Filtered, N-Order Power-of-Cosine, Sinc FIR Filter [Loxx]Filtered, N-Order Power-of-Cosine, Sinc FIR Filter is a Discrete-Time, FIR Digital Filter that uses Power-of-Cosine Family of FIR filters. This is an N-order algorithm that allows up to 50 values for alpha, orders, of depth. This one differs from previous Power-of-Cosine filters I've published in that it this uses Windowed-Sinc filtering. I've also included a Dual Element Lag Reducer using Kalman velocity, a standard deviation filter, and a clutter filter. You can read about each of these below.
Impulse Response
What are FIR Filters?
In discrete-time signal processing, windowing is a preliminary signal shaping technique, usually applied to improve the appearance and usefulness of a subsequent Discrete Fourier Transform. Several window functions can be defined, based on a constant (rectangular window), B-splines, other polynomials, sinusoids, cosine-sums, adjustable, hybrid, and other types. The windowing operation consists of multipying the given sampled signal by the window function. For trading purposes, these FIR filters act as advanced weighted moving averages.
A finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
What is a Standard Deviation Filter?
If price or output or both don't move more than the (standard deviation) * multiplier then the trend stays the previous bar trend. This will appear on the chart as "stepping" of the moving average line. This works similar to Super Trend or Parabolic SAR but is a more naive technique of filtering.
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
What is a Dual Element Lag Reducer?
Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array:
2 * coeff - coeff
The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing .
Whats a Windowed-Sinc Filter?
Windowed-sinc filters are used to separate one band of frequencies from another. They are very stable, produce few surprises, and can be pushed to incredible performance levels. These exceptional frequency domain characteristics are obtained at the expense of poor performance in the time domain, including excessive ripple and overshoot in the step response. When carried out by standard convolution, windowed-sinc filters are easy to program, but slow to execute.
The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
For our purposes here, we are used a normalized Sinc function
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
Related indicators
Variety, Low-Pass, FIR Filter Impulse Response Explorer
STD-Filtered, Variety FIR Digital Filters w/ ATR Bands
STD/C-Filtered, N-Order Power-of-Cosine FIR Filter
STD/C-Filtered, Truncated Taylor Family FIR Filter
STD/Clutter-Filtered, Kaiser Window FIR Digital Filter
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt
Educational
Variety, Low-Pass, FIR Filter Impulse Response Explorer [Loxx]Variety Low-Pass FIR Filter, Impulse Response Explorer is a simple impulse response explorer of 16 of the most popular FIR digital filtering windowing techniques. Y-values are the values of the coefficients produced by the selected algorithms; X-values are the index of sample. This indicator also allows you to turn on Sinc Windowing for all window types except for Rectangular, Triangular, and Linear. This is an educational indicator to demonstrate the differences between popular FIR filters in terms of their coefficient outputs. This is also used to compliment other indicators I've published or will publish that implement advanced FIR digital filters (see below to find applicable indicators).
Inputs:
Number of Coefficients to Calculate = Sample size; for example, this would be the period used in SMA or WMA
FIR Digital Filter Type = FIR windowing method you would like to explore
Multiplier (Sinc only) = applies a multiplier effect to the Sinc Windowing
Frequency Cutoff = this is necessary to smooth the output and get rid of noise. the lower the number, the smoother the output.
Turn on Sinc? = turn this on if you want to convert the windowing function from regular function to a Windowed-Sinc filter
Order = This is used for power of cosine filter only. This is the N-order, or depth, of the filter you wish to create.
What are FIR Filters?
In discrete-time signal processing, windowing is a preliminary signal shaping technique, usually applied to improve the appearance and usefulness of a subsequent Discrete Fourier Transform. Several window functions can be defined, based on a constant (rectangular window), B-splines, other polynomials, sinusoids, cosine-sums, adjustable, hybrid, and other types. The windowing operation consists of multipying the given sampled signal by the window function. For trading purposes, these FIR filters act as advanced weighted moving averages.
A finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
What's a Low-Pass Filter?
