Short Sale Restriction (SSR) Level - Intraday and daily chartsThis script plots the Short Sale Restriction (SSR) Level relative to the previous day's closing price. It works on any time frame from 1 minute to daily, showing the correct level even during the extended session.
The Short Sale Restriction (SSR) is a rule of the Securities and Exchange Commission (SEC) that restricts traders from short-selling stocks that are rapidly decreasing in value in an attempt to profit from the price drop. The rule was introduced in 2010, after the 2008 financial crisis, to prevent market manipulation and excessive volatility.
The SSR works as follows: when the price of a particular stock drops 10% compared to the previous day's closing price, the SSR is triggered and a temporary limitation is imposed on traders' ability to short-sell that stock for the rest of the trading day and the following day. During the SSR activation period, traders can still short-sell, but only if the sale is "covered" by another long position on the same stock.
Knowledge of the SSR level is especially important for day traders because it helps them to plan their trading strategies in advance, avoiding situations where short-selling becomes more difficult. Additionally, if a stock has exceeded the SSR threshold, traders can expect an increase in price volatility.
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Vigilant Asset Allocation G4 Backtesting EngineThis script was based off of an idea that @CubanEmissary had so the description and some of the code that @CubanEmissary built on TradingView was used.
Vigilant Asset Allocation G4 (VAA G4) is a dual-momentum based investment strategy that aggressively monitors the market and reallocates portfolio funds based on the relative momentums of user-defined risk assets and safety assets. It was created by Wouter Keller and JW Keuning, based on their paper "Breadth Momentum and Vigilant Asset Allocation." In contrast to traditional dual momentum strategies, VAA G4 monitors the market itself through the two asset types. When all risk assets have positive momentum, the portfolio is allocated entirely into the risk asset with the strongest momentum At any other time, the portfolio is allocated entirely into the safety asset with the strongest momentum. The combination of breadth momentum with a very defensive reallocation trigger results in a strategy which captures alpha consistently.
The Strategy Rules:
1. Calculate each asset's momentum score on each monthly close:
momentumScore = (12*(currentMonthlyClose/lastMonthlyClose))+(4*(currentMonthlyClose/thirdLastMonthlyClose))+(2*(currentMonthlyClose/sixthLastMonthlyClose))+(currentMonthlyClose/twelvethLastMonthlyClose)-19
2. If all risk asset momentums are positive, allocate entire portfolio to the risk asset with the strongest momentum.
3. If any risk asset's momentum is negative, allocate entire portfolio to the safety asset with the strongest momentum.
4. Reevaluate at the end of each month.
Caveats:
1. It seems like TradingView only has limited price data for these tickers that are listed in the strategy. So it is best to start the strategy when they all have ample data (~ June 2nd, 2008)
2. This backtesting engine is basic and doesn't account for slippage and trading fees. So I implemented a basic "trading fee" input that will subtract a trading fee whenever the strategy makes a trade at the end of the month.
3. It is assumed in this engine that the trades will be made the exact second a new monthly bar opens up.
4. MUST USE ON MONTHLY CHART. It is hard-coded to work on monthly chart, if you open it on a daily chart , the Sharpe, Sortino, & CAGR calculations might not be right as well as the momentum score
Global (World) Monetary Supply M2 (measured in USD)This is the Global Monetary Supply M2 of the richest and most populous countries that have info from at least 2008
It is measured in USD (converting the M2 of each of the countries respective currencies and virtually converting them into USD)
This is less than the global liquidity as it does not include the countries' assets in other currencies (on their balance sheets), it only focuses on the monetary supply of each of the countries own currencies.
Dark Energy Divergence OscillatorThe Dark Energy Divergence Oscillator (DEDO)
What makes The Universe grow at an accelerating pace?
Dark Energy.
What makes The Economy grow at an accelerating pace?
Debt.
Debt is the Dark Energy of The Economy.
I pronounce DEDO "Deed-oh", but variations are fine with me.
Note: The Pine Script version of DEDO is improved from the original formula, which used a constant all-time high calculation in the normalization factor. This was technically not as accurate for calculating liquidity pressure in historical data because it meant that historical prices were being tested against future liquidity factors. Now using Pine, the functions can be normalized for the bar at the time of calculation, so the liquidity factors are normalized per candle, not across the entire series, which feels like an improvement to me.
Thought Process:
It's all about the liquidity. What I started with is a correlation between major stock indices such as SPX and WRESBAL , a balance sheet metric on FRED
After September 2008, when QE was initiated, many asset valuations started to follow more closely with liquidity factors. This led me to create a function that could combine asset prices and liquidity in WRESBAL , in order to calculate their divergence and chart the signal in TradingView.
The original formula:
First, we don't want "non-QE" data. we only want data for the market affected by QE .
