The Next Pivot (With History) [Mxwll]Introducing "The Next Pivot (With History)"!
With permission from the author @KioseffTrading
The script "The Next Pivot" has been restructured to show historical projections!
Features
Find the most similar price sequence per time frame change.
Forecast almost any public indicator! Not just price!
Forecast any session i.e. 4Hr, 1Hr, 15m, 1D, 1W
Forecast ZigZag for any session
Spearmen
Pearson
Absolute Difference
Cosine Similarity
Mean Squared Error
Kendall
Forecasted linear regression channel
The image above shows/explains some of the indicator's capabilities!
Additionally, you can project almost any indicator!
Should load times permit it, the script can search all bar history for a correlating sequence. This won't always be possible, contingent on the forecast length, correlation length, and the number of bars on the chart.
If a load time error occurs, simple reduce the "Bars Back To Search" parameter!
The script can only draw 500 bars into the future. For whatever time frame you are on and the session you wish to project, ensure it will not exceeded a 500-bar forecast!
Reasonable Assessment
The script uses various similarity measures to find the "most similar" price sequence to what's currently happening. Once found, the subsequent price move (to the most similar sequence) is recorded and projected forward.
So,
1: Script finds most similar price sequence
2: Script takes what happened after and projects forward
While this may be useful, the projection is simply the reaction to a possible one-off "similarity" to what's currently happening. Random fluctuations are likely and, if occurring, similarities between the current price sequence and the "most similar" sequence are plausibly coincidental.
Thanks!

# Predictions

Golden Level Predictions v1.0Golden Level Predictions (GLP) Trading Indicator
This script introduces a custom trading indicator named "GLP" tailored for the TradingView platform. It offers various price levels derived from Fibonacci calculations and other mathematical models, assisting traders in pinpointing potential overpriced and discounted price levels.
Key Features:
User Inputs : Users have the flexibility to select their desired timeframe, with options ranging from Weekly, Daily, Monthly, and more. Additionally, they can opt to showcase Fibonacci lines and the associated prices within these levels.
Price Level Calculations :
- Employs constants such as the Golden Ratio (PHI) and Pi (PI) to extract various multipliers and factors.
- Assesses if the current asset is a cryptocurrency and tweaks calculations accordingly.
- Determines overpriced and discounted price levels, drawing from the current open price and past data.
Fibonacci Levels :
- For each overpriced and discounted level, the script computes intermediary Fibonacci levels, including 23.6%, 38.2%, 50%, 61.8%, and 78.6% (the 3rd level is excluded due to plot limitations).
- These levels are illustrated on the chart, granting traders a more detailed view of price targets.
Visual Elements :
- Projects horizontal lines to the subsequent selected indicator interval for every calculated price level.
- Exhibits potential percentage gains or losses at each tier, indicating the prospective price alteration upon reaching that level.
- Differentiates overpriced (green) and discounted (red) levels using color codes. A neutral price is depicted in yellow.
Anticipated Close Calculation : Offers a projected closing price for the current timeframe, based on a myriad of factors.
This indicator is particularly effective with cryptocurrencies due to their inherent volatility. It's also compatible with stocks and is most efficient with tickers that provide volume data.