A low-pass filter is the type of frequency domain filter that is used for smoothing sound, image, or data. This is different from a high-pass filter that is used for sharpening data, images, or sound.
Whats a Windowed-Sinc Filter?
Windowed-sinc filters are used to separate one band of frequencies from another. They are very stable, produce few surprises, and can be pushed to incredible performance levels. These exceptional frequency domain characteristics are obtained at the expense of poor performance in the time domain, including excessive ripple and overshoot in the step response. When carried out by standard convolution, windowed-sinc filters are easy to program, but slow to execute.
The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
For our purposes here, we are used a normalized Sinc function
Included Windowing Functions
N-Order Power-of-Cosine (this one is really N-different types of FIR filters)
Hamming
Hanning
Blackman
Blackman Harris
Blackman Nutall
Nutall
Bartlet Zero End Points
Bartlet-Hann
Hann
Sine
Lanczos
Flat Top
Rectangular
Linear
Triangular
If you wish to dive deeper to get a full explanation of these windowing functions, see here: en.wikipedia.org
Related indicators
STD-Filtered, Variety FIR Digital Filters w/ ATR Bands
STD/C-Filtered, N-Order Power-of-Cosine FIR Filter
STD/C-Filtered, Truncated Taylor Family FIR Filter
STD/Clutter-Filtered, Kaiser Window FIR Digital Filter
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt
Automatic Order Block + Imbalance by D. BrigagliaThis script combines automatic orderblock and imbalance tracking.
Bullish OB - Blue
Bullish Imbalance - Green
Bearish OB - Red
Bearish Imbalance - Orange
Please note that the actual definitions of orderblock and imbalance are not respected in this script for the sake of simplicity. Scripts that are too complex may overfit some particular chart. Since there is no way to translate the actual ob and imb definitions into pinescript language, I decided to keep it simple.
Ideally, you want to see a bullish OB followed by buy side imbalance, or viceversa. OBs that are broken weakly are generally invalidated, ones that are broken strongly generally become breakers, and you can use them as good support/resistance levels.
Also, a good thing you can do when an OB and an imbalance match, is going in the lower timeframes and catching the structure reversal in the OB or imbalance zone. That may provide excellent RR trades. Always trade with OB that confirm the HTF trend.
Nothing in my content on tradingview is considerable investment advice.
Bollinger Pair TradeNYSE:MA-1.6*NYSE:V
Revision: 1
Author: @ozdemirtrading
Revision 2 Considerations :
- Simplify and clean up plotting
Disclaimer: This strategy is currently working on the 5M chart. Change the length input to accommodate your needs.
For the backtesting of more than 3 months, you may need to upgrade your membership.
Description:
The general idea of the strategy is very straightforward: it takes positions according to the lower and upper Bollinger bands.
But I am mainly using this strategy for pair trading stocks. Do not forget that you will get better results if you trade with cointegrated pairs.
Bollinger band: Moving average & standard deviation are calculated based on 20 bars on the 1H chart (approx 240 bars on a 5m chart). X-day moving averages (20 days as default) are also used in the background in some of the exit strategy choices.
You can define position entry levels as the multipliers of standard deviation (for exp: mult2 as 2 * standard deviation).
There are 4 choices for the exit strategy:
SMA: Exit when touches simple moving average (SMA)
SKP: Skip SMA and do not stop if moving towards 20D SMA, and exit if it touches the other side of the band
SKPXDSMA: Skip SMA if moving towards 20D SMA, and exit if it touches 20D SMA
NoExit: Exit if it touches the upper & lower band only.
Options:
- Strategy hard stop: if trade loss reaches a point defined as a percent of the initial capital. Stop taking new positions. (not recommended for pair trade)
- Loss per trade: close position if the loss is at a defined level but keeps watching for new positions.
- Enable expected profit for trade (expected profit is calculated as the distance to SMA) (recommended for pair trade)
- Enable VIX threshold for the following options: (recommended for volatile periods)
- Stop trading if VIX for the previous day closes above the threshold
- Reverse active trade direction if VIX for the previous day is above the threshold
- Take reverse positions (assuming the Bollinger band is going to expand) for all trades
Backtesting:
Close positions after a defined interval: mark this if you want the close the final trade for backtesting purposes. Unmark it to get live signals.