So, find SPX on the day of pre-QE: 1255.08 and subtract that from the 2022 top 4818.62 = 3563.54
With this post-QE SPX range, now you can normalize the price level simply by dividing by the range = ( SPX -1255.08)/3563.54)
Normalization produces values from 0 to 1 so that they can be compared with other normalized figures.
In order to test the 0 to 1 normalized SPX range measure against the liquidity number, WRESBAL , it's the same idea: normalize it using the max as the denominator and you get a 0 to 1 liquidity index:
( WRESBAL /4276000000000)
Subtract one from the other to get the divergence:
(( WRESBAL /4276000000000)-(( SPX -1255.08)/3563.54))*10
x10 to reduce decimal places, but this option is configurable in DEDO's input settings tab.
Positive values indicate there's ample liquidity to hold up price or even create bullish momentum in some cases. Negative values mean price levels are potentially extended beyond what liquidity levels can support.
Note: many viewers of the charts on social media wanted the values to go down in alignment with price moving down, so inverting the chart is what I do with Option + I. I like the fact that negative values represent a deficit in liquidity to hold up price but that's just me.
Now with Pine Script and some help from other liquidity focused accounts on TradingView , I was able to derive a script that includes central bank liquidity and Reverse Repo liquidity drain, all in one algorithm, with adjustable settings.
Central bank assets included in this version:
-JPY (Japan)
-CNY (China)
-UK (British Pound)
-SNB (Swiss National Bank)
-ECB (European Central Bank )
Central Bank assets can be adjusted to an allocation % so that the formula is adjusted for the market cap of the asset.
A handy table in the lower right corner displays useful information about the asset market cap, and percentage it represents in the liquidity pool.
Reverse repo soak is also an optional addition in the Input settings using the RRPONTSYD value from FRED. This value is subtracted from global liquidity used to determine divergence since it is swept away from markets when residing in the Fed's reverse repo facility.
There is an option to draw a line at the Zero bound. This provides a convenience so that the line doesn't keep having to be redrawn on every chart. The normalized equation produces a value that should oscillate around zero, as price/valuation grows past liquidity support, falls under it, and repeats in cycles.
Boyle Trinomial Options Pricing Model [Loxx]Boyle Trinomial Options Pricing Model is an options pricing indicator that builds an N-order trinomial tree to price American and European options. This is different form the Binomial model in that the Binomial assumes prices can only go up and down wheres the Trinomial model assumes prices can go up, down, or sideways (shoutout to the "crab" market enjoyers). This method also allows for dividend adjustment.
The Trinomial Tree via VinegarHill Finance Labs
A two-jump process for the asset price over each discrete time step was developed in the binomial lattice. Boyle expanded this frame of reference and explored the feasibility of option valuation by allowing for an extra jump in the stochastic process. In keeping with Black Scholes, Boyle examined an asset (S) with a lognormal distribution of returns. Over a small time interval, this distribution can be approximated by a three-point jump process in such a way that the expected return on the asset is the riskless rate, and the variance of the discrete distribution is equal to the variance of the corresponding lognormal distribution. The three point jump process was introduced by Phelim Boyle (1986) as a trinomial tree to price options and the effect has been momentous in the finance literature. Perhaps shamrock mythology or the well-known ballad associated with Brendan Behan inspired the Boyle insight to include a third jump in lattice valuation. His trinomial paper has spawned a huge amount of ground breaking research. In the trinomial model, the asset price S is assumed to jump uS or mS or dS after one time period (dt = T/n), where u > m > d. Joshi (2008) point out that the trinomial model is characterized by the following five parameters: (1) the probability of an up move pu, (2) the probability of an down move pd, (3) the multiplier on the stock price for an up move u, (4) the multiplier on the stock price for a middle move m, (5) the multiplier on the stock price for a down move d. A recombining tree is computationally more efficient so we require:
ud = m*m
M = exp (r∆t),
V = exp (σ 2∆t),
dt or ∆t = T/N
where where N is the total number of steps of a trinomial tree. For a tree to be risk-neutral, the mean and variance across each time steps must be asymptotically correct. Boyle (1986) chose the parameters to be:
m = 1, u = exp(λσ√ ∆t), d = 1/u
pu =( md − M(m + d) + (M^2)*V )/ (u − d)(u − m) ,
pd =( um − M(u + m) + (M^2)*V )/ (u − d)(m − d)
Boyle suggested that the choice of value for λ should exceed 1 and the best results were obtained when λ is approximately 1.20. One approach to constructing trinomial trees is to develop two steps of a binomial in combination as a single step of a trinomial tree. This can be engineered with many binomials CRR(1979), JR(1979) and Tian (1993) where the volatility is constant.
