Average Range @coldbrewroshTaking the average daily range from low to high or high to low isn't the "best" way to get an idea of how much to set targets. So, I made this indicator to make the system better.
This indicator calculates the daily range from Open to High on Bullish Days & Open to Low on Bearish Days .
Nobody can catch the absolute low of the day on bullish days and get out at the high but one can enter at a reasonable price around the open ( 17:00 EST ) .
To complement the Average Range, another table shows the movement in the opposite direction.
For Instance: On Bullish Days how much it moved from Open to Low so that we have an idea of where to put the stop loss and vice versa. The time ranges calculated are the last 5 days, last 1 month, last 3 months & last 1 year.
Note #1: Even though the date range is predefined, it has a different meaning. For Instance: date range of last 5 days means "calculation of the range of last 5 bullish daily candles & not last 5 days" .
Note #2: Exclusive to Forex at the time of posting this.
Statistics
Fiat Currency and Gold Indices (FGXY) CandlesA modification of my previous indicator "Crypto Index (DXY) Candles". The idea was to create a similar currency basket to the standard DXY, but from the perspective of other currencies. Still using the standard DXY weights, this indicator allows you to create a tailored index for other currencies, provided that a currency pair exists for each of the 6 components. This means that even currencies that aren't included should work in theory; just find the 3 character currency prefix used by tradingview and give it a shot! This indicator is useful for gauging how well countries/currencies are holding up and when paired with the standard DXY may help see potential inflection points. For use on longer time frames (~1h-~3d) as some of the data being pulled seems to have issues on lower timeframes.
SAFE MARGINE_OSHi dear Investors!
Here I present you my last prepared indicator that works with searching on the most visited prices in a period. It also take an average of them which is described here as balance line.
Inputs:
+BACKWARD: range of your search area on history from current moment.
+MEMORY: number of memory stacks that would be used to save previous calculated values for taking an average.
+REFRESH: this parameter is in mili-seconds and describe saving data in memory stacks.
+METHODOLOGY:
++OC-BASED: OPEN/CLOSE would be used for calculations
++HL-BASED: HIGH/LOW would be used for calculations
++MID-BASED: HL2/OHLC4 would be used for calculations
Please do not forget to 'BOOST' the script if you use it!
Happy trading!
PlanB4.
Financial Data Spreadsheet [By MUQWISHI]The Financial Data Spreadsheet indicator displays tables in the form of a spreadsheet containing a set of selected financial performances of a company within the most recent reported period. Analyzing Financial data is one of the classic methods to evaluate whether the company’s stock price is overvalued or undervalued based on its income statement, balance sheet, and cash flow statement. This indicator might be practical to investors to collect needed data of a company to analyze and compare it with other companies on a TradingView chart or print it in spreadsheet form.
█ OVERVIEW
█ BEST PRACTICES
Due to strict limitations on calling request.financial() function, I tried to develop the table with the best ways to be more dynamic to move and the ability to join multiple tables into a spreadsheet. Users can add up to 20 instruments and 2 financial metrics per table. However, it’s possible to add many tables with other financial metrics, then connect them to the main table.
Credits: The idea of joining multiple tables inspired by @QuantNomad Screener for 40+ instruments
█ INDICATOR SETTINGS
1- Moving Table toward right-left up-down from its origin.
2- Hiding Column Title checkmark. Useful for adding a joined table underneath with additional instruments.
3- Hiding Instruments Title checkmark. Useful for adding a joined table on the right with other financial metrics.
4- Shade Alternate Rows checkmark. I believe it’ll make the table easier to read.
5- Selecting Financial Period. (Year, Quarter).
6- Entering a currency.
7- Choosing a financial ID for each column. There’re over 200 financial IDs. Source: What financial data is available in Pine? — TradingView
8- Optional to highlight values in between.
9- Entering the ticker’s symbol with the ability to activate/deactivate.
█ TIP
For best technical performance, use the indicator in a 1D timeframe.
Please let me know if you have any questions.
Thank you.
Open Interest Denominated in QuoteOpen Interest indicator in TradingView doesn't have option to denominate in quote, so I made one.
TechnicalRating█ OVERVIEW
This library is a Pine Script™ programmer’s tool for incorporating TradingView's well-known technical ratings within their scripts. The ratings produced by this library are the same as those from the speedometers in the technical analysis summary and the "Rating" indicator in the Screener , which use the aggregate biases of 26 technical indicators to calculate their results.
█ CONCEPTS
Ensemble analysis
Ensemble analysis uses multiple weaker models to produce a potentially stronger one. A common form of ensemble analysis in technical analysis is the usage of aggregate indicators together in hopes of gaining further market insight and reinforcing trading decisions.
Technical ratings
Technical ratings provide a simplified way to analyze financial markets by combining signals from an ensemble of indicators into a singular value, allowing traders to assess market sentiment more quickly and conveniently than analyzing each constituent separately. By consolidating the signals from multiple indicators into a single rating, traders can more intuitively and easily interpret the "technical health" of the market.
Calculating the rating value
Using a variety of built-in TA functions and functions from our ta library, this script calculates technical ratings for moving averages, oscillators, and their overall result within the `calcRatingAll()` function.
The function uses the script's `calcRatingMA()` function to calculate the moving average technical rating from an ensemble of 15 moving averages and filters:
• Six Simple Moving Averages and six Exponential Moving Averages with periods of 10, 20, 30, 50, 100, and 200
• A Hull Moving Average with a period of 9
• A Volume-Weighted Moving Average with a period of 20
• An Ichimoku Cloud with a conversion line length of 9, base length of 26, and leading span B length of 52
The function uses the script's `calcRating()` function to calculate the oscillator technical rating from an ensemble of 11 oscillators:
• RSI with a period of 14
• Stochastic with a %K period of 14, a smoothing period of 3, and a %D period of 3
• CCI with a period of 20
• ADX with a DI length of 14 and an ADX smoothing period of 14
• Awesome Oscillator
• Momentum with a period of 10
• MACD with fast, slow, and signal periods of 12, 26, and 9
• Stochastic RSI with an RSI period of 14, a %K period of 14, a smoothing period of 3, and a %D period of 3
• Williams %R with a period of 14
• Bull Bear Power with a period of 50
• Ultimate Oscillator with fast, middle, and slow lengths of 7, 14, and 28
Each indicator is assigned a value of +1, 0, or -1, representing a bullish, neutral, or bearish rating. The moving average rating is the mean of all ratings that use the `calcRatingMA()` function, and the oscillator rating is the mean of all ratings that use the `calcRating()` function. The overall rating is the mean of the moving average and oscillator ratings, which ranges between +1 and -1. This overall rating, along with the separate MA and oscillator ratings, can be used to gain insight into the technical strength of the market. For a more detailed breakdown of the signals and conditions used to calculate the indicators' ratings, consult our Help Center explanation.
Determining rating status
The `ratingStatus()` function produces a string representing the status of a series of ratings. The `strongBound` and `weakBound` parameters, with respective default values of 0.5 and 0.1, define the bounds for "strong" and "weak" ratings.
The rating status is determined as follows:
Rating Value Rating Status
< -strongBound Strong Sell
< -weakBound Sell
-weakBound to weakBound Neutral
> weakBound Buy
> strongBound Strong Buy
By customizing the `strongBound` and `weakBound` values, traders can tailor the `ratingStatus()` function to fit their trading style or strategy, leading to a more personalized approach to evaluating ratings.
Look first. Then leap.
█ FUNCTIONS
This library contains the following functions:
calcRatingAll()
Calculates 3 ratings (ratings total, MA ratings, indicator ratings) using the aggregate biases of 26 different technical indicators.
Returns: A 3-element tuple: ( [(float) ratingTotal, (float) ratingOther, (float) ratingMA ].
countRising(plot)
Calculates the number of times the values in the given series increase in value up to a maximum count of 5.
Parameters:
plot : (series float) The series of values to check for rising values.
Returns: (int) The number of times the values in the series increased in value.
ratingStatus(ratingValue, strongBound, weakBound)
Determines the rating status of a given series based on its values and defined bounds.
Parameters:
ratingValue : (series float) The series of values to determine the rating status for.
strongBound : (series float) The upper bound for a "strong" rating.
weakBound : (series float) The upper bound for a "weak" rating.
Returns: (string) The rating status of the given series ("Strong Buy", "Buy", "Neutral", "Sell", or "Strong Sell").
Kioseff Trading - AI-Powered Strategy Optimizer Introducing the Kioseff Trading AI-Powered Strategy Optimizer
Optimize and build your trading strategy with ease, no matter your experience level. The Kioseff Trading AI-Powered Strategy Optimizer allows traders to efficiently test and refine strategies with thousands of different profit targets and stop loss settings. Integrated with TradingView's backtester, this tool simplifies strategy optimization, strategy testing, and alert setting, enabling you to enhance your strategy with AI-driven insights.
