BTC Europe-America-Asia market backgrounds - 1h max frameMarket indicator - Europe - America - Asia.
Based on my trading experience. Therefore, time zones are of different sizes.
Циклический анализ
Stable Coin Dominance RSIThe Stable Coin Dominance RSI evaluates the relative dominance of stable coins within the crypto ecosystem as compared to the total market cap. As stable coin dominance rises, it suggests that market participants are exiting out of crypto assets and into dollar pegged stable coins. The opposite is true inversely; as stable coin dominance diminishes, it suggests that market participants are divesting out of stable coins and into crypto assets.
Stable coin dominance can be expressed as a percentage of the total market cap as follows: Stable Coins / Total Crypto. The Stable Coin Dominance RSI indicator uses this percentage and converts it into an oscillator using the formula for the relative strength index.
The calculation for the indicator is: RSI
The users can select from USDT and USDC, two most dominant stable tokens by market cap, and compare their relative dominance against Bitcoin and the alt market.
The Stable Coin Dominance RSI may be useful on larger timeframes when attempting to identify the market’s appetite for risk along with oversold and undersold readings which may indicate pivots or turn arounds along market extremes.
The limitation of the indicator lies in the fact that stable coins continue to make up a growing percentage of the total market cap over time and thus comparisons to earlier cycles will not be a perfect apples-to-apples evaluation. This being said, the smoothing function of the RSI’s look back helps to moderate these comparative differences.
BTMM R.NOODLEThis script aims to encompass the style of Traders Reality, Steve M (BTTM), and Stacey Burke (youtube)
yesterday and last wk high and low
Custom watermark with examples (good for fullscreen multicharts)
can set it to show the year and week x of year
Colored candles for ays of week.
The theory behind these colors are that monday and tuesday set your initial balance for the week . having color seperation helps identify the balance
mon-tues = initial balance :green
wed +thu : blue
friday: red
Session boxes and daily initial balance
displays the asian and european trading session as one
displays the ny session first 3 hrs
how to combine sessions, weekly ib, daily ib, and sessions
will add pip lines 00/.50 increments at a later date
Yearly Monthly Vertical Lines [MsF]This indicator that draws vertical lines for each year and month starting from the specified date and time.
For example, starting from August 26th, 2022,
The annual vertical lines are Aug 26th, 2022, Aug 26th, 2021, Aug 26th, 2020, Aug 26th, 2019, Aug 26th, 2018, Aug 26th, 2017...
It is useful to check the action of the chart at the event of each year and month.
Caution:
Since there is a limit to the Bar-Count that can be specified in the script language, depending on the chart period setting It may not be displayed.
-------------------------
指定した日時を起点として、年、月ごとに垂直線（縦線）を描画するインジケータです。
例えば、2022/9/1を起点とした場合、
年次のラインは、2021/9/1、2020/9/1、2019/9/1、2018/9/1、2017/9/1、2016/9/1...
に描画します。
年、月ごとのイベント時のチャートの動きを確認するのに便利です。
※過去イベント時の価格変動確認の際、手作業で垂直線を引くのが面倒なため、作成しました。
＜注意＞
スクリプト言語で指定できるBarCntに制限がある為、チャートの期間設定によっては
表示されない場合があります。※今後改善の余地あり
SATAN Cycle BitcoinWith this indicator I want to dismantle the Pi Cycle Bitcoin indicator since the community thinks that it has a mathematical basis based on the Pi number, nothing is further from reality, the indicator uses averages which, divided, result in the Pi number but in the code uses some multipliers to adjust the crossing of averages and I demonstrate it with this indicator in which if you add the averages you get the number 666, is the demon behind the Bitcoin cycles? NO, it is only an adjustment of averages and multipliers. With this I only intend to alert the community that the indicator is 0% reliable.
High low volatile
its purpose to make this script is learning how many spread on each product.
its help trader and me to estimate spread of High and low for make a better trade
Money management :
Its also useful to help trader to manage number of lot to take a risk on that trade
How it work?
