation, Ripple's market cap will reach $2 trillion.
Note that these studies have been conducted only to better understand price movements and are not a financial advice.
Indicator : Financial Table■ What is Financial Table?
Financial table is the table shows the finanacial data over period of time (Quartery : FQ, Annually : FY).
These incluse 3 tables,
1) Income Statement (Revenue, Net Profit (or Net Income) and EPS) .
2) Balance Sheet (Current Asset, Total Asset, Liabilites and Share Holder's Equity).
3) Cash Flow ( Cash Flow from Operating Activities, Investment, Financing and Free Cash Flow)
This data will allow us to get understanding of the status of a company financial status over time.
■ How to make it?
1) Get Financial Data
2) Decare array
3) Store the array if conditions are met.
4) Generate table
■ How to use?
1) You can select the report period : FQ (Quarterly) or FY (Annually).
2) You can also select the data to plot (Revenue, Net Profit and EPS).
3) Select how many quarter or year you want to get (It is available from 2014 only).
4) Customize text size and position of the table as you wish.
I'm new and appriciate your suggestion.
Williams Vix Fix + BB & RVI (Top/Bottom) & SqueezeLegend :
- When line touches or crosses red band it is Top signal (Williams Vix Fix)
- When line touches or crosses blue band it is Bottom signal (Williams Vix Fix)
- Red dot at the top of indicator is a Top signal (Relative Volatility Index)
- Blue dot at the top of indicator is a Bottom signal (Relative Volatility Index)
- Gray dot at the bottom of indicator is a Squeeze signal
This is an attempt to make use of the main features of all 4 of these very popular Volatility tools :
- Williams Vix Fix + Bollinger Bands (as per Larry Williams idea, link )
- Relative Volatility Index (RVI)
- The crossing of Keltner Channel by the Bollinger Bands (Squeeze)
The goal is to find the best tool to find bottoms and top relative to volatility . This is a simple combination, but I find it very useful personally
(no need to reinvent the wheel, just need to find what works best)
The idea is that Williams Vix Fix + Bollinger Bands already give the main volatility bottom and top (Bottom are more accurate).
So instead of trying to modify it, I chose to compliment it by mapping with points when the Relative Volatility Index (RVI) reached the
top/bottom thresholds (red dot means top and blue dot means bottom). That way we can easily see when both indicators find a top or bottom relative
to volatility (of course this needs to be then confirmed with a momentum indicator rally).
In addition, I added the squeeze because this quickly shows the potential breakouts.
For ideas on how to continue this work, it would be very interesting to be able to create a probability of a bottom and top relative to volatility using the
Williams Vix Fix + Bollinger Bands and "Relative Volatility Index" signals as both work well and give top or bottom the other doesn't see.
Woobull BTC Top CapA close approximation of Willy Woo's Top Cap indicator.
Top Cap is BTC's market cap cumulative average x 35
Since trading view lacks the data from 2010 to 2014 that is used for the calculation, initial values are taken from Willy Woo's chart.
The indicator must be applied to a CRYPTOCAP:BTC chart and daily timeframe
Day of month gain-lossSince March 2014 to date, on the 24 of each month, the crypto market has:
- lost more than 1% 30 times
- gained more than 1% 32 times
Stochastic based on Closing Prices - Identify and Rank TrendsStochClose is a trend indicator that can be used on its own to measure trend strength, in a scan to rank a group of securities according to trend strength or as part of a trend following strategy. Moreover, it acts as a volatility-adjusted trend indicator that puts securities on an equal footing.
StochClose measures the location of the current close relative to the close-only high-low range over a given period of time. In contrast to the traditional Stochastic Oscillator, this indicator only uses closing prices. Traditional Stochastic uses intraday highs and lows to calculate the range. The focus on closing prices reduces signal noise caused by intraday highs and lows, and filters out errant or irrationally exuberant price spikes.
Here are some examples when the high or low was out of proportion and suspect. Perhaps most famously, there were errant spike lows in dozens of ETFs in August 2015 (XLK, IJR, ITB). There were other spikes in VMBS (October 2014), IJR (October 2008) and KRE (May 2011). Elsewhere, there were suspicious spikes in IEI (April 2020), CHD (March 2020), CCRN (March 2020) and FNB (March 2020)
The preferred setting to identify medium and long-term uptrends is 125 days with 5 days smoothing. 125 days covers around six months. Thus, StochClose(125,5) is a 5-day SMA of the 125-day Stochastic based on closing prices. Smoothing with the 5-day SMA introduces a little lag, but reduces whipsaws and signal noise.
StochClose fluctuates between 0 and 100 with 50 as the midpoint. Values above 80 indicate that the current price is near the high end of the 125-day range, while values below 20 indicate that price is near the low end of the range. For signals, a move above 60 puts the indicator firmly in the top half of the range and points to an uptrend. A move below 40 puts the indicator firmly in the bottom half of the range and points to a downtrend.