Key Features:
Comprehensive Testing : Simultaneously test thousands of profit targets and stop losses to fine-tune your strategy.
Dual Strategy Optimization : Adjust and optimize both long and short strategies for balanced performance.
AI Integration : Elevate your strategy with heuristic-based adaptive learning, turning it into a smart, AI-assisted system.
Detailed Analysis : View critical metrics like profit factor, win rate, max drawdown, and equity curve, presented in a strategy script format.
Customizable Alerts : Set alerts for the best version of your strategy.
Flexible Risk Management : Optimize various stop loss types, including profit targets, limit orders, OCO orders, trailing stops, and fixed stops.
Targeted Goals : Choose optimization goals like highest win rate, maximum net profit, or most efficient profit.
Indicator Compatibility : Integrate any strategy/indicator, whether it’s your creation, a favorite author’s, or any public TradingView indicator.
Accessible Design : Navigate a user-friendly interface suitable for traders of all skill levels. No code required.
Precision Lock-In : “Lock” your optimal profit target or stop loss to drill down into precision testing of other variables.
How it works
It's important to remember that merely having the AI-Powered Strategy Optimizer on your chart doesn't automatically provide you with the best strategy. You need to follow the AI's guidance through an iterative process to discover the optimal settings for your strategy.
The Trading Strategy Optimizer is a versatile tool tailored for both non-coding traders and seasoned algorithmic trading professionals. Let's start with no-code-required instructions on how to use the optimizer.
Instructions: How To Optimize Your Strategy Without Code
1. Build your strategy in the settings
The image above shows explanations for each key setting.
Note: This example uses the RSI indicator to initiate a long trade whenever it dips below the 30 mark.
Ensure that the indicator you wish to optimize is already applied to your chart . This enables the Trading Strategy Optimizer to interact with the indicator and finetune profit targets and stop losses effectively.
Because the indicator is plotted on the chart I can access the indicator with the Trading Strategy Optimizer and optimize profit targets and stop losses for it.
2. Leverage AI Recommendations
Optimization Prompt: After you load your strategy, the tool advises you on new TP and SL levels that could be more profitable.
When your strategy is set, the tool gives you tips for where to set your profit goal (TP) and your stop loss to help you optimize your strategy. It'll tell you if there's a better range for these settings based on past results.
Follow Suggestions: Keep updating your TP and SL according to the tool's suggestions until it says "Best Found".
Final Result: The last image shows the best settings found by the indicator.
(Optional Step 3)
3. Lock the profit target or stop loss to further fine tune your strategy
Continue following the AI’s suggestion until “Best Found” is displayed.
Note: you can select lock either your stop loss or profit target for fine tuning. For this demonstration we will lock our profit target.
Code-Required Instructions (Optional)
You can backtest more code-intensive strategies, such as harmonic patterns, traditional chart patterns, candlestick patterns, Elliot wave, etc., by coding the entry condition in your own script and loading it into the Trading Strategy Optimizer. Let's dial in on how to achieve this!
1. You must create an integer variable in your script with an initial value of "0".
2. Define your entry condition in the code. Once complete, assign the value "1" to the variable you created if the entry condition is fulfilled.
3. Plot your variable.
4. Select the plotted variable in the settings for the Trading Strategy Optimizer
The image above shows a coded entry condition for the linear regression channel (which can be any indicator). When price crosses under and closes below the lower line our variable "strategyEntryVariable" is assigned the value "1".
The Trading Strategy Optimizer will treat this change in value from "0" to "1" as an entry signal and enter long/short up to 1000 times at the price where the entry condition was fulfilled.
5. Test Your Strategy
The image above shows the completion of the process! Keep applying the steps we described. Stick with the AI's recommendations until you see “Best Found” show up.
By following these instructions, you can build, test, and optimize almost any trading indicator or strategy!
So, just note that the Trading Strategy Optimizer considers a change in value of a plotted variable from "0" to "1" as an entry signal! So long as you follow this rule you should be able to test and optimize any conceivable, Pine Script compatible strategy!
AI Mode
AI Mode incorporates Heuristic-Based Adaptive Learning to fine-tune trading strategies in a continuous manner. This feature consists of two main components:
Heuristic-Based Decision Making: The algorithm evaluates multiple versions of your strategy using specific metrics such as Profit and Loss (PNL), Win Rate, and Most Efficient Profit. These metrics act as heuristics to assist the algorithm in identifying suitable profit targets and stop losses for trade execution.
Online Learning: The algorithm updates the performance evaluations of each strategy based on incoming market data. This enables the system to adapt to current market conditions.
Incorporating both heuristic-based decision-making and online learning, this feature aims to provide a framework for trading strategy optimization.
Settings
AI Mode Aggressiveness:
Description: The "AI Mode Aggressiveness" setting allows you to fine-tune the AI's trading behavior. This setting ranges from "Low" To "High, with higher aggressiveness indicating a more assertive trading approach.
Functionality: This feature filters trading strategies based on a proprietary evaluation method. A higher setting narrows down the strategies that the AI will consider, leaning towards more aggressive trading. Conversely, a lower setting allows for a more conservative approach by broadening the pool of potential strategies.
Adaptive Learning Aggressiveness:
Description: When Adaptive Learning is enabled, the "Adaptive Learning Aggressiveness" setting controls how dynamically the AI adapts to market conditions using selected performance metrics.
Functionality: This setting impacts the AI's responsiveness to shifts in strategy performance. By adjusting this setting, you can control how quickly the AI moves away from strategies that may have been historically successful but are currently underperforming, towards strategies that are showing current promise.
Additional Settings
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
Monthly ReturnsDisplays monthly and yearly returns in tabular format along with maximum, minimum, average returns and standard deviations.
This uses boxes to build the table and as maximum boxes that could be used is 500, it displays up to 32 years of returns. However, for maximum, minimum, average and standard deviation calculations, it uses data from all months since inception.
This requires timeframe to be set to one month (1M). Cell widths correspond to years. For the first year, cell widths may be shorter and there could be overlap of numbers as nothing could be drawn before the first bar.
Provide sufficient space for the table to render properly. Zooming out or less space may lead to overlapping of numbers.
SPX IC Intraday & StatsThis indicator was designed for traders who make intraday Iron Condors with the SPX.
It basically has 2 main parts:
Part 1:
The indicator shows the distance, in real time, between the current SPX price and the IC selling wings (calls and puts wings) that have been selected. This result is displayed in percentages and in points. In the upper right corner (for calls) and lower right corner (for puts). The label will change color as the price moves closer to or further away from the sell strike. Darker red color as it approaches the wings, lighter green color as the price approaches the center of the Iron Condor.
Part 2:
Statistical dashboard showing the number of times in the past that from a certain hour (the current time) to the market close (4pm US East Time) the SPX moved outside or inside the width of the selected wings. The position of the table is configurable. By default, it's in the upper right corner.
Dashboard:
First Date: the first day of the sample (this varies depending on the timeframe, the higher the timeframe the larger the sample is, as TradingView counts more bars backwards, by default TV offers 10000 bars for the PRO version). Recommended 5 minutes.
Range Time: refers to the time range of the sample. From the current time to 4pm (US East Time).
Deviation Wing (%): It's the deviation (in percentage) based on the current time, from the current SPX price to the wing strikes. It takes the smaller value, either call or put wing.
Total Days: the number of days in the sample.
In range Days: the number of days that the price remained in that range from the current time to the market close.
Out of Range Days: the number of days the price ended outside that range from the current time to the market close.
Configuration:
Open Time (Hour): IC opening hour.
Open time (Minute): IC opening minutes.
Width Wings: width of the wings from the SPX price at the configured time.
Mini Rectangles: Refers to include within the IC 4 deviations (mini rectangles) to better visualize where the SPX price is with reference to the wings. Darker red color as it gets closer to the wings, lighter green color as the price gets closer to the center of the Iron Condor.
Add SMAs: Add SMAs 8, 20 and 50 to the IC chart.
Include Fundamental Days: Include or not in the sample important news days. They are considered: FED Meetings, CPI Reports, Unemployment Reports, Powell Speech.
Include Days: (Monday, Tuesday, Wednesday, Thursday, Friday). Include (or not) in the sample the chosen days.
Apply Filters Since: Sampling start date. Per default: False.
Table Position: Dashboard position.
Note 1: It's recommended to use the Dark Theme Color of TradingView.
Note 2: this indicator will only work in intraday timeframes less than 30 minutes (1m, 2m, 5m, 10m, 15m, 30m) and will only show results while the market is open.
