OhManLan Golden CloudThis indicator is a modification of the popular Ichimoku indicator, build high/low channels using the Golden Ratio, Volume-weighted average price allows smoother components.
high/low channels moves based on Fibo Levels (Golden Ratio: 1.618).
- Settings -
The indicator can be adjusted to your needs.
- How to use -
OhManLan Golden can be used a Support/Resistance , Stop loss, Trailing stop and Price target.
Volume-weighted average price allows smoother components.
Can be used with other indicators such as Moving Average Convergence Divergence (MACD).
Cicli
Adaptive, Double Jurik Filter Moving Average (AJFMA) [Loxx]Adaptive, Double Jurik Filter Moving Average (AJFMA) is moving average like Jurik Moving Average but with the addition of double smoothing and adaptive length (Autocorrelation Periodogram Algorithm) and power/volatility {Juirk Volty) inputs to further reduce noise and identify trends.
What is Jurik Volty?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you.
What is adaptive Jurik volatility?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Included
- Double calculation of AJFMA for even smoother results
Adaptive Look-back/Volatility Phase Change Index on Jurik [Loxx]Adaptive Look-back, Adaptive Volatility Phase Change Index on Jurik is a Phase Change Index but with adaptive length and volatility inputs to reduce phase change noise and better identify trends. This is an invese indicator which means that small values on the oscillator indicate bullish sentiment and higher values on the oscillator indicate bearish sentiment
What is the Phase Change Index?
Based on the M.H. Pee's TASC article "Phase Change Index".
Prices at any time can be up, down, or unchanged. A period where market prices remain relatively unchanged is referred to as a consolidation. A period that witnesses relatively higher prices is referred to as an uptrend, while a period of relatively lower prices is called a downtrend.
The Phase Change Index (PCI) is an indicator designed specifically to detect changes in market phases.
This indicator is made as he describes it with one deviation: if we follow his formula to the letter then the "trend" is inverted to the actual market trend. Because of that an option to display inverted (and more logical) values is added.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you.
What is adaptive Jurik volatility
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers, 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average (KAMA) and Tushar Chande’s variable index dynamic average (VIDYA) adapt to changes in volatility. By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic, relative strength index (RSI), commodity channel index (CCI), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
Included
-Your choice of length input calculation, either fixed or adaptive cycle
-Invert the signal to match the trend
-Bar coloring to paint the trend
Happy trading!
Ehlers Autocorrelation Periodogram [Loxx]Ehlers Autocorrelation Periodogram contains two versions of Ehlers Autocorrelation Periodogram Algorithm. This indicator is meant to supplement adaptive cycle indicators that myself and others have published on Trading View, will continue to publish on Trading View. These are fast-loading, low-overhead, streamlined, exact replicas of Ehlers' work without any other adjustments or inputs.
Versions:
- 2013, Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers
- 2016, TASC September, "Measuring Market Cycles"
Description
The Ehlers Autocorrelation study is a technical indicator used in the calculation of John F. Ehlers’s Autocorrelation Periodogram. Its main purpose is to eliminate noise from the price data, reduce effects of the “spectral dilation” phenomenon, and reveal dominant cycle periods. The spectral dilation has been discussed in several studies by John F. Ehlers; for more information on this, refer to sources in the "Further Reading" section.
As the first step, Autocorrelation uses Mr. Ehlers’s previous installment, Ehlers Roofing Filter, in order to enhance the signal-to-noise ratio and neutralize the spectral dilation. This filter is based on aerospace analog filters and when applied to market data, it attempts to only pass spectral components whose periods are between 10 and 48 bars.
Autocorrelation is then applied to the filtered data: as its name implies, this function correlates the data with itself a certain period back. As with other correlation techniques, the value of +1 would signify the perfect correlation and -1, the perfect anti-correlation.
Using values of Autocorrelation in Thermo Mode may help you reveal the cycle periods within which the data is best correlated (or anti-correlated) with itself. Those periods are displayed in the extreme colors (orange) while areas of intermediate colors mark periods of less useful cycles.
What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm?
From his Ehlers' book mentioned above, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
How to use this indicator
The point of the Ehlers Autocorrelation Periodogram Algorithm is to dynamically set a period between a minimum and a maximum period length. While I leave the exact explanation of the mechanic to Dr. Ehlers’s book, for all practical intents and purposes, in my opinion, the punchline of this method is to attempt to remove a massive source of overfitting from trading system creation–namely specifying a look-back period. SMA of 50 days? 100 days? 200 days? Well, theoretically, this algorithm takes that possibility of overfitting out of your hands. Simply, specify an upper and lower bound for your look-back, and it does the rest. In addition, this indicator tells you when its best to use adaptive cycle inputs for your other indicators.