Bitcoin Market Cap wave model weeklyThis Bitcoin Market Cap wave model indicator is rooted in the foundation of my previously developed tool, the : Bitcoin wave model
To derive the Total Market Cap from the Bitcoin wave price model, I employed a straightforward estimation for the Total Market Supply (TMS). This estimation relies on the formula:
TMS <= (1 - 2^(-h)) for any h.This equation holds true for any value of h, which will be elaborated upon shortly. It is important to note that this inequality becomes the equality at the dates of halvings, diverging only slightly during other periods.
Bitcoin wave model is based on the logarithmic regression model and the sinusoidal waves, induced by the halving events.
This chart presents the outcome of an in-depth analysis of the complete set of Bitcoin price data available from October 2009 to August 2023.
The central concept is that the logarithm of the Bitcoin price closely adheres to the logarithmic regression model. If we plot the logarithm of the price against the logarithm of time, it forms a nearly straight line.
The parameters of this model are provided in the script as follows: log(BTCUSD) = 1.48 + 5.44log(h).
The secondary concept involves employing the inherent time unit of Bitcoin instead of days:
'h' denotes a slightly adjusted time measurement intrinsic to the Bitcoin blockchain. It can be approximated as (days since the genesis block) * 0.0007. Precisely, 'h' is defined as follows: h = 0 at the genesis block, h = 1 at the first halving block, and so forth. In general, h = block height / 210,000.
Adjustments are made to account for variations in block creation time.
The third concept revolves around investigating halving waves triggered by supply shock events resulting from the halvings. These halvings occur at regular intervals in Bitcoin's native time 'h'. All halvings transpire when 'h' is an integer. These events induce waves with intervals denoted as h = 1.
Consequently, we can model these waves using a sin(2pih - a) function. The parameter determining the time shift is assessed as 'a = 0.4', aligning with earlier expectations for halving events and their subsequent outcomes.
The fourth concept introduces the notion that the waves gradually diminish in amplitude over the progression of "time h," diminishing at a rate of 0.7^h.
Lastly, we can create bands around the modeled sinusoidal waves. The upper band is derived by multiplying the sine wave by a factor of 3.1*(1-0.16)^h, while the lower band is obtained by dividing the sine wave by the same factor, 3.1*(1-0.16)^h.
The current bandwidth is 2.5x. That means that the upper band is 2.5 times the lower band. These bands are forming an exceptionally narrow predictive channel for Bitcoin. Consequently, a highly accurate estimation of the peak of the next cycle can be derived.
The prediction indicates that the zenith past the fourth halving, expected around the summer of 2025, could result in Total Bitcoin Market Cap ranging between 4B and 5B USD.
The projections to the future works well only for weekly timeframe.
Enjoy the mathematical insights!

The Next Pivot [Kioseff Trading]Hello!
This script "The Next Pivot" uses various similarity measures to compare historical price sequences to the current price sequence!
Features
Find the most similar price sequence up to 100 bars from the current bar
Forecast price path up to 250 bars
Forecast ZigZag up to 250 bars
Spearmen
Pearson
Absolute Difference
Cosine Similarity
Mean Squared Error
Kendall
Forecasted linear regression channel
The image above shows/explains some of the indicator's capabilities!
The image above highlights the projected zig zag (pivots) pattern!
Colors are customizable (:
Additionally, you can plot a forecasted LinReg channel.
Should load times permit it, the script can search all bar history for a correlating sequence. This won't always be possible, contingent on the forecast length, correlation length, and the number of bars on the chart.
Reasonable Assessment
The script uses various similarity measures to find the "most similar" price sequence to what's currently happening. Once found, the subsequent price move (to the most similar sequence) is recorded and projected forward.
So,
1: Script finds most similar price sequence
2: Script takes what happened after and projects forward
While this may be useful, the projection is simply the reaction to a possible one-off "similarity" to what's currently happening. Random fluctuations are likely and, if occurring, similarities between the current price sequence and the "most similar" sequence are plausibly coincidental.
That said, if you have any ideas on cool features to add please let me know!
Thank you (:

Monte Carlo Price ProbabilitiesMonte Carlo simulations have been a popular tool in the world of finance, risk analysis, and decision making for decades. In this post, I will take you through the history of Monte Carlo simulations and explain how I implemented this powerful technique in Pine Script. This implementation can help traders and investors in various time frames to better understand the potential future price movements of financial instruments based on historical data.
History of Monte Carlo Simulations
The Monte Carlo method was named after the famous Monte Carlo Casino in Monaco, as the technique involves using random sampling to approximate solutions to mathematical problems. The method was first introduced by Stanislaw Ulam, a mathematician working on the Manhattan Project in the 1940s. Ulam realized that using random sampling could provide approximate solutions to complex problems that were otherwise difficult or impossible to solve analytically.
Over the years, Monte Carlo simulations have found applications in various fields, including physics, engineering, and finance. In the world of finance, the method has been used to model stock price movements, option pricing, portfolio optimization, and risk management.
Implementation in Pine
In my implementation of Monte Carlo simulations in Pine, I created a script that allows users to input several parameters such as the arbitrary price, number of simulations, number of steps into the future, and the start bar index. The start bar index is a crucial setting for running the script on lower time frames, as it helps to ensure that the script runs smoothly for a given symbol.
The script then calculates the log return of each bar and categorizes them into green (positive) or red (negative) moves. It uses these historical price movements to calculate the probabilities of future price movements for each step in the simulation.
The core of the Monte Carlo simulation lies in the `monte()` function, which generates random numbers to determine if the next price movement will be green or red, and then selects a move size based on its probability. The `sim()` function runs multiple simulations using the `monte()` function and stores the results in an array.
Finally, the script calculates the probability of the arbitrary price being reached in the future based on the results of the simulations. It also plots the probability on the chart, allowing users to visually assess the potential future price movements of the financial instrument.
Using the Monte Carlo Simulation
To use the Monte Carlo simulation in Pine, you need to input the desired parameters such as the arbitrary price, number of simulations, number of steps into the future, and the start bar index. For some symbols, you may need to set the start bar index to around 10k to ensure that the script runs smoothly.
Once you have input the parameters and run the script, you will see the probability of reaching the arbitrary price plotted on the chart. This can provide a valuable insight into the potential future price movements of the financial instrument based on historical data, helping you make more informed trading and investment decisions.
Conclusion
Monte Carlo simulations have a rich history and have proven to be a valuable tool in various fields, including finance. My implementation of Monte Carlo simulations in Pine allows traders and investors to better understand the potential future price movements of financial instruments in various time frames. By evaluating the probabilities of reaching specific price levels, users can make more informed decisions and better manage their risk.

Linear Regress on Price And VolumeLinear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the dependent variable and the independent variable(s) and attempts to fit a straight line that best describes the relationship.
In the context of predicting the price of a stock based on the volume, we can use linear regression to build a model that relates the price of the stock (dependent variable) to the volume (independent variable). The idea is to use lookback period to predict future prices based on the volume.
To build this indicator, we start by collecting data on the price of the stock and the volume over a selected of time or by default 21 days. We then plot the data on a scatter plot with the volume on the x-axis and the price on the y-axis. If there is a clear pattern in the data, we can fit a straight line to the data using a method called least squares regression. The line represents the best linear approximation of the relationship between the price and the volume.
Once we have the line, we can use it to make predictions. For example, if we observe a certain volume, we can use the line to estimate the corresponding price.
It's worth noting that linear regression assumes a linear relationship between the variables. In reality, the relationship between the price and the volume may be more complex, and other factors may also influence the price of the stock. Therefore, while linear regression can be a useful tool, it should be used in conjunction with other methods and should be interpreted with caution.

predictions_LUKE_MACVICARThis indicator is the output of our prediction algorithm. You can use these lines to see where the price may head for the day. These lines are great support and resistance in the market and you can play off them accordingly.

Leavitt Convolution [CC]The Leavitt Convolution indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very similar to my Leavitt Projection script and I forgot to mention that both of these indicators are actually predictive moving averages. The Leavitt Convolution indicator doubles down on this idea by creating a prediction of the Leavitt Projection which is another prediction for the next bar. Obviously this means that it isn't always correct in its predictions but it does a very good job at predicting big trend changes before they happen. The recommended strategy for how to trade with these indicators is to plot a fast version and a slow version and go long when the fast version crosses over the slow version or to go short when the fast version crosses under the slow version. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal.
This is another indicator in a series that I'm publishing to fulfill a special request from @ashok1961 so let me know if you ever have any special requests for me.