Further reading:
A Lattice Framework for Option Pricing with Two State
Trinomial tree via wikipedia
Inputs
Spot price: select from 33 different types of price inputs
Calculation Steps: how many iterations to be used in the Trinomial model. In practice, this number would be anywhere from 5000 to 15000, for our purposes here, this is limited to 220.
Strike Price: the strike price of the option you're wishing to model
Market Price: this is the market price of the option; choose, last, bid, or ask to see different results
Historical Volatility Period: the input period for historical volatility ; historical volatility isn't used in the Trinomial model, this is to serve as a comparison, even though historical volatility is from price movement of the underlying asset where as implied volatility is the volatility of the option
Historical Volatility Type: choose from various types of implied volatility , search my indicators for details on each of these
Option Base Currency: this is to calculate the risk-free rate, this is used if you wish to automatically calculate the risk-free rate instead of using the manual input. this uses the 10 year bold yield of the corresponding country
% Manual Risk-free Rate: here you can manually enter the risk-free rate
Use manual input for Risk-free Rate? : choose manual or automatic for risk-free rate
% Manual Yearly Dividend Yield: here you can manually enter the yearly dividend yield
Adjust for Dividends?: choose if you even want to use use dividends
Automatically Calculate Yearly Dividend Yield? choose if you want to use automatic vs manual dividend yield calculation
Time Now Type: choose how you want to calculate time right now, see the tool tip
Days in Year: choose how many days in the year, 365 for all days, 252 for trading days, etc
Hours Per Day: how many hours per day? 24, 8 working hours, or 6.5 trading hours
Expiry date settings: here you can specify the exact time the option expires
Included
Option pricing panel
Loxx's Expanded Source Types
Related indicators
Implied Volatility Estimator using Black Scholes
Cox-Ross-Rubinstein Binomial Tree Options Pricing Model
Cox-Ross-Rubinstein Binomial Tree Options Pricing Model [Loxx]Cox-Ross-Rubinstein Binomial Tree Options Pricing Model is an options pricing panel calculated using an N-iteration (limited to 300 in Pine Script due to matrices size limits) "discrete-time" (lattice based) method to approximate the closed-form Black–Scholes formula. Joshi (2008) outlined varying binomial options pricing model furnishes a numerical approach for the valuation of options. Significantly, the American analogue can be estimated using the binomial tree. This indicator is the complex calculation for Binomial option pricing. Most folks take a shortcut and only calculate 2 iterations. I've coded this to allow for up to 300 iterations. This can be used to price American Puts/Calls and European Puts/Calls. I'll be updating this indicator will be updated with additional features over time. If you would like to learn more about options, I suggest you check out the book textbook Options, Futures and other Derivative by John C Hull.
***This indicator only works on the daily timeframe!***
A quick graphic of what this all means:
In the graphic, "n" are the steps, in this case we can do up to 300, in production we'd need to do 5-15K. That's a lot of steps! You can see here how the binomial tree fans out. As I said previously, most folks only calculate 2 steps, here we are calculating up to 300.
Want to learn more about Simple Introduction to Cox, Ross Rubinstein (1979) ?
Watch this short series "Introduction to Basic Cox, Ross and Rubinstein (1979) model."
Limitations of Black Scholes options pricing model
This is a widely used and well-known options pricing model, factors in current stock price, options strike price, time until expiration (denoted as a percent of a year), and risk-free interest rates. The Black-Scholes Model is quick in calculating any number of option prices. But the model cannot accurately calculate American options, since it only considers the price at an option's expiration date. American options are those that the owner may exercise at any time up to and including the expiration day.
What are Binomial Trees in options pricing?
A useful and very popular technique for pricing an option involves constructing a binomial tree. This is a diagram representing different possible paths that might be followed by the stock price over the life of an option. The underlying assumption is that the stock price follows a random walk. In each time step, it has a certain probability of moving up by a certain percentage amount and a certain probability of moving down by a certain percentage amount. In the limit, as the time step becomes smaller, this model is the same as the Black–Scholes–Merton model.
What is the Binomial options pricing model ?
This model uses a tree diagram with volatility factored in at each level to show all possible paths an option's price can take, then works backward to determine one price. The benefit of the Binomial Model is that you can revisit it at any point for the possibility of early exercise. Early exercise is executing the contract's actions at its strike price before the contract's expiration. Early exercise only happens in American-style options. However, the calculations involved in this model take a long time to determine, so this model isn't the best in rushed situations.
What is the Cox-Ross-Rubinstein Model?
The Cox-Ross-Rubinstein binomial model can be used to price European and American options on stocks without dividends, stocks and stock indexes paying a continuous dividend yield, futures, and currency options. Option pricing is done by working backwards, starting at the terminal date. Here we know all the possible values of the underlying price. For each of these, we calculate the payoffs from the derivative, and find what the set of possible derivative prices is one period before. Given these, we can find the option one period before this again, and so on. Working ones way down to the root of the tree, the option price is found as the derivative price in the first node.