************************************
Spanish Version:
Este indicador fue diseñado para los traders que hacen intradías de Iron Condors con el SPX.
Básicamente cuenta con 2 partes principales:
Parte 1:
El indicador muestra la distancia, en tiempo real, entre el actual precio del SPX y las alas vendedoras (alas calls y puts) que se hayan seleccionado. Este resultado se muestra en porcentajes y en puntos. En la esquina superior derecha (para los calls) e inferior derecha (para los puts). El label cambiará de color a medida que el precio se acerque o aleje del precio de las alas. Color rojo más oscuro a medida que se acerque a las alas, color verde claro cuando el precio se acerque al centro del Iron Condor.
Parte 2:
Dashboard estadístico que muestra la cantidad de veces que desde una determinada hora (el tiempo actual) hasta el cierre del mercado (4pm US East Time) el SPX se movió fuera o dentro del ancho de las alas seleccionadas. La posición de la tabla es configurable. Por defecto esta en la esquina superior derecha.
Dashboard:
First Date: el primer día de la muestra (esto varía dependiendo el timeframe, a mayor timeframe la muestra es mayor, ya que TradingView contará más barras para atrás (por default TV ofrece 10000 barras para la versión PRO). Recomendado 5 minutos.
Range Time: se refiere al rango horario de la muestra. Desde la hora actual hasta las 4pm (US East Time)
Deviation Wing (%): Es la desviación en porcentaje en base a la hora actual, desde el precio actual del SPX hasta el ancho de las alas. Toma el menor valor, sea al ala call o al ala put.
Total Days: la cantidad de días de la muestra.
In range Days: la cantidad de días que el precio se mantuvo en ese rango desde la hora actual hasta el cierre del mercado
Out of Range Days: la cantidad de días que el precio terminó fuera de ese rango desde la hora actual hasta el cierre del mercado.
Configuración:
Open Time (Hour): Hora de apertura del IC.
Open time (Minute): Minutos de apertura del IC.
Width Wings: ancho de las alas desde el precio del SPX a la hora configurada.
MiniRectangles: Se refiere a incluir dentro del IC 4 desviaciones (mini rectángulos) para visualizar de mejor manera donde se encuentra el precio del SPX con referencia a las alas. Color rojo más oscuro a medida que se acerque a las alas, color verde claro cuando el precio se acerque al centro del Iron Condor.
Add SMAs: Agrega al cuadro las SMA 8, 20 y 50.
Include Fundamental Days: Incluye o no en la muestra días de noticias importantes. Son considerados: FED Meetings, CPI Reports, Unemployment Reports, Powell Speech.
Include Days: (Monday, Tuesday, Wednesday, Thursday, Friday). Incluye (o no) en la muestra los días elegidos.
Apply Filters Since: Fecha de comienzo del muestreo.
Table Position: Posición del dashboard.
Nota 1: se recomienda usar el Dark Theme Color de TradingView.
Nota 2: este indicador solo funcionará en temporalidades intradías menores a 30 minutos (1m,2m,5m,10m,15m,30m) y Solo mostrará resultados mientras el mercado esté abierto, o sea en tiempo real.
Markets vs Inflation [x7.am]Markets vs Inflation(CPI US) also known as Inflation-Adjusted Return.
The inflation-adjusted return is the measure of return that takes into account the time period's inflation rate. The purpose of the inflation-adjusted return metric is to reveal the return on an investment after removing the effects of inflation.
Removing the effects of inflation from the return of an investment allows the investor to see the true earning potential of the security without external economic forces. The inflation-adjusted return is also known as the real rate of return or required rate of return adjusted for inflation. It is a more accurate measure of investment performance than the nominal rate of return.
The inflation-adjusted return accounts for the effect of inflation on an investment's performance over time.
Also known as the real return, the inflation-adjusted return provides a more realistic comparison of an investment's performance.
Inflation will lower the size of a positive return and increase the magnitude of a loss.
Assume you have saved $10,000 to buy a car but decide to invest the money for a year before buying to ensure that you have a small cash cushion left over after getting the car. Earning 5% interest, you have $10,500 after 12 months. However, because prices increased by 3% during the same period due to inflation, the same car now costs $10,300.
Consequently, the amount of money that remains after you buy the car—which represents your increase in purchasing power—is $200, or 2% of your initial investment. This is your real rate of return, as it represents the amount that you gained after accounting for the effects of inflation.
Markets vs Inflation indicators use in 1 months interval
SP:SPX , INDEX:BTCUSD , TVC:GOLD , TVC:DJI
Position Size ToolUpdated - Version 2
This tool is used to calculate the size of a trade.
Settings - Type in total account size and % of capital that can be risked on each trade.
The table will display:
Column 1 - Stop placement based on low, mid or high value of the current candle.
Column 2 - Percent risk on the trade.
Column 3 - Amount of shares that can be traded (calculated from account size, risk and selected stop placement).
Green color is intended for long position, stop at the low of the candle.
Red color is intended for short position, stop at the high of the candle.
Middle value can shift between either color since its measured from open to close.
AlexD Market annual seasonalityThe indicator displays the percentage of bullish days with a given date over several years.
This allows you to determine the days of the year when the price usually goes up or down.
Indicator has a built-in "simple moving average" shifted back by half a period, due to which the delay of this smoothing is removed.
Z-Score Buy and Sell SignalsHello everyone!
Happy Holidays, Merry/Happy Christmas!
Here is my Christmas gift to you to show my appreciation of your support and engagement over the past year!
This is the Z-Score Buy and Sell Signal indicator!
How it works:
It works by looking at the Z-Score of an equities close price and looking for previous areas over reversals over the defined period of time.
It also looks at areas that are overbought or oversold (manifested by Z-Scores greater than or less than 2 Standard Deviations away) and displays them as bar colour changes.
Historic reversals are signaled with buy and/or sell signals.
Oversold is signaled with a green bar colour change (colour can be customaized).
Overbought is signaled with an orange bar colour change.
How to use it:
You can use it with support resistance or other indicators. You can use this on both the larger and small timeframes, depending on the style of trader you are.
You can modify the input length to look back on shorter or longer periods.
As a general rule from my experience using it, if you are using the shorter timeframes (i.e. 1 minute tfs), its best to look back between 50 and 75 candles for most equities.
If you are looking at the larger timeframes (i.e. Daily, 1 to 2 hour, etc.) its best to set the input value to between 500 to 800.
But, as always, you should check to ensure the indicator is providing correct signals by reviewing the previous signals to ensure that they adequate identified reversals.
It is also best not to use this alone as your sole indicator. It is meant to be supplementary to other indicators/support resistance/chart patterns you are using to guide your trades. This will not replace good TA and a good understand of the stock and its likely trajectory.
As always, please feel free to share your comments/feedback/questions and recommendations below.
As always, I do customary tutorial videos for my indicators, so please see below for an in-depth video tutorial should you want to see it in action:
Otherwise, happy holidays everyone! And all of the best over this Christmas weekend to you and your loved ones!
Multi-Polar WorldA new macro analysis tool for easily analyzing the multi-polar world's economic powerhouses / spheres of influence, making for an easy to use visual when comparing a number of statistics:
GDP, GDP per Capita, External Debt, Government Debt, Exports, Imports, Gold Reserves, Employed Persons, Military Expenditure, Population, Bank Lending Rate, Balance of Trade, Central Bank Balance Sheet, M2 Money Supply, and CPI . Includes option to provide the total for each pole, or view individually for more detailed comparison. Meant to be used when analyzing the macro-economic conditions/trends in conjunction with other "Big Picture" type indicators when adjusting your macro framework.
Liquidation Levels - By LeviathanThis indicator generates liquidation levels of over-leveraged traders and utilizes a variety of different tools to analyze data such as Open Interest and Volume to provide an edge in your trading system and help you with valuable market insights.
The concept of this indicator is inspired by the platform Hyblock and their Liquidation Levels tool.
The script offers a lot of flexibility in settings, so please read these instructions and test out different parameters to see what works best for you. Here is a short overview of all inputs, one by one.
PRIMARY SOURCE OF DATA
This input allows you to choose the primary source of data that the script uses to calculate liquidation levels. I encourage you to test them all and see which works best for the assets and timeframes you trade. You can also regularly switch between to see confluences.
My personal favorite inputs (and also most accurate ones) are Open Interest, Open Interest + Volume (OI+VOL), and Open Interest + CVD (OI+CVD), as they utilize Open Interest in the calculations, which is the most important factor when it comes to analyzing position opening/closing and market activity in general. The other options that do not include Open Interest (Volume, CVD) were mainly added to provide the possibility to use the indicator on pairs where OI data is not available. Please note that this indicator is built on Tradingview and can only use data (e.g., Open Interest) provided by Tradingview, which is unfortunately quite limited. This is why I recommend using the script with OI-based data source inputs on Binance's perpetual futures pairs, as this is where OI data is available. The volume-based data source inputs can be used on spot pairs, forex, indices, and other markets, where Open Interest data is not provided.