Use custom interval: Backtest specific time periods.
Other Options:
- Use EMA: use an exponential moving average for the calculations instead of simple moving average
- Not against XDSMA: do not take a position against 20D SMA (if X is selected as 20) (recommended for pairs with a clear trend)
- Not in XDSMA 1 DEV: do not take a position in 20D SMA 1*standart deviation band (recommended if you need to decrease # of trades and increase profit for trade)
- Not in XDSMA 2 DEV: do not take a position in 20D SMA 2*standart deviation band
Session management:
- Not in session: Session start and end times can be defined here. If you do not want to trade in certain time intervals, mark that session.(helps to reduce slippage and get more realistic backtest results)
PipMotionFXHi guys,
If you are looking to add some watermark into your charts. You can use this indicator.
You can add add a title and a subtitle, if you want to write in diferents lines, you can use as you can see in the script.
All the features are customizable: position, text size, text color, background.
Enjoy it.
[EDU] Close Open Estimation Signals (COE Signals)EN:
Close Open Estimation ( aka COE ) is a very simple swing-trading indicator based on even simpler idea. This indicator is from my educational series, which means that I just want to share with another way to look at the market in order to broaden your knowledge .
Idea :
Let's take n previous bars and make a sum a of close - open -values of each bar. Knowledgeable of you may already see the similarity to RSI calculation idea . Now let's plot this sum and see what we have now.
We can see, that whenever COE crosses over 0-level, uptrend begins, and if COE crosses under 0-level, downtrend begins. The speed of such signals can be adjusted by changing lookback period: the lower the lookback, the faster signals you get, but high-quality ones can be obtained only via not-so-fast lookback as when the market is consolidating or volatility is to high, there can be many garbage signals, like 95+% of other indicators have.
Let's explore more and calculate volatility of COE(v_coe in the code): current COE - previous CEO .
Now it appears that when v_coe crosses over 0-level, it's a signal, that this is a new low and soon the uptrend will follow. Analogically for crossing under 0-level .
I guess now you understood what these all are about: COE crossings show global trend signals , while Volatility COE ( v_coe or VCOE ) crossings show reversal points .
For signals I further calculated volatility of VCOE(VVCOE) and then volatility of VVCOE(VVVCOE). Why? Because for me they seem to be more accurate, but you are welcome to experiment and figure best setups for yourself and by yourself, I just share my opinion and experience .
COE can be helpful only in high liquidity markets with good trend or wide sideways .
If you want to experiment with COE, just copy the code and play with it. Curious of you will probably find it helpful eventhough the idea is way too simple.
By it's perfomance COE can probably beat QQE at open price settings.
(use open of the price at indicator to get zero repaint! )
Examples :
If you any questions, feel free to DM me or leave comments.
Good luck and take your profits!
- Fyodor Tarasenko
RU:
Close Open Estimation ( aka COE ) — это очень простой индикатор свинг-трейдинга, основанный на еще более простой идее. Этот индикатор из моей образовательной серии, а это значит, что я просто хочу поделиться с другим взглядом на рынок , чтобы расширить ваши знания .
Идея :
Возьмем n предыдущих баров и составим сумму a из close - open -значений каждого бара. Знающие люди могут уже заметить сходство с идеей расчета RSI . Теперь давайте построим эту сумму и посмотрим, что у нас сейчас есть.
Мы видим, что всякий раз, когда COE пересекает выше 0-уровня, начинается восходящий тренд , а если COE пересекает ниже 0-уровня, начинается нисходящий тренд. Скорость таких сигналов можно регулировать изменением ретроспективы: чем меньше ретроспектива, тем быстрее вы получаете сигналы, но качественные можно получить только через не- такой быстрый взгляд назад, как когда рынок консолидируется или волатильность слишком высока, может быть много мусорных сигналов, как у 95+% других индикаторов.
Давайте рассмотрим больше и рассчитаем волатильность COE(v_coe в коде): текущий COE - предыдущий CEO .