Inputs
Spot price: select from 33 different types of price inputs
Calculation Steps: how many iterations to be used in the Binomial model. In practice, this number would be anywhere from 5000 to 15000, for our purposes here, this is limited to 300
Strike Price: the strike price of the option you're wishing to model
% Implied Volatility: here you can manually enter implied volatility
Historical Volatility Period: the input period for historical volatility; historical volatility isn't used in the CRRBT process, this is to serve as a sort of benchmark for the implied volatility,
Historical Volatility Type: choose from various types of implied volatility, search my indicators for details on each of these
Option Base Currency: this is to calculate the risk-free rate, this is used if you wish to automatically calculate the risk-free rate instead of using the manual input. this uses the 10 year bold yield of the corresponding country
% Manual Risk-free Rate: here you can manually enter the risk-free rate
Use manual input for Risk-free Rate? : choose manual or automatic for risk-free rate
% Manual Yearly Dividend Yield: here you can manually enter the yearly dividend yield
Adjust for Dividends?: choose if you even want to use use dividends
Automatically Calculate Yearly Dividend Yield? choose if you want to use automatic vs manual dividend yield calculation
Time Now Type: choose how you want to calculate time right now, see the tool tip
Days in Year: choose how many days in the year, 365 for all days, 252 for trading days, etc
Hours Per Day: how many hours per day? 24, 8 working hours, or 6.5 trading hours
Expiry date settings: here you can specify the exact time the option expires
Take notes:
Futures don't risk free yields. If you are pricing options of futures, then the risk-free rate is zero.
Dividend yields are calculated using TradingView's internal dividend values
This indicator only works on the daily timeframe
Included
Option pricing panel
Loxx's Expanded Source Types
Barndorff-Nielsen and Shephard Jump Statistic [Loxx]The following comments and descriptions are from from "Problems in the Application of Jump Detection Tests to Stock Price Data" by Michael William Schwert; Professor George Tauchen, Faculty Advisor.
This indicator applies several jump detection tests to intraday stock price data sampled at various frequencies. It finds that the choice of sampling frequency has an effect on both the amount of jumps detected by these tests, as well as the timing of those jumps. Furthermore, although these tests are designed to identify the same phenomenon, they find different amounts and timing of jumps when performed on the same data. These results suggest that these jump detection tests are probably identifying different types of jump behavior in stock price data, so they are not really substitutes for one another.
In recent years there has been a great deal of interest in studying jumps in asset price movements. Reasons why it is important to know when and how frequently jumps occur include risk management and the pricing and hedging of derivative contracts. Investors would benefit greatly from knowing the properties of jumps, since large instantaneous drops in asset prices result in large instantaneous losses. The effect of jumps on derivative pricing is equally significant, especially considering the important role derivatives play in modern financial markets. When asset price movements are continuous, investors can perfectly hedge derivative contracts such as options, but when jumps occur, they cause a change in the derivative price that is non-linear to the change in the price of the underlying asset. Thus, jumps introduce an unhedgeable risk to the holders of derivative contracts.
The ability to identify realized jumps in the financial markets could provide helpful information such as how frequently jumps occur, how large the jumps are, and whether they tend to occur in clusters. With this goal in mind, several authors have developed tests to determine whether or not an asset price movement is a statistically significant jump. These tests take advantage of the high-frequency intraday price data available today through electronic sources. Barndorff-Nielsen and Shephard (2004, 2006) use the difference between an estimate of variance and a jump-robust measure of variance to detect jumps over the course of a day. Approaching the problem differently, Jiang and Oomen (2007) exploit high order sample moments of returns to identify days that include jumps. Aїt-Sahalia and Jacod (2008) also exploit high order sample moments of returns to detect jumps by comparing price data sampled at two different frequencies. Lee and Mykland (2007) test for jumps at individual price observations by scaling returns by a local volatility measure. While these tests employ different strategies for detecting jumps, they are all designed to identify the same phenomenon.
For this indicator we are focused on the Barndorff-Nielsen and Shephard jump statistic.
Barndorff-Nielsen and Shephard (2004, 2006) developed a test that uses high-frequency price data to determine whether there is a jump over the course of a day. Their test compares two measures of variance: Realized Variance, which converges to the integrated variance plus a jump component as the time between observations approaches zero; and Bipower Variation, which converges to the integrated variance as the time between observations approaches zero, and is robust to jumps in the price path, an important fact for this application. The integrated variance of a price process is the integral of the square of the σ(t) term in (2.2.2), taken over the course of a day. Since prices cannot be observed continuously, one cannot calculate integrated variance exactly, and must estimate it instead.
For our purposes here, this is calculated as:
r = log(p /p )
This the geometric return from time ti-1 to time ti.