To summarize the use of Primary Source of Data input:
1. Open Interest - use on Binance’s perpetual futures or anywhere else where OI data is available
2. OI + VOL - use on Binance’s perpetual futures or anywhere else where OI data is available
3. OI + CVD - use on Binance’s perpetual futures or anywhere else where OI data is available
4. CVD - use on any pair you wish
5. Volume - use on any pair you wish
DIRECTIONAL BIAS
If turned on, the Directional bias function uses volume and some other calculations to predict which side’s liquidation levels are more likely to be filled and only keep those levels on your chart.
- Directional Bias for Liquidation Levels
Turned ON: only one side’s liquidation levels are shown on the chart
Turned OFF: all liquidation levels are shown on the chart
- Directional Bias for Liquidation Level Bubbles
Turned ON: only one side’s liquidation level bubbles are shown on the chart
Turned OFF: all liquidation level bubbles are shown on the chart
I've separated directional bias options between Liquidation Levels and Liquidation Level Bubbles because sometimes it can be useful to have one of them ON and the other one OFF.
REDUCE SENSITIVITY
The Reduce Sensitivity option allows you to multiply the thresholds for "Smaller Size Liquidation," "Medium Size Liquidation," and "Large Size Liquidation" simultaneously. This is a useful feature as it enables you to easily filter larger positions and their liquidations without having to manually alter the standard deviation multipliers, which will be discussed in further detail later in the article.
The default value is 1, which means that it does nothing.
Increasing it above 1 will increase all thresholds and therefore generate fewer liquidation levels but with larger relative sizes.
Decreasing it below 1 will lower all thresholds and therefore generate more liquidation levels but with both smaller and larger relative sizes.
BASE
This input gives you the possibility to choose between four different bases, from which the liquidation levels will be generated.
CLOSE ⇒ Levels are plotted above and below the close of the candle
HALF ⇒ Levels are plotted above and below the middle of the candle ((high+low)/2)
VWAP ⇒ Levels are plotted above and below the volume-weighted average price
VWMA ⇒ Levels are plotted above and below the volume-weighted moving average
I suggest that you backtest them and see what works best for you.
LIQUIDATION LEVEL BUBBLES
If enabled, "Liquidation Level Bubbles" mark the beginning of a new liquidation level and indicate the relative size of liquidations that would occur if the price were to reach that level.
Liquidation Bubbles or Liquidation Levels in general appear when there is a large influx of new positions and logically, (significant) new positions lead to (significant) new liquidation levels. Liquidation Bubbles can be used to estimate the size and therefore significance of a given liquidation level. It could be argued that the price is more likely to be attracted to larger Liquidation Level Bubbles. While this is often true, it is not always the case as the strong momentum created by large positions can sustain for a prolonged period before reversing and filling the remaining levels Similarly to other features in this indicator, significant new positions are identified and filtered using standard deviation thresholds and their multipliers. New positions are considered significant when newly opened positions exceed the threshold for "Smaller Size Liquidation," leading to the creation of new liquidation levels and bubbles.
1. If new positions exceed the first standard deviation multiplier ("Smaller Size Liquidation Level"), but do not exceed “Medium Size Liquidation Level”, a smaller-sized bubble appears.
2. If new positions exceed the second standard deviation multiplier (”Medium Size Liquidation Level”), but do not exceed “Large Size Liquidation Level”, a medium-sized bubble appears.
3. If new positions surpass the third standard deviation multiplier (”Large Size Liquidation Level”), a large-sized bubble appears.
Significant opened positions are identified and filtered by size using three "thresholds" in the form of standard deviations, which can be modified in the indicator settings, in the section called "Standard Deviation Multipliers".
**Increasing** the standard deviation multipliers of Liquidation Level Bubbles effectively increases the threshold for a given bubble to occur, making the conditions for its appearance stricter.
⇒ Fewer Liquidation Levels - just larger positions are included
**Decreasing** the standard deviation multipliers of Liquidation Level Bubbles effectively decreases the threshold for a given bubble to occur, making the conditions for its appearance looser.
⇒ More Liquidation Levels - smaller positions are included
Keep in mind that this should always be the case:
*Smaller Size Liquidation Level < Medium Size Liquidation Level < Large Size Liquidation Level*
MARKET ORDER BUBBLES
"Market Order Bubbles" is my experimental concept used as a way to analyze large volumes and visualize the market activity at significant levels under certain conditions. It is based on my imitation of CVD which is also used in other parts of the script. The aim of this concept is to give you an idea about the real-time heavy market buying and selling, which could indicate eg. large liquidations, large entries/exits. in order to add confluence to your analysis. Please note that this concept is still in its early stages and may be confusing as it might have been poorly implemented. I recommend taking the time to thoroughly read through this section in order to fully understand it. On the other hand, early backtesting results appear very promising, as the win rate of countertrading buy and sell bubbles under certain conditions was fairly high (70%). I will continue working on this tool, so stay tuned for future updates.
**Market Buy Order Bubbles** appear above the price and possibly signal the following:
- Short positions being liquidated (exit short = buy order)
- New traders entering late longs based on FOMO (enter long = buy order)
- New short positions (sells) of “smarter” traders absorbing liquidated/closed shorts (buys)
⇒ Possible reversal to the downside - look for the liquidation levels of longs to get filled
**Market Sell Order Bubbles** appear below the price and possibly signal the following:
- Long positions being liquidated (exit long = sell order)
- New traders entering late shorts based on FOMO (enter short = sell order)
- New long positions (buys) of “smarter” traders absorbing liquidated/closed longs (sells)
⇒ Possible reversal to the upside - look for the liquidation levels of shorts to get filled
significant volumes are identified and filtered using three “thresholds” in the form of standard deviations, which can be modified in indicator settings.
1. If buy/sell volume surpasses the first standard deviation (”**Smaller Size Market Orders**”), then the **smaller-sized bubble** appears.
2. If buy/sell volume surpasses the second standard deviation (”**Medium Size Market Orders**”), then the **medium-sized bubble** appears.
3. If buy/sell volume surpasses the third standard deviation (”**Large Size Market Orders**”), then the **large-sized bubble** appears.
**Increasing** the standard deviation multipliers effectively increases the threshold for a given bubble to appear, making the conditions for its occurrence more strict.
**Decreasing** the standard deviation multipliers effectively decreases the threshold for a given bubble to appear, making the conditions for its occurrence more strict.
Keep in mind that this should always be the case:
*Smaller Size Market Orders < Medium Size Market Orders < Large Size Market Orders*
COLOR CANDLES
The Color Candles function is a useful and interesting feature that will enhance your analysis with additional context. If enabled, the indicator will color the chart's candles based on different data. Currently, there are three options to choose from, with more to be added in future updates.
Color Candles: OI DELTA
This option will color the candles to reflect Open Interest Delta. If there is a net increase in open positions (positive Open Interest Delta), the candle will be colored green. If there is a net decrease in open positions (negative Open Interest Delta), the candle will be colored red. It is important to note that this option only functions on pairs for which Tradingview provides OI data
Color Candles: VOLUME
This option utilizes volume data to help you identify the trend and momentum, coloring the candles accordingly - upward impulses are colored green and downward impulses are colored red. This makes it easier to recognize trend reversals and adjust your directional bias accordingly.
Color Candles: STOCH
This option uses the Stochastic Oscillator and SMA to color the candles, helping you identify momentum as upward green moves transition to downward red moves and vice versa.
LEVERAGE RATIO
Leverage ratio is another experimental concept I have added to this script. If turned on, it provides a broad insight into whether the market is relatively over-leveraged or if the leverage is relatively low. The aim is to use it as a confluence in anticipating incoming volatility and possibly use it to understand other aspects of market activity. It is still in its early stages of development and needless to say, it only works on pairs where Open Interest data is provided by Tradingview.
Crosses above price = Leverage is relatively high
Crosses below price = Leverage is relatively low
MARKET INFO SCREENER, LEVEL LEGEND, HIDE FILLED LINES
The last three inputs of Basic Settings section:
”The Market Info Screener” feature will display a small panel on the right side of your chart that provides useful data about the market, including Open Interest, Volume, the aggressive side of traders, and the Leverage Ratio. More data coming in future updates.
"Level Legend" option will display a small legend on the right side of the chart, helping you or others viewing the chart to understand what the objects on the chart mean.
"Hide Filled Lines" option will hide liquidation levels that have already been filled, only displaying the active ones in order to reduce clutter on your chart.
APPEARANCE
The “Appearance” settings offer a variety of modifications for colors, styles, and visibility.
