Open, Open +/- EMA ATR Lines with LabelsThis indicator provides traders with a clear visualization of the opening price and its potential movement range for a specific timeframe, based on the Exponential Moving Average (EMA) of the Average True Range (ATR).
Features:
Opening Price Line: A green line representing the opening price for the chosen timeframe.
EMA ATR Lines:
An upper blue line, calculated as the opening price plus the EMA of the ATR.
A lower blue line, calculated as the opening price minus the EMA of the ATR.
Labels: Each line comes with a label on its right side, displaying the price level and, for the EMA ATR lines, the distance in pips from the opening price.
Custom Timeframes: Users can select their desired timeframe for calculations, making this tool versatile for different trading strategies.
Usage:
The EMA-smoothed ATR provides a measure of volatility. By plotting this value above and below the opening price, traders get a sense of potential price movement for the selected timeframe. This can be particularly useful for setting stop losses, take profit levels, or identifying breakout points.
For instance, if the price breaks above the upper EMA ATR line, it might indicate a potential upward move, especially if other market conditions align.
Customization:
Timeframe: Choose from various timeframes like 1-minute, 5-minutes, daily, weekly, and more.
ATR Length: Adjust the length of the ATR for more or less sensitivity.
This indicator is designed to offer traders a quick way to gauge potential price movement for their chosen timeframe. By combining the principles of the opening price and volatility measured by the EMA-smoothed ATR, it provides a straightforward yet powerful tool for various trading scenarios.
Forecasting
Liquidity Depth [Pro+]Description:
Liquidity Depth Pro+ is a trading tool with a remarkable adaptability and perfectly aligned with the intricate demands of the futures, forex, and bond markets. This indicator is based on a concept taught by the Inner Circle Trader (ICT), who explains that institutions tend to dig deeper into Liquidity Pools above highs and below lows. Specifically, ICT mentions how in Forex these Liquidity Depths are classically manifested as 10-20-30 pips respectively.
This tool allows the Analyst to adapt this concept based on their understanding of price. It delves into the essence of institutional trading, exposing deeper liquidity depth pursued by institutional giants and astute bank traders that lay further than the mere extremities of price.
CME_MINI:NQ1! Example (Tuesday):
Price raids Monday's low
Price raids Friday's low
Price digs deeper into one of Friday's Deep Liquidity Pools
Low of the Day Reversal
Note: the Depths used in this example are 30-60-90 points.
Key Features:
Versatility Across Assets: Liquidity Depth Pro+ is finely tailored for futures, forex, and bond markets, making it an all-encompassing solution suitable for a broad range of financial instruments.
Timeframe Customization: Liquidity Depth Pro+ allows users to decide Timeframe Liquidity empowering the analyst with flexibility.
Historical Pools: Choose up to the last 20 highs and lows to mark liquidity pools from the User Selected Timeframe.
Universal Trading Style: Regardless of your trading approach, be it trend-following or reversal models, this indicator embraces all styles. It offers a holistic perspective to navigating liquidity zones above highs and below lows of the chosen Timeframe.
Visual Precision: This indicator visualizes the liquidity depth with a customizable style, allowing the analyst to frame the position of deeper liquidity pools above highs and below lows.
Liquidity Table: Keep track of liquidity levels and unlock faster decision making by taking advantage of the visual Liquidity Table cues.
Adaptive Table Colors: When price is above your desired liquidity pool high, the table will match the liquidity high color to indicate a current liquidity raid or deeper pool being attacked. Vice versa, when price is below your desired liquidity pool low, the table will match the liquidity low color.
Real-Time Alerts: Save Time with live alerts that provide valuable insights into potential opportunities and liquidity purges at your desired liquidity levels.
Other Features:
Choose the Depth Type ("Auto", "Value", "Ticks", "Pips"). The “Auto” feature will select the best unit of measurement for the depths based on the current market on chart.
Choose to show up to Three Liquidity Depths.
Customize the Liquidity Line Style.
Customize the Liquidity Line Color.
Customize the Liquidity Line Width.
Customize Table Size and Location
Usage Guidance:
Add Liquidity Depth to your Tradingview chart.
Customize your desired Timeframe and Liquidity Depths to align with your personal preference.
