Accumulation Stage Identifier and Strategy around for TradingIn the psychology of trading at any market condition, there are four stage usually occurs on any tickers.
Stage 1 -> Neglect phase or consolidation phase
It occurs when the company does not produce the expected result and waiting for next result.
It can extend for days, weeks, months and years. Never give entry at this stage though that blue-chip told to be cheaper in price.
Stage 2 -> Accumulation
It occurs when the company's earning and sales consistently grows.
It can extend for days, weeks, months but should not expect the continues increase in price, as there will be potential pull-back which can be considered as opportunity to accumulate.
If the company fundamental is good, just give some space at the time of pullback.
Most of the time, the pullback volume will be low to compare to volume at the time of increase.
Usually, the stock that is going through accumulation stage will definitely trade above 200SMA and short term MA will be greater than long term moving average.
Continues the highest high and highest low along with volume.
Stage 3 -> Distribution
It occurs when the company's earning and sales stagnated due to certain reason.
It can extend for days, weeks while the price and volume highly volatile.
High volume while the price low
Typically, the stock that is going through distribution stage will certainly trade below 200SMA and short term MA will be lesser than long term moving average.
Continues the lowest high and lowest low along with volume.
Stage 4 -> Capitalization
Price reaches the 52W low while volume spikes on big down.
In each stage, the price & volume are perfect indicator to highlight the situation and the trader with proper discipline and patients can certainly reap the fruitful outcome of accumulation stage.
Based on this explanation, here is the strategy that is created with 50,90 & 200 Simple moving average and price volume trends (PVT) indicator applied on MACD to signal whenever the PVT convergence and divergence.
Note:
As the indicator designed to signal on the ticker that trade above 200 moving average, it is good to use this strategy on companies that are fundamental strong.
Whenever, there is pull back happens, the strategy might signal for exit, however, here comes the traders patient based on the conviction on the particular chosen stocks.
White being patient is good, disciplinary in following the strategy also important. Hence, consider the action when the stock goes opposite direction from your expectation.
Hope this strategy would help you find the profit.
Happy investing.
Pesquisar nos scripts por "文华财经tick价格"
T3 Volatility Quality Index (VQI) w/ DSL & Pips Filtering [Loxx]T3 Volatility Quality Index (VQI) w/ DSL & Pips Filtering is a VQI indicator that uses T3 smoothing and discontinued signal lines to determine breakouts and breakdowns. This also allows filtering by pips.***
What is the Volatility Quality Index ( VQI )?
The idea behind the volatility quality index is to point out the difference between bad and good volatility in order to identify better trade opportunities in the market. This forex indicator works using the True Range algorithm in combination with the open, close, high and low prices.
What are DSL Discontinued Signal Line?
A lot of indicators are using signal lines in order to determine the trend (or some desired state of the indicator) easier. The idea of the signal line is easy : comparing the value to it's smoothed (slightly lagging) state, the idea of current momentum/state is made.
Discontinued signal line is inheriting that simple signal line idea and it is extending it : instead of having one signal line, more lines depending on the current value of the indicator.
"Signal" line is calculated the following way :
When a certain level is crossed into the desired direction, the EMA of that value is calculated for the desired signal line
When that level is crossed into the opposite direction, the previous "signal" line value is simply "inherited" and it becomes a kind of a level
This way it becomes a combination of signal lines and levels that are trying to combine both the good from both methods.
In simple terms, DSL uses the concept of a signal line and betters it by inheriting the previous signal line's value & makes it a level.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
Included
Signals
Alerts
Related indicators
Zero-line Volatility Quality Index (VQI)
Volatility Quality Index w/ Pips Filtering
Variety Moving Average Waddah Attar Explosion (WAE)
***This indicator is tuned to Forex. If you want to make it useful for other tickers, you must change the pip filtering value to match the asset. This means that for BTC, for example, you likely need to use a value of 10,000 or more for pips filter.
Modified Covariance Autoregressive Estimator of Price [Loxx]What is the Modified Covariance AR Estimator?
The Modified Covariance AR Estimator uses the modified covariance method to fit an autoregressive (AR) model to the input data. This method minimizes the forward and backward prediction errors in the least squares sense. The input is a frame of consecutive time samples, which is assumed to be the output of an AR system driven by white noise. The block computes the normalized estimate of the AR system parameters, A(z), independently for each successive input.
Characteristics of Modified Covariance AR Estimator
Minimizes the forward prediction error in the least squares sense
Minimizes the forward and backward prediction errors in the least squares sense
High resolution for short data records
Able to extract frequencies from data consisting of p or more pure sinusoids
Does not suffer spectral line-splitting
May produce unstable models
Peak locations slightly dependent on initial phase
Minor frequency bias for estimates of sinusoids in noise
Order must be less than or equal to 2/3 the input frame size
Purpose
This indicator calculates a prediction of price. This will NOT work on all tickers. To see whether this works on a ticker for the settings you have chosen, you must check the label message on the lower right of the chart. The label will show either a pass or fail. If it passes, then it's green, if it fails, it's red. The reason for this is because the Modified Covariance method produce unstable models
H(z)= G / A(z) = G / (1+. a(2)z −1 +…+a(p+1)z)
You specify the order, "ip", of the all-pole model in the Estimation order parameter. To guarantee a valid output, you must set the Estimation order parameter to be less than or equal to two thirds the input vector length.
The output port labeled "a" outputs the normalized estimate of the AR model coefficients in descending powers of z.
The implementation of the Modified Covariance AR Estimator in this indicator is the fast algorithm for the solution of the modified covariance least squares normal equations.
Inputs
x - Array of complex data samples X(1) through X(N)
ip - Order of linear prediction model (integer)
Notable local variables
v - Real linear prediction variance at order IP
Outputs
a - Array of complex linear prediction coefficients
stop - value at time of exit, with error message
false - for normal exit (no numerical ill-conditioning)
true - if v is not a positive value
true - if delta and gamma do not lie in the range 0 to 1
true - if v is not a positive value
true - if delta and gamma do not lie in the range 0 to 1
errormessage - an error message based on "stop" parameter; this message will be displayed in the lower righthand corner of the chart. If you see a green "passed" then the analysis is valid, otherwise the test failed.