I use High and low to calculate the different between on them
and I add an EMA 10 and EMA 200 to estimate spread of each product in many bar
On second picture , I use it on GOLD 1H timeframe . You can see that Gold in hour trade on my country time (thailand) It has more spread on 19.00-23.00 pm (in US time is 7.00-11.00)
It help trader to set time when to trade and when to leave
thank you
Window Periods// Work in progress.. visualize event windows
// to do:
// - assign sector relevance (add opec events and weight more to commodity typed instruments, whereas revenue forecasts of walmart won't be as relevant to commodities)
// - create weighting onto macro probability valuators
// - create means to store window weight/values in an accessible way so that additional scripts can access this data (post hidden text in window?) or maybe just duplicate arrays elsewhere in additional code (terrible option)
// - correct colors to scale within like types of events (opex the same, rebalancing periods the same, political and financial meeting the same color etc..)
// - find a way to make transfer the timestamps or date strings as const variables usable within functions. v5 sends as series and cannot be converted back making this an overly complicated case instead of a function/library
// - add dates back to 2017 for better referencing
// - convert a number of the variables to inputs so they can be controlled in-chart, such as transparancy, and color of each window period?
Trend Identifier StrategyTrend Identifier Strategy for 1D BTC.USD
The indicator smoothens a closely following moving average into a polynomial like plot and assumes 4 staged cycles based on the first and the second derivatives. This is an optimized strategy for long term buying and selling with a Sortino Ratio above 3. It is designed to be a more profitable alternative to HODLing. It can be combined with 'Accumulation/Distribution Bands & Signals' and 'Exponential Top and Bottom Finder'.
Intraday Background Time RangesThis simple script was written for studying recurring intraday behaviours of financial instruments. With it, you can highlight up to 13 customizable time ranges on your chart, filling the corresponding background space with colors you prefer. You can then write a note for each range and it will be shown in the optional related table.
The experience shows that every financial instrument has its own personality. With this in mind, the script can be useful to study intraday charts with the purpose of discovering recurring behaviours of specific instruments over a certain time range and under specific circumstances (normal days, earnings days, days with catalysts, etc.) This can help the trader to deeply understand the instrument personality, and therefore also to decide whether to enter or exit the market if its behaviour meets or not his expectations.
Please note that this script only works on minute/hourly charts.
ET TurnaroundMondayET TurnaroundMonday - two red bars, on Monday go long and exit when close > yeasterday High
OBV Trend Indicator by Bruno MoscanOBV Trend is a modification I made in other scripts, so that after months of testing, I got a very accurate indicator as to the right moment to enter a high-yield trade.
The red line indicates the very short-term OBV variation, counting the last 3 candles.
The yellow line shows the short-term OBV variation, counting the last 9 candles.
The white line shows the medium-term OBV variation, counting the last 21 candles.
The trend is up (Bull Market) while the order of the lines on the chart is Red/Yellow/White.
The trend is Bear Market while the order of the lines on the chart is White/Yellow/Red.
If the Yellow line is in the first position, either from bottom to top, or from top to bottom, it is a sign that the trend is changing, reversing, wait for the lines to organize before entering the movement.
When the lines are so close together that it is difficult to understand the correct order of the lines, it means that you are facing the best possible entry point. Wait for the lines to separate, at the first perfect food signal as described above, enter the movement, at this point, your stoploss may be very short, positioned at the base of the last candlestick.
The indicator works best on H1, H2, H4 and D1. In the Weekly until the signal reversal occurs, you can lose several days, sometimes weeks. On minute charts, there are many reversals and you can be stopped many times.
Good trends.
*Translate by Google Tradutor*
*In Brazil, we speack Portuguese, not Spanish or English :p *
VXD Cloud Edition for Python-Binance-bots.VXD Cloud Edition for Python-Binance-bots.
to overcome sideways market conditions this cloud configured for low timeframe.
every TA is same as VXD Cloud Edition but custom alert message for bots.
Risk:Reward Calculation
Risk of Ruin Setting can now selected between Fixed $ or %
if Buy your Stoploss will be Swing low
if Sell your Stoploss will be Swing high and can be setting at Pivot Setting
then Auto Position Sizing and TP line will be calculated form there and will show in Orange color line (Draw Position Box is available)
Tailing SL when price greater than RR=1
Alert Setting
{{strategy.order.alert_message}}
Python-Bot
github.com
There are 2 mode : one-way mode and hedge mode is different script in my Github profile.
read README.MD and there's video tutorial in thai language.