StochClose values can also be ranked to separate the leaders from the laggards. In contrast to Rate-of-Change and Percentage Above/Below a Moving Average, StochClose acts as a volatility-adjusted indicator that can identify trend strength or weakness. The Consumer Staples SPDR is unlikely to win in a Rate-of-Change contest with the Technology SPDR. However, it is just as easy for the Consumer Staples SPDR to get in the top of its range as it is for the Technology SPDR. StochClose puts securities on an equal footing.
StochClose measures trend direction and trend strength with one number. The indicator value tells us immediately if the security is trending higher or lower. Furthermore, we can compare this value against the values for other securities. Securities with higher StochClose values are stronger than those with lower values.
Volatility StopThis is a new version of the classic Volatility Stop originally published in 2014 by admin and written in Pine v1. While the code has evolved, its logic is identical. It is an ATR-based trend detector that can also be used as a stop. It belongs to the same family of indicators as:
• Charles Le Beau's Chandelier Exit ,
• Olivier Seban's Super Trend , and
• Sylvain Vervoort's Average True Range Trailing Stop .
Unlike the Chandelier Exit , Volatility Stop will not move against the trend.
This new version is written in Pine v4. The indicator can be used as a chart overlay, like the original. The calculations have been functionalized for easier reuse, so it is now easier to lift the logic out of the script and use it in others.
Features
• Choice of 2 color themes.
• Choice of display as a line, circles, diamonds or arrows. The line can be used with the other shapes. If no line is required, set its thickness to zero.
• Same default of length=20 and ATR factor=2 used in the original Volatility Stop.
• 3 alerts: on any trend change, or on changes into up or downtrends only. Alerts should be configured to trigger Once Per Bar Close .
Original version:
Look first. Then leap.
Donchian Channels Alert SystemThis time I was using an script/indicator that was originally written by ChrisMoody on 12-14-2014, as turned it an Alert System to know whether the price breaks above or below the donchian channel of 20 periods.
I would like to show you how easy is to use it and configure it to receive this alerts in your phone.
Here's a page about what Donchian Channels are for the ones that don't know already: www.investopedia.com
This simple indicator prints a green alert when the close price crosses above the upper band of the DC, and a red alert when the price crosses the lower band of the DC.
When these conditions are met, the indicator throws an alertcondition signal. You can personalize your Trading View alerts using this information.
Create new alert, In the condition select "DC Alert System" and below select "Break Above" or "Break Below" depending on the case you're looking for. Save the Alert and voila.
Simply as that.
The Donchian Channel period is set to 20 by default, but you can update it to what suit best for you.
Hope this is a +1 to your trade alert system.
Have a good day!
If you like this script please give me a like and comment below.
Murreys Math Lines Box OR Ratio PivotsI'm publishing my second script, though nothing extraordinary, I believe there is user group for Murry Math indies and the only "proper one" (According to my usage) I found was of RicardoSantos, here is the link :
He developed that script in 2014 and it is in need of update to Pine V4 and I'm doing the needful as its user.
All the updates from my end are listed below:
1. Updated to Pine V4
2. Automatic octave selection
3. In auto mode one can switch octave
4. This script is color coded with intention of use on dark theme, one can change the colors to use it on white background with simple few clicks as pinelines have been used
Other thing I want to add is that usage of this is not very clear to many users, so I'll do little explaining here;
Lets start with what is Octave? Octave is basically distance between square of two whole numbers, this is hard-fast method to calculate, Murry has made it far more complicated to use practically. In mathematical formula terms it could be something like this for script trading at 11890 (CMP)
Step 1: Square Root of CMP i.e Square Root of 11890 = 109.041 = Rounded to 109
Step 2: You can either take one whole number higher or lower than 109, which is 108 or 110. We will take 108
Step 3: Square of 108 = 11664 and Square of 109 = 11881
Step 4: Octave => Distance between (Lowlevel) 11664 and (Higherlevel) 11881
I've automated it so you don't need to calculate, but there is also manual entry possible if you want to calculate octaves yourself, there are different ways to calculate and some like to just take High and Low's of the day or week or month, whatever you like. When I used it I did it strictly this way, so automation is based on it. This is very subjective matter so don't ask to change the calculation of this, if I started doing that every second person would ask me to modify it to different calculation..and thats...just not possible to do.
This is output for calculation we just did above
This is octave shift option (Which basically shifts to next whole number square in above calculation)
Normal nomenclature on octaves and important color codes
+2/8: Extreme overbought = Blue Color and solid line
+1/8: OverBought
8/8: Hardest line to rise above (overbought) = White Color and solid line
7/8: Fast reverse line (weak)
6/8: Pivot reverse line = Yellow Color and solid line
5/8: Upper trading range
----------------------------------------
4/8: Major reversal line = Green Color and solid line
----------------------------------------
3/8: Lower trading range
2/8: Pivot reverse line = Blue Color and solid line
1/8: Fast reverse line (weak)
0/8: Hardest line to fall below (oversold) = White Color and solid line
-1/8: Oversold
-2/8: Extreme Oversold = Yellow Color and solid line
Other lines that I've not mentioned color codes for are minor and are usually plotted in dotted format.