Indicator inputs
LastBar = bars backward from current bar to test estimate reliability
PastBars = how many bars are we going to analyze
LPOrder = Order of Linear Prediction, and for Modified Covariance AR method, this must be less than or equal to 2/3 the input frame size, so this number has a max value of 0.67
FutBars = how many bars you'd like to show in the future. This algorithm will either accept or reject your value input here and then project forward
Further reading
Spectrum Analysis-A Modern Perspective 1380 PROCEEDINGS OF THE IEEE, VOL. 69, NO. 11, NOVEMBER 1981
Related indicators
Levinson-Durbin Autocorrelation Extrapolation of Price
Weighted Burg AR Spectral Estimate Extrapolation of Price
Helme-Nikias Weighted Burg AR-SE Extra. of Price
Itakura-Saito Autoregressive Extrapolation of Price
Modified Covariance Autoregressive Estimator of Price
intraday_bondsStatistics for assisting with intraday bond trading, using five minute periods and one hour ranges. There are two tables, a volatility table and a correlation table. The correlation table shows the correlation of five minute returns (absolute) between the four different bond contracts that trade on the CME. The volatility table shows for each contract:
- The current realized volatility, based on the previous one hour of realized volatility. This figure is annualized for easy comparison with options contracts.
- The current realized volatility's z-score, based on all available data.
- The tick range of an "N" standard deviation move over one hour. Choose "N" using the stdevs input.
- The previous hour's true range (high - low).
The ranges are expressed in ticks.
Intrabar OBV/PVTI got this idea from @fikira's script Intrabar-Price-Volume-Change-experimental
The indicator calculates OBV and PVT based on ticks. Since, the indicator relies on live ticks, it only starts execution after it is put on the charts. The script can be useful in analysing intraday buy and sell pressure. Details are color coded based on the values.
Data is presented in simple tabular format.
Formula for OBV and PVT can be found here:
www.investopedia.com
www.investopedia.com
Volume Prism RibbonNASDAQ:SPWR
The purpose of this script is to give insight into the volume action. The relative volume is calculated (based on 400 ticks) with the volume of down days (close-close <0) being given a negative value. This function is then summed over 100 ticks. WMA's are used to generate a rainbow ribbon who's color order is easily recognized buy all of us. Watch and Warning points are added using crossover points. I find it to be a good supplement to my favorite Buy/Sell indicator. In addition to the wrapping of the ribbon, pay attention to where the zero line is as well.
Technical Analysis Consulting Table (TACT)Inspired by Tradingview's own "Technical Analysis Summary", I present to you a table with analogous logic.
You can track any ticker you want, no matter your chart. You can even have multiple tables to track multiple tickers. By default it tracks the Total Crypto Cap.
You can change the resolution you want to track. By default it is the same as the chart.
You can position the table to whichever corner of the chart you want. By default it draws in the bottom right corner.
Background colors and text size can be adjusted.
Indicators Used:
Oscillators
RSI(14)
STOCH(14, 3, 3)
CCI(20)
ADX(14)
AO
Momentum(10)
MACD(12, 26)
STOCH RSI(3, 3, 14, 14)
%R(14)
Bull Bear Power
UO(7,14,28)
Moving Averages
EMA(5)
SMA(5)
EMA(10)
SMA(10)
EMA(20)
SMA(20)
EMA(30)
SMA(30)
EMA(50)
SMA(50)
EMA(100)
SMA(100)
EMA(200)
SMA(200)
Ichimoku Cloud(9, 26, 52, 26)
VMWA(20)
HMA(9)
Pivots
Traditional
Fibonacci
Camarilla
Woodie
WARNING: I have observed up to a couple of seconds of signal jitter/delay, so use it with caution in very small resolutions (1s to 1m).
I hope you enjoy this and good luck with your trading. Suggestions and feedback are most welcome.
Bitcoin CME Gaps [NeoButane]Simple script that checks for gaps in price from CME. tickerid(x, y, sess) doesn't seem to be applying correctly for the ticker specified at the moment so there are a couple of 'gaps' peppered on lower timeframes.
Gaps are legitimate price levels to look as a support or resistance. The theory is that volume needs to be gap filled, but I currently believe it's an easy entry/exit trade for those who can move the market. I don't think there is sound analysis behind the why, but it is real.
QuantNomad - Simple Custom Screener in PineScriptQuite often I need to run screeners with the custom condition, but unfortunately, in TradingView it's impossible.
I created an example script to show how you can create a simple custom screener in Pine Script on your own.
It's not very good, it requires some manual adjustments, it can be improved in some ways, but I think it might work for some tasks.
What do you think? Do you have a better way to implement custom screeners in TradingView?
To run your own conditions you need to implement them in:
customFunc() function and for every ticker you want to include in your search add 2 lines like these with newly defined variable:
s1 = security('BTCUSD', '1', customFunc())
and
scr_label := s1 ? scr_label + 'BTCUSD\n' : scr_label
I'm not sure that it will work well for more than a few dozen tickers.
But I hope it will be helpful for you.