Pls study app.py and it's script before deploy for your own safty and your own risk, I'm NOT responsible for your loss.
Stochastic Rsi+Ema - Auto Buy Scalper Scirpt v.0.3Simple concept for a scalping script, written for 5 minute candles, optimized for BTC.
1st script I've created from scratch, somewhat from scratch. Also part of the goal of this one is to hold coin as often as possible, whenever it's sideways or not dropping significantly.
Designed to buy on the stochastic bottoms (K>D and rising, and <17)
Then and sell after 1 of 3 conditions;
a. After the price goes back up at least 1 % and then 1-2 period ema reversal
b. After the rsi reversal (is dropping) and K<D Flip
c. Stop loss at -1.5%
Bias AnalyzerName: Bias Analyzer
Category: Market Analyzer
Timeframe: 1H and 1D, depending on the Analysis type.
Technical Analysis: Usually when we think about a Trading System we start from an idea. This idea comes normally from observation and the study of the market.
Have we ever observed a market - for example Bitcoin - and thought that it increases at the start of a USA session? Great, this is a well-known category of Trading System and the purpose of the Bias Analyzer is to study these phenomena.
There are different types of Trading System that we can classify considering the market in-efficiency that we use to our advantage. In this case we make the Bias. Literally "inclination" or "presupposition" or precisely "tendency" of the price to go up or down in a temporal way.
The characteristics of the Bias depend on how much the Bias is persistent on the market since the analysed period. therefore we can consider:
Hourly Bias : analysing the hourly behaviours during the week. Trades normally last from a few hours to a few days.
Seasonal Bias : analysing the behaviour of the weeks in the monthly or annual context, evaluating the seasons.
Suggested usage: The possibilities of the tool are infinite, these are some scenarios of use:
Development of Intraday Trading Systems based on Hourly Bias with possible filters for specific days of the week.
Development of a Multi-day Trading System based on daily Bias with monthly analysis.
To identify the best day to execute our investment through Dollar Cost Average with a bit of healthy buy the dip
Main features:
Hourly Summary organized in Week
The cells contain the sum of the various price deltas for the single hour. The transparency indicates the frequency in which the candles close positive or negative. This information is available both in a synthetic way, as in the first column "Sum", and for each day of the week.
Hourly Details organized in different entry/exit
Shows the cumulative data of the various deltas, considering the purchase and the sale at certain times. In the rows are represented the buying hours and in the columns the selling hours.
Daily Summary organized in Months
The cells contain the summation of the various price deltas for the single day.
Hourly Details organized in different entry/exit
Allows to visualise the detailed analysis table, choosing to do it for all the months or for a specific month and shows the cumulative data of the various deltas, considering the purchase and the sale in certain days.
Configuration: You can configure the tool easily and completely.
Analysis
Calculate from Close to Open : this is the core of the whole analysis where the "Price Delta" to be calculated is defined. At this moment there is the possibility to calculate the distance between opening and closing.
Calculate in Percent or Cash : this allows to calculate the Price Delta in Percent or in Cash.
Analysis on 1H Timeframe
Show Hourly Summary on : allows to visualise the summary analysis table of the week. The cells contain the sum of the various price deltas for the single hour. The transparency indicates the frequency in which the candles close positive or negative. This information is available both in a synthetic way, as in the first column "Sum", and for each day of the week. At the bottom left there is also data which allows us to understand how many candles are being analysed. At the bottom of each day it is possible to visualise the cumulative data of the day. The position of the table is customizable.
Show Hourly Details of on : allows to visualise the detailed analysis table, choosing to do it for all days or for a specific day, and shows the cumulative data of the various deltas, considering the purchase and the sale at certain times. In the rows are represented the buying hours and in the columns the selling hours. For example, going to the table "All Days" we can see in the cell of row 13 and in column 22 the cumulative data of a possible buy on 13 and a sell at the end of 22. To facilitate the research of the values there is a configurable transparency system.