Then, Realized Variance and Bipower Variation are described by the following functions (see code for details)
realizedVariance(float src, int per)
and
bipowerVariance(float src, int per)
Huang and Tauchen (2005) also consider Relative Jump, a measure that approximates the percentage of total variance attributable to jumps:
RJ = (RV - BV) / RV
This statistic approximates the ratio of the sum of squared jumps to the total variance and is useful because it scales out long-term trends in volatility so one can compare the relative contribution of jumps to the variance of two price series with different volatilities.
To develop a statistical test to determine whether there is a significant difference between RV and BV, one needs an estimate of integrated quarticity. Andersen, Bollerslev, and Diebold (2004) recommend using a jump-robust realized Tri-Power Quarticity, I've included commentary in code to better explain how this indicator is collocated. See code for details.
How to use this indicator
When the bars turn gray, it's an indication that a jump has occurred in the market. It serves a warning that price jumped. I've included a percent point function (or inverse cumulative distribution function) to cutoff Z-score values depicted by histogram values. The top line at 3 is the empirical maximum Z-score value a serves merely as a point of reference. The Red line is the cutoff line calculated using PPF. When the histogram is green, no jumps have been detected. This indicator also includes alerts, signals, and bar coloring. I've also expanded the possible source types using my own Expanded Source Types library so you can test different log return methods as inputs. It is recommended to use window sizes of 7, 16, 78, 110, 156, and 270 returns for sampling intervals of 1 week, 1 day, 1 hour, 30 minutes, 15 minutes, and 5 minutes, respectively.
If you'ed like to better understand PPF, see here: Distributions in python
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Cycle-Period Adaptive, Linear Regression Slope Oscillator [Loxx]Cycle-Period Adaptive, Linear Regression Slope Oscillator is an osciallator that solves for the Linear Regression slope and turns it into an oscillator. This is a very simple calculation and uses one of Ehler's first implementations of his cycle period calculations. The output slope value is smoothed after calculation and before being drawn. This is a sort of momentum indicator and has a rich history with Forex traders around the world.
What is the Cycle Period?
The spectral content of the data are measured in a bank of contiguous filters as described in "Measuring Cycle Periods" in the March 2008 issue of Stocks & Commodities Magazine. The filter having the strongest output is selected as the current dominant cycle period. The cycle period is measured as the number of bars contained in one full cycle period.
What is Linear Regression?
In statistics, linear regression is a linear approach for modeling the relationship between a scalar response and one or more explanatory variables. The case of one explanatory variable is called simple linear regression; for more than one, the process is called multiple linear regression.
Included:
Bar coloring
2 signal types
Alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
ChillLax Percent Up and Down
Show the days that the stock is up 5% (default) or down 5%, from the previous close.
Useful to spot a cluster of those days. It indicates violent, choppy environment. It's better to keep your boat docked in choppy waters. Eg: see QQQ in 2008, 2000.
S&P 500 Earnings Yield SpreadThis indicator compares the attractiveness of equities relative to the risk-free rate of return, by comparing the earnings yields of S&P 500 companies to the 10Y treasury yields. "Earnings yield" refers to the net income attributable to shareholders divided by the stock's price - effectively the inverse of the PE ratio. The tangible meaning of this metric is "the annual income received by (attributable to) shareholders as a percent of the price paid to receive said income." Therefore, earnings yield is comparable to bond yields, which are "the annual income received by bond holders as a percent of the price paid to receive said income."
This indicator subtracts the earnings yield of S&P 500 companies from the current 10-year treasury bond yield, creating a "spread" between the yields that determines whether equities are currently an attractive investment relative to bonds. That is, if the S&P 500 earnings yield exceeds the 10Y treasury yield, then equity investors are receiving more attributable income per dollar paid than bondholders, which could be an indication that equities are an attractive purchase relative to the risk-free rate. The same applies vice-versa; if the 10Y treasury yield exceeds that of the S&P 500 earnings yield, then equities may not be an attractive investment relative to the risk-free rate.
Since data on S&P 500 companies' earnings yields are pulled on a monthly basis, this indicator should be used on a monthly timeframe or longer. Historical data has shown that the critical zones for the indicator are at -4% and +3%, i.e. when equities are trading with a 4% greater yield than 10Y T-bonds and when equities are trading with a 3% lower yield than 10Y T-bonds, respectively. In the "Oversold" case (-4%), equities are trading at a steep discount to the risk-free rate and has often represented a strong buying opportunity. In the "Overbought" case (+3%), equities are trading at a premium to the risk-free rate, which may be an indication that caution should be exercised within the stock market. When the indicator first crosses into "Oversold" territory, this has historically been near a the bottom of a crash on the S&P 500. When the indicator first crosses into the "Overbought" territory, this has often precipitated a correction of 15% on the S&P 500.