Observe where the Liquidity Lines manifest above and below your chosen Timeframe’s highs and lows respectively, once they are raided.
Leverage this invaluable information to frame the narrative, whether you opt to pursue liquidity or capitalize on post-purge reversals.
These tools are available ONLY on the TradingView platform.
Terms and Conditions
Our charting tools are products provided for informational and educational purposes only and do not constitute financial, investment, or trading advice. Our charting tools are not designed to predict market movements or provide specific recommendations. Users should be aware that past performance is not indicative of future results and should not be relied upon for making financial decisions. By using our charting tools, the purchaser agrees that the seller and the creator are not responsible for any decisions made based on the information provided by these charting tools. The purchaser assumes full responsibility and liability for any actions taken and the consequences thereof, including any loss of money or investments that may occur as a result of using these products. Hence, by purchasing these charting tools, the customer accepts and acknowledges that the seller and the creator are not liable nor responsible for any unwanted outcome that arises from the development, the sale, or the use of these products.
Finally, the purchaser indemnifies the seller from any and all liability. If the purchaser was invited through the Friends and Family Program, they acknowledge that the provided discount code only applies to the first initial purchase of the Toodegrees Premium Suite subscription. The purchaser is therefore responsible for cancelling – or requesting to cancel – their subscription in the event that they do not wish to continue using the product at full retail price. If the purchaser no longer wishes to use the products, they must unsubscribe from the membership service, if applicable. We hold no reimbursement, refund, or chargeback policy. Once these Terms and Conditions are accepted by the Customer, before purchase, no reimbursements, refunds or chargebacks will be provided under any circumstances.
By continuing to use these charting tools, the user acknowledges and agrees to the Terms and Conditions outlined in this legal disclaimer.
Euclidean Distance Predictive Candles [SS]Finally releasing this, its been in the works for the past 2 weeks and has undergone many iterations.
I am not sure if I am 100% happy with it yet, but I guess its best to release and get feedback to make improvements.
So this is the Euclidean distance predictive candle indicator and what it does is exactly what it sounds like, it uses Euclidean distance to identify similar candles and then plot the candles and range that immediately proceeded like candles.
While this is using a general machine learning/data science approach (Euclidean distance), I do not employ the KNN (Nearest Neighbors) algo into this. The reason being is it simply offered no predictive advantage than isolating for the last case. I tried it, I didn't like it, the results were not improve and, at times, acutally hindered so I ditched it. Perhaps it was my approach but using some other KNN indicators, I just don't really find them all that more advantageous to simply relying on the Law of Large Numbers and collecting more data rather than less data (which we will get into later in this explanation).
So using this indicator:
There is a lot of customizability here. And the reason is, not all settings are going to work the same for all tickers. To help you narrow down your parameters, I have included various backtest results that show you how the model is performing. You see in the AMZN chart above, with the current settings, it is performing optimally, with a cumulative range pass of 99% (meaning that, of all the cases, the indicator accurately predicted the next day high OR low range 99% of the time), and the ability to predict the candle slightly over 52%.
The recommended settings, from me, are as follows:
So these are generally my recommended settings.
Euclidian Tolerance: This will determine the parameters to look for similar candles. In general, the lower the tolerance, the greater the precision. I recommend keeping it between 0.5, for tickers with larger prices (like ES1! futures or NQ1!) or 0.05 for tickers with lower TPs, like SPY or QQQ.
If the ED Tolerance is too extreme that the indicator cannot find identical setups, it will alert you:
But in general, the more precise you can get it, the better.
Anchor Type: You will see the option to anchor by "Predicted Open" or by "Previous Close". I suggest sticking with anchoring by predicted open. All this means is, it is going to anchor your range, candle, high and low targets by the predicted open price. Anchoring by previous close will anchor by the close of yesterday. Both work okay, but in general the results from anchoring to predicted open have higher pass rates and more accurately depict the candle.
Euclidean Distance Measurement Type: You can choose to measure by candle body or from high to low wicks. I haven't played around with measuring from high to low wicks all that much, because candle body tends to do the job. But remember, ED is a neutral measurement. Which means, its not going to distinguish between a red or green candle, just the formation of the candle. Thus, I tend to recommend, pragmatically, not to necessarily rely on the candle being red or green, but one the formation of the candle (where are the wicks going, are there more bearish wicks or bullish wicks) etc. Examples will follow.