And remember:
Past performance does not guarantee future results.
ec tEST cODE FOR pERCENT DIFERENCE ////////////////////////////////////////////////////////////
// Copyright by HPotter v1.0 04/04/2015
// Percent difference between price and MA
////////////////////////////////////////////////////////////
study(title="Percent difference between price and MA")
source = close
useCurrentRes = input(true, title="Use Current Chart Resolution?")
resCustom = input(title="Use Different Timeframe? Uncheck Box Above", type=resolution, defval="60")
smd = input(true, title="Show MacD & Signal Line? Also Turn Off Dots Below")
sd = input(true, title="Show Dots When MacD Crosses Signal Line?")
sh = input(true, title="Show Histogram?")
macd_colorChange = input(true,title="Change MacD Line Color-Signal Line Cross?")
hist_colorChange = input(true,title="MacD Histogram 4 Colors?")
res = useCurrentRes ? period : resCustom
fastLength = input(12, minval=1), slowLength=input(26,minval=1)
signalLength=input(9,minval=1)
fastMA = ema(source, fastLength)
slowMA = ema(source, slowLength)
Length = input(9, minval=1)
Length2= input(36,minval=1)
Length3= input(81,minval=1)
AveragePrice= input(9,minval=1)
Length5= input(3,minval=1)
xSMA = (sma(close, Length)+sma(close, Length2)+sma(close, Length3))/3
pSAM=sma(close, AveragePrice)
nRes = (pSAM - xSMA) * 100 / close
signalnRes = sma(nRes, signalLength)
macd = nRes
signal = sma(macd, signalLength)
hist = macd - signal
outMacD = security(tickerid, res, macd)
outSignal = security(tickerid, res, signal)
outHist = security(tickerid, res, hist)
histA_IsUp = outHist > outHist and outHist > 0
histA_IsDown = outHist < outHist and outHist > 0
histB_IsDown = outHist < outHist and outHist <= 0
histB_IsUp = outHist > outHist and outHist <= 0
//MacD Color Definitions
macd_IsAbove = outMacD >= outSignal
macd_IsBelow = outMacD < outSignal
plot_color = hist_colorChange ? histA_IsUp ? aqua : histA_IsDown ? blue : histB_IsDown ? red : histB_IsUp ? maroon :yellow :gray
macd_color = macd_colorChange ? macd_IsAbove ? lime : red : red
signal_color = macd_colorChange ? macd_IsAbove ? yellow : yellow : lime
circleYPosition = outSignal
// MA COLOR DEFINITION
maColor = change(nRes)>0 ? green : change(nRes)<0 ? red : na
mA_IsAbove = nRes> 0
mA_IsBelow = nRes< 0
plot( nRes, color=maColor, style=line, title="MMA", linewidth=2)
//plot(smd and signalnRes ? signalnRes : na, title="Signal Line", color=signal_color, style=line ,linewidth=2)
//plot(smd and outMacD ? outMacD : na, title="MACD", color=macd_color, linewidth=4)
//plot(smd and outSignal ? outSignal : na, title="Signal Line", color=signal_color, style=line ,linewidth=2)
//plot(sh and outHist ? outHist : na, title="Histogram", color=plot_color, style=histogram, linewidth=4)
plot(sd and cross(outMacD, outSignal) ? circleYPosition : na, title="Cross", style=circles, linewidth=4, color=macd_color)
hline(0, '0 Line', linestyle=solid, linewidth=2, color=white)
//////ALERT cONDITION////
src = input(close)
ma_1 = sma(src, 20)
ma_2 = sma(src, 10)
c = cross(ma_1, ma_2)
alertcondition(c, title='Red crosses blue', message='Red and blue have crossed!')
d = cross(outMacD, outSignal)
alertcondition(d, title='GOING DOWN', message='SELL!')
//
//e = cross(outSignal, outMacD)
//alertcondition(E, title='GOING UP', message='BUY!')
BTC World Price: Multi-Exchange VWAPBTC World Price: Multi-Exchange VWAP
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WHAT IT DOES
What you see above are not Bitmex candles, but this indicator's.
Bitcoin is listed on multiple exchanges. Many people have called for a single global index that would quote BTC price and volume across all exchanges: this script is such a virtual aggregate (formerly: Multi-Listed , Volume-Weighted Average Price ).
It will, independently for each tick, for any time-frame:
- Quote the price (O, H, L, C) and volume from Bitfinex (USD), Binance (USDT), bitFlyer (Yen), Bithumb (S. Korean Won), Coinbase (USD), Kraken (EUR) and even Bitmex (USD Contracts).
- Weight each price with the corresponding volume of the exchange.
- Quote the FOREX conversion rate in USD for each currency (USDJPY etc.)
- Finally return global average price (candles) in USD.
- Additionally provide (H+L)/2 etc. values.
No more "on Coinbase this" or "on Bitstamp that", you've now got a global overview!
See CoinMarketCap: Markets for reference. I've included alternative exchanges in the comments at the top of the script.
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HOW TO USE IT
Basically just add it to your chart and use the indicator's candles instead of the chart's main ticker.
By default, BTC World Price will display candles only, but you can also display OHLC & averages (in whichever style you want).
You may indeed want to hide the main symbol (top-left corner, click the 'eye' button next to its name), or switch it to something else than candles/bars (e.g. line).
Make sure "Scale Price Chart Only" is disabled if you want to use the auto-zoom feature. (if other indicators are messing your zoom, you can try to select "Line with Breaks" or "Area with Breaks" to allow these to overflow from the main window)
By clicking the triangle next to the indicator's name, you can select "Visual Order" (e.g "Bring to Front").
You can select regular Candles or Heikin-Ashi in Options.
In the Format > Inputs tab, you can select which exchanges to quote. By default, all of them are enabled.
The script also exposes the following typical values to the backend, which you can use as Price Source for other indicators: (e.g. MA, RSI, in their "Format > Input" tab)
Open Price (grey)
High Price (green)
Low Price (red)
Close Price (white)
(H + L)/2 (light blue)
(H + L + C)/3 (blue)
(O + H + L + C)/4 (purple)
They are all hidden by default (by means of maximum transparency).
In the Format > Style tab, you can change their color, transparency and style (line, area, etc), as well as uncheck Candles and Wicks to hide these.
If you are using "Indicator Last Value" and want to clear the clutter from all these values, simply uncheck them in Style. They will still be available as Price Source for other indicators.
You can also choose to scale it to the left, right (default) or "screen" (no scaling).
Once you're satisfied with your Style, you may click "Default"> "Save as default" in the botton-left. Everytime you load the indicator, it will look the same. ("Reset Settings" will reset to the script's defaults)
__________________________
Please leave feedback below in comments or pm me directly for bugs and suggestions.