Теперь кажется, что когда v_coe пересекает уровень 0, это сигнал о том, что это новый минимум и вскоре последует восходящий тренд . Аналогично для пересечения под 0-уровнем .
Думаю, теперь вы поняли, о чем все это: COE пересечения показывают глобальные сигналы тренда , а пересечения Volatility COE ( v_coe или VCOE ) показывают точки разворота .
Для сигналов я дополнительно рассчитал волатильность VCOE(VVCOE), а затем волатильность VVCOE(VVVCOE). Почему? Потому что для меня они кажутся более точными, но вы можете поэкспериментировать и подобрать оптимальные настройки для себя и для себя, я просто делюсь своим мнением и опытом .
COE может быть полезен только на рынках с высокой ликвидностью и хорошим трендом или широким боковиком .
Если вы хотите поэкспериментировать с COE, просто скопируйте код и поэкспериментируйте с ним. Любознательные из вас, вероятно, сочтут это полезным, хотя идея слишком проста.
По своей результативности СОЕ может составить конкуренцию широко известному QQE, используя open цены.
(используйте open цены на индикаторе, чтобы получить нулевую перерисовку! )
Примеры :
Если у вас есть вопросы, пишите мне в личные сообщения или оставляйте комментарии.
Удачи и профита всем!
- Федор Тарасенко
ABC 123 Harmonic Ratio Custom Range Interactive█ OVERVIEW
This indicator was designed based on Harmonic Trading : Volume One written by Scott Carney.
This is about harmonic ratios which expanded through retracement and projection.
Derivation is pretty much explained here such as Primary, Primary Derivation, Secondary Derivation and Secondary Derivation Extreme.
Derivation value depends on minimum retracement or maximum projection.
This derivation value utilize Fibonacci value which later expand to Harmonic Ratio.
█ INSPIRATION
Inspired by design, code and usage of CAGR . Basic usage of custom range / interactive, pretty much explained here . Credits to TradingView.
This build is based and visualized upon Harmonic Trading Ratios.
This build also was stripped down from XABCD Harmonic Pattern Custom Range Interactive .
█ CREDITS
Scott Carney, Harmonic Trading : Volume One (Page 18)
█ FEATURES
Table can positioned by any position and font size can be resized.
Labels can be either changed to alphabets or numbers.
█ HOW TO USE
Draw points from Point A to Point C.
Dont worry about magnet, point will attached depends on High or Low of the candle.
█ USAGE / TIPS EXAMPLES (Description explained in each image)
Kalman Gain Parameter MechanicsFrequently asked question is to explain how Gain parameter works in kalman funtion. This script serves as a visual representation of Gain parameter of Kalman function used in HMA-Kalman & Trendlines script. (The function creator's name was misspeled in that script as Kahlman)
To see better results set your Chart's timeframe to Daily.
Fourier Extrapolator of 'Caterpillar' SSA of Price [Loxx]Fourier Extrapolator of 'Caterpillar' SSA of Price is a forecasting indicator that applies Singular Spectrum Analysis to input price and then injects that transformed value into the Quinn-Fernandes Fourier Transform algorithm to generate a price forecast. The indicator plots two curves: the green/red curve indicates modeled past values and the yellow/fuchsia dotted curve indicates the future extrapolated values.
What is the Fourier Transform Extrapolator of price?
Fourier Extrapolator of Price is a multi-harmonic (or multi-tone) trigonometric model of a price series xi, i=1..n, is given by:
xi = m + Sum( a*Cos(w*i) + b*Sin(w*i), h=1..H )
Where:
xi - past price at i-th bar, total n past prices;
m - bias;
a and b - scaling coefficients of harmonics;
w - frequency of a harmonic ;
h - harmonic number;
H - total number of fitted harmonics.
Fitting this model means finding m, a, b, and w that make the modeled values to be close to real values. Finding the harmonic frequencies w is the most difficult part of fitting a trigonometric model. In the case of a Fourier series, these frequencies are set at 2*pi*h/n. But, the Fourier series extrapolation means simply repeating the n past prices into the future.
