Rolling HTF Liquidity Levels [CHE]█ OVERVIEW
This indicator displays a Rolling HTF Liquidity Levels . Contrary to HTF Liquidity Levels indicators which use a fix time segment, Rolling HTF Liquidity Levels calculates using a moving window defined by a time period (not a simple number of bars), so it shows better results.
This indicator is inspired by
The indicator introduces a new representation of the previous rolling time frame highs & lows (DWM HL) with a focus on untapped levels.
█ CONCEPTS
Untapped Levels
It is popularly known that the liquidity is located behind swing points or beyond higher time frames highs/lows.
Rolling HTF Liquidity Levels uses a moving window, it does not exhibit the static of the HTF Liquidity Levels plots.
█ HOW TO USE IT
Load the indicator on an active chart (see the Help Center if you don't know how).
Time period
By default, the script uses an auto-stepping mechanism to adjust the time period of its moving window to the chart's timeframe. The following table shows chart timeframes and the corresponding time period used by the script. When the chart's timeframe is less than or equal to the timeframe in the first column, the second column's time period is used to calculate the Rolling HTF Liquidity Levels:
Chart Time
timeframe period
1min 🠆 1H
5min 🠆 4H
1H 🠆 1D
4H 🠆 3D
12H 🠆 1W
1D 🠆 1M
1W 🠆 3M
By default, the time period currently used is displayed in the lower-right corner of the chart. The script's inputs allow you to hide the display or change its size and location.
This indicator should make trading easier and improve analysis. Nothing is worse than indicators that give confusingly different signals.
I hope you enjoy my new ideas
best regards
Chervolino
Forecasting
SPIRA This is my signal generator indicator.
It uses mean reversion principles in order to find the best times to potentially enter the market in line with larger trends.
It creates a buy-stop / sell-stop signal with plotted levels indicating entry, stop-loss, and take profit levels.
This indicator has been extensively tested and is intended for use on the 5M chart, although all potential is available on any timeframe.
This script provides a clear forecast for potential price movements and capitalizes on frequent impulsive moves in the market before they occur.
The stop-entry model allows for trades to be placed before hand, and invalidated due to various criteria involving range-boxes or level breaks.
Q-TrendQ-Trend is an multipurpose indicatorm that can be used for swing- and trend-trading equally on any timeframe (non-volatile markets are better for this thing).
Settings:
Trend period - used to calculate trend line in the special moments(will explain below);
ATR Multiplier - changes sensitivity. The higher the multiplier = the more sensitive it is.
Also option to smooth source data (helps get cleaner signals, as always).
How to use?
Signals are given on the chart. Also ou can use trend line as S/R line.
The idea behind:
Terms:
SRС = Source
TL = trend line;
MP = ATR multiplier;
ATR = ATR :)
TL = (highest of source P-bars back + lowest of source P-bars back) / 2
Epsilon = MP * ATR
I was thinking for a week about combining volatility and relation between highest and lowest price point. That why I called indicator Q-Trend = Quantitative Trend , as I was trying to think about price in a mathematical way.
Okay, time to go philosophical:
1) TL is shows good price trend, but as it is slow enough and not enough informative, we need add additional conditions to produce signals.
2) Okay, so what can we add as conditions? We need to take volatility into account, as it is crucial in the moments of market uncertainty. So let's use ATR (Average True Range) somehow. My idea is that if SRC breaks TL + ATR , then it means that there will be upmove and we update our TL . Analogically for SRC breaking TL - ATR (breaks are crosses of TL +- ATR lines) .
Conclusion:
- if SRC breaks TL + ATR , it is a BUY signal and update of trend line;
- if SRC breaks TL - ATR , it is a SELL signal and update of trend line;
I think that such indicator already exisits on TradingView, as I've already saw something similar, but long ago, so please don't report, if such thing already exists.
But if not, then I hope, that you will gain some profits with Q-Trend :)
I will continue my work on this thing, so stay tuned.
Trade with your own risks and have your profits!
Wish you all the best!
- Tarasenko Fyodor
Miyagi BacktesterMiyagi: The attempt at mastering something for the best results.
Miyagi indicators combine multiple trigger conditions and place them in one toolbox for traders to easily use, produce alerts, backtest, reduce risk and increase profitability.
The Miyagi Backtester is a standalone backtester which is to be applied to the chart after the Miyagi indicator to be backtested.
The backtester can only backtest one script at a time, and is meant to backtest ONCE PER BAR CLOSE entries.
It is currently not possible to backtest ONCE PER BAR entries.
The backtester will allow users to all Miyagi Indicators using DCA strategies to show returns over a selectable time period.
The backtester allows leverage, and as such users should be aware of the Maximum Amount for Bot Usage and Leverage Required Calculations.
The DCA Selector switch will allow users to backtest with, or without DCA.
Static DCA is used within the backtester and allows users to see DCA Statistics on closed trades.
How to use the Miyagi Backtester
Step 1: Apply the Miyagi Indicator of Choice to backtest (4in1/10in1/Strend).
DATE AND TIME RANGE:
-Date and time range to backtest.
TRADE:
-Entry source to backtest. Please select the "Outbound Entry Signal Sender"
-Trade Direction to backtest. This can be helpful to backtest according to your strategy (long or short).
-Take Profit % to backtest. This is the percent take profit to backtest. Slippage can be accounted for on the "Properties" tab.
-Stoploss % to backtest. This is the percent stoploss to backtest.
DCA:
DCA Checkbox: Enable the DCA Checkbox to backtest with DCA. Disable it to backtest without DCA.
Leverage: Input the Leverage you will trade with.
Base Order Size (% Equity): This is the Base order (BO) size to backtest in % of equity.
Safety Order Size (% Equity): This is the Safety order (SO) size to backtest in % of equity.