The "Line Style" input allows you to choose the style and width of the liquidation level lines. You can also select which levels to display, as well as the color of the liquidation level lines and bubbles. The "Max Number of Lines" input allows you to specify the number of level lines you want on a chart. If you feel that there is too much clutter, you can decrease this number, and old lines will be removed. Please note that Tradingview has a built-in limit of 500 lines on a given chart, so this value cannot be set above 500. The "Market Buy/Sell Bubble Style" input can be used to modify the color and style of the market order bubbles. Enabling the "Show Settings Screener" option will display a screener with a list of all your settings on the right side of the chart, making it easier to share your preferred settings with others."
STANDARD DEVIATION MULTIPLIERS
This is where you can set the standard deviation thresholds for Liquidation Levels and Market Order Bubbles. These values can be customized to your preference, as the default values may not be suitable for your needs or you may want to experiment with different values to see more or fewer liquidation levels or market order bubbles on your chart. Personally, I sometimes use this feature to increase the Large Size Market Orders or Large Size Liquidation Levels multipliers so that large-sized bubbles are only assigned to extremely large positions or volumes.
If you want to only analyze larger positions or volumes, you can increase the "Smaller Size" multipliers, which act as a minimum threshold.
If you also want to include even smaller positions or volumes in your analysis, you can decrease the values of the "Smaller Size" multipliers, which act as a minimum threshold
Please note that the "Reduce Sensitivity" option in Basic Settings affects the standard deviation multipliers of the liquidation levels. Changing the "Reduce Sensitivity" value will equally multiply all of them (the ratio between Smaller, Medium, and Large multiplier values will remain the same)
STANDARD DEVIATION LENGTH
"Standard Deviation Length" defines the length (number of bars) used in all calculations that utilize the standard deviation function in this script. Decreasing it will generate Liquidation Levels and Liquidation Bubbles based more on recent data. Increasing it will generate Liquidation Levels and Liquidation Bubbles based on more long-term data.
CVD LENGTH
"CVD Length" refers to the length used in calculating cumulative volumes. Decreasing it will generate Liquidation Levels, Market Order Bubbles, and apply Directional bias based more on recent data. Increasing it will generate those based on more long-term data."
LEVEL CALCULATION MODES
“Level Calculation Modes” enable you to switch between three modes of calculating the distance between the base and actual liquidation levels.
COPYRIGHT
"Copyright" option will add your username to the copyright section at the bottom of the chart, giving you credit for your analysis if you post it somewhere.
Neon Juliet - PreviewThere is no TLDR, but there is a summary at the end. I strongly encourage to read full description before trying it out. Enjoy!
Background
=========
Having successful and adamant trading systems typically consists of two (oversimplified) elements: signals and risk management system. In most zero-sum games, such as trading, signals must offer an advantage against the market, and risk management system provides a safety mechanism to allow the system to exist in the future. Let me explain.
Say, I have a solid risk management system: it is diversified, with take profit and stop loss thresholds set for low risk, on average I trade less than 3% of my assets, and there’s a loss recovery mechanism, etc. Hypothetically, it’s pristine. Now, let’s trade this portfolio against a flip of a coin, essentially a signal that provides 50% probability of things turning out in my favour. How profitable is such system? My answer: it isn’t. I might be able to sustain this system for some time, but eventually this system is going to have to loosen risk restrictions to stay ahead of the commissions and borrowing costs, resulting in overtime detrimental trend.
Conversely, if the signals provide greater than 50% confidence of things turning out in my favour, but risk management is poor, I’d expect such system to end up in a disaster soon, perhaps after a few euphoric gains. (I’d isolate a top-notch signals, say >90% confidence, in another bucket, but this idealistic system is non-achievable in my practice, so I’ll leave it be)
Neon Juliet was developed to offer an advantage against given markets. Probabilities generated by this model are statistical historical outcomes. This model developed using only price action and is unable to consume any other data or price data across instruments. In other words, it doesn’t know anything you don’t see already on a chart.
Neon J performs best on complex instruments where there’s great diversity of actors and considerable daily volume .
Methodology
==========
In principle, Neon J is based on Bayes’ Theorem. Simply put, prior knowledge of price action ( aka patterns) provides basis for probability of future price action development (ex. long or short trend).
The training process is implemented outside of this script mainly due to Pine Script limitations. This script, however, contains inference portion of the model.
As input for training, daily candle data is used. From this data, feature engineering step of the training develops features, like price average divergence/convergence (think MACD ), price strength (think RSI , ADX ); multiple periods used to diversify long and short patterns. This is done to develop a “state” that is reflective of recent price development. Ex. what we’d call a trend is just a strong and consistent upward price action, but we’d need to look at most recent N candles and their pattern to know that.
Once features are developed, I train a model using Reinforcement Learning technique. Simply put, this technique allows an agent to interact with a trading simulator and take actions (ex. go long, go short, etc.). After many iterations, the agent learns conditions (patterns) that lead to positive outcomes and those that lead to negative outcomes. This learning is quantitative, which means there’s a way to tell which probabilities are strong and which are weak. These probabilities are indicated by this script.
Trained Neon J models are instruments-specific. Meaning, that model for DJI is not compatible with SP500 or any other instrument. Experimentally, I proved that such approach over-performs generalizable models (those that are trained on data from multiple instruments)
Neon J currently only support daily time frame. The limitation is purely practical to reduce the development load and model size.
Results
======
Tests show 60%-70% success rate (on average, some instruments are worse than that, some better) of individual signal when threshold is set to 0.3 (roughly equivalent to 65% probability). This is calculated with Pine Script Strategy with the following entry/exit rules:
Entry when individual signal (a dot) is above 0.3 (long) or below -0.3 (short)
Exit when 14-period smooth signal (a column) is above 0.0 (short exit) or below 0.0 (long exit)
No stop loss or take profit levels.
Pyramiding is set to 100 (to allow unrestricted action of all signals)
All trades are closed on last tested bar (to conclude all signals in-flight)
Percent Profitable is what we take as success rate in the context of this assessment. This number represents how many signals were profitable vs all signals actioned.
It is also worth noting that this assessment was performed on a time period previously unseen by the model. Simply put, we only train a model with data up until date X, then we test starting from date X onward. This ensures that the assessment is unbiased by the model already “knowing” the future. In practice, this gives confidence that future (unknown) market dynamics is going to be representative of our test results.
Be aware, the above “strategy” is not my recommended usage of this signal, it is simply an assessment technique that is meant to be as simple and unconstrained as possible.
How to use this script
================
The script calculates a probability. A term probability here is used in a loose form and means “a numeric value in roughly -1 to 1 space that represents the likelyhood of bullish or bearish price action”. Keep in mind that probability values can go over 1.0 or below -1.0. This is due to the fact that these value are normalized to -1/1 space using 95-percentile (this detail is largely unimportant for usability’s sake).
Indications
--------------
Dots (circles) indicate individual probability value on any given bar. Indicated value on a given bar indicates the probability of future price action. High (positive) values indicate high probability of long action in the future. Low (negative) values indicate high probability of short action in the future. You should interpret future as a gradient (a trend developing slowly over time) instead of being isolated to what’s immediately follows (ex. next bar)
Columns (histogram) provided as convenient view of smoothed probabilities of last N bars. This is controlled by the Smoothing parameter and defaults to 14.
Parameters
---------------
Model parameter is the backbone of this script. It is a required parameter and it is unique for each instrument. Example models provided at the end (see below). This parameter is a long 10000+ character representation of a model.
The script has two additional parameters for configuring interpretation: Threshold and Smoothing.
Threshold controls the level at which values change color (ex. above 0.3, turn neon blue, and below -0.3 turn neon purple).
Smoothing parameter provides a way to smooth out individual probabilities into a exponential moving average with the periods provided. This average is indicated using columns on the indicator.
Model expiration
----------------------
Models are valid for 1 month after training. This is done by design to prevent model deterioration. A month is proven to be a maximum period of time to hold model performance steady. After that, deterioration is likely to occur. Optimal time for model lifetime is 10 days (this is what I use for live trading), and of course most optimal (but unpractical for now) is to re-train daily.
Validity indicated with blue-tinted indicator background, while red-tinted background indicates expired period.
Preview
======
This script is released as a public script for anyone to try. My motives for this release are two-fold:
To subject the model to a variety of conditions, including traders with different experiences trading different instruments (subject to specific models offered of course). Essentially, my own testing is not enough to grasp a full breadths of scenarios. I’d like to harden it and understand where it is strong and where it might fall short (pun intended).
Get an idea on how Neon J might be useful when making trading decision. I tried to make the representation of the signals unconstrained and unopinionated, so there’s room to explore and experiment. I found that Neon J can be packaged in a number of different ways.