Niteya Multi Ticker Dollar-Based Pricing Ver 1.3The main purpose of the indicator is to make a future price estimation based on the highest dollar-based price of the stock in the past, especially for stocks that exceed their past prices in chart currency terms. There should be no expectation that this prediction will necessarily come true.
A table with six columns and 19 rows (excluding the header) is created on the graph, positioned bottom and left.
The first column contains the ticker code, the second column contains the highest historical price of the stock in currency, the third column contains the past high price of the stock in USD, the fourth column contains the closing price, the fifth column contains the value obtained by multiplying the past highest USD price of the stock by the daily dollar price, and the sixth column is includes the rate of increase.
Using the indicator interface, you can select the ticker value in the first row of the table from among 22 different values via a selection box, and for the 18 rows below, you can directly type the ticker name.
* The currency of the chart must be compatible with the dollar conversion currency. For example, if the conversion currency is "USDTRY", the currency of the chart should be "TRY".
All stocks in the indicator are randomly selected. Investment information, stock selections, comments and recommendations herein are not within the scope of investment consultancy. Investment consultancy service is provided within the framework of investment consultancy agreement to be signed between brokerage houses, portfolio management companies, non-deposit banks and the customer.
Türkçe açıklama
Göstergenin temel amacı, özellikle grafik para birimi (TRY) bazında geçmiş fiyatlarının üzerine çıkmış hisselerde, hissenin geçmişteki en yüksek dolar bazlı fiyatını esas alarak, geleceğe yönelik bir fiyat tahmininde bulunmaktır. Bu tahminin mutlaka gerçekleşeceği beklentisi olmamalıdır.
Grafik üzerinde, üste ve ortalanmış olarak, altı sütun ve başlık kısmı hariç 19 satırlık bir tablo oluşturulmaktadır.
İlk sütun hisse kodunu, ikinci sütun hissenin geçmiş en yüksek fiyatını TRY olarak, üçüncü sütun hissenin geçmiş en yüksek fiyatını USD olarak, dördüncü sütun kapanış fiyatını, beşinci sütun hissenin geçmiş en yüksek USD fiyatının günlük dolar kuru ile çarpılarak elde edilen değeri, altıncı sütun ise artış oranını içerir.
Gösterge arayüzünü kullanarak, tablonun ilk satırındaki ticker (hisse) değerini 22 farklı değer arasından (BIST 100 ve 21 şirket) bir seçim kutusu yoluyla, altta yer alan 18 satır için ise, doğrudan hisse adını yazabilirsiniz.
* Grafiğin para birimi dolar çevrim kuru ile uyumlu olmalıdır. Örneğin, çevrim kuru "USDTRY" ise, grafiğin para birimi "TRY" olmalıdır.
Gösterge içinde yer alan tüm hisseler rastgele seçilmiştir. Buradaki yatırım bilgileri, hisse seçimleri, yorum ve tavsiyeleri yatırım danışmanlığı kapsamında değildir. Yatırım danışmanlığı hizmeti, aracı kurumlar, portföy yönetim şirketleri, mevduat kabul etmeyen bankalar ile müşteri arasında imzalanacak yatırım danışmanlığı sözleşmesi çerçevesinde sunulmaktadır.

EMA PredictionThis script predicts future EMA values assuming that the price remains as configured (-50% to +50%).

RK's 07 ∴ Moving Average Ribbon with Momentum Adjusted by DGTHello folks!
In my search for new ways to get faster and better market responses, I found this brilliant Indicator here on Trading View.
I rewrite all the code with my own functions and styles.
So... This is my adaptation to excellent script "Momentum adjusted Moving Average by DGT" from the user dgtrd
In dgtrd's words: "A brand new Moving Average, calculated using Momentum, Acceleration and Probability (Psychological Effect).
Momentum adjusted Moving Average( MaMA ) is an indicator that measures Price Action by taking into consideration not only Price movements but also its Momentum, Acceleration and Probability.
MaMA , provides faster responses comparing to the regular Moving Average"
The original post is here: 👇
T∴F∴A∴
Rodrigo Kazuma

Magic 8-Ball [QuantNomad]Sometimes get tired and what to create something fun and useless )
Here I developed a magic 8-ball. You can apply it to the chart, and it randomly will show you a prediction unique for your symbol/candle time.
Please don't take this prediction seriously; there is 0 rationale behind it. However, I believe it can outperform some traders here on TradginView =)
So it will show you one of the following messages:
Buy
Strong Buy
HODL
Sell
Strong Sell
Ask again later
Better not tell you now
Neutral
Cannot predict now
Very doubtful

Dumb Indicator 17 - Retracement and market directionsThis indicator shows when the market is oversold or overbought changing the bar colors as the High+Low/2 going to extreme.

4Hours optimized CARHey!
I have been working on this script the lasts days, it's a collaboration with an uruguayan mate.
#####IMPORTANT#####
* It has been optimized for 4h charts, use it on other at your own risk.
* Always use Stop Loss, since it might give false signals after a long trend.
* I'm still working on it, I'm going to add candlestick pattern filtering.
Thanks for your support, greetings.