Number of DCA Orders: This is the maximum amount of DCA orders to place, or total DCA orders.
Price Deviation (% from initial order): This is the percent at which the first safety is placed.
Safety Order Step Scale: This is the scale at which is applied to the deviation for the step calculation to determine next SO placement.
Safety Order Volume Scale: This is the scale at which is applied to the safety orders for the volume calculation to determine SO Volume.
Real world DCA Example:
The process is as follows.
Base Order: This is your initial order size, $100 used for Base Order
Safety Order: This is your first safety order size, which is placed at the deviation. $100 Safety Order, it is good to keep the same size as your BO for your scaling to be effective.
Price deviation: This is the deviation at which your first Safety order is placed. 0.3-0.75% used by most of our members.
Safety Order Volume Scale: This is the scale at which is applied to the safety orders for the volume calculation. Scale of 2 used, which means that SO2 = (SO1) * 2, or $200. This scaling is typical for all following orders and as such SO3 = (SO2) *2, or $400.
Safety Order Step Scale: This is the scale at which is applied to the deviation for the step calculation. This is similar to the volume scale however the last order percentage is added.
Scale of 2 used, which means that SO2 % = ((Deviation) * 2) + (SO1%). (0.5% *2) + (0.5) = 1.5%.
This scaling is typical for all following orders except that the prior deviation is used and as such SO3 = ((Prior%) * 2) + (Deviation). (1.5% * 2) +(0.5%) or 3.5%.
Total SO Number: The calculations will continue going until the last SO. It is helpful to understand the amount of SO’s and scaling determines how efficient your DCA is.
Backtester Outputs include:
Net Profit to display net profit
Daily Net Profit to estimate
Percent Profitable which shows ratio of winning trades to losing trades.
Total Trades
Winning Trades
Losing Trades (only applicable if stoploss is used)
Buy & Hold Return (of the backtested asset) to compare if the strategy used beats buy & hold return.
Avg Trade Time is very helpful to see average trade time.
Max Trade Time is very helpful to see the maximum trade time.
Total Backtested Time will return total backtested time.
Initial Capital which is taken from the Properties tab.
Max amount for Bot Usage which can be helpful to see bot usage.
Leverage Required will show you the leverage required to sustain the DCA configuration.
Total SO Deviation will allow users to see the drop coverage their DCA provides.
Max Spent which is a % of total account spent on one trade.
Max Drawdown which displays the maximum drawdown of any trade.
Max % distance from entry shows the maximum distance price went away from entry prior to the trade closing.
Max SO Used which shows the maximum number of SO's used on a single trade
Avg SO Used which shows the average number of SO's used in all closed trades.
Deals closing with BO Only calculation will show how many trades are closed without DCA.
Deals closing with 1-7 SOs calculation will show how many trades are closed with DCA, and allow for fine-tuning.
Happy Trading!
This script will be effective to backtest and produce the best settings for each timeframe and pair across all STP Scripts.
This will take a lot of the manual work out of backtesting for our users while improving profit potential.
Happy Trading!
VOLQ Sigma TableThis indicator replaces the implied volatility of VOLQ with the daily volatility and reflects that value into the price on the NDX chart to create the VOLQ standard deviation table.
It will only be useful for stocks related to the Nasdaq Index.
For example, NDX, QQQ or so.
And we want to predict the range of weekly fluctuations by plotting those values as a line in the future.
It is expressed as High 2σ by adding the standard deviation 2 sigma value of the VOLQ value from last week's closing price.
It is expressed as High 1σ by adding the standard deviation 1 sigma value of the VOLQ value from last week's closing price.
It is expressed as Low 1σ by subtracting the standard deviation 1 sigma value of the VOLQ value from the closing price of the previous week.
It is expressed as Low 2σ by subtracting the standard deviation 2 sigma value of the VOLQ value from last week's closing price.
1day predicts daily fluctuations.
2day predicts 2-day fluctuations.
3day predicts 3-day fluctuations.
4day predicts 4-day fluctuations.
5day predicts 5-day fluctuations.
In the settings you can select the start date to display the VOLQ line via input.
-----------------------------
What motivated me to create this indicator?
From my point of view, the reason for classifying vix volq historical volatility (realized volatility) is that the most important point is that VIXX and VolQ are calculated from implied volatility. It can be standardized as one-month volatility. There are many strike prices, but exchanges use the implied volatility of options traded on their own exchanges.
Because historical volatility depends on how the period is set, to compare with VIXX, we compare it with a month, that is, 20 business days. One-month implied volatility means (actually different depending on the strike price), because option traders expect that the one-month volatility will be this much, and it is the volatility created by volatility trading.
So we see it as the volatility expected by derivatives traders, especially volatility traders.
I'm trying to infer what the market thinks will fluctuate this much from the numbers generated there.
SFC Smart Money Manipulation - Liquidity, StructureThis indicator shows very important information about the market.
Features:
- Market structure
- Important Ranges
- Liquidity
- Trading session
- Daily Checklist
Market structure
Market structure is the behaviour, condition, and current flow of the market. It highlights support and resistance levels, swing highs, and swing lows. A trend is simply a consistent direction of price movement over time. Market structure can tell you if the market is trending or not.
Market structure is a lagging indicator, because Highs and Lows must to be created in order to define the structure properly. The structure provide the most important information about the market.
Market structure can provide early signals about the trend.
- If the structure continues to break in the same direction, it means that the trend is healthy and will continue (BoS).
- If the structure break in the opposite direction, means that the trend may reverse or pause for a while (CHoCH).
Important ranges
- Asia Range - it is important intraday range and can provide early information if the day will be bullish or bearish.
- Most recent High/Low - determine the last swing
- Premium/ Discount zone with Fibonacci levels - the institutions always want to buy in discount and sell in premium.