Usage Example 1
Let's say you're using "Adaptive Qualitative Quantitative Estimation (QQE) ". This indicator has the option of adaptive cycle inputs. When the "Ehlers Autocorrelation Periodogram " shows a period of high correlation that adaptive cycle inputs work best during that period.
Usage Example 2
Check where the dominant cycle line lines, grab that output number and inject it into your other standard indicators for the length input.
Ehlers Adaptive Relative Strength Index (RSI) [Loxx]Ehlers Adaptive Relative Strength Index (RSI) is an implementation of RSI using Ehlers Autocorrelation Periodogram Algorithm to derive the length input for RSI. Other implementations of Ehers Adaptive RSI rely on the inferior Hilbert Transformer derive the dominant cycle.
In his book "Cycle Analytics for Traders Advanced Technical Trading Concepts", John F. Ehlers describes an implementation for Adaptive Relative Strength Index in order to solve for varying length inputs into the classic RSI equation.
What is an adaptive cycle, and what is the Autocorrelation Periodogram Algorithm?
From his Ehlers' book mentioned above, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average (KAMA) and Tushar Chande’s variable index dynamic average (VIDYA) adapt to changes in volatility. By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic, relative strength index (RSI), commodity channel index (CCI), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator.This look-back period is commonly a fixed value. However, since the measured cycle period is changing, as we have seen in previous chapters, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the autocorrelation periodogram algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
What is Adaptive RSI?
From his Ehlers' book mentioned above, page 137:
"The adaptive RSI starts with the computation of the dominant cycle using the autocorrelation periodogram approach. Since the objective is to use only those frequency components passed by the roofing filter, the variable "filt" is used as a data input rather than closing prices. Rather than independently taking the averages of the numerator and denominator, I chose to perform smoothing on the ratio using the SuperSmoother filter. The coefficients for the SuperSmoother filters have previously been computed in the dominant cycle measurement part of the code."
Happy trading!
Buying power against Bitcoin and EthereumI created a simple tool where you can input your capital (in USD) and it will track your buying power against Bitcoin and Ethereum.
A handy tool for Dollar Cost Averaging and trend following systems.
Default value: You have 1000$
Formula: Buying power = Capital / Underlying assets
Adaptive Qualitative Quantitative Estimation (QQE) [Loxx]Adaptive QQE is a fixed and cycle adaptive version of the popular Qualitative Quantitative Estimation (QQE) used by forex traders. This indicator includes varoius types of RSI caculations and adaptive cycle measurements to find tune your signal.
Qualitative Quantitative Estimation (QQE):
The Qualitative Quantitative Estimation (QQE) indicator works like a smoother version of the popular Relative Strength Index (RSI) indicator. QQE expands on RSI by adding two volatility based trailing stop lines. These trailing stop lines are composed of a fast and a slow moving Average True Range (ATR).
There are many indicators for many purposes. Some of them are complex and some are comparatively easy to handle. The QQE indicator is a really useful analytical tool and one of the most accurate indicators. It offers numerous strategies for using the buy and sell signals. Essentially, it can help detect trend reversal and enter the trade at the most optimal positions.
Wilders' RSI:
The Relative Strength Index ( RSI ) is a well versed momentum based oscillator which is used to measure the speed (velocity) as well as the change (magnitude) of directional price movements. Essentially RSI , when graphed, provides a visual mean to monitor both the current, as well as historical, strength and weakness of a particular market. The strength or weakness is based on closing prices over the duration of a specified trading period creating a reliable metric of price and momentum changes. Given the popularity of cash settled instruments (stock indexes) and leveraged financial products (the entire field of derivatives); RSI has proven to be a viable indicator of price movements.
RSX RSI:
RSI is a very popular technical indicator, because it takes into consideration market speed, direction and trend uniformity. However, the its widely criticized drawback is its noisy (jittery) appearance. The Jurk RSX retains all the useful features of RSI , but with one important exception: the noise is gone with no added lag.
Rapid RSI:
Rapid RSI Indicator, from Ian Copsey's article in the October 2006 issue of Stocks & Commodities magazine.