Analysis on 1D Timeframe
Show Daily Summary on : allows to visualise the summary analysis table of the month. The cells contain the summation of the various price deltas for the single day: The first row is the summation of all days of the month for all months in the analysis period, while the other rows represent the analysis for the various days of the individual months.
Show Daily Details of on : allows to visualise the detailed analysis table, choosing to do it for all the months or for a specific month and shows the cumulative data of the various deltas, considering the purchase and the sale in certain days. In the rows are represented the buying days and in the columns the selling days. For example, going to the table "All Months" we can see in the cell at row 1 and at column 3 the cumulative of a possible purchase on the 1st and the sale on the 3rd. To facilitate the research of the values, there is a configurable transparency system.
Table Layout
Size : allows to define the size of the text in the table.
Precision : it is possible to define the decimal precision of the calculations presented in the tables.
Transparency Factor : allows the application of a multiplication factor when the table calculates the transparency of detail tables.
Colours : allows to specify the colours of Profit, Loss and Neutral, besides to adapt a style coherent with the Dark Mode or Light Mode of Trading View
Volatility Filter
It is possible to directly apply a filter to the time series on which the delta is calculated. The volatility filter uses the ATR - an indicator that allows you to calculate the volatility in a given period. Briefly: the higher the ATR value, the higher the volatility. Therefore the filter works by comparing the volatility on two periods and indicates compression or expansion.
Backtest Dates
In order to facilitate the identification of in-sample and out-of-sample data, as well as the degradation of a given behaviour, it is possible to specify a period in which to do the analysis.
Pi CycleImplementation of Pi Cycle Top and Pi Cycle Bottom with clear visualization
Pi Circle Top is slightly modified to be closer to Pi sma 113 & sma (355) * 2)
Pi Circle Bottom is ISO sma 571 & ema 150
Script will adapt to timeframe but under hourly it might not work due to the high amount of candles to take into account.
Moon Phases/Apogee & Perigee/Eclipses/North Node by BTThis script helps us to display date of different lunar properties on price chart. Simply following items could be used to see dates.
New moon
Full moon
Apogee
Perigee
North node
I've used following web site (fourmilab.ch) for obtaining exact dates, according to web site "All dates and times are Universal time (UTC); to convert to local time add or subtract the difference between your time zone and UTC"
deseasonalized_modDescription:
As in the decomposition of time series data, the indicator was modified from the deseasonalized process, to avoid overfitting as in the original format, aiming to exclude seasonal component (shorter time-period in consideration), thus leaving trend and irregularity. The indicator is expected to identify short-term trends, based on the given timeframe.
Notes:
Length set to 10-bar as default, while reducing the length to e.g. 8 increase more responsive of the fitted data, however inline to increase a false trend identification (bias-variance trade-off). To be noted, the indicator wasn't a momentum indicator, thus can only expect the trend identifier instead. If the price is above the fitted line (indicator), and experiencing a retracement, regularly expected to be bounced around the fitted line (support), while if breaks to the downside, expected a trend reversal, and become a (resistance).
Feature:
Buy/Long overlay signal
Sell/Short overlay signal
Alert
Cycle-Period Adaptive, Linear Regression Slope Oscillator [Loxx]Cycle-Period Adaptive, Linear Regression Slope Oscillator is an osciallator that solves for the Linear Regression slope and turns it into an oscillator. This is a very simple calculation and uses one of Ehler's first implementations of his cycle period calculations. The output slope value is smoothed after calculation and before being drawn. This is a sort of momentum indicator and has a rich history with Forex traders around the world.
What is the Cycle Period?
The spectral content of the data are measured in a bank of contiguous filters as described in "Measuring Cycle Periods" in the March 2008 issue of Stocks & Commodities Magazine. The filter having the strongest output is selected as the current dominant cycle period. The cycle period is measured as the number of bars contained in one full cycle period.
What is Linear Regression?
In statistics, linear regression is a linear approach for modeling the relationship between a scalar response and one or more explanatory variables. The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression.
Included:
Bar coloring
2 signal types
Alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
Time Sessions - S/RThis scripts shows you some time sessions such as NY open, NY mid, London Open and Daily Close for UTC - 0
It also plots resistance and support based on the last 15 candles but you can change it in settings.