Liquidity
Areas where a lot of traders get into the market and theirs stop losses are obvious. So the banks will manipulate the price to clear these stop losses, before price go in real direction. The banks will always hunt the liquidity.
The major liquidity is:
- Doji candle - displayed
- Double/Triple Highs or Lows - displayed
- Fair value gaps - displayed
- Imbalances - displayed
- Trend lines
- Big wicks
Trading Sessions
Price and Time theory is very significant in Smart Money Concept. The banks do not just place orders chaotically. They place it in specific time.
The indicator shows the Asia, London and New York intraday sessions and the kill zones.
Kill Zone - most manipulated time in the day, where institutions try to wipe out the retail traders and establish the true move.
Daily Checklist
Simple, but very useful checklist. It shows the most important daily steps in order qualitative analysis to be created.
How to use
1) Use the swing highs and lows and check the current structure.
2) Look where is the major liquidity. By default orange colour. When liquidity is retested from the price ,it change the colour from orange to gray. Retested liquidity is no more significant for the banks.
3) Use the important ranges to define the pullbacks or reversals or trading ranges.
4) Use the trading sessions and kill zones to place orders in the right time.
5) Use the "daily checklist" every day - step by step. It helps trader to analyse the current market.
Settings
-Show pivots, Pivot confirmation candles, Equal Highs/Lows sensitivity
-Show structures breaks
-Show most recent high/low
-Show Asia range
-Show premium/discount zone with Fibonacci levels
-Show liquidity, Colour of liquidity, Color of retested liquidity, Doji settings
-Show Trading sessions
-Show daily checklist
ILM Seasonality Big Moves - TableUse this script on Daily Timeframe.
This script calculates Daily Moves ( Intraday / Close basis ) and buckets them into 1% / 2% / 3% moves
Also calculates MAX DD for the financial year from Peak to Trough
Helps in identifying volatility of the instrument and high drawdowns due to volatility
Pinbar by BirdCoinIt is the most customizable Pinbars indicator that you can find.
The indicator autonomously detects the Pinbars to which filters can be placed. The available adjustments are:
- The spread of the Pinbars
- The wick and the body ratio of the Pinbars
- The volume of the Pinbars
- Number of the previous candles that the Pinbars hunted
Happy trading!
~ Birdcoin
Variety Distribution Probability Cone [Loxx]Variety Distribution Probability Cone forecasts price within a range of confidence using Geometric Brownian Motion (GBM) calculated using selected probability distribution, volatility, and drift. Below is detailed explanation of the inner workings of the indicator and the math involved. While normally this indicator would be used by options traders, this can also be used by regular directional traders who wish to observe a forecast of the confidence interval of possible prices over time.
What is a Random Walk
A random walk is a path which consists of a set of random steps. The starting point is zero and following movement may be one step to the left or to the right with equal probability. In the random walk process, there is no observable trend or pattern which are followed by the objects that is the movements are completely random. That is why the prices of a stock as it moves up and down can be modeled by random a walk process.
Stock Prices and Geometric Brownian Motion
Brownian motion, as first conceived by the botanist Robert Brown (1827), is a mathematical model used to describe random movements of small particles in a fluid or gas. These random movements are observed in the stock markets where the prices move up and down, randomly; hence, Brownian motion is considered as a mathematical model for stock prices.
P(exp(lnS0 + (mu + 1/2*sigma^2)t - z(0.05)*sigma*t^0.5) <= St <= exp(lnS0 + (mu + 1/2*sigma^2)t + z(0.05)*sigma*t^0.5)) = 0.95
Probability Distributions
Typically the normal distribution is used, but for our purposes here we extend this to Student t-distribution, Cauchy, Gaussian KDE, and Laplace
Student's t-Distribution
The probability density function of the Student’s t distribution is given by
g(x) = (L(v+1)/2) / L(v/2) * 1 / L(sqrt(v)) * (1 + x^2/v) ^ (-(v+1)/2)
with v degrees of freedom and v >= 0, denoted by X ~ t(v). The mean is 0 and the variance is v/(v-2). It is known that as v tends to infinity, the Student’s t-distribution tends to a standard normal probability density function, which has a variance of one. Blattberg and Gonedes were the first to propose that stock returns could be modeled by this distribution. (Blattberg and Gonedes, 1974) Platen and Sidorowicz later reaffirmed these findings.(Platen and Rendek, 2007) Finally, Cassidy, Hamp, and Ouyed used these findings to derive the Gosset formula, which is the Student t version of the Black-Scholes model.(Cassidy et al., 2010) They found that v = 2.65 provides the best fit when looking at the past 100 years of returns. They realized that as markets become more turbulent, the degrees of freedom should be adjusted to a smaller value.(Cassidy et al., 2010)
Cauchy Distribution
The probability density function of the Cauchy distribution is given by
f(x) = 1 / (theta*pi*(1 + ((x-n)/v)))
where n is the location parameter and theta is the scale parameter, for -infinity < x < infinity and is denoted by X ~ CAU(L,v). This model is similar to the normal distribution in that it is symmetric about zero, but the tails are fatter. This would mean that the probability of an extreme event occurring lies far out in the distributions tail. Using a crude example, if the normal distribution gave a probability of an extreme event occurring of 0.05% and the “best case” scenario of this event occurring 300 years, then using the Cauchy distribution one would find that the probability of occurring would be around 5% and now the “best case” scenario might have been reduced to only 63 years. Thus giving extreme events more of a likelihood of occurring. The mean, variance, and higher order moments are not defined (they are infinite); this implies that n and theta cannot be related to a mean and standard deviation. The Cauchy distribution is related to the Student’s t distribution T ~ CAU(1,0) when v = 1. In 1963, Benoit Mandelbrot was the first to suggest that stock returns follow a stable distribution, in particular, the Cauchy distribution.(Mandelbrot, 1963) His work was validated by Eugene Fama in 1965.(Fama, 1965) Recent research by Nassim Taleb came to the same conclusion as Mandelbrot, saying that stock returns follow a Cauchy distribution, as reported in his New York Times best-seller book “The Black Swan”.(Taleb, 2010)
Laplace Distribution
In probability theory and statistics, the Laplace distribution is a continuous probability distribution named after Pierre-Simon Laplace. It is also sometimes called the double exponential distribution, because it can be thought of as two exponential distributions (with an additional location parameter) spliced together along the abscissa, although the term is also sometimes used to refer to the Gumbel distribution. The difference between two independent identically distributed exponential random variables is governed by a Laplace distribution, as is a Brownian motion evaluated at an exponentially distributed random time. Increments of Laplace motion or a variance gamma process evaluated over the time scale also have a Laplace distribution.