Quinn-Fernandes algorithm find sthe harmonic frequencies. It fits harmonics of the trigonometric series one by one until the specified total number of harmonics H is reached. After fitting a new harmonic , the coded algorithm computes the residue between the updated model and the real values and fits a new harmonic to the residue.
see here: A Fast Efficient Technique for the Estimation of Frequency , B. G. Quinn and J. M. Fernandes, Biometrika, Vol. 78, No. 3 (Sep., 1991), pp . 489-497 (9 pages) Published By: Oxford University Press
Fourier Transform Extrapolator of Price inputs are as follows:
npast - number of past bars, to which trigonometric series is fitted;
nharm - total number of harmonics in model;
frqtol - tolerance of frequency calculations.
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
"Caterpillar" SSA inputs are as follows:
lag - How much lag to introduce into the SSA algorithm, the higher this number the slower the process and smoother the signal
ncomp - Number of Computations or cycles of of the SSA algorithm; the higher the slower
ssapernorm - SSA Period Normalization
numbars =- number of past bars, to which SSA is fitted
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Related Fourier Transform Indicators
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Related Projection Forecast Indicators
Itakura-Saito Autoregressive Extrapolation of Price
Helme-Nikias Weighted Burg AR-SE Extra. of Price
Related SSA Indicators
End-pointed SSA of FDASMA
End-pointed SSA of Williams %R
Fractal Dimension Index Adaptive Period [Loxx]Fractal Dimension Index Adaptive Period is the adaptive period out of Fractal Dimension Index Adaptivity. This isn't an indicator that shows a signal, instead, it's to be used as auxiliary support and an educational tool to create other indicators. This value can be injected into other indicators to make those indicators Fractal Dimension Index Adaptive.
What is the Fractal Dimension Index?
The goal of the fractal dimension index is to determine whether the market is trending or in a trading range. It does not measure the direction of the trend. A value less than 1.5 indicates that the price series is persistent or that the market is trending. Lower values of the FDI indicate a stronger trend. A value greater than 1.5 indicates that the market is in a trading range and is acting in a more random fashion.
Included
Loxx's Expanded Source Types
Risk Calculation Table - Amount BasedHello, this is my first script, and I believe that understanding the Risk and Reward is also the first essential step to become a successful trader.
Well maybe there are a lot of script like this but I think no one was suitable for me, so I learnt how to make one.
I think I need to explain some aspects about this script:
Input Section :
1. Entry = Entry Price.
2. SL = Stop Loss Price.
3. TP = Take Profit Price.
4. Amount = How much dollars you trade on this trade.
4. Ticker's Decimal = The number behind the decimal, to adjust this just type how much 0 you want behind the decimal.
Output Section :
1. You can adjust the lines plotted on the chart to automatically enter your entry, stop loss, and take profit price.
2. The table's appearance can be repositioned and resized.
3. The terms in the table, I think it's clear enough for everyone to understand.
If there are any critics or suggestions, I will appreciate it so much.
Greetings from Indonesia :)
Reserve Balances with Federal Reserve BanksReserve balances with Federal Reserve Banks are the difference between "total factors supplying reserve funds" and "total factors, other than reserve balances, absorbing reserve funds." This item includes balances at the Federal Reserve of all depository institutions that are used to satisfy reserve requirements and balances held in excess of balance requirements. It excludes reserves held in the form of cash in bank vaults, and excludes service-related deposits
Trend EMAHello Everyone. This is a new indicator which helps you to follow the trend & find out the support and resistance in Intraday. Time frame is best for this indicator is 5mins. You can also use Vwap which you get in this indicator. This is an Indicator which I'm using to manage my trades.
N.B. I don't insist you to use this. I'm not RESPONSIBLE for your profit and loss after using this indicator.
Consolidated IndicatorI have attempted to combine all the parameters to decide on the entry and exit points for stocks. The indicator combines
1) EMAs
2)PSAR
3)ATR
The script also attempts to show the risk-reward
Trading Guidance institutional ZoneThis is the institutional zone indicator. it used in 5 Minute Timmeframe.