Some notable "misses," crashes that this indicator missed, include the 1973 stock market crash and the 2008 global recession. However, both of these cases were largely precipitated by unprecedented economic events, as opposed to stocks simply being "Overbought" relative to treasury yields. Nonetheless, this indicator should form only a small portion of your fundamental analysis, as there are many macroeconomic factors that could lead to major corrections besides the impact of treasury yields. Furthermore, it should also be noted that since markets are "forward looking," future earnings growth or interest rate hikes may become "priced into" both the stock and bond markets, affecting the outputs of this indicator. However, since both the stock and bond markets should account for these factors simultaneously, the impact has historically been minimized.
I hope you find this indicator to be beneficial to your strategies. Stay safe, and happy trading.
Jurik Moving Average//Sup TV. This script is inspired by (and dedicated to) closure of sales (today, Oct 20 '21) of the famous Jurik Research.
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Jurik Research, the real people who been doing real things by using the real instruments, while many others been reading books "How to become a billionaire in 2 days", watching 5687 hours videos of how to use RSI, and studying+applying machine learning to everything cuz suddenly it became trendy xD
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This is my remake of the original Jurik Moving Average (JMA) based on all the info I managed to get my hands on, some stuff is dated back to 2008 or smth.
The whole point of this filter, the point missed by other attempts of its remakes even posted there on TV, is that it takes into account volatility and adjusts its speed based on it.
Think about it as an EMA, where the alpha parameter is dynamic.
Now, by all means I'm not claiming that's this is the perfect replica of the original algo. I've tested it a lot, looks like it's working legit...
But we all can see together whether it's legit or nah, besides, the official sales are closed since today, you feel me?
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@everget, does it differs from yours closed one?
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Live Long And Prosper
Take America Back Version 1.0So basically, the when the price goes down a little, it buys, and when it goes up a little it buys. The only indicators are account balances, and the price, that’s it. Now I wish that Pine Script had a function or variable in which I could recall the balances of specific portions of the portfolio, but it doesn’t. So, I had to improvise. Now for this to work accurately, all of the money needs to be in the “base” side before the bot can begin. Now, the thing about this is that it does not re-invest the amount that is “saved” to all but guarantee the balance will go up. However, as this goes up it will not add up as quickly in order to allow more wiggle room so that the bot does not work itself into a corner. If you want to keep some of your base, enter how much you want to keep it in the initial “saved” setting, as long as you allow at least enough to be equal to the default quantity value. Also, I recommend you setting the pyramiding setting to the result of the base value divided by the default quantity value. The default quantity value is how much you invest, measured in the base currency.
This would have been sooooo much easier if pine script could allow me to recall specific balances, but maybe a future one will.
Finally, THIS is why I made this program, I wanted to create something that would prevent the little ones from being stepped on by the big players who don't always play fair.
Besides, cryptocurrencies were made in response to the 2008 financial meltdown that caused a global recession. This decentralized currency is not just the money of the banks, the corporations, or the governments, but the money of the people. Use this tool to level the massive wealth inequality in my country and take America Back.
I will post more links and updates later once my reputation score goes up. I will discuss more about what influenced me to make this program and as some advise and possible future improvements as well.
Premier Stochastic Oscillator (PSO) [andre_007]This is a improved version of Premier Stochastic Oscillator (PSO), coded by "LazyBear".
"The indicator was first introduced by technical analyst Lee Leibfarth in the August 2008 issue of the journal Technical Analysis of Stocks & Commodities".
Inprovements:
The script was update to version 4 of PineScript.
Added support for diferents times frames.
For example, now it's possible to stay in intraday and at same time see a weekly version of this indicator.
Possibility to customize the thresholds.
Introduction to indicator:
"Stochastic oscillators have long been used to help traders and investors identify areas where trend changes are likely.
Leibfarth developed the PSO to take advantage of a standard stochastic oscillator's strengths while enhancing it to become more reactive to market activity.
The result is a faster indicator that provides earlier signals for potential trend changes".
More info:
www.investopedia.com
[blackcat] L3 Ehlers Enhanced Corona Swing PositionLevel: 3
Background
John F. Ehlers introuced Enhaced Corona Swing Position Indicator in April, 2012.
Function
John Ehlers's corona indicators, "Corona Charts," provide a "multidimensional" view of market activity. In the article "Corona Charts" in Nov, 2008, John Ehlers further developed his earlier work on market cycles. A new kind of indicator was presented that uses a glow-like effect to present another dimension of data. Implementation of corona charts in pine v4 helps detect dominant cycles in data. It provides a corona chart for swing positions indicator. He made an enhanced version of corona chart after 4 years, which could be used as a range filter to detect sideways and trends. Dr . Ehlers claims that a corona is displayed when the market is in a trend and there is little cyclic component. The swing position indicator shows the phasing of the data within the dominant cycle. In a pure cycle the Swing Position will trace out the shape of a sine wave.