SMART4TRADER-Margin ZONEIndicator based on marginal zones (according to Mityukov Sergey). In open source.
Formula for calculating the margin:
Margin size / cost tick * minimum price change
Example:
EURUSD = 2100 $ / 6.25 $ * 0.00005 points = 0.01680 points
....
For currency pairs where USD is in the first place it is necessary to write so that the indicator is taken away from zero
Iff (ticker == "USDCAD", (0- (950/5 * 0.00005)),
//////////////////////////////////////////////////////////////////////////////////////////////////
Индикатор на основе маржинальных зон (по Митюкову Сергею). В открытом исходном коде.
Формула рассчета маржи:
размер маржи / стоимость тика * минимальное ценовое изменение
Пример:
EURUSD = 2100 $ / 6.25 $ * 0.00005 points = 0.01680 points
....
Для валютных пар где USD стоит на первом месте нужно писать так, чтобы показатель отнимался от нуля
iff (ticker=="USDCAD", (0-(950/5*0.00005)),
//////////////////////////////////////////////////////////////////////////////////////////////////
Convert Yuan value symbols to USDIGNORE PREVIOUS SCRIPT/POST (titled: "yuan normiz")
If you like to look add symbols that are valued in China's Yuan and want to convert them to USD accurately then this is the perfect script for you.
"I'm not sure if this script is for me. Does my setup apply here?"
If either of these resemble your chart setup then this is for you:
Example 1: You have COINBASE:BTCUSD on your main chart often add to compare Bitstamp:btcusd and Okcoin:btccny.
Example 2: You have SPY or SPX (or DJIA etc) as your main chart but like to add other composites to compare like SSE(Shanghai Stock Exchange index) to your main chart.
This takes the symbol of your choice (default is BTCCHINA:BTCCNY) that is expressed in Yuan and divides it by the corresponding value of IDC's USDCNH ticker. Not the last value of USDCNH, but the respective tick mark----BTCCNY's close 3 months ago is divided by USDCNH's close 3 months ago.
Live Market - Performance MonitorLive Market — Performance Monitor
Study material (no code) — step-by-step training guide for learners
________________________________________
1) What this tool is — short overview
This indicator is a live market performance monitor designed for learning. It scans price, volume and volatility, detects order blocks and trendline events, applies filters (volume & ATR), generates trade signals (BUY/SELL), creates simple TP/SL trade management, and renders a compact dashboard summarizing market state, risk and performance metrics.
Use it to learn how multi-factor signals are constructed, how Greeks-style sensitivity is replaced by volatility/ATR reasoning, and how a live dashboard helps monitor trade quality.
________________________________________
2) Quick start — how a learner uses it (step-by-step)
1. Add the indicator to a chart (any ticker / timeframe).
2. Open inputs and review the main groups: Order Block, Trendline, Signal Filters, Display.
3. Start with defaults (OB periods ≈ 7, ATR multiplier 0.5, volume threshold 1.2) and observe the dashboard on the last bar.
4. Walk the chart back in time (use the last-bar update behavior) and watch how signals, order blocks, trendlines, and the performance counters change.
5. Run the hands-on labs below to build intuition.
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3) Main configurable inputs (what you can tweak)
• Order Block Relevant Periods (default ~7): number of consecutive candles used to define an order block.
• Min. Percent Move for Valid OB (threshold): minimum percent move required for a valid order block.
• Number of OB Channels: how many past order block lines to keep visible.
• Trendline Period (tl_period): pivot lookback for detecting highs/lows used to draw trendlines.
• Use Wicks for Trendlines: whether pivot uses wicks or body.
• Extension Bars: how far trendlines are projected forward.
• Use Volume Filter + Volume Threshold Multiplier (e.g., 1.2): requires volume to be greater than multiplier × average volume.
• Use ATR Filter + ATR Multiplier: require bar range > ATR × multiplier to filter noise.
• Show Targets / Table settings / Colors for visualization.
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4) Core building blocks — what the script computes (plain language)
Price & trend:
• Spot / LTP: current close price.
• EMA 9 / 21 / 50: fast, medium, slow moving averages to define short/medium trend.
o trend_bullish: EMA9 > EMA21 > EMA50
o trend_bearish: EMA9 < EMA21 < EMA50
o trend_neutral: otherwise
Volatility & noise:
• ATR (14): average true range used for dynamic target and filter sizing.
• dynamic_zone = ATR × atr_multiplier: minimum bar range required for meaningful move.
• Annualized volatility: stdev of price changes × sqrt(252) × 100 — used to classify volatility (HIGH/MEDIUM/LOW).
Momentum & oscillators:
• RSI 14: overbought/oversold indicator (thresholds 70/30).
• MACD: EMA(12)-EMA(26) and a 9-period signal line; histogram used for momentum direction and strength.
• Momentum (ta.mom 10): raw momentum over 10 bars.
Mean reversion / band context:
• Bollinger Bands (20, 2σ): upper, mid, lower.
o price_position measures where price sits inside the band range as 0–100.
Volume metrics:
• avg_volume = SMA(volume, 20) and volume_spike = volume > avg_volume × volume_threshold
o volume_ratio = volume / avg_volume
Support & Resistance:
• support_level = lowest low over 20 bars
• resistance_level = highest high over 20 bars
• current_position = percent of price between support & resistance (0–100)
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5) Order Block detection — concept & logic
What it tries to find: a bar (the base) followed by N candles in the opposite direction (a classical order block setup), with a minimum % move to qualify. The script records the high/low of the base candle, averages them, and plots those levels as OB channels.
How learners should think about it (conceptual):
1. An order block is a signature area where institutions (theory) left liquidity — often seen as a large bar followed by a sequence of directional candles.
2. This indicator uses a configurable number of subsequent candles to confirm that the pattern exists.
3. When found, it stores and displays the base candle’s high/low area so students can see how price later reacts to those zones.