The probability density function of the Cauchy distribution is given by
f(x) = 1/2b * exp(-|x-µ|/b)
Here, µ is a location parameter and b > 0, which is sometimes referred to as the "diversity", is a scale parameter. If µ = 0 and b=1, the positive half-line is exactly an exponential distribution scaled by 1/2.
The probability density function of the Laplace distribution is also reminiscent of the normal distribution; however, whereas the normal distribution is expressed in terms of the squared difference from the mean µ, the Laplace density is expressed in terms of the absolute difference from the mean. Consequently, the Laplace distribution has fatter tails than the normal distribution.
Gaussian Kernel Density Estimation
In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights. KDE is a fundamental data smoothing problem where inferences about the population are made, based on a finite data sample. In some fields such as signal processing and econometrics it is also termed the Parzen–Rosenblatt window method, after Emanuel Parzen and Murray Rosenblatt, who are usually credited with independently creating it in its current form. One of the famous applications of kernel density estimation is in estimating the class-conditional marginal densities of data when using a naive Bayes classifier, which can improve its prediction accuracy.
Let (x1, x2, ..., xn) be independent and identically distributed samples drawn from some univariate distribution with an unknown density f at any given point x. We are interested in estimating the shape of this function f. Its kernel density estimator is:
f(x) = 1/nh * sum(k(x-xi)/h, n)
where K is the kernel—a non-negative function—and h > 0 is a smoothing parameter called the bandwidth. A kernel with subscript h is called the scaled kernel and defined as Kh(x) = 1/h K(x/h). Intuitively one wants to choose h as small as the data will allow; however, there is always a trade-off between the bias of the estimator and its variance.
The probability density function of Gaussian Kernel Density Estimation is given by
f(x) = 1 / (v * 2*pi)^0.5 * exp(-(x - m)^2 / (2 * v))
where v is the bandwidth component h squared
KDE Bandwidth Estimation
Bandwidth selection strongly influences the estimate obtained from the KDE (much more so than the actual shape of the kernel). Bandwidth selection can be done by a "rule of thumb", by cross-validation, by "plug-in methods" or by other means. The default is Scott's Rule.
Scott's Rule
n ^ (-1/(d+4))
with n the number of data points and d the number of dimensions.
In the case of unequally weighted points, this becomes
neff^(-1/(d+4))
with neff the effective number of datapoints.
Silverman's Rule
(n * (d + 2) / 4)^(-1 / (d + 4))
or in the case of unequally weighted points:
(neff * (d + 2) / 4)^(-1 / (d + 4))
With a set of weighted samples, the effective number of datapoints neff
is defined by:
neff = sum(weights)^2 / sum(weights^2)
Manual input
You can provide your own bandwidth input. This is useful for those who wish to run external to TradingView Grid Search Machine Learning algorithms to solve for the bandwidth per ticker.
Inverse CDF of KDE Calculation
1. Create an array of random normalized numbers, using an inverse CDF of a normal distribution of mean of zero
and standard deviation one
2. Create a line space range of values -3 to 3
3. Create a Gaussian Kernel Density Estimate CDF by iterating over the line space array created in step 2. For each line space item, find the mean difference between the line space and the random variable divided by the bandwidth.
4. Derive test statistics from the resulting KDE inverse CDF, we use cubic spline interpolation to solve for line space value for a given alpha computed using the user selected probability percent value in the settings.
Volatility
Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility. That drawback is taken into account in the Garman-Klass's volatility estimator.
Garman-Klass
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It considered being 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1))
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by θ.
θavg(var ;M) + (1 − θ)avg(var ;N) = 2θvar/(M+1-(M-1)L) + 2(1-θ)var/(M+1-(M-1)L)
Solving for θ can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as θ.
Manual
User input % value
Drift
Cost of Equity / Required Rate of Return (CAPM)
Standard Capital Asset Pricing Model used to solve for Cost of Equity of Required Rate of Return. Due to the processor overhead required to compute CAPM, the user must plug in values for beta, alpha, and expected market return using Loxx's CAPM indicator series. Used for stocks.
Mean of Log Returns
Average of the log returns for the underlying ticker over the user selected period of evaluation. General purpose use.
Risk-free Rate (r)
10, 20, or 30 year bond yields for the user selected currency. Under equilibrium the drift of the empirical GBM must be the risk-free rate. If the price process is a GBM under the empirical measure, then a consequence of viability is that it is also a GBM under an equivalent (risk-neutral) measure.
Risk-free Rate adjusted for Dividends (r-q)
This is the Risk-free Rate minus the Dividend Yield.
Forex (r-rf)
This is derived from the Garman and Kohlhagen (1983) modified Black-Scholes model can be used to price European currency options. This is simply the diffeence between Risk-free Rate of the Forex currency in question. This is used for Forex pricing.