Resources on complete technique to trade and importance of levels (highly recommended to read carefully before trading), if you don't know how to get this for free don't worry you can just google Murrey math and you will find it somewhere, its just that it would be in little scattered manner.
www.scribd.com
Enjoy!
Easy to Use Stochastic + RSI StrategyA simple strategy that yields some great results.
CODE VARIABLES
LINE 2 - Here you can change your currency and amount you want to invest on each entry.
LINE 10/11/12 - Here we establish what date we want to start backtesting from. Simply change the defval on each line to change the date (In the code below we start on Jan 1st, 2014).
LINES 19 through 27 - Here we set our Stochastic and RSI sensitivity (Currently %K = 14, %D = 3, RSI = 14). Change these to your preference.
LINE 39/41 - Here we execute our orders (Currently set when %K crosses %D under the 20 value and RSI is less than 50 to BUY, %K crosses %D above the 80 value and RSI is greater than 60 to SELL). Change these to your preference.
NOTE: As a beginner you may not want to short stock, therefore LINE 6 was added to only allow long positions.
I didn't overlay the RSI value over the Stochastics because it was too cluttered. Just add the RSI indictor seperately to your layout.
As always, couple this with trend following and exit/entry rules to make the profitability even higher!
Cheers!
Golden Ratio Macro Top IndicatorsThis is inspired by Philip Swift's Golden Ratio Multiplier research however it uses the 300 DMA to predict the Macro Cycle Top's Price. It still uses the 350 DMA * 2 and 111 DMA to predict the top's date (the two cross).
111 DMA (Orange) crosses the 350 DMA * 2 (Green) predicts the Macro Cycle Top Date
300 DMA * 3 (Red) predicts the Current Macro Cycle Top Price
300 DMA * 5 (Yellow) predicted the 2018 Macro Cycle Top Price
300 DMA * 8 (Blue) predicted the 2014 Macro Cycle Top Price
Golden RatioThis is inspired by Philip Swift's Golden Ratio Multiplier research however it uses the 300 DMA to predict the Macro Cycle Top's Price. It still uses the 350 DMA * 2 and 111 DMA to predict the top's date (the two cross).
111 DMA (Orange) crosses the 350 DMA * 2 (Green)= Macro Cycle Top Date
300 DMA * 3 (Red) predicts the Current Macro Cycle Top Price
300 DMA * 5 (Yellow) predicted the 2018 Macro Cycle Top Price
300 DMA * 8 (Blue) predicted the 2014 Macro Cycle Top Price
Bryant Adaptive Moving Average@ChartArt got my attention to this idea.
This type of moving average was originally developed by Michael R. Bryant (Adaptrade Software newsletter, April 2014). Mr. Bryant suggested a new approach, so called Variable Efficiency Ratio (VER), to obtain adaptive behaviour for the moving average. This approach is based on Perry Kaufman' idea with Efficiency Ratio (ER) which was used by Mr. Kaufman to create KAMA.
As result Mr. Bryant got a moving average with adaptive lookback period. This moving average has 3 parameters:
Initial lookback
Trend Parameter
Maximum lookback
The 2nd parameter, Trend Parameter can take any positive or negative value and determines whether the lookback length will increase or decrease with increasing ER.
Changing Trend Parameter we can obtain KAMA' behaviour
To learn more see www.adaptrade.com
Log ProjectionReferences an old log projection made in October of 2014
bitcointalk.org
You can see today's value for that old projection here:
www.wolframalpha.com(2.9065++*+ln((number+of+days+since+2009+Jan+09)%2Fdays)+-+19.493)
This script attempts to improve error based on data we've seen since the original. That ends up looking more like this:
www.wolframalpha.com(2.7065++*+ln((number+of+days+since+2009+Jan+09)%2Fdays)+-+18.2500)
Log Projection ErrorReferences an old log projection made in October of 2014
bitcointalk.org
You can see today's value for that old projection here:
www.wolframalpha.com(2.9065++*+ln((number+of+days+since+2009+Jan+09)%2Fdays)+-+19.493)
This script attempts to improve error based on data we've seen since the original. That ends up looking more like this:
www.wolframalpha.com(2.7065++*+ln((number+of+days+since+2009+Jan+09)%2Fdays)+-+18.2500)
Log Projection OrigReferences an old log projection made in October of 2014
bitcointalk.org
You can see today's value for that old projection here:
www.wolframalpha.com(2.9065++*+ln((number+of+days+since+2009+Jan+09)%2Fdays)+-+19.493)