RapidRSI resembles Wilder's RSI , but uses a SMA instead of a WilderMA for internal smoothing of price change accumulators.
VHF Adaptive Cycle:
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Band-pass Adaptive Cycle:
Even the most casual chart reader will be able to spot times when the market is cycling and other times when longer-term trends are in play. Cycling markets are ideal for swing trading however attempting to “trade the swing” in a trending market can be a recipe for disaster. Similarly, applying trend trading techniques during a cycling market can equally wreak havoc in your account. Cycle or trend modes can readily be identified in hindsight. But it would be useful to have an objective scientific approach to guide you as to the current market mode.
There are a number of tools already available to differentiate between cycle and trend modes. For example, measuring the trend slope over the cycle period to the amplitude of the cyclic swing is one possibility.
We begin by thinking of cycle mode in terms of frequency or its inverse, periodicity. Since the markets are fractal ; daily, weekly, and intraday charts are pretty much indistinguishable when time scales are removed. Thus it is useful to think of the cycle period in terms of its bar count. For example, a 20 bar cycle using daily data corresponds to a cycle period of approximately one month.
When viewed as a waveform, slow-varying price trends constitute the waveform's low frequency components and day-to-day fluctuations (noise) constitute the high frequency components. The objective in cycle mode is to filter out the unwanted components--both low frequency trends and the high frequency noise--and retain only the range of frequencies over the desired swing period. A filter for doing this is called a bandpass filter and the range of frequencies passed is the filter's bandwidth.
Included:
-Toggle on/off bar coloring
-Customize RSI signal using fixed, VHF Adaptive, and Band-pass Adaptive calculations
-Choose from three different RSI types
Visuals:
-Red/Green line is the moving average of RSI
-Thin white line is the fast trend
-Dotted yellow line is the slow trend
Happy trading!
Trend IdentifierTrend Identifier for 1D BTC.USD
It smoothens a closely following moving average into a polynomial like plot.
And assumes 4 stage cycles based on the first and second derivatives.
Green: Bull / Exponential Rise
Yellow: Distribution
Red: Bear / Exponential Drop
Blue: Accumulation
Red --> Blue --> Green: indicates the start of a bull market
Green --> Yellow --> Red: indicates the start of a bear market
Green --> Yellow: Start of a distribution phase, take profits
Red --> Blue: Start of a accumulation phase, DCA
Hybrid, Zero lag, Adaptive cycle MACD [Loxx]TASC's March 2008 edition Traders' Tips includes an article by John Ehlers titled "Measuring Cycle Periods," and describes the use of bandpass filters to estimate the length, in bars, of the currently dominant price cycle.
What are Dominant Cycles and Why should we use them?
Even the most casual chart reader will be able to spot times when the market is cycling and other times when longer-term trends are in play. Cycling markets are ideal for swing trading however attempting to “trade the swing” in a trending market can be a recipe for disaster. Similarly, applying trend trading techniques during a cycling market can equally wreak havoc in your account. Cycle or trend modes can readily be identified in hindsight. But it would be useful to have an objective scientific approach to guide you as to the current market mode.
There are a number of tools already available to differentiate between cycle and trend modes. For example, measuring the trend slope over the cycle period to the amplitude of the cyclic swing is one possibility.
We begin by thinking of cycle mode in terms of frequency or its inverse, periodicity. Since the markets are fractal; daily, weekly, and intraday charts are pretty much indistinguishable when time scales are removed. Thus it is useful to think of the cycle period in terms of its bar count. For example, a 20 bar cycle using daily data corresponds to a cycle period of approximately one month.
When viewed as a waveform, slow-varying price trends constitute the waveform's low frequency components and day-to-day fluctuations (noise) constitute the high frequency components. The objective in cycle mode is to filter out the unwanted components--both low frequency trends and the high frequency noise--and retain only the range of frequencies over the desired swing period. A filter for doing this is called a bandpass filter and the range of frequencies passed is the filter's bandwidth .
Indicator Features
-Zero lag or Regular MACD/signal calculation
- Fixed or Band-pass Dominant Cycle for MACD and Signal MA period inputs
-10 different moving average options for both MACD and Signal MA calculations
-Separate Band-pass Dominant Cycle calculations for both MACD and Signal MA calculations
- Slow-to-Fast Band-pass Dominant Cycle input to tweak the ratio of MACD MA input periods as they relate to each other
Distance From Moving AverageThis indicator shows the distance between the current price and the Moving Average price.