Range Prediction Type: You can filter the range prediction type by last instance (in which, it will pull the previous identical candle and plot the next candle that followed it, adjusted for the current ranges) or "Average of All Cases". So this is where we need to talk a little bit about the law of large numbers.
In general, in statistics, when you have a huge amount of random data, the law of large numbers stipulates that, within this randomness should be repeated events. This is why sometimes chart patterns work, sometimes they don't. When we filter by the average of all cases, we are relying on the law of large numbers. In general, if you are getting good Backtest readings from Last Instance, then you don't need to use this function. But it provides an alternative insight into potential candle formations next day. Its not a bad idea to compare between the two and look for similarities and differences.
So now that we have covered the boring details, let's get into how to use the indicator and some examples.
So the indicator is plotting the range and candle for the next day. As such, we are not looking at the current candle being plotted, but we are looking at the previous candle (see image below for example):
The green arrow shows the prediction for Friday, along with the corresponding result. The purple arrow shows the prediction for Monday which we have yet to realize.
So remember when you are using this, you need to look at the previous candle, and not the candle that it is currently plotting with realtime data, because it is plotting for the next candle.
If you are plotting by last instance, the indicator will tell you which day it is pulling its data from if you have opted to toggle on the demographic data:
You can see the green arrow pointing to the date where it is pulling from. This data serves as the example candle with the candle proceeding this date being the anchored candle (or the predicted candle).
Price Targets and Probability:
In the chart, you can see the green arrow pointing to the green portion of the table. In this table, it will give you the current TPs. These represent the current time target price, which means, the TPs shown here are for Friday. On Monday, the table will update with the TPs for Monday, etc. If you want to view the TPs in advance, you can view them from the actual candle itself.
Below the TPs, you see a bullish 7:6. It means, in a total of 13 cases, the next candle was bullish 7 times and bearish 6 times. Where do we see the number of cases? In the demographic table as well:
Auxiliary functions
Because you are using the previous candle, if you want to avoid confusion, you can have the indicator plot the price targets over the predicted candle, to anchor your attention so to speak. Simply select "Label" in the "Show Price Targets" section, which will look like this:
You can also ask the indicator to plot the demographic data of Higher High, Low, etc. information. What this does is simply looks at all the cases and plots how many times higher highs, lows, lower lows, highs etc. were made:
This will just count all of the cases identified and plot the number of times higher highs, lows, etc. were made.
Concluding Remarks
This is a kind of complex indicator and I can appreciate it may take some getting used to.
I will try to post a tutorial video at some point next week for it, so stay tuned for that.
But this isn't designed to make your life more complicated, just to help give you insights into potential outcomes for the next day or hour or 5 minute (it can be used on all timeframes).
If you find it helpful, great! If not, that's okay, too :-).
Please be aware, this is not my forte of indicators. I am not a data scientist or programmer. My background is in Epi and we don't use these types of data science approaches, so if you have any suggestions or critiques, feel free to share them below.
Otherwise, I hope you enjoy!
Take care everyone and safe trades!
Bullish vs. Bearish Candle CounterFollowing an exhaustive analysis of the most recent 50,000 candles within a given currency pair, a notable equilibrium between bearish and bullish candles has emerged as a persistent market phenomenon. This equilibrium, indicative of the market's continuous endeavor to establish parity, has spurred the development of the following indicator.
The indicator meticulously scrutinizes the preceding 100 candles, promptly triggering an on-chart marker when either bullish or bearish candle counts surpass the threshold of 60%. This marker serves as an invaluable tool, providing traders with a potential signal for the initiation of a trend reversal.
As such, this indicator serves as a valuable asset in a trader's toolkit, offering insights into shifts in market sentiment and the prospect of emerging trends.
Key Features:
- Customizable Candle Count: Traders can set the number of candlesticks to be analyzed in the input parameters, allowing flexibility in their analysis.
- Bullish and Bearish Percentage: Users can define their desired percentage for both bullish and bearish candles in the indicator's settings. The indicator calculates the percentage of each candle type within the specified range.
- Arrow Signals: The indicator plots arrows above or below the current candle, indicating bullish or bearish conditions based on the defined percentage thresholds. A green arrow signifies bullish sentiment, while a red arrow denotes bearish sentiment.