Days and Session
This indicator optionally displays 2 informations:
- The Day of the week
- The New session's Candle
You can turn off/on the displayed information
Disclaimer: Scripts that I post publicly are experimental. They are not financial advices. Always backtest your ideas using your own methodologies.
PA-Adaptive TRIX Log [Loxx]PA-Adaptive TRIX Log is a Phase Accumulation Adaptive TRIX Log indicator. This adaptation smooths the signal to catch larger trends.
What is TRIX?
TRIX is a momentum oscillator that displays the percent rate of change of a TEMA . It was developed in the early 1980's by Jack Hutson, an editor for "Technical Analysis of Stocks and Commodities" magazine. With its triple smoothing, TRIX is designed to filter insignificant price movements. In his article he uses a logarithm of a price (which is in many versions, left out).
What is the Phase Accumulation Cycle?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
Included
Bar coloring
2 signal options
Alerts
Poly Cycle [Loxx]This is an example of what can be done by combining Legendre polynomials and analytic signals. I get a way of determining a smooth period and relative adaptive strength indicator without adding time lag.
This indicator displays the following:
The Least Squares fit of a polynomial to a DC subtracted time series - a best fit to a cycle.
The normalized analytic signal of the cycle (signal and quadrature).
The Phase shift of the analytic signal per bar.
The Period and HalfPeriod lengths, in bars of the current cycle.
A relative strength indicator of the time series over the cycle length. That is, adaptive relative strength over the cycle length.
The Relative Strength Indicator, is adaptive to the time series, and it can be smoothed by increasing the length of decreasing the number of degrees of freedom.
Other adaptive indicators based upon the period and can be similarly constructed.
There is some new math here, so I have broken the story up into 5 Parts:
Part 1:
Any time series can be decomposed into a orthogonal set of polynomials .
This is just math and here are some good references:
Legendre polynomials - Wikipedia, the free encyclopedia
Peter Seffen, "On Digital Smoothing Filters: A Brief Review of Closed Form Solutions and Two New Filter Approaches", Circuits Systems Signal Process, Vol. 5, No 2, 1986
I gave some thought to what should be done with this and came to the conclusion that they can be used for basic smoothing of time series. For the analysis below, I decompose a time series into a low number of degrees of freedom and discard the zero mode to introduce smoothing.
That is:
time series => c_1 t + c_2 t^2 ... c_Max t^Max
This is the cycle. By construction, the cycle does not have a zero mode and more physically, I am defining the "Trend" to be the zero mode.
The data for the cycle and the fit of the cycle can be viewed by setting
ShowDataAndFit = TRUE;
There, you will see the fit of the last bar as well as the time series of the leading edge of the fits. If you don't know what I mean by the "leading edge", please see some of the postings in . The leading edges are in grayscale, and the fit of the last bar is in color.
I have chosen Length = 17 and Degree = 4 as the default. I am simply making sure by eye that the fit is reasonably good and degree 4 is the lowest polynomial that can represent a sine-like wave, and 17 is the smallest length that lets me calculate the Phase Shift (Part 3 below) using the Hilbert Transform of width=7 (Part 2 below).
Depending upon the fit you make, you will capture different cycles in the data. A fit that is too "smooth" will not see the smaller cycles, and a fit that is too "choppy" will not see the longer ones. The idea is to use the fit to try to suppress the smaller noise cycles while keeping larger signal cycles.
Part 2:
Every time series has an Analytic Signal, defined by applying the Hilbert Transform to it. You can think of the original time series as amplitude * cosine(theta) and the transformed series, called the quadrature, can be thought of as amplitude * sine(theta). By taking the ratio, you can get the angle theta, and this is exactly what was done by John Ehlers in . It lets you get a frequency out of the time series under consideration.
Amazon.com: Rocket Science for Traders: Digital Signal Processing Applications (9780471405672): John F. Ehlers: Books
It helps to have more references to understand this. There is a nice article on Wikipedia on it.