Key Signal
DomCyc ---> Dominant Cycle
Raster ---> Corona Raster Array
Pros and Cons
100% John F. Ehlers definition translation, even variable names are the same. This help readers who would like to use pine to read his book.
Remarks
The 79th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Carl's BOTTOM DETECTOR: Williams %R + normalized ATRThis script is based on Williams %r and normalized ATR.
When William%R indicates extreme oversold conditions
and the ATR indicates extreme volatility at the same time,
then it prints an arrow below the candle.
It is based on the concept that swing lows and market bottoms
are characterized by extreme oversold momentum and
extreme volatility.
The highest tf's like the daily, show you perfect market bottoms for btc.
If you zoom in it's still good to find swing highs and lows, if necessary
you can tweak the settings.
Next to that I added grey, red, and green vertical bands to the chart.
This is based on the VIX, the SPX volatility index.
Whenever the volatility of the S&P500 crosses above a specific level
it prints a colored background band behind the candle.
Grey means high volatility, red extreme volatility (like in the covid
crisis and 2008 crisis), and green means the same as grey, but indicates
it came after a red zone and could mean strong bullish bounce momentum.
You can tweak the thresholds for the grey/green and read areas.
Stochastic based on Closing Prices - Identify and Rank TrendsStochClose is a trend indicator that can be used on its own to measure trend strength, in a scan to rank a group of securities according to trend strength or as part of a trend following strategy. Moreover, it acts as a volatility-adjusted trend indicator that puts securities on an equal footing.
StochClose measures the location of the current close relative to the close-only high-low range over a given period of time. In contrast to the traditional Stochastic Oscillator, this indicator only uses closing prices. Traditional Stochastic uses intraday highs and lows to calculate the range. The focus on closing prices reduces signal noise caused by intraday highs and lows, and filters out errant or irrationally exuberant price spikes.
Here are some examples when the high or low was out of proportion and suspect. Perhaps most famously, there were errant spike lows in dozens of ETFs in August 2015 (XLK, IJR, ITB). There were other spikes in VMBS (October 2014), IJR (October 2008) and KRE (May 2011). Elsewhere, there were suspicious spikes in IEI (April 2020), CHD (March 2020), CCRN (March 2020) and FNB (March 2020)
The preferred setting to identify medium and long-term uptrends is 125 days with 5 days smoothing. 125 days covers around six months. Thus, StochClose(125,5) is a 5-day SMA of the 125-day Stochastic based on closing prices. Smoothing with the 5-day SMA introduces a little lag, but reduces whipsaws and signal noise.
StochClose fluctuates between 0 and 100 with 50 as the midpoint. Values above 80 indicate that the current price is near the high end of the 125-day range, while values below 20 indicate that price is near the low end of the range. For signals, a move above 60 puts the indicator firmly in the top half of the range and points to an uptrend. A move below 40 puts the indicator firmly in the bottom half of the range and points to a downtrend.
StochClose values can also be ranked to separate the leaders from the laggards. In contrast to Rate-of-Change and Percentage Above/Below a Moving Average, StochClose acts as a volatility-adjusted indicator that can identify trend strength or weakness. The Consumer Staples SPDR is unlikely to win in a Rate-of-Change contest with the Technology SPDR. However, it is just as easy for the Consumer Staples SPDR to get in the top of its range as it is for the Technology SPDR. StochClose puts securities on an equal footing.
StochClose measures trend direction and trend strength with one number. The indicator value tells us immediately if the security is trending higher or lower. Furthermore, we can compare this value against the values for other securities. Securities with higher StochClose values are stronger than those with lower values.
Rate Of Change - Weekly SignalsRate of Change - Weekly Signals
This indicator gives a potential "buy signal" using Rate of Change of SPX and VIX together,
using the following criteria:
SPX Weekly ROC(10) has been BELOW -9 and now rises ABOVE -5
*PLUS*
VIX Weekly ROC(10) has been ABOVE +80 and now falls BELOW +10
The background will turn RED when ROC(SPX) is below -9 and ROC(VIX) is above +80.
The background will turn GREEN when ROC(SPX) is above -5 and ROC(VIX) is below +10.
So the potential "buy signal" is when you start to get GREEN BARS AFTER RED - usually with
some white/empty bars in between...but wait for the green. This indicates that the volatility
has settled down, and the market is starting to turn up.
This indicator gives excellent entry points, but be careful of the occasional false signals.
See Nov. 2001 and Nov. 2008, in both cases the market dropped another 25-30% before the final
bottom was formed. Always have an exit strategy, especially when buying in after a downtrend.