How to Use:
- Adjust Parameters: In the indicator settings, users can customize the number of candlesticks to be analyzed, as well as set their preferred percentages for both bullish and bearish conditions.
- Interpret Arrows: The indicator generates arrows above or below the current candle, reflecting the prevailing market sentiment. A green arrow suggests a bullish bias, while a red arrow indicates a bearish bias.
- Trade with Confidence: Traders can use this indicator as a tool to gauge market sentiment and make informed trading decisions. It helps identify potential entry and exit points based on the chosen percentage thresholds.
MAutoFloorCeiling* MAutoFloorCeiling Indicator *
The MAutoFloorCeiling indicator is a powerful algorithm utilizing Wyckoffian concepts of Supply, Demand, and Volume Climaxes to determine and draw Support / Resistance levels automatically. It is the culmination of over 2 years of research. Drawing Support / Resistance lines automatically is a tremendous benefit to the trader as this provides structure to price and exposes market movement as well as which areas price is likely to respect or break out of.
* WHAT THE SCRIPT DOES *
The MAutoFloorCeiling algorithm draws Floor and Ceiling lines automatically. The price points at which these lines are drawn at are areas of increasing Supply, Demand, or Volume Climax respective to their Price Levels. Areas of Volume Climaxes are often respected by price, since price tends to return to them or break out of them, and hence form powerful Support / Resistance levels.
* HOW TO USE IT *
Floor and Ceiling lines correspond to Support and Resistance lines. When a line is draw consider the following questions
Is it a top / bottom?
Is it support / resistance?
Is it a breakout / breakdown?
Is it a pullback?
* HOW IT WORKS *
1. There are 2 types of lines: Floors and Ceilings
2. A Floor Line is drawn when there is a "Selling Volume Bias" (Volume Climaxes on downward price movement)
More Floor Lines get drawn if market continues to go lower combined with a "Selling Volume Bias"
3. A ceiling line is drawn when there is a "Buying Volume Bias" (Volume Climaxes on upward price movement)
More ceiling lines get drawn if market continues to go higher combined with a "Buying Volume Bias"
4. There is a 1 bar delay to confirm the creation of a new floor / ceiling line.
Once the new floor / ceiling is created, it draws forward with no delay.
* EXAMPLE AND USE CASES *
MAutoFloorCeiling draws lines that can be used as effective Support / Resistance Levels, Breakout Lines, and Pullback areas. Studying the Volume at these levels can provide insight as to where price is likely to go.
You can scan for Trend Like behavior such as
More Demand on Higher High = Increase in Volume on a Higher Ceiling
More Supply on Lower Low = Increase in Volume on a Lower Floor
You can scan for divergences such as
Less Demand on Higher High = Lower volume on a Higher Ceiling
Less Supply on on Lower Low = Lower volume on a Lower Floor
Pullbacks
A lower ceiling is representative of a pullback when price is going down.
A higher floor is representative of a pullback when price is going up.
You can inspect instances where the thrust of price is shortened, which means the distance between Ceiling or Floor lines becomes less as price struggles to continue in the direction it was moving. Or conversely the thrust of price as shown by the Floor / Ceiling lines can expand, which is indicative of a trend forming.
* AUTHOR *
This script is published by MBoxWave LLC
Multiperiod Volume Pressure Indicator
Description:
The Volume Pressure Indicator is a powerful tool designed to assess market sentiment based on a combination of price and volume data. By analyzing buy and sell pressure within specific lookback periods, this indicator provides valuable insights into the intensity of market buying and selling activities. Traders can use this information to make informed decisions, especially during periods of price consolidation or trend reversal.
Key Features:
- **Multi-Period Analysis:** Utilizes multiple lookback periods (1, 2, and 4) to calculate buy and sell pressures, offering a nuanced view of market dynamics over different timeframes.
- **Pressure Calculation:** Computes buy and sell pressures based on price range and closing values, providing a comprehensive understanding of market participant behavior.
- **Color-Coded Bars:** Visualizes market sentiment by coloring bars according to the number of positive (buy pressure > sell pressure) periods observed within the specified lookback periods.