Martingale (0)
When the drift parameter is 0, geometric Brownian motion is a martingale. In probability theory, a martingale is a sequence of random variables (i.e., a stochastic process) for which, at a particular time, the conditional expectation of the next value in the sequence is equal to the present value, regardless of all prior values. Typically used for futures or margined futures.
Manual
User input % value
Additional notes
Indicator can be used on any timeframe. The T (time) variable used to annualize volatility and inside the GBM formula is automatically calculated based on the timeframe of the chart.
Confidence interval of volatility is calculated using an inverse CDF of a Chi-Squared Distribution. You change the volatility input used to create the probability cones from from realized volatility to upper or lower confidence levels of volatility to better visualize extremes of range. Generally, you'd stick with realized volatility.
Days per year should be 252 for everything but Cryptocurrency. These are days trader per year. Maximum future forecast bars is 365. Forecast bars are limited to the maximum of selected days per year.
Includes the ability to overlay option expiration dates by bars to see the range of prices for that date at that bar
You can select confidence % you wish for both the cone in general and the volatility. There are three levels for the cones, this will show on the three different levels up and down on the chart.
The table on the right displays important calculated values so you don't have to remember what they are or what settings you selected
All values are annualized no matter the timeframe.
Additional distributions and measures of volatility and drift will be added in future releases.
Spider Lines For Bitcoin (Daily And Weekly)I haven't seen any indicator do this, so I decided to publish this to form automatic Spider Charts without actually going through the effort of drawing them!
This script charts dotted lines (spider lines) all over, depicting support and resistance levels.
It works by connecting some candles from the 2018 bear market to the candle from 1st July 2019, followed by extending the lines to the right, making support and resistance levels for the future. The script works only for the daily and weekly charts for Bitcoin.
The levels are accurate to a good extent.
If the lines don't load, zoom out until the 2018 bear market top and it should load then.
Have fun with this indicator!
Seasonality Table - Tabular FormThis indicator displays the seasonality data for any instrument (index/stock/futures/currency) in a tabular data.
User can change the start of the year for analysis from the inputs.
Year is represented in rows and Month is represented in cols.
This indicator uses Monthly Data feed to calculate the % change
Summary data for the month is displayed as the last row
TRENDsignalsindicator_MTF► DESCRIPTION
This indicator calculate works in 2 directions:
1) Calculate SMA & VWAP trends at a fixed value: so it values the price actions according the VWAP level to reach the perfect entrypoint
2) Set the value found at a different timeframe(4Hs if u use tradingview TF of 15 mins)
This combination is useful to identify the trend
To help the trader, I placed BUY/SELL signals on the second candle of the same color changed.
Furthermore, I placed:
- HH and LL of the day(green and red lines) and of the current Week(white lines): these lines help the traders to identify the relative supports and resistances
- line red and gray(with big arrows at the start of them): to identify others supports and resistance
► HOW TO USE IT:
1) Entry when a signal(buy/sell) appears or when candles change color: yellow is long, red is short
2) Evaluate where the candle is: for example, if you get a signal "buy", near the Weekly line LL, it's the perfect entry point. The same is if u get a "SELL" signal near the upper white line, it's the perfect moment to enter short.
3) Take profit: we suggest to take profit when RSI is overbought or oversold, that we've pointed thanks the following signals:
- colored circles
- small diamonds
- white circles
- Big white diamonds
► Legend:
BARCOLORS: Yellow is long and red is short moment
MINIARROW buy/sell alert u when the color of candls change
COLORED CIRCLES: indicates when Rsi is oversold or overbought. We identify them like good moment to take profit
BIG ARROW: Identify support and resistance level
SMALL DIAMOND: Use it Like TP. Possible small swing of price can happen
WHITE CIRCLE: Use it Like TP. Possible small swing of price can happen
BIG WHITE DIAMOND: Use it Like TP. Possible big swing of price can happen
So it's suggested to trade just near this supports and resistence using the right direction: when you have a reversal signal near one of the daily or weekly line, it's a good moment to entry
PLEASE COMMENT HERE BELOW ANY QUESTION ABOUT THIS STUDY
Implied Move with NASA Ideas & Price LineThis script allows you to customize the Implied Move Percentage and fully customize the way it is shown.
Can be used on any stock that has earnings and works based on the Implied Move (Percent).
Basically, it lets you visualize how the stock moved after reporting earnings and seeing if it reached the implied move or not.
This is helpful as it's important to know what earnings are worth keeping an eye on and which should be avoided.
There is also an added custom text input which was inspired to make from a frogman named NASA.
It lets you input whatever text you want on whatever price you want.
To summarize, it's basically a Post-It Note that you can add to any price level for any stock.
Alerts can be set if wanted, They can be alerted for the Implied Moves (If the stock price goes Above/ Below the set percentage) and NASA Ideas (if the stock price goes Above/ Below the set price).
There is also an added custom price line which is mostly for having a nonintrusive price line and label.
This price line and label can be switched to show the (Open, High, Low, Close, Extended High, Extended Low, Yesterday's Open, Yesterday's High, Yesterday's Low, and Yesterday's Close).
MINI SPXThis is the XSP version of SPX, basically it's just the price of SPX divided by 10 and shown using labels.
Should only be used on SPX to watch the price of XSP since XSP doesn't have real-time data ATM.
Can be used on any time frames.
This script allows you to view the Daily (O, H, L, C) and Yesterday's (O, H, L, C) with a non intrusive price line.
Allows for extra customization of the price lines and labels.
Relative Market Status by @WilliamBeliniWhat is the impact for Volume to the Prices?
To respond this question, I formulate the hipótesis if a little Volume change a lot the Price, it's a reversion signal, and if a lot of Volume change a little the price, it´s because the price is established.