Implementation note for learners: the tool keeps a limited history of OB lines (ob_channels). When new OBs exceed the count, the oldest lines are removed — good practice to avoid clutter.
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6) Trendline detection — idea & interpretation
• The script finds pivot highs and lows using a symmetric lookback (tl_period and half that as right/left).
• It then computes a trendline slope from successive pivots and projects the line forward (extension_bars).
• Break detection: Resistance break = close crosses above the projected resistance line; Support break = close crosses below projected support.
Learning tip: trendlines here are computed from pivot points and time. Watch how changing tl_period (bigger = smoother, fewer pivots) alters the trendlines and break signals.
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7) Signal generation & filters — step-by-step
1. Primary triggers:
o Bullish trigger: order block bullish OR resistance trendline break.
o Bearish trigger: bearish order block OR support trendline break.
2. Filters applied (both must pass unless disabled):
o Volume filter: volume must be > avg_volume × volume_threshold.
o ATR filter: bar range (high-low) must exceed ATR × atr_multiplier.
o Not in an existing trade: new trades only start if trade_active is false.
3. Trend confirmation:
o The primary trigger is only confirmed if trend is bullish/neutral for buys or bearish/neutral for sells (EMA alignment).
4. Result:
o When confirmed, a long or short trade is activated with TP/SL calculated from ATR multiples.
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8) Trade management — what the tool does after a signal
• Entry management: the script marks a trade as trade_active and sets long_trade or short_trade flags.
• TP & SL rules:
o Long: TP = high + 2×ATR ; SL = low − 1×ATR
o Short: TP = low − 2×ATR ; SL = high + 1×ATR
• Monitoring & exit:
o A trade closes when price reaches TP or SL.
o When TP/SL hit, the indicator updates win_count and total_pnl using a very simple calculation (difference between TP/SL and previous close).
o Visual lines/labels are drawn for TP and updated as the trade runs.
Important learner notes:
• The script does not store a true entry price (it uses close in its P&L math), so PnL is an approximation — treat this as a learning proxy, not a position accounting system.
• There’s no sizing, slippage, or fee accounted — students must manually factor these when translating to real trades.
• This indicator is not a backtesting strategy; strategy.* functions would be needed for rigorous backtest results.
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9) Signal strength & helper utilities
• Signal strength is a composite score (0–100) made up of four signals worth 25 points each:
1. RSI extreme (overbought/oversold) → 25
2. Volume spike → 25
3. MACD histogram magnitude increasing → 25
4. Trend existence (bull or bear) → 25
• Progress bars (text glyphs) are used to visually show RSI and signal strength on the table.
Learning point: composite scoring is a way to combine orthogonal signals — study how changing weights changes outcomes.
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10) Dashboard — how to read each section (walkthrough)
The dashboard is split into sections; here's how to interpret them:
1. Market Overview
o LTP / Change%: immediate price & daily % change.
2. RSI & MACD
o RSI value plus progress bar (overbought 70 / oversold 30).
o MACD histogram sign indicates bullish/bearish momentum.
3. Volume Analysis
o Volume ratio (current / average) and whether there’s a spike.
4. Order Block Status
o Buy OB / Sell OB: the average base price of detected order blocks or “No Signal.”
5. Signal Status
o 🔼 BUY or 🔽 SELL if confirmed, or ⚪ WAIT.
o No-trade vs Active indicator summarizing market readiness.
6. Trend Analysis
o Trend direction (from EMAs), market sentiment score (composite), volatility level and band/position metrics.
7. Performance
o Win Rate = wins / signals (percentage)
o Total PnL = cumulative PnL (approximate)
o Bull / Bear Volume = accumulated volumes attributable to signals
8. Support & Resistance
o 20-bar highest/lowest — use as nearby reference points.
9. Risk & R:R
o Risk Level from ATR/price as a percent.
o R:R Ratio computed from TP/SL if a trade is active.
10. Signal Strength & Active Trade Status
• Numeric strength + progress bar and whether a trade is currently active with TP/SL display.
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11) Alerts — what will notify you
The indicator includes pre-built alert triggers for:
• Bullish confirmed signal
• Bearish confirmed signal
• TP hit (long/short)
• SL hit (long/short)
• No-trade zone
• High signal strength (score > 75%)
Training use: enable alerts during a replay session to be notified when the indicator would have signalled.
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12) Labs — hands-on exercises for learners (step-by-step)
Lab A — Order Block recognition
1. Pick a 15–30 minute timeframe on a liquid ticker.
2. Use default OB periods (7). Mark each time the dashboard shows a Buy/Sell OB.
3. Manually inspect the chart at the base candle and the following sequence — draw the OB zone by hand and watch later price reactions to it.
4. Repeat with OB periods 5 and 10; note stability vs noise.
Lab B — Trendline break confirmation
1. Increase trendline period (e.g., 20), watch trendlines form from pivots.
2. When a resistance break is flagged, compare with MACD & volume: was momentum aligned?
3. Note false breaks vs confirmed moves — change extension_bars to see projection effects.
Lab C — Filter sensitivity
1. Toggle Use Volume Filter off, and record the number and quality of signals in a 2-day window.
2. Re-enable volume filter and change threshold from 1.2 → 1.6; note how many low-quality signals are filtered out.
Lab D — Trade management simulation
1. For each signalled trade, record the time, close entry approximation, TP, SL, and eventual hit/miss.
2. Compute actual PnL if you had entered at the open of the next bar to compare with the script’s PnL math.
3. Tabulate win rate and average R:R.
Lab E — Performance review & improvement
1. Build a spreadsheet of signals over 30–90 periods with columns: Date, Signal type, Entry price (real), TP, SL, Exit, PnL, Notes.
2. Analyze which filters or indicators contributed most to winners vs losers and adjust weights.
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13) Common pitfalls, assumptions & implementation notes (things to watch)
• P&L simplification: total_pnl uses close as a proxy entry price. Real entry/exit prices and slippage are not recorded — so PnL is approximate.