Read the part about the discrete Hilbert Transform:
en.wikipedia.org
If you really want to understand how to go from continuous to discrete, look up this article written by Richard Lyons:
www.dspguru.com
In the indicator below, I am calculating the normalized analytic signal, which can be written as:
s + i h where i is the imagery number, and s^2 + h^2 = 1;
s= signal = cosine(theta)
h = Hilbert transformed signal = quadrature = sine(theta)
The angle is therefore given by theta = arctan(h/s);
The analytic signal leading edge and the fit of the last bar of the cycle can be viewed by setting
ShowAnalyticSignal = TRUE;
The leading edges are in grayscale fit to the last bar is in color. Light (yellow) is the s term, and Dark (orange) is the quadrature (hilbert transform). Note that for every bar, s^2 + h^2 = 1 , by construction.
I am using a width = 7 Hilbert transform, just like Ehlers. (But you can adjust it if you want.) This transform has a 7 bar lag. I have put the lag into the plot statements, so the cycle info should be quite good at displaying minima and maxima (extrema).
Part 3:
The Phase shift is the amount of phase change from bar to bar.
It is a discrete unitary transformation that takes s + i h to s + i h
explicitly, T = (s+ih)*(s -ih ) , since s *s + h *h = 1.
writing it out, we find that T = T1 + iT2
where T1 = s*s + h*h and T2 = s*h -h*s
and the phase shift is given by PhaseShift = arctan(T2/T1);
Alas, I have no reference for this, all I doing is finding the rotation what takes the analytic signal at bar to the analytic signal at bar . T is the transfer matrix.
Of interest is the PhaseShift from the closest two bars to the present, given by the bar and bar since I am using a width=7 Hilbert transform, bar is the earliest bar with an analytic signal.
I store the phase shift from bar to bar as a time series called PhaseShift. It basically gives you the (7-bar delayed) leading edge the amount of phase angle change in the series.
You can see it by setting
ShowPhaseShift=TRUE
The green points are positive phase shifts and red points are negative phase shifts.
On most charts, I have looked at, the indicator is mostly green, but occasionally, the stock "retrogrades" and red appears. This happens when the cycle is "broken" and the cycle length starts to expand as a trend occurs.
Part 4:
The Period:
The Period is the number of bars required to generate a sum of PhaseShifts equal to 360 degrees.
The Half-period is the number of bars required to generate a sum of phase shifts equal to 180 degrees. It is usually not equal to 1/2 of the period.
You can see the Period and Half-period by setting
ShowPeriod=TRUE
The code is very simple here:
Value1=0;
Value2=0;
while Value1 < bar_index and math.abs(Value2) < 360 begin
Value2 = Value2 + PhaseShift ;
Value1 = Value1 + 1;
end;
Period = Value1;
The period is sensitive to the input length and degree values but not overly so. Any insight on this would be appreciated.
Part 5:
The Relative Strength indicator:
The Relative Strength is just the current value of the series minus the minimum over the last cycle divided by the maximum - minimum over the last cycle, normalized between +1 and -1.
RelativeStrength = -1 + 2*(Series-Min)/(Max-Min);
It therefore tells you where the current bar is relative to the cycle. If you want to smooth the indicator, then extend the period and/or reduce the polynomial degree.
In code:
NewLength = floor(Period + HilbertWidth+1);
Max = highest(Series,NewLength);
Min = lowest(Series,NewLength);
if Max>Min then
Note that the variable NewLength includes the lag that comes from the Hilbert transform, (HilbertWidth=7 by default).
Conclusion:
This is an example of what can be done by combining Legendre polynomials and analytic signals to determine a smooth period without adding time lag.
________________________________
Changes in this one : instead of using true/false options for every single way to display, use Type parameter as following :
1. The Least Squares fit of a polynomial to a DC subtracted time series - a best fit to a cycle.
2. The normalized analytic signal of the cycle (signal and quadrature).
3. The Phase shift of the analytic signal per bar.
4. The Period and HalfPeriod lengths, in bars of the current cycle.
5. A relative strength indicator of the time series over the cycle length. That is, adaptive relative strength over the cycle length.
MACDV DASHBOARDFrom Riliza MACD-V Volatility Normalisation and another knowledge I am just her follower, Try to make dashboard to study the market for my self.
Rev00
- MACDV with momentum
-Still need to optimize and revise many thing.
-Any wrong could you please help feed back , Not much experience.