Key Features:
Show the distance between price and Moving Average (Read Distance Calculation for more information)
Show Historic Highs and Lows
Show Highest High and Lowest Low
Show current Highest High, current Lowest Low and current distance
Key Indicator Settings:
1. Distance Calculation
There are two ways to calculate the distance:
Spread - Calculate the difference between the price and the moving average
Percentage - Calculate the percentage change between the price and the moving average
2. Moving Average Types
There are 5 different Moving Averages:
EMA
SMA
WMA
VWMA
HMA
3. Highest High and Lowest Low
You can show or hide the Highest High and the Lowest Low plots of the series
4. Historic Highs and Lows
You can show or hide past Highs and Lows of the series
Lookback Length - Let's you adjust the frequency of local highs and lows of the series
5. Current Values
You can show or hide current value labels
US/CA Bond Yield CurveEasy Viewing of 4 different duration bond yields for US and Canada. Bond prices and bond yields are excellent indicators of the economy as a whole, and of inflation in particular. A bond's yield is the discount rate that can be used to make the present value of all of the bond's cash flows equal to its price. Good as part of a macro set.
Bitcoin Bottom Detector: W TimeframeUse this indicator in the weekly time frame:
One of the most widely used indicators for identifying the Bitcoin market bottom is the 200-week moving average. This indicator works based on the ratio of price to the value of the 200-week moving average. When the indicator enters the lower blue part (overflow area), it indicates the bitcoin is in the bottom of the market.
Exponential Top and Bottom FinderThis is an indicator to identify possible tops and bottoms after exponential price surges and drops, it works best on ETH 1D, but you can also use it for bitcoin and altcoins.
It's based on stochastic first and second derivatives of a close moving average
BlackMEX - Production CostBitcoin's Value as determined by Joules of energy input only
Calculations per Medium article EV = (Energy-in) / (Supply Growth Rate) * (Fiat Factor)
Historic Energy Efficiency data can only be entered monthly due to processing speed constraints of below data load and should be considred an estimate only.
Energy Efficiency Data requires manual updating. Currently accurate as of 28 December 2019
Bitcoin Production Cost
Cambridge Bitcoin Electricity Consumption Index (CBECI) - Bitcoin's global electricity consumption in TwH.
NB: Uses MONTHLY averages of raw data from CBECI. TV script run-time is too slow with Daily/Weekly data here.
This requires manual updating once a month for ongoing accuracy.
Bitcoin Price Temperature: Weekly TimeframeUse this oscillator at weekly timeframes:
The Bitcoin Price Temperature (BPT) is an oscillator that models the number of standard deviations the price has moved away from the 4-yr moving average. This seeks to establish a mean reversion model based on the cyclical nature of Bitcoin halving and investment cycles. The BPT bands then establish price levels that coincide with specific standard deviation multiples to identify fair and extreme valuations.
Coined By:
DilutionProof
Interpretation:
Values above 6 indicate extremely high price areas: (TOP OF THE MARKET)
Areas below 0.2 indicate extremely low price areas: (BOTTOM OF THE MARKET)
TASC 2022.6 Ehlers' Loops - SectorsInspired by the latest TASC article, the crocker graph is expanded to show 5 tickers.
for commodity also draws a side box with current tickers candles so it can be used as standalone.
Signal generatorThis simple script generates signals for testing the connection from TradingView to a REST API client via the webhook and demonstrates very basic concepts of gerenating alerts within the script.
This script also demonstrates how to visualize when a buy or a sell should take place and how to use diagnostics text for bug fixes/informational purposes.
This is for testing and learning only. Do not use with real money as losses WILL occur. This script is for educational purposes only and should only be used with demo accounts, never with real money .
Buy signals are generated when closing price is less then opening price.
Sell Signals are generated when closing price is greater then opening price.
Can also be used to test signal counting and very rudimentary dollar cost averaging.
Fibonacci Zone Oscillator With MACD HistogramThe columns
After I found a way to calculate a price as a percent of the middle line of the KeltCOG Channel in the KCGmut indicator (published), I got the idea to use the same trick in the Fbonacci Zone Channel (also published), thus creating an oscillator.
I plot the percent’s as columns with the color of the KeltCOG Channel. Because the channels I created and published (i.e. Fibonacci Zone, Donchian Fibonacci Trading Tool, Keltner Fibzones, and KeltCOG) all use Fibonacci zones, this indicator also reports the position of the close in their zones.