This is one of 3 indicators created to improve this hipótesis, named:
1. Relative Volume Prices Index by @WilliamBelini (RVPI)
2. Relative Market Status by @WilliamBelini (RMS)
3. Trade Trigger RVPI by @WilliamBelini (TTR)
- The first show you the effect from volume to the prices, meas the sensibility of the variation;
- The second show you the feeling of the market by cicles, based at the cumulative average sensibility from the RVPI indicator;
- The third show you a trigger to trading positions, with the analysis of the historical RVPI data, based on the normal distribution of the futures price variation, by previos RVPI values and some rules created based on data behaviors identified.
To the end of this work, I can comprove the hipótesis, with simulations trading based from the TTR.
How we can´t monetize our work here, on TradingView platform, I´m disponibilize 2 of 3 indicators for you here free. If you want to have the third, discover how to contact with me (@ ;), and for me will be a pleasure to help you.
Relative Volume Prices Index by @WilliamBeliniWhat is the impact for Volume to the Prices?
To respond this question, I formulate the hipótesis if a little Volume change a lot the Price, it's a reversion signal, and if a lot of Volume change a little the price, it´s because the price is established.
This is one of 3 indicators created to improve this hipótesis, named:
1. Relative Volume Prices Index by @WilliamBelini (RVPI)
2. Relative Market Status by @WilliamBelini (RMS)
3. Trade Trigger RVPI by @WilliamBelini (TTR)
- The first show you the effect from volume to the prices, meas the sensibility of the variation;
- The second show you the feeling of the market by cicles, based at the cumulative average sensibility from the RVPI indicator;
- The third show you a trigger to trading positions, with the analysis of the historical RVPI data, based on the normal distribution of the futures price variation, by previos RVPI values and some rules created based on data behaviors identified.
To the end of this work, I can comprove the hipótesis, with simulations trading based from the TTR.
How we can´t monetize our work here, on TradingView platform, I´m disponibilize 2 of 3 indicators for you here free. If you want to have the third, discover how to contact with me (@ ;), and for me will be a pleasure to help you.
Capital Asset Pricing Model (CAPM) [Loxx]Capital Asset Pricing Model (CAPM) demonstrates how to calculate the Cost of Equity for an underlying asset using Pine Script. This script will only work on the monthly timeframe. While you can change the default inputs, you should study what CAPM is and how this works before doing so. This indicator pulls various types of data from SPY from various timeframes to calculate risk-free rates, market premiums, and log returns. Alpha and Beta are computed using the regression between underlying asset and SPY. This indicator only calculates on the most recent data. If you wish to change this, you'll have to save the script and make adjustments. A few examples where CAPM is used:
Used as the mu factor Geometric Brownian Motion models for options pricing and forecasting price ranges and decay
Calculating the Weighted Average Cost of Capital
Asset pricing
Efficient frontier
Risk and diversification
Security market line
Discounted Cashflow Analysis
Investment bankers use CAPM to value deals
Account firms use CAPM to verify asset prices and assumptions
Real estate firms use variations of CAPM to value properties
... and more
Details of the calculations used here
Rm is calculated using yearly simple returns data from SPY, typically this is just hard coded as 10%.
Rf is pulled from US 10 year bond yields
Beta and Alpha are pulled form monthly returns data of the asset and SPY
In the past, typically this data is purchased from investments banks whose research arms produce values for beta, alpha, risk free rate, and risk premiums. In 2022 ,you can find free estimates for each parameter but these values might not reflect the most current data or research.
History
The CAPM was introduced by Jack Treynor (1961, 1962), William F. Sharpe (1964), John Lintner (1965) and Jan Mossin (1966) independently, building on the earlier work of Harry Markowitz on diversification and modern portfolio theory. Sharpe, Markowitz and Merton Miller jointly received the 1990 Nobel Memorial Prize in Economics for this contribution to the field of financial economics. Fischer Black (1972) developed another version of CAPM, called Black CAPM or zero-beta CAPM, that does not assume the existence of a riskless asset. This version was more robust against empirical testing and was influential in the widespread adoption of the CAPM.
Usage
The CAPM is used to calculate the amount of return that investors need to realize to compensate for a particular level of risk. It subtracts the risk-free rate from the expected rate and weighs it with a factor – beta – to get the risk premium. It then adds the risk premium to the risk-free rate of return to get the rate of return an investor expects as compensation for the risk. The CAPM formula is expressed as follows:
r = Rf + beta (Rm – Rf) + Alpha
Therefore,
Alpha = R – Rf – beta (Rm-Rf)
Where:
R represents the portfolio return
Rf represents the risk-free rate of return
Beta represents the systematic risk of a portfolio
Rm represents the market return, per a benchmark
For example, assuming that the actual return of the fund is 30, the risk-free rate is 8%, beta is 1.1, and the benchmark index return is 20%, alpha is calculated as:
Alpha = (0.30-0.08) – 1.1 (0.20-0.08) = 0.088 or 8.8%
The result shows that the investment in this example outperformed the benchmark index by 8.8%.
The alpha of a portfolio is the excess return it produces compared to a benchmark index. Investors in mutual funds or ETFs often look for a fund with a high alpha in hopes of getting a superior return on investment (ROI).
The alpha ratio is often used along with the beta coefficient, which is a measure of the volatility of an investment. The two ratios are both used in the Capital Assets Pricing Model (CAPM) to analyze a portfolio of investments and assess its theoretical performance.
To see CAPM in action in terms of calculate WACC, see here for an example: finbox.com
Further reading
en.wikipedia.org
Position Tool█ OVERVIEW
This script is an interactive measurement tool that can be used to evaluate or keep track of trades. Like the long and short position drawing tools, it calculates a risk reward ratio and a risk-adjusted position size from the entry, stop and take profit levels, but it also does much more:
• It can be used to configure long or short trades.
• All monetary values can be expressed in any number of currencies.