• No position sizing or money management: the script doesn’t compute position size from equity or risk percent.
• Signal confirmation logic: composite "signal_strength" is a simple 4×25 point scheme — explore different weights or additional signals.
• Order block detection nuance: the script defines the base candle and checks the subsequent sequence. Be sure to verify whether the intended candle direction (base being bullish vs bearish) aligns with academic/your trading definition — read the code carefully and test.
• Trendline slope over time: slope is computed using timestamps; small differences may make lines sensitive on very short timeframes — using bar_index differences is usually more stable.
• Not a true backtester: to evaluate performance statistically you must transform the logic into a strategy script that places hypothetical orders and records exact entry/exit prices.
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14) Suggested improvements for advanced learners
• Record true entry price & timestamp for accurate PnL.
• Add position sizing: risk % per trade using SL distance and account size.
• Convert to strategy. (Pine Strategy)* to run formal backtests with equity curves, drawdowns, and metrics (Sharpe, Sortino).
• Log trades to an external spreadsheet (via alerts + webhook) for offline analysis.
• Add statistics: average win/loss, expectancy, max drawdown.
• Add additional filters: news time blackout, market session filters, multi-timeframe confirmation.
• Improve OB detection: combine wick/body, volume spike at base bar, and liquidity sweep detection.
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15) Glossary — quick definitions
• ATR (Average True Range): measure of typical range; used to size targets and stops.
• EMA (Exponential Moving Average): trend smoothing giving more weight to recent prices.
• RSI (Relative Strength Index): momentum oscillator; >70 overbought, <30 oversold.
• MACD: momentum oscillator using difference of two EMAs.
• Bollinger Bands: volatility bands around SMA.
• Order Block: a base candle area with subsequent confirmation candles; a zone of institutional interest (learning model).
• Pivot High/Low: local turning point defined by candles on both sides.
• Signal Strength: combined score from multiple indicators.
• Win Rate: proportion of signals that hit TP vs total signals.
• R:R (Risk:Reward): ratio of potential reward (TP distance) to risk (entry to SL).
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16) Limitations & assumptions (be explicit)
• This is an indicator for learning — not a trading robot or broker connection.
• No slippage, fees, commissions or tie-in to real orders are considered.
• The logic is heuristic (rule-of-thumb), not a guarantee of performance.
• Results are sensitive to timeframe, market liquidity, and parameter choices.
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17) Practical classroom / study plan (4 sessions)
• Session 1 — Foundations: Understand EMAs, ATR, RSI, MACD, Bollinger Bands. Run the indicator and watch how these numbers change on a single day.
• Session 2 — Zones & Filters: Study order blocks and trendlines. Test volume & ATR filters and note changes in false signals.
• Session 3 — Simulated trading: Manually track 20 signals, compute real PnL and compare to the dashboard.
• Session 4 — Improvement plan: Propose changes (e.g., better PnL accounting, alternative OB rule) and test their impact.
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18) Quick reference checklist for each signal
1. Was an order block or trendline break detected? (primary trigger)
2. Did volume meet threshold? (filter)
3. Did ATR filter (bar size) show a real move? (filter)
4. Was trend aligned (EMA 9/21/50)? (confirmation)
5. Signal confirmed → mark entry approximation, TP, SL.
6. Monitor dashboard (Signal Strength, Volatility, No-trade zone, R:R).
7. After exit, log real entry/exit, compute actual PnL, update spreadsheet.
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19) Educational caveat & final note
This tool is built for training and analysis: it helps you see how common technical building blocks combine into trade ideas, but it is not a trading recommendation. Use it to develop judgment, to test hypotheses, and to design robust systems with proper backtesting and risk control before risking capital.
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20) Disclaimer (must include)
Training & Educational Only — This material and the indicator are provided for educational purposes only. Nothing here is investment advice or a solicitation to buy or sell financial instruments. Past simulated or historical performance does not predict future results. Always perform full backtesting and risk management, and consider seeking advice from a qualified financial professional before trading with real capital.
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P/B Ratio (Per Share) vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Book (P/B) ratio in relation to Bollinger Bands and its historical median.
- 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context.
- 💡 Vertical green shading is applied when the P/B ratio drops below the lower Bollinger Band, which is calculated directly from the P/B ratio itself — not price. This condition often signals the ticker may be oversold.
- 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk.
- 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry.
- ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017.
- ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete.
- ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence.
- 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity.
- 📢 Idea is based on the script "Historical PE ratio vs median" by haribotagada
- 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed.
P/E Ratio vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Earnings (P/E) ratio in relation to Bollinger Bands and its historical median.
- 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context.
- 💡 Vertical green shading is applied when the P/E ratio drops below the lower Bollinger Band, which is calculated directly from the P/E ratio itself — not price. This condition often signals the ticker may be oversold.
- 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk.
- 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry.
- ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017.
- ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete.
- ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence.
- 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity.
- 📢 Idea is based on the script "Historical PE ratio vs median" by haribotagada
- 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed.
P/S Ratio vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Sales (P/S) ratio in relation to Bollinger Bands and its historical median.
- 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context.
- 💡 Vertical green shading is applied when the P/S ratio drops below the lower Bollinger Band, which is calculated directly from the P/S ratio itself — not price. This condition often signals the ticker may be oversold.
- 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk.
- 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry.
- ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017.
- ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete.
- ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence.
- 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity.
- 📢 Idea is based on the script "Historical PE ratio vs median" by @haribotagada
- 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed.
Strat Failed 2-Up/2-Down Scanner v2**Strat Failed 2-Up/2-Down Scanner**
The Strat Failed 2-Up/2-Down Scanner is designed for traders using The Strat methodology, developed by Rob Smith, to identify key reversal patterns in any market and timeframe. This indicator detects two specific candlestick patterns: Failed 2-Up (bearish) and Failed 2-Down (bullish), which signal potential reversals when a directional move fails to follow through.
