More than three Candles in a roww Changes color of more than three candles in a row, when there are consecutive candles of same color green or red
Cycles
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).
Buy/Sell on the levelsThis script is generally
My describe is:
There are a lot of levels we would like to buy some crypto.
When the price has crossed the level-line - we buy, but only if we have the permission in array(2)
When we have bought the crypto - we lose the permission for buy for now(till we will sell it on the next higher level)
When we sell some crypto(on the buying level + 1) we have the permission again.
There also are 2 protect indicators. We can buy if these indicators both green only(super trend and PIVOT )
Jun 12
Release Notes: Hello there,
Uncomment this section before use for real trade:
if array.get(price_to_sellBue, i) >= open and array.get(price_to_sellBue, i) <= close// and
//direction < 0 and permission_for_buy != 0
Here is my script.
In general - this is incredible simple script to use and understand.
First of all You can see this script working with only long orders, it means we going to get money if crypto grows only. Short orders we need to close the position on time.
In this script we buy crypto and sell with step 1% upper.
You can simply change the step by changing the price arrays.
Please note, if You want to see where the levels of this script is You Have to copy the next my indicator called LEVEL 1%
In general - if the price has across the price-level we buy some crypto and loose permission for buying for this level till we sell some crypto. There is ''count_of_orders" array field with value 2. When we bought some crypto the value turns to 0. 0 means not allowed to by on this level!!! The script buy if the bar is green only(last tick).
The script check every level(those we can see in "price_to_sellBue" array).
If the price across one of them - full script runs. After buying(if it possible) we check is there any crypto for sell on the level.
We check all levels below actual level( of actual level - ''i'' than we check all levels from 0 to i-1).
If there is any order that has value 0 in count of orders and index <= i-1 - we count it to var SELL amount and in the end of loop sell all of it.
Pay attention - it sells only if price across the level with red bar AND HAS ORDERS TO SELL WHICH WAS BOUGHT BELOW!!!
In Strategy tester it shows not-profitables orders sometimes, because if You have old Long position - it sells it first. First in - first out.
If the price goes down for a long time and You sell after 5 buys You sell the first of it with the highest value.
There is 2 protection from horrible buying in this strategy. The first one - Supertrend. If the supertrend is red - there is no permission for buy.
The second one - something between PIVOT and supertrend but with switcher.
If the price across last minimum - switcher is red - no permission for buy and the actual price becomes last minimum . The last maximum calculated for last 100 bars.
When the price across last maximum - switcher is green, we can buy. The last minimum calculation for last 100 bars, last maximum is actual price.
This two protections will save You from buying if price get crash down.
Enjoy my script.
Should You need the code or explanation, You have any ideas how to improve this crypt, contact me.
Vladyslav.
Jun 12
Release Notes: Here has been uncommented the protection for buy in case of price get down.
5 hours ago
Release Notes: Changed rages up to actual price to make it work
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
BUY/SELL on the levels onlyMy discribe is:
There ara a lot of levels we would like to buy some crypto.
When the price has acrossed the level-line - we buy, but only if we have the permission in array(2)
When we have bought the crypto - we lose the permission for buy for now(till we will sell it on the next hiegher level)
When we sell some crypto(on the buying level + 1) we have the permission again.
There also are 2 protect indicators. We can buy if these indicators both green only(supertrend and PIVOT)
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
VXD SupercycleVXD is a brand new indicator and still developing. to minimize stop losses and overcome sideways market conditions, Higher Timeframe are recommended
Trend lines
-using Rolling VWAP as trend line to determined if Volume related to a certain price.
-you can switch RVWAP to EMA in the setting
ATR
-trailing 12*ATR and 2.4 Mutiplier
Pivot point and Rejected Block
Pivot show last High and low of a price in past bars
Rejected Block show when that High or Low price are important level to determined if it's Hidden Divergence or Divergence
Symbols on chart show Premium and Discount Prices
X-Cross - show potential reversal trend with weak volume .
O-circle - show potential reversal trend with strong volume .
Setting
Momentum: RSI = 25 , RSI MA = 14
Trend: Rolling VWAP and ATR and Subhag
Trailing STOP: ATR 12 x 2.4
Highlight Bars color when volume is above SMA 6
SMA200 act as TP Line
Risk:Reward Calculation
if Buy your Stoploss will be previous Pivot low
if Sell your Stoploss will be previous Pivot high and will be calculated form there, then show TP in Orange color line
VXD เป็นระบบเทรดที่ผมทดลองเอาหลาย ๆ ไอเดีย ทั้งจาก Youtube facebook และกลุ่มคนต่าง ๆ มารวบรวมไว้ แล้วตกผลึกขึ้นมาเป็นระบบนี้ ใน Timeframe ใหญ่ ๆ สามารถลากได้ทั้ง Cycle กันเลย
Trend lines
-ใช้ Rolling VWAP ของแอพ Tradingview (สามารถตั้งแค่าเป็น EMA ได้)
ATR
-ใช้ค่า ATR 12 Mutiplier 2.4
Pivot point and Rejected Block
Pivot โชว์เส้น High low และมีผลกับออเดอร์ หากแท่งเทียนปิดทะลุเส้นนี้
Rejected Block วาดแนวรับ-ต้าน อัตโนมัติ ใช้ประกอบ RSI ว่ามี Divergence หรือไม่
สัญลักษณ์ต่าง ๆ
X-Cross - แท่งกลืนกิน วอลุ่มน้อย
O-circle - แท่งกลืนกิน มีวอลุ่ม
Setting
Momentum: RSI = 25 , RSI MA = 14
Trend: Rolling VWAP and ATR and Subhag
Trailing STOP: ATR 12 x 2.4
Highlight Bars color when volume is above SMA 6
SMA200 act as TP Line
Risk:Reward Calculation
หาก Buy จุด SL จะอยู่ที่ Pivot low
หาก Sell จุด SL จะอยู่ที่ Pivot high และระบบจะคำนวณจากตรงนั้น จากนั้นแสดงเป็นเส้น TP สีส้ม
This Strategy Combined the following indicators and conditioning by me
ATR , RSI , EMA , SMA
Rolling VWAP - /script/ZU2UUu9T-Rolling-VWAP/
Regression Lines - Subhag form Subhag Ghosh /script/LHHBVpQu-Subhag-Ghosh-Algo-Version-for-banknifty/
Rejection Block , Pivots , High Volume Bars and PPDD form Super OrderBlock / FVG / BoS Tools by makuchaku & eFe /script/aZACDmTC-Super-OrderBlock-FVG-BoS-Tools-by-makuchaku-eFe/
ขอให้รวยครับ.
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)
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.