Strategy and Use:
Blue column: Close in uptrend area, 4 supports, 0 resistance, ready to rally up.
Green column: Close in buyers area, 3 supports, 1 resistance, looking up.
Gray column: Close in center area 2 supports, 2 resistances, undecided.
Yellow column: Close in sellers area 1 support, 3 resistances, looking down.
Red column: Close in downtrend area, 0 support, 4 resistances, ready to rally down.
I use this indicator in a layout with three timeframes which I use for stock picking, I pick all stocks with a blue column in every timeframe, the indicator is so clear that I can flip through the 50 charts of my universe of high liquid European blue chips in 15 minutes to make a list of these stocks.
Because I use it in conjunction with KeltCOG I also gave it a ‘script sets lookback’ option which can be checked with a feedback label and switched off in the inputs.
The MACD histogram
I admire the MACD because it is spot on when predicting tops and bottoms. It is also the most sexy indictor in TA. Actually just the histogram is needed, so I don’t show the macd-line and the signal line. I use the same lookback for the slow-ma as for the columns, set the fast-ma to half and the signal-line to a third of the general lookback. Therefore I gave the lookback a minimum value of 6, so the signal gets at least a lookback of 2.
The histogram is plotted three times, first as a whitish area to provide a background, then the colums of the Fibzone Oscillator are plotted, then the histogram as a purple line, which contrasts nicely and then as a hardly visible brown histogram.
The input settings give the option to show columns and histogram separate or together.
Strategy and use:
I think about the columns as showing a ‘longer term chosen momentum’ and about the histogram as a ‘short term power momentum’. I use it as additional information.
Enjoy, Eykpunter.
Bitcoin OnChain & Other MetricsHi all,
In these troubled times, going back to fundamentals can sometimes be a good idea 😊
I put this one up using data retrieved from “Nasdaq Data Link” and their “Blockchain.com” database.
Here is a good place to analyses some Bitcoin data “outside” its price action with 25 different data sets.
Just go to the settings menu and display the ones you are interested in.
If you want me to add more metrics, feel free to DM or comment below!
Hope you enjoy 😉
Tycoon Gann Currently this script will work for the stocks and futures price trading in between 1000 - 100000. In our future update we will add another feature that will give you access to all the price digit stocks futures and currencies too .
This is purely based on a secret method of Tycoon Infotek as a research of GANN levels we found in our experience these WD GANN Trend angle based calculations giving us some important hints to watch . Degree offset from previous close price add 90 degree to calculate resistance and substract 90 to get resistance levels . green color dots denotes buying pressure zone and red color denotes selling pressure zones . yellow line indicates neutral sign
These levels not only shows us the Support and resistance . It clearly intimate us the strength of selling and buying pressure naturally occurred once the price reaches the zones.
3D GATOR %HLThis indicator tracks the 3 day trading bots and measures the high and the low (%).
Usually a trend can change or continue every 3 days.
When volatility decreases and both values are the same gator is going to open its jaws so it's a good time to open a position long. Avoid shorts during low volatility.
On the other hand when volatility increases, and gator has its jaws wide open is a good time to look for shorts.
That's pretty much it.
This indicator was designed by me and created by Marketwatcher.
multiple_ma_envelope
Description:
Moving Average is a well-known though simple technical analysis tool, that can be applied in most trading journeys. By adding an envelope (a certain amount above and below the moving averages, cited from Investopedia), the indicator aligned its aim to identify the reversal area i.e. when the price reaches the envelopes, the price tends to have a reverse. In this indicator, the improvement is by adding multiple envelopes at once, thus can identify the further phase of the reverse area when the price apparently continues current direction.
Upper Band = MA * (1 + %envelope)
Lower Band = MA * (1 - %envelope)
Notes:
1). In this indicator, the default value of the moving average utilized is set to 10, 20, 50, 100 respectively
2). The band initial value is set to 0.2, and increases by 0.2 for each increasing MA Length
Feature:
1). Multiple Moving Average Envelope
2). Information Table as displayed Rolling Deviation, Rolling Maximum Drawdown, and Value-at-Risk
1 year ROI BUY ZONEThis indicator is comparing price with price 1 year ago. This will generate ROI which could be positive or negative.
If ROI switches from negative to positive or vice versa it will generate zone
This zone could have minimum days to filter false signals
Buy signal could be added when ROI reaches some value ( -65% for example)