How I use this indicator, pretty much as shown in the preview. Weekly SPX as the main chart with
some medium/long moving averages to identify the trend, VIX added as a "Compare Symbol" in red,
and then the Weekly ROC signals below.
For the ROC graphs, you can show SPX+VIX together, SPX alone, or VIX alone. I prefer to display
them separately because they don't scale well together (VIX crowds out the SPX when it spikes).
Background color is still based on both SPX/VIX together, regardless of which graph is shown.
Note that there is no VIX data available on Trading View prior to 1990, so for those dates the
formula is using only ROC(SPX) and the assigned thresholds (-9 and -5, or whatever you choose).
Overbought or Oversold? Check Distance From MAMoving averages are one of the most basic tools for technical analysts. They can be useful for both trend analysis and for mean reversion.
But how can you know when price is historically overbought or oversold relative to a moving average? Distance from MA can help.
This indicator calculates the distance from a moving average as a percentage and plots the result as an oscillator. Values above 0 appear in green, while negative readings are colored red.
This chart highlights the depth of the S&P 500's recent selloff. As you can see, the close dipped to 25 percent below its 50-day SMA on Monday. That was its most oversold condition since November 20, 2008 -- in the middle of the subprime financial crisis.
Distance from MA can handle five types of moving average. Simply change the "AvgType" input according to this key:
1 - Simple Moving Average
2 - Exponential Moving Average
3 - Hull Moving Average
4 - Weighted Moving Average
5 - Volume-Weighted Moving Average
Congestion Index by KatsanosCONGESTION INDEX
Market movements can be characterized by two distinct types or phases. In the first, the market shows trending movements which have a directional bias over a period of time. The second type of market behavior is periodic or cyclic motion, where the market shows no consistent directional bias and trades between two levels. This type of market results in the failure of trend-following indicators and the success of overbought/oversold oscillators. Both phases of the market require the use of different types of indicator. Trending markets need trend-following indicators such as moving averages, moving average convergence/divergence (MACD), and so on. Trading range markets need oscillators such as the relative strength index (RSI) and stochastics, which use overbought and oversold levels. The age-old problem for many trading systems is their inability to determine if a trending or trading range market is at hand. Trend-following indicators, such as the MACD or moving averages, tend to be whipsawed as markets enter a nontrending congestion phase. On the other hand, oscillators (which work well during trading range markets) are often too early to buy or sell in a trending market. Thus, identifying the market phase and selecting the appropriate indicators is critical to a system’s success. The congestion index attempts to identify the market’s character by dividing the actual percentage that the market has changed in the past x days by the extreme range according to the following formula:
Readings between+20 and−20indicate congestion or oscillating mode. Crossing over the 20 line from below indicates the start of a rising trend. Conversely, the start of a down turn is indicated by crossing under−20 from above. The CI can also be used as an overbought/oversold oscillator.
It was taken from İntermarket Trading Strategies book of by Markos Katsanos.Read the book.
D1:=Input(“DAYS IN CONGESTION”,1,500,15);
CI:=ROC(C,D1-1,%)/((HHV(H,D1)-LLV(L,D1))/(LLV(L,D1)+.01)+.000001);
Mov ( CI ,3,E)
(Copyright Markos Katsanos 2008)
Premium Stochastic OscillatorThe PSO is a rewired version of a short-period stochastic. Unlike a standard stochastic oscillator, this indicator is normalized to register neutral values at zero while providing greater sensitivity to short-term price moves. This indicator uses a central zero line as a reference point and will oscillate above and below this point as price fluctuates. In addition, the PSO is smoothed by using a double exponential moving average to provide a more even response to turns in the market.
(from TASC magazine, August 2008 issue).
The Premium Stochastic Oscillator was introduced by technical analyst Lee Leibfarth.
Asymmetrical RSIThis indicator was originally developed by Sylvain Vervoort (Stocks & Commodities, V.26:11 (October, 2008): "ARSI, The Asymmetrical RSI").
Schaff Trend CycleThis indicator was originally developed by Doug Schaff in the 1990s (published in 2008).
@WACC Volatility Weighted PUT/CALL Positions [SPX]This indicator is based on Volatility and Market Sentiment. When volatility is high, and market sentiment is positive, the indicator is in a low or 'buy state'. When volatility is low and market sentiment is poor, the indicator is high.
The indicator uses the VIX as it's volatility input.
The indicator uses the spread between the Call Volume on SPX/SPY and the Put Volume.
This is pulled from CVSPX and PVSPX.
When volatility and put/call reaches a critical level, such as the levels present in a crisis or a sell off, the line will be green. See Sept 2015, 2008, and Feb 2018.
This level can be edited in the source code.
As the indicator is based on Put/Call, the indicator works best on larger time frames as the put/call ratio becomes a more discernible measure of sentiment over time.