At this moment the script is closed-source. I might consider open-sourcing this script in future depending on how much feedback I get from this submission and whether it’d be deemed useful to others.
Summary
=======
Neon J is a set of probabilistic models for predicting future price action with ~65% accuracy. It indicates individual signals (circles) for probability of price action in a foreseeable future, while smoothed signals (columns) are provided for a more dynamic view of probable price action. Blue circle - strong long probability; Purple circle - strong short probability. Blue column - strong long trend ahead or in-progress; Purple column - strong short trend ahead or in-progress.
To use it, copy models below and provide them an input to “model” parameter when applying to a chart. Models are instrument-specific. Only daily (D) charts should be used.
The script is provided for evaluation purposes.
Models!
======
At last, here are the models (a piece of text you need to input in script parameters for each instrument)
TVC:DJI :
DJI|20121220|20221220|0.597,-0.032,0.0,-0.121,0.0,0.866,-0.046,0.0,-0.091,0.0|1.492,0.1,0.0,-0.162,0.0,-0.669,-0.037,0.0,-0.042,0.0|0.07,0.374,0.0,0.305,0.0,0.085,0.488,0.0,0.26,0.0|0.249,-0.257,0.0,0.529,0.0,-0.018,-0.233,0.0,0.502,0.0|0,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,30,10,10,10,10,10,10,30,30,10,10,10,10,60,10,10,10,20,10,40,10,10,10,80,10,10,60,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,20,20,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,10,10,10,20,10,10,10,20,10,10,10,10,20,10,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,30,10,10,10,10,20,50,10,10,10,10,10,10,30,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,20,30,10,10,10,10,10,50,10,10,10,10,60,10,10,10,10,10,40,10,10,10,10,10,20,30,10,10,10,10,60,10,10,10,10,10,20,10,10,10,10,10,10,10,40,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,50,10,10,10,10,10,50,10,10,10,10,50,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,20,20,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,10,30,10,10,10,10,10,10,40,10,10,10,10,10,40,10,10,10,10,10,10,10,30,10,10,10,10,10,10,20,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,20,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,20,20,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,40,10,10,10,10,10,20,20,10,10,10,10,10,10,40,10,10,10,10,10,10,10,10,30,10,10,10,10,10,30,20,10,10,10,10,10,20,20,10,10,10,10,10,10,10,10,30,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,30,10,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,50,10,10,10,10,20,40,10,10,10,70,10,10,10,10,60,10,10,10,10,10,10,40,10,10,10,10,10,10,10,10,20,10,10,10,10,10,10,10,30,10,10,10,10,10,10,40,10,10,10,10,10,10,40,10,10,10,10,10,50,10,10,10,10,70,10,10,10|-645,-188,-7,-97,-4,29,-18,90,60,-7,-30,117,-226,-82,-49,77,-245,53,78,221,-72,280,245,400,683,268,-74,-15,-106,-102,-3,251,302,536,47,3,-6,-179,-56,101,-62,172,176,98,-15,-71,-18,200,61,-249,-30,-38,1,94,-2,-9,47,79,-35,-15,34,-30,76,120,39,96,-47,-11,-61,-21,124,-704,0,-248,112,-193,143,-27,-14,133,170,-20,-17,-2,-120,61,-98,-32,-2,79,-2,109,-35,-16,132,-44,-63,-168,205,-28,919,235,-34,-53,-23,-243,-68,-26,-35,-54,60,-37,28,-91,-3,-21,-47,79,-127,229,61,59,-49,-139,-63,-43,91,201,-19,-80,-27,120,-122,-141,-100,-32,-25,-98,-27,50,-2,-65,-138,-7,-36,-9,53,-36,-36,-64,-11,216,-5,-664,-19,74,82,-83,-3,-66,21,386,-454,-1002,-282,-7,-52,-30,-9,-16,-148,-131,112,-484,-96,97,93,-13,-162,-49,38,31,-5,-199,-22,205,153,-29,14,-41,-222,-225,-145,107,70,-3,-8,-7,-20,-247,37,96,268,362,-95,706,-69,60,70,120,-34,-65,-152,-69,-7,69,-76,71,-5,384,109,-102,-484,-3,34,60,-20,380,244,678,292,-48,-2,-154,-17,-62,105,486,597,212,-26,-21,-310,-29,-22,-90,285,-204,-92,-290,-6,-516,-42,-16,127,-47,-7,-72,-247,76,-47,-13,43,-26,43,89,-38,30,-21,-106,-78,113,-19,-13,-8,-12,-12,362,247,-4,50,76,64,-14,-52,-16,-93,-172,53,-1,32,99,22,-75,-4,-9,31,70,116,-54,-61,-3,-55,-19,-15,176,143,-11,134,144,-11,-28,-47,-29,-136,-75,99,64,-9,-2,-24,-43,30,-161,-179,82,175,129,115,-71,-396,-202,101,-9,139,-6,-31,-312,-111,2,0,-234,-21,-52,-31,-12,-26,-37,-144,-23,68,23,-16,149,60,-64,10,-7,-8,46,210,393,-5,96,-56,89,48,475,176,20,-10,-31,-29,34,76,41,178,38,-32,-94,-33,76,-5,91,-15,123,72,-46,-13,-11,0,-37,-244,-161,155,-8,-3,165,23,77,16,-117,35,-74,-5,-107,-286,-24,-263,-14,-37,-5,-196,-290,-576,-188,41,-20,-98,-34,-45,-45,-242,40,60,-7,-10,-17,-43,73,48,-25,-8,-40,-27,-2,-5,42,73,-6,-23,8,-16,63,167,21,-99,-47,-119,-36,-59,192,158,115,123,54,-28,-1,-90,-169,-71,-72,114,156,-141,155,64,42,-88,69,-75,76,94,-4,65,102,152,-9,10,-17,-192,67,-10,-343,-90,-43,-106,12,-9,-79,-10,-73,-461,-509,-75,99,-57,0,-27,80,-156,-198,-642,-363,33,47,-28,-40,-43,-8,9,-27,-67,41,26,0,6,-49,-29,-60,32,70,34,-2,-9,-40,-240,-152,21,189,49,67,12,-12,-2,16,31,200,193,211,-150,-84,-45,58,75,44,260,128,105,-9,-11,-1,82,-94,184,-53,266,326,-55,-209,-9,54,85,308,-14,60,420,160,-39,-81,-17,-10,77,108,-28,257,-104,-53,-59,-128,-5,-13,8,119,-20,-130,-49,-9,-3,-23,-46,150,194,263,-214,-12,72,-6,-22,25,-10,290,-41,-21,-18,-1,-17,-42,-14,-21,0,-4,-23,-1,-1,-13,172,-9,224,86,-9,-2,-22,176,-6,33,186,-61,-187,-46,-33,94,172,0,16,-12,-37,59,103,118,194,1000,44,4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VANTAGE:SP500 :
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BINANCE:BTCUSD
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For more models, see a link on bio (description length limitation in this description restricts me to publish more).
Unimportant details
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“Neon” is the project code name, “J” is the iteration (versions “A” to “I” all led to a solid “J”)
Formatting options here make formatting very difficult, so forgive me poor readability.
Seasonal tendency: week-on-week % change and 10yr Averages-shows week-on-week % change, and 10yr averages of these % changes
-scan across the 10yr averages to get a good idea of the seasonality of an asset
-best used on commodities with strong seasonal tendencies (Gold, Wheat, Coffee, Lean hogs etc)
-works only on daily timeframe
-by default it will compare SMA(length) in the following way, BTC: Sunday cf previous Sunday | ES/Gold: Monday cf previous Monday
-for most assets, 5 daily bars in a week (SMA(5)) => that's the default. For BTC can change this to 7.
~~inputs:
-change input year to show any previous decade of asset's history; the table will display over that year on the chart
-choose expression for Average of % change week on week: SMA, ohlc4, vwma, vwap (default SMA)
-choose number of daily bars in a week (i.e. SMA length)
-change label sizes/colors
~~notes:
-When applied to current year: will print the 10yr average for previous weeks in the year; 9yr average for future weeks in the year
-drawings and SMA plot on the above chart are just to show visually how the week's average is calculated, and how this lines up with the label
-current week of year will highlight in large font orange by default
-the first 2 weeks of the year are omitted because of a bug i can't figure out, which throws out bad numbers.
-cannot print all the values for each of previous 10yrs; 'code too long' error. Could likely do this via using matrices but would require a rewrite
17th Dec 2022
@twingall
Economic Calendar Events NickShows Economic Events for possible trade setups. Different events like GOP and CPI. It also works in a way if you want to avoid a trade based on the news.