• The value of tick/pip movement (which varies with the position's size) is displayed in the currency you have selected.
• The CAGR ( Compound Annual Growth Rate ) for the trade can be displayed.
• It does live tracking of the position.
• You can configure alerts on entries and exits.
█ HOW TO USE IT
Load the indicator on an active chart (see here if you don't know how).
When you first load this script on a chart, you will enter an interactive selection mode where the script asks you to pick three points in price and time on your chart by clicking on the chart. Directions will appear in a blue box at the bottom of the screen with each click of the mouse. The first selection is the entry point for the trade you are considering, which takes into account both the time and level you choose, the next are the take profit and stop levels. Once you have selected all three points, the script will draw trade zones and labels containing the trade metrics. The script determines if the trade is a long or short from the position of the take profit and stop loss levels in relation to the entry price. If the take profit level is above the entry price, the stop must be below and vice versa, otherwise an error occurs.
You can change levels by dragging the handles that appear when you select the indicator, or by entering new values in the script's settings. The only way to re-enter interactive mode is to re-add the indicator to your chart.
Once you place the position tool on a chart, it will appear at the same levels on all symbols you use. If your scale is not set to "Scale price chart only", the position tool's levels will be taken into account when scaling the chart, which can cause the symbol's bars to be compressed. If your scale is set to "Scale price chart only", the position tool will still be there, but it will not impact the scale of the chart's bars, so you won't see it if it sits outside the symbol's price scale.
If you select the position tool on your chart and delete it, this will also delete the indicator from the chart. You will need to re-add it if you want to draw another position tool. You can add multiple instances of the indicator if you need a position tool on more than one of your charts.
█ FEATURES
Display
The position tool displays the following information for entries:
• The entry's price level with an '@' sign before it.
• Open or Closed P&L : For an open trade, the "Open P&L" displays the difference in money value between the entry level and the chart's current price.
For a closed trade, the "Closed P&L" displays the realized P&L on the trade.
• Quantity : The trade size, which takes into account the risk tolerance you set in the script's settings.
• RR : The reward to risk ratio expresses the relationship of the distance between the entry and the take profit level vs the entry and the stop level.
Example: A $100 stop with a $100 target will have a ratio of 1:1, whereas a $200 target with the same stop will have a 2:1 ratio.
• Per tick/pip : Represents the money value of a tick or pip movement.
• CAGR : The Compound Annual Growth Rate will be displayed on the main order label on trades that exceed one day in duration.
This value is calculated the same way as in our CAGR Custom Range indicator.
If the trade duration is less than one day, the metric will not be present in the display.
The stop and take profit levels display:
• Their price level with an '@' sign before it.
• Their distance from the entry in money value, percentage and ticks/pips.
• The projected end money value of the position if the level is reached. These values are calculated based on the trade size and the currency.
Currency adjustments
This indicator modifies the trade label's colors and values based on the final Profit and Loss (P&L), which considers the dynamic exchange rate between base and conversion currencies in its calculations when the conversion currency is a specified value other than the default. Depending on the cross rate between the base and account currencies, this process can yield a negative P&L on an otherwise successful simulated trade.
For instance, if your account is in currency XYZ, you might buy 10 Apple shares at $150 each, with the XYZ to USD exchange rate being 2:1. This purchase would cost you 3000 units of XYZ. Suppose that later on, the shares appreciate to $170 each, and you decide to sell. One might expect this trade to result in profit. However, if the exchange rate has now equalized to 1:1, the return on selling the shares, calculated in XYZ, would only be 1700 units, resulting in a loss of 1300 units XYZ.
The indicator will mark the P&L and the target labels in red in such cases, regardless of whether the market price reached the profit target, as the trade produced a net loss due to reduced funds after currency conversion. Conversely, an otherwise unsuccessful position can result in a net profit in the account currency due to conversion rate fluctuations. The final losses or gains appear in the label metrics, and the corresponding color coding reflects the trade's success or failure.
Settings
The settings in the "Trade sizing" section are used to calculate the position size and the monetary value of trades. Two types of risk can be chosen from the menu; a percentage based risk calculation, or a fixed money value. The risk is used to calculate the quantity of units to purchase to achieve that level of risk exposure. Example: An account size of $1000 and 10% risk will have a projected end amount of $900 if the stop loss is hit. The quantity is a product of this relationship; a projected number of units to allow for the equivalent of $100 of risk exposure over the change in price from the entry to the stop value.
The "Trade levels" allow you to manually set the entry, take profit and stop levels of an existing position tool on your chart.
You can control the appearance of the tool and the values it displays in the settings following these first two sections.
Alerts
Three alerts that will trigger when you configure an alert on this indicator. The first will send an alert when the entry price is breached by price action if that price has not already been breached in the previous price history. This is dependant on the entry location you select when placing the indicator on the chart. The other two alerts will trigger when either the stop loss or the take profit level is breached to signal that a trade exit has occurred.
█ NOTES FOR Pine Script™ CODERS
• Interactive inputs are implemented for input.time() and input.price() . These specialized input functions allow users to interact with a script.
You can create one interactive input for both time and price values by using the same `inline` argument in a pair of input.time() and input.price() function calls.
• We use the `cagr()` function from our ta library.
• The script uses the runtime.error() function to throw an error if the stop and limit prices are not placed on opposing sides of the entry price.
• We use the `currency` parameter in a request.security() call to convert currencies.
Look first. Then leap.
DB Change Forecast ProDB Change Forecast Pro
What does the indicator do?
The DB Change Forecast Pro is a unique indicator that uses price change on HLC3 to detect buy and sell periods along with plotting a linear regression price channel with oversold and undersold zones. It also has a linear regression change forecast mode to optionally project market direction.
Change is calculated by taking a two-bar change of HLC3 and dividing that by the price or, optionally, a fixed divisor.