**What It Does**
- **Failed 2-Up**: Identifies a bearish candle where the low and high are higher than the previous candle’s low and high, but the close is below the open, indicating a failed attempt to continue an uptrend. These are marked with a red candlestick, a red downward triangle above the bar, and a table entry.
- **Failed 2-Down**: Identifies a bullish candle where the high and low are lower than the previous candle’s high and low, but the close is above the open, signaling a failed downtrend. These are marked with a green candlestick, a green upward triangle below the bar, and a table entry.
- A table in the top-right corner displays the signal type ("Failed 2-Up" or "Failed 2-Down") and the ticker symbol for quick reference.
- Alerts are provided for both patterns, making the indicator compatible with TradingView’s screener for automated scanning.
**How It Works**
The indicator analyzes each candlestick’s high, low, and close relative to the previous candle:
- Failed 2-Up: `low > low `, `high > high `, `close < open`.
- Failed 2-Down: `high < high `, `low < low `, `close > open`.
When these conditions are met, the indicator applies visual markers (colored bars and triangles) and updates the signal table. Alert conditions trigger notifications for integration with TradingView’s alert system.
**How to Use**
1. Apply the indicator to any chart (stocks, forex, crypto, etc.) on any timeframe (e.g., 1-minute, hourly, daily).
2. Monitor the chart for red (Failed 2-Up) or green (Failed 2-Down) candlesticks with corresponding triangles.
3. Check the top-right table for the latest signal and ticker.
4. Set alerts by selecting “Failed 2-Up Detected” or “Failed 2-Down Detected” in TradingView’s alert menu to receive notifications (e.g., via email or app).
5. Use the signals to identify potential reversal setups in conjunction with other Strat-based analysis, such as swing levels or time-based strategies.
**Originality**
Unlike other Strat indicators that may focus on swing levels or complex candlestick combinations, this scanner specifically targets Failed 2-Up and Failed 2-Down patterns with clear, minimalist visualizations (bars, triangles, table) and robust alert functionality. Its simplicity makes it accessible for both novice and experienced traders using The Strat methodology.
**Ideal For**
Day traders, swing traders, and scalpers looking to capitalize on reversal signals in trending or ranging markets. The indicator is versatile for any asset class and timeframe, enhancing trade decision-making with The Strat’s pattern-based approach.
MacD Alerts MACD Triggers (MTF) — Buy/Sell Alerts
What it is
A clean, multi-timeframe MACD indicator that gives you separate, ready-to-use alerts for:
• MACD Buy – MACD line crosses above the Signal line
• MACD Sell – MACD line crosses below the Signal line
It keeps the familiar MACD lines + histogram, adds optional 4-color histogram logic, and marks crossovers with green/red dots. Works on any symbol and any timeframe.
How signals are generated
• MACD = EMA(fast) − EMA(slow)
• Signal = SMA(MACD, length)
• Buy when crossover(MACD, Signal)
• Sell when crossunder(MACD, Signal)
• You can compute MACD on the chart timeframe or lock it to another timeframe (e.g., 1h MACD on a 4h chart).
Key features
• MTF engine: choose Use Current Chart Resolution or a custom timeframe.
• Separate alert conditions: publish two alerts (“MACD Buy” and “MACD Sell”)—ideal for different notifications or webhooks.
• Visuals: MACD/Signal lines, optional 4-color histogram (trend & above/below zero), and crossover dots.
• Heikin Ashi friendly: runs on whatever candle type your chart uses. (Tip below if you want “regular” candles while viewing HA.)
Settings (Inputs)
• Use Current Chart Resolution (on/off)
• Custom Timeframe (when the above is off)
• Show MACD & Signal / Show Histogram / Show Dots
• Color MACD on Signal Cross
• Use 4-color Histogram
• Lengths: Fast EMA (12), Slow EMA (26), Signal SMA (9)
How to set alerts (2 minutes)
1. Add the script to your chart.
2. Click ⏰ Alerts → + Create Alert.
3. Condition: choose this indicator → MACD Buy.
4. Options: Once per bar close (recommended).
5. Set your notification method (popup/email/webhook) → Create.
6. Repeat for MACD Sell.
Webhook tip: send JSON like
{"symbol":"{{ticker}}","time":"{{timenow}}","signal":"BUY","price":"{{close}}"}
(and “SELL” for the sell alert).
Good to know
• Symbol-agnostic: use it on crypto, stocks, indices—no symbol is hard-coded.
• Timeframe behavior: alerts are evaluated on bar close of the MACD timeframe you pick. Using a higher TF on a lower-TF chart is supported.
• Heikin Ashi note: if your chart uses HA, the calculations use HA by default. To force “regular” candles while viewing HA, tweak the code to use ticker.heikinashi() only when you want it.
• No repainting on close: crossover signals are confirmed at bar close; choose Once per bar close to avoid intra-bar noise.
Disclaimer
This is a tool, not advice. Test across timeframes/markets and combine with risk management (position sizing, SL/TP). Past performance ≠ future results.
Daily Manipulation Probability Dashboard📜 Summary
Tired of getting stopped out on a "Judas Swing" just before the price moves in your intended direction? This indicator is designed to give you a statistical edge by quantifying the daily manipulation move.
The Daily Manipulation Probability Dashboard analyzes thousands of historical trading days to reveal the probability of the initial "stop-hunt" or "fakeout" move reaching certain percentage levels. It presents this data in a clean, intuitive dashboard right on your chart, helping you make more data-driven decisions about stop-loss placement and entry timing.
🧠 The Core Concept
The logic is simple but powerful. For every trading day, we measure two things:
Amplitude Above Open (AAO): The distance price travels up from the daily open (High - Open).
Amplitude Below Open (ABO): The distance price travels down from the daily open (Open - Low).
The indicator defines the "Manipulation" as the smaller of these two moves. The idea is that this smaller move often acts as a liquidity grab to trap traders before the day's primary, larger move ("Distribution") begins.