Kioseff Trading - AI-Optimized RSIAI-Optimized RSI
Introducing AI-Optimized RSI: a streamlined solution for traders of any skill level seeking to rapidly test and optimize RSI. Capable of analyzing thousands of strategies, this tool cuts through the complexity to identify the most profitable, reliable, or efficient approaches.
Paired with TradingView's native backtesting capabilities, the AI-Optimized RSI learns from historical performance data. Set up is easy for all skill levels, and it makes fine-tuning trading alerts and RSI straightforward.
Features
Purpose : Uncover optimal RSI settings and entry levels with precision. Say goodbye to random guesses and arbitrary indicator use—this tool provides clear direction based on data.
Target Performance : You set the goal, and AI-RSI seeks it out, whether it's maximizing profits, efficient trading, or achieving the highest win rate.
AI-Powered : With intelligent AI recommendations, the tool dynamically fine-tunes your RSI approach, steering you towards ideal strategy performance.
Rapid Testing : Evaluate thousands of RSI strategies.
Dual Direction : Perfect both long and short RSI strategies with equal finesse.
Deep Insights : Access detailed metrics including profit factor, PnL, win rate, trade counts, and more, all within a comprehensive strategy script.
Instant Alerts : Set alerts and trade.
Full Customization : Test and optimize all RSI settings, including cross levels, profit targets and stop losses.
Simulated Execution : Explore the impact of limit orders and other trade types through simulation.
Integrative Capability : Combine your own custom indicators or others from the TradingView community for a personalized optimization experience.
Flexible Timeframes : Set your optimization and backtesting to any date range.
Key Settings
The image above shows explanations for a list of key settings for the optimizer.
Direction : This setting controls trade direction: Long or Short.
Entry Condition : Define RSI entry: Select whether to trigger trades on RSI crossunders or crossovers.
RSI Lengths Range : Choose the range of RSI periods to test and find the best one.The AI will find the best RSI period for you.
RSI Cross Range : Set the range for RSI levels where crosses trigger trade signals. The AI will find the best level for you.
Combinations : Select how many RSI strategies to compare.
Optimization Type : Choose the goal for optimization and the AI: profit, win rate, or efficiency.
Profit Target : Set your profit target with this setting.
Stop Loss : Decide your maximum allowable loss (stop loss) per trade.
Limit Order : Specify whether to include limit orders in the strategy.
Stop Type : Choose your stop strategy: a fixed stop loss or a trailing stop.
How to: Find the best RSI for trading
It's important to remember that merely having the AI-Optimized RSI on your chart doesn't automatically provide you with the best strategy. You need to follow the AI's guidance through an iterative process to discover the optimal RSI settings and strategy.
1.Starting Your Strategy Setup
Begin by deciding your goals for each trade: your profit target and stop loss. You'll also choose how to manage your stops – whether they stay put (fixed) or move with the price (trailing), and whether you want to exit trades at a specific price (limit orders). Keep the initial settings for RSI lengths and cross ranges at their default to give the tool a broad testing field. The AI's guidance will refine these settings to pinpoint the most effective ones through a process of comprehensive testing.
The image above shows our chart prior to any optimization efforts.
Note: the settings shown above in the key settings section will be used to start our demonstration.
2. Follow AI’s suggestions
Optimization Prompt: After loading your strategy, the indicator will prompt you to change the RSI length range and RSI level range to a better performing range.
Continue changing the RSI length range and RSI level range to match the indicator's suggestions until "Best Found" is displayed!
The image above shows results after we applied the tool’s suggestions. New suggestions have appeared, and we will continue to apply them.
Continue to adjust settings as recommended by the optimizer. If no better options are found, the optimizer will suggest increasing the number of combinations. Repeat this process until the optimizer indicates that the optimal setting has been identified.
Success! With the "Best Found" notification, an optimized RSI is now active. The AI will keep refining the strategy based on ongoing performance, ensuring continuous optimization.
AI Mode
AI Mode incorporates Heuristic-Based Adaptive Learning to fine-tune trading strategies in a continuous manner. This feature consists of two main components:
Heuristic-Based Decision Making: The algorithm evaluates multiple RSI-based trading strategies using specific metrics such as Profit and Loss (PNL), Win Rate, and Most Efficient Profit. These metrics act as heuristics to assist the algorithm in identifying suitable strategies for trade execution.
Online Learning: The algorithm updates the performance evaluations of each strategy based on incoming market data. This enables the system to adapt to current market conditions.
Incorporating both heuristic-based decision-making and online learning, this feature aims to provide a framework for trading strategy optimization.
Settings
AI Mode Aggressiveness:
Description: The "AI Mode Aggressiveness" setting allows you to fine-tune the AI's trading behavior. This setting ranges from “Low” to “High”, with “High” indicating a more assertive trading approach.
Functionality: This feature filters trading strategies based on a proprietary evaluation method. A higher setting narrows down the strategies that the AI will consider, leaning towards more aggressive trading. Conversely, a lower setting allows for a more conservative approach by broadening the pool of potential strategies.
Adaptive Learning Aggressiveness:
Description: When Adaptive Learning is enabled, the "Adaptive Learning Aggressiveness" setting controls how dynamically the AI adapts to market conditions using selected performance metrics.
Functionality: This setting impacts the AI's responsiveness to shifts in strategy performance. By adjusting this setting, you can control how quickly the AI moves away from strategies that may have been historically successful but are currently underperforming, towards strategies that are showing current promise.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
Signal AnalyzerThis library contains functions that try to analyze trading signals performance.
Like the % of average returns after a long or short signal is provided or the number of times that signal was correct, in the inmediate 2 candles after the signal.
Trend Finder with Coefficient of VariationCoefficient of variation (“COV”) is a statistical measure used to describe the variability of values within a data set, it’s calculated by taking the standard deviation divided by the mean.
Traditionally, COV is applied to the expected returns of competing investment portfolios. A risk adverse investor prefers to accept a portfolio with a relatively lower COV value.
On the other hand, when applying COV to price charts, the difference is that instead of looking at expected returns, we now treat price as the source of data. We look at price from a moving average perspective. This script purely focuses on price.
What this indicator does:
Firstly, to go over the parameters:
Let ‘n’ be the lookback period for computing COV, and ‘m’ be the period for comparing the ranking of COVs.
Logics in a nutshell:
This program will (A) calculate the COV by dividing the moving standard deviation by moving average over ‘n’ bars, and then (B) illustrate the relationship of how COV at each bar ranks compared to COVs over past ‘m’ bars. We use a color scale (default black and yellow) for visualizing ranking in terms of percentiles. If COV is below its median value, then we assume that price is consolidating.
Hypothesis:
Using COV on top of regular SMA signals should reduce a lot of unwanted noise such as consecutive crossovers during ranging-periods. Traders want volatility, but not too much of it when sniping for entry opportunities (speaking of initial position; need to add to winning positions after, but this is for another topic). For this reason, the median value of COV is suitable as a metric for signals.
Applications:
We use the median value of COV to form a decision rule. A signal is generated when COV > median(COV,m), and the direction of trend is determined based on relative position of price with respect to sma(price,n). When the value of COV is increasing, it can also be thought of seeing Bollinger Bands beginning to bulge. When trends begin, this program will plot triangles to signify entry opportunities.
ASE Additionals v1ASE Additionals is a statistics-driven indicator that combines multiple features to provide traders with valuable statistics to help their trading. This indicator offers a customizable table that includes statistics for VWAP with customizable standard deviation waves.
Per the empirical rule, the following is a schedule for what percent of volume should be traded between the standard deviation range:
+/- 1 standard deviation: 68.26% of volume should be trading within this range
+/- 2 standard deviation: 95.44% of volume should be trading within this range
+/- 3 standard deviation: 99.73% of volume should be trading within this range
+/- 4 standard deviation: 99.9937% of volume should be trading within this range
+/- 5 standard deviation: 99.999943% of volume should be trading within this range
+/- 6 standard deviation: 99.9999998% of volume should be trading within this range
The statistics table presents five different pieces of data
Volume Analyzed: Amount of contracts analyzed for the statistics
Volume Traded Inside Upper Extreme: Calculated by taking the amount of volume traded inside the Upper Extreme band divided by the total amount of contracts analyzed
Volume Traded Inside Lower Extreme: Calculated by taking the amount of volume traded inside the Lower Extreme band divided by the total amount of contracts analyzed
Given the user’s inputs, they will see the upper and lower extremes of the day. For example, if the user changed the inner st. dev input to 2, 95.44% of the volume should be traded within the inner band. If the user changed the outer st. dev input to 3, 99.73% of the volume should be traded within the outer band. Thus, statistically, 2.145% ((99.73%-95.44%)/2) of volume should be traded between the upper and lower band fill.