A fast-moving change cloud is then calculated and displayed as the "regular version" plot (shown in light gray). When the cloud bottom is above low, a buy zone is detected. When the cloud top is below the high, a sell zone is detected.
The linear regression price channel is calculated similarly but using a much slower change rate. The linear regression price channel shows reasonable high, low and HLC3 ranges. At the bar's opening, the channel will be more compact and come fairly accurate about 1/4 into the bar timeframe.
The change forecasted price is projected on the right side of the current bar to indicate the current timeframe direction. Please note this forecasting feature is shown in orange when it's early in the timeframe and gray when the timeframe is more likely to produce an accurate direction forecast for the upcoming bar.
You can use these projected dashed lines to see possible market movements for the Current bar and possible market direction for the next bar. Kindly note these projects change; they should be used to understand possible extreme highs/lows for the current bar or market direction.
The indicator includes an optional change forecast projection feature hidden by default. It will project the market forecast channel with an offset of 1. The forecast is defaulted to an offset of 1 to show market direction. However, you can modify to zero the offset to show the current bar forecast and forecast history.
How should this indicator be used?
First, very important,
1. Settings > Set Symbol to Desired
2. Settings > Set High Timeframe to "Chart"
3. Settings > Ensure "Use price as divisor" is checked.
It's recommended to use this indicator in higher timeframes. Buy and sell signals are displayed in real-time. However, waiting until 1/4 to 1/2 into the current bar is recommended before taking action, and change can happen.
The buy/sell signals (zones) provide recommendations on playing a long vs. a short. When in a buy sone, only play longs. When in a sell zone, only play shorts.
Then use the linear regression price channel oversold and undersold zones to optionally open and close positions within the buy/sell zones.
For example, consider opening a long in a buy zone when the linear regression price channel shows undersold. Then consider closing the long when the price moves into the linear regression oversold or higher. Then repeat as long as it's in the buy zone. Then vice versa for sell zones and shorting.
At basic design, buy in the buy zone, sell or short in the sell zone. If you are up for higher trading frequencies, use the linear regression price channel as described in the example above.
Please note, as, with all indicators, you may need to adjust to fit the indicator to your symbol and desired timeframe.
This is only an example of use. Please use this indicator as your own risk and after doing your due diligence.
Does the indicator include any alerts?
Yes,
"DB CFHLC3: Signal BUY" - Is triggered when a buy signal is fired.
"DB CFHLC3: Signal SELL" - Is triggered when a sell signal is fired.
"DB CFHLC3: Zone BUY" - Is triggered when a buy zone is detected.
"DB CFHLC3: Zeon SELL" - Is triggered when a sell zone is detected.
"DB CFHLC3: Oversold SELL" - Is triggered when the price exceeds the oversold level.
"DB CFHLC3: Undersold BUY" - Is triggered when the price goes below the undersold level.
Any other tips?
Once you have configured the indicator for your symbol and chart timeframe. Meaning the plots are displayed over the price. Check out larger timeframes such as W, 2W, 3W, 4W, M, and 4M. It works wonderfully for showing market lows and highs for long-term investing too!
Another, tip is to combine it with your favorite indicator, such as TTM Squeeze or MACD for confirmation purposes. You may be surprised how fast the indicator shows market direction changes on higher timeframes.
You can just as easily use a high timeframe such as D, 2D, or 3D for day trading due to how the linear price channel works.
Why am I not selling this indicator?
I would like to bless the TradingView community, and I enjoy publishing custom indicators.
If you enjoy this indicator, please consider leaving a thumbs up or a comment for others to know about your experience or recommendations.
Enjoy!
RSI Trend Veracity (RSI TV)The RSI only plots itself between a high and a low value. It does not show its bullish/bearish sentiment.
The RSI TV shows the sentiment and helps anticipate the RSI trend but not the price trend.
When the Trend Veracity Line is in green, there is bullish sentiment. When it is in red, there is bearish sentiment.
The closer the lines get to their extremities, the more the current trend of the RSI is exhausted.
It works quite well even in choppy markets. See notes in the picture for more details.
High and Low PredictorWhat can I Do:
Update at 12:01 AM everyday.
Predict the Highest and the Lowest price of each day.
Color of the lines: the red color suggests downward while the green color suggests upward.
It is able to predict the highest and the lowest price of each day by calculating the history prices,
but please notice this is only prediction! Not one hundred percent correct!
The data suggests chances to buy and to sell, and the history data is also available for review.
If the switch of resistance is turned on, the resistance will be displayed on the chart directly for reference.
How to use it?
First, choose the period of 60 minutes/1 hour,
Second, add alerts, when price hits the predicted prices, it will send you alert alarm.
Adaptive Rebound Line Bands (ARL Bands)These bands consist of 4 ARLs (See: Adaptive Rebound Line ('ARL'/AR Line)) that help accurately spot price rebounds.
It is excellent for 15 minute scalping and price-action trading.
See notes in the picture above for more details.
Note: "Top Deviation" is the deviation of the top 'ARL', "High Deviation" is for the high 'ARL', etc.
Chart CAGR█ OVERVIEW
This simple script displays in the lower-right corner of the chart the Growth Rate and the Compound Annual Growth Rate (CAGR) between the open of the chart's first visible bar and the close of its last bar. As you zoom/scroll the chart, calculations will adjust to the chart's bars. You can change the location and colors of the displayed text in the script's settings.
If you need to calculate the CAGR between any two points on the chart, see our CAGR Custom Range indicator.
█ FOR Pine Script™ CODERS
Like our Chart VWAP publication, this script calculates on the range of visible bars utilizing the new Pine Script™ functions announced here and the VisibleChart library by PineCoders . It also uses the `cagr()` function from our ta library, which was recently augmented with many new functions.
Look first. Then leap.