This tool focuses exclusively on providing deep statistical insight into this crucial manipulation phase.
🛠️ How to Use This Tool
This dashboard is designed to be a practical part of your daily analysis and trade planning.
1. Smarter Stop-Loss Placement
This is the primary use case. The "Prob. (%)" column tells you the historical chance of the manipulation move being at least a certain size.
Example: If the table shows that for EURUSD, the ≥ 0.25% level has a probability of 30%, you can flip this information: there is a 70% probability that the daily manipulation move will be less than 0.25%.
Action: Placing your stop-loss just beyond a level with a low probability gives you a statistically sound buffer against typical stop-hunts.
2. Entry Timing and Patience
The live arrow (→) shows you where the current day's manipulation falls.
Example: If the arrow is pointing at ≥ 0.10% and you know there is a high probability (e.g., 60%) of the manipulation reaching ≥ 0.20%, you might wait for a deeper pullback before entering, anticipating that the "Judas Swing" hasn't completed yet.
3. Assessing Daily Character
Quickly see if the current day's action is unusual. If the manipulation move is already in a very low probability zone (e.g., > 1.00%), it might indicate that your Bias is wrong, or signal a high-volatility day or a potential trend reversal.
📊 Understanding the Dashboard
Ticker: The top-right shows the current symbol you are analyzing.
→ (Arrow): Points to the row that corresponds to the current, live day's manipulation amplitude.
Manip. Level: The percentage threshold being analyzed (e.g., ≥ 0.20%).
Days Analyzed: The raw count of historical days where the manipulation move met or exceeded this level.
Prob. (%): The key statistic. The cumulative probability of the manipulation move being at least the size of the level.
⚙️ Settings
Position: Choose where you want the dashboard to appear on your chart.
Text Size: Adjust the font size for readability.
Max Historical Days to Analyze: Set the number of past daily candles to include in the statistical analysis. A larger number provides a more robust sample size.
I believe this tool provides a unique, data-driven edge for intraday traders across all markets (Forex, Crypto, Stocks, Indices). Your feedback and suggestions are highly welcome!
- @traderprimez
PnL_EMA_TRACK12_PRO_3.3_full_adjusted# Multi-Ticker Support
Manage up to 12 tickers simultaneously.
- For each symbol, input share quantities, entry prices, and two optional additional entry points (E2, E3) with their own shares and offset percentages.
- Dynamic handling of inputs using arrays for easier maintenance and scalability.
# Average Cost and PnL Calculation
- Computes weighted average entry costs across all position parts (E1 and optionally E2 and E3).
- Calculates real-time Profit & Loss (PnL) both in USD and percentage relative to the current price.
- Color-coded values: green for profit, red for loss — for quick visual feedback.
# Moving Averages as Benchmarks
- Uses daily EMAs (10, 21, 65) and 15-minute SMA 200 as reference levels.
- Calculates percentage deviations of these moving averages from the average entry price.
- Calculates dollar differences based on the total shares held.
# Chart Visualization
- Draws a dashed yellow line for the average cost of each position.
- Optionally draws two additional lines and labels for E2 (blue) and E3 (purple) if activated.
- Lines extend to the right to emphasize current relevance.
- Labels can be positioned left or right, with customizable horizontal offset.
# Interactive Table in Chart
- Positions the info table in any chosen corner or center of the chart (top/right/left/middle, etc.).
- Displays symbol, PnL (dollar and percentage), and deviations to key EMAs and SMA.
- Colors PnL values according to profit or loss for instant clarity.
# User-Friendly Settings
- Flexible font size options for both the table and labels.
- Customizable colors for positive and negative values (default green/red).
- Choice of label position and X-axis offset to fit your chart style.
X-Day Capital Efficiency ScoreThis indicator helps identify the Most Profitable Movers for Your fixed Capital (ie, which assets offer the best average intraday profit potential for a fixed capital).
Unlike traditional volatility indicators (like ATR or % change), this script calculates how much real dollar profit you could have made each day over a custom lookback period — assuming you deployed your full capital into that ticker daily.
How it works:
Calculates the daily intraday range (high − low)
Filters for clean candles (where body > 60% of the candle range)
Assumes you invested the full amount of capital ($100K set as default) on each valid day
Computes an average daily profit score based on price action over the selected period (default set to 20 days)
Plots the score in dollars — higher = more efficient use of capital
Why It’s Useful:
Compare tickers based on real dollar return potential — not just % volatility
Spot low-priced, high-volatility stocks that are better suited for intraday or momentum trading
Inputs:
Capital ($): Amount you're hypothetically deploying (e.g., 100,000)
Look Back Period: Number of past days to average over (e.g., 20)
Essa - Multi-Timeframe LevelsEnhanced Multi‐Timeframe Levels
This indicator plots yearly, quarterly and monthly highs, lows and midpoints on your chart. Each level is drawn as a horizontal line with an optional label showing “ – ” (for example “Apr 2025 High – 1.2345”). If two or more timeframes share the same price (within two ticks), they are merged into a single line and the label lists each timeframe.
A distance table can be shown in any corner of the chart. It lists up to five active levels closest to the current closing price and shows for each level:
level name (e.g. “May 2025 Low”)
exact price
distance in pips or points (calculated according to the instrument’s tick size)
percentage difference relative to the close
Alerts can be enabled so that whenever price comes within a user-specified percentage of any level (for example 0.1 %), an alert fires. Once price decisively crosses a level, that level is marked as “broken” so it does not trigger again. Built-in alertcondition hooks are also provided for definite breaks of the current monthly, quarterly and yearly highs and lows.
Monthly lookback is configurable (default 6 months), and once the number of levels exceeds a cap (calculated as 20 + monthlyLookback × 3), the oldest levels are automatically removed to avoid clutter. Line widths and colours (with adjustable opacity for quarterly and monthly) can be set separately for each timeframe. Touches of each level are counted internally to allow future extension (for example visually emphasising levels with multiple touches).