In the chart above, the bands are the 2nd and 3rd standard deviation inputs. We notice that out of the 151 Million Contracts , the actual percentage of volume traded in the upper extreme was 2.7% , and the actual percentage of the volume traded in the lower extreme was 3.3% . Given the empirical rule, about 2.145% of the volume should be traded in the upper extreme band, and 2.145% of the volume should be traded in the lower extreme band. Based on the statistics table, the empirical rule is true when applied to the volume-weighted average price.
The trader should recognize that statistics is all about probability and there is a margin for error, so the bands should be used as a bias, not an entry. For example, given the +/-2 and 3 standard deviations, statistically, if 2.145% of the volume is traded within the upper band extreme, you shouldn’t look for a long trade if the current price is in the band. Likewise, if 2.145% of the volume is traded within the lower band extreme, you shouldn’t look for a short trade if the current price is in the band.
Additionally, we provide traders with the Daily, Weekly, and Monthly OHLC levels. Open, High, Low, and Close are significant levels, especially on major timeframes. Once price has touched the level, the line changes from dashed/dotted to solid.
Features
VWAP Price line and standard deviation waves to analyze the equilibrium and extremes of the sessions trend
Previous Day/WEEK/Month OHLC levels provide Major timeframe key levels
Settings
VWAP Equilibrium: Turn on the VWAP line
VWAP Waves: Turn on the VWAP standard deviation waves
Inner St. Dev: Changes the inner band standard deviation to show the percentage of volume traded within
Outer St. Dev: Changes the outer band standard deviation to show the percentage of volume traded within
Upper Extreme: Change the color of the upper VWAP wave
Lower Extreme: Change the color of the lower VWAP wave
Wave Opacity: Change the opacity of the waves (0= completely transparent, 100=completely solid)
Statistics Table: Turn on or off the statistics table
Statistics Table Settings: Change the Table Color, Text Color, Text Size, and Table Position
Previous Day/Week/Month OHLC: Choose; All, Open, Close, High, Low, and the color of the levels
OHLC Level Settings: Change the OHLC label color, line style, and line width
How to Use
The VWAP price line acts as the 'Fair Value' or the 'Equilibrium' of the price session. Just as the VWAP Waves show the session's upper and lower extreme possibilities. While we can find entries from VWAP , our analysis uses it more as confirmation. OHLC levels are to be used as support and resistance levels. These levels provide us with great entry and target opportunities as they are essential and can show pivots in price action.
Kioseff Trading - AI-Optimized Supertrend
AI-Optimized Supertrend
Introducing AI-Optimized Supertrend: a streamlined solution for traders of any skill level seeking to rapidly test and optimize Supertrend. Capable of analyzing thousands of strategies, this tool cuts through the complexity to identify the most profitable, reliable, or efficient approaches.
Paired with TradingView's native backtesting capabilities, the AI-Optimized Supertrend learns from historical performance data. Set up is easy for all skill levels, and it makes fine-tuning trading alerts and Supertrend straightforward.
Features
Rapid Supertrend Strategy Testing : Quickly evaluate thousands of Supertrend strategies to find the most effective ones.
AI-Assisted Optimization : Leverage AI recommendations to fine-tune strategies for superior results.
Multi-Objective Optimization : Prioritize Supertrend based on your preference for the highest win rate, maximum profit, or efficiency.
Comprehensive Analytics : The strategy script provides an array of statistics such as profit factor, PnL, win rate, trade counts, max drawdown, and an equity curve to gauge performance accurately.
Alerts Setup : Conveniently set up alerts to be notified about critical trade signals or changes in performance metrics.
Versatile Stop Strategies : Experiment with profit targets, trailing stops, and fixed stop losses.
Binary Supertrend Exploration : Test binary Supertrend strategies.
Limit Orders : Analyze the impact of limit orders on your trading strategy.
Integration with External Indicators : Enhance strategy refinement by incorporating custom or publicly available indicators from TradingView into the optimization process.
Key Settings
The image above shows explanations for a list of key settings for the optimizer.
Set the Factor Range Limits : The AI suggests optimal upper and lower limits for the Factor range, defining the sensitivity of the Supertrend to price fluctuations. A wider range tests a greater variety, while a narrower range focuses on fine-tuning.
Adjust the ATR Range : Use the AI's recommendations to establish the upper and lower bounds for the Average True Range (ATR), which influences the Supertrend's volatility threshold.
ATR Flip : This option lets you interchange the order of ATR and Factor values to quicky test different sequences, giving you the flexibility to explore various combinations and their impact on the Supertrend indicator's performance.
Strategies Evaluated : Adjust this setting to determine how many Supertrend strategies you want to assess and compare.
Enable AI Mode : Turn this feature on to allow the AI to determine and employ the optimal Supertrend strategy with the desired performance metric, such as the highest win rate or maximum profitability.
Target Metric : Adjust this to direct the AI towards optimizing for maximum profit, top win rates, or the most efficient profits.
AI Mode Aggressiveness : Set how assertively the AI pursues the chosen performance goal, such as highest profit or win rate.
Strategy Direction : Choose to focus the AI's testing and optimization on either long or short Supertrend strategies.
Stop Loss Type : Specify the stop loss approach for optimization—fixed value, a trailing stop, or Supertrend direction changes.
Limit Order : Decide if you want to execute trades using limit orders for setting your profit targets, stop losses, or apply them to both.
Profit Target : Define your desired profit level when using either a fixed stop loss or a trailing stop.
Stop Loss : Define your desired stop loss when using either a fixed stop loss or a trailing stop.
How to: Find the best Supertrend for trading
It's important to remember that merely having the AI-Optimized Supertrend on your chart doesn't automatically provide you with the best strategy. You need to follow the AI's guidance through an iterative process to discover the optimal Supertrend settings and strategy.
Optimizing Supertrend involves adjusting two key parameters: the Factor and the Average True Range (ATR). These parameters significantly influence the Supertrend indicator's sensitivity and responsiveness to price movements.
Factor : This parameter multiplies the ATR to determine the distance of the Supertrend line from the price. Higher values will create a wider band, potentially leading to fewer trade signals, while lower values create a narrower band, which may result in more signals but also more noise.
ATR (Average True Range) : ATR measures market volatility. By using the ATR, the Supertrend adapts to changing market volatility; a higher ATR value means a more volatile market, so the Supertrend adjusts accordingly.
During the optimization process, these parameters are systematically varied to determine the combination that yields the best performance based on predefined criteria such as profitability, win rate, or risk management efficiency. The optimization aims to find the optimal Factor and ATR settings.
1.Starting Your Strategy Setup
Begin by deciding your goals for each trade: your profit target and stop loss, or if all trades exit when Supertrend changes direction. You'll also choose how to manage your stops – whether they stay put (fixed) or move with the price (trailing), and whether you want to exit trades at a specific price (limit orders). Keep the initial settings for Supertrend Factor Range and Supertrend ATR Range at their default to give the tool a broad testing field. The AI's guidance will refine these settings to pinpoint the most effective ones through a process of comprehensive testing.
Demonstration Start: We'll begin with the settings outlined in the key settings section, using Supertrend's direction change to the downside as our exit signal for all trades.
2. Continue applying the AI’s suggestions
Keep updating your optimization settings based on the AI's recommendations. Proceed with this iterative optimization until the "Best Found" message is displayed, signaling that the most effective strategy has been identified.
While following the AI's suggestions, we've been prompted with a new suggestion: increase the
number of strategies evaluated. Keep following the AI's new suggestions to evaluate more strategies. Do this until the "Best Found" message shows up.
Success! We continued to follow the AI’s suggestions until “Best Found” was indicated!
AI Mode
AI Mode incorporates Heuristic-Based Adaptive Learning to fine-tune trading strategies in a continuous manner. This feature consists of two main components:
Heuristic-Based Decision Making: The algorithm evaluates multiple Supertrend-based trading strategies using metrics such as Profit and Loss (PNL), Win Rate, and Most Efficient Profit. These metrics act as heuristics to assist the algorithm in identifying suitable strategies for trade execution.
Online Learning: The algorithm updates the performance evaluations of each strategy based on incoming market data. This enables the system to adapt to current market conditions.
Incorporating both heuristic-based decision-making and online learning, this feature aims to provide a framework for trading strategy optimization.
AI Mode Settings
AI Mode Aggressiveness:
Description: The "AI Mode Aggressiveness" setting allows you to fine-tune the AI's trading behavior. This setting ranges from “Low” to “High”, with “High” indicating a more assertive trading approach.
Functionality: This feature filters trading strategies based on a proprietary evaluation method. A higher setting narrows down the strategies that the AI will consider, leaning towards more aggressive trading. Conversely, a lower setting allows for a more conservative approach by broadening the pool of potential strategies.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.