How to Use:
- **Color Coding:** Green bars represent periods where buy pressure dominates, indicating potential buying interest. Yellow bars suggest a balance between buy and sell pressures. Red bars signal periods dominated by sell pressure, indicating potential selling interest.
- **Lookback Periods:** Shorter lookback periods (e.g., 1) offer insights into immediate market sentiment, while longer periods (e.g., 4) provide a broader perspective. Analyzing multiple periods can help traders confirm trends and anticipate reversals.
Customization:
- **Lookback Periods:** Adjust the length of the lookback periods (1, 2, and 4) to match your trading style and timeframe preferences.
Disclaimer:
Trading involves risk, and past performance is not indicative of future results. Always conduct thorough analysis and apply proper risk management techniques before making trading decisions.
Usage Scenarios:
- **Trend Confirmation:** Use the indicator to confirm the strength of an ongoing trend. Consistent green bars can validate a bullish trend, while red bars may confirm a bearish trend.
- **Reversal Signals:** Look for transitions in bar colors to identify potential trend reversals. A shift from green to yellow/red or vice versa can indicate changing market sentiment.
- **Divergence Analysis:** Compare price movements with the indicator's bar colors. Divergence between price trends and bar colors may signal upcoming price movements.
Machine Learning using Neural Networks | EducationalThe script provided is a comprehensive illustration of how to implement and execute a simplistic Neural Network (NN) on TradingView using PineScript.
It encompasses the entire workflow from data input, weight initialization, implicit neuron calculation, feedforward computation, backpropagation for weight adjustments, generating predictions, to visualizing the Mean Squared Error (MSE) Loss Curve for monitoring the training phase.
In the visual example above, you can see that the prediction is not aligned with the actual value. This is intentional for demonstrative purposes, and by incrementing the Epochs or Learning Rate, you will see these two values converge as the accuracy increases.
Hyperparameters:
Learning Rate, Epochs, and the choice between Simple Backpropagation and a verbose version are declared as script inputs, allowing users to tailor the training process.
Initialization:
Random initialization of weight matrices (w1, w2) is performed to ensure asymmetry, promoting effective gradient updates. A seed is added for reproducibility.
Utility Functions:
Functions for matrix randomization, sigmoid activation, MSE loss calculation, data normalization, and standardization are defined to streamline the computation process.
Neural Network Computation:
The feedforward function computes the hidden and output layer values given the input.
Two variants of the backpropagation function are provided for weight adjustment, with one offering a more verbose step-by-step computation of gradients.
A wrapper train_nn function iterates through epochs, performing feedforward, loss computation, and backpropagation in each epoch while logging and collecting loss values.
Training Invocation:
The input data is prepared by normalizing it to a value between 0 and 1 using the maximum standardized value, and the training process is invoked only on the last confirmed bar to preserve computational resources.
Output Forecasting and Visualization:
Post training, the NN's output (predicted price) is computed, standardized and visualized alongside the actual price on the chart.
The MSE loss between the predicted and actual prices is visualized, providing insight into the prediction accuracy.
Optionally, the MSE Loss Curve is plotted on the chart, illustrating the loss trajectory through epochs, assisting in understanding the training performance.
Customizable Visualization:
Various inputs control visualization aspects like Chart Scaling, Chart Horizontal Offset, and Chart Vertical Offset, allowing users to adapt the visualization to their preference.
-------------------------------------------------------
The following is this Neural Network structure, consisting of one hidden layer, with two hidden neurons.
Through understanding the steps outlined in my code, one should be able to scale the NN in any way they like, such as changing the input / output data and layers to fit their strategy ideas.
Additionally, one could forgo the backpropagation function, and load their own trained weights into the w1 and w2 matrices, to have this code run purely for inference.
-------------------------------------------------------
While this demonstration does create a “prediction”, it is on historical data. The purpose here is educational, rather than providing a ready tool for non-programmer consumers.
Normally in Machine Learning projects, the training process would be split into two segments, the Training and the Validation parts. For the purpose of conveying the core concept in a concise and non-repetitive way, I have foregone the Validation part. However, it is merely the application of your trained network on new data (feedforward), and monitoring the loss curve.
Essentially, checking the accuracy on “unseen” data, while training it on “seen” data.
-------------------------------------------------------
I hope that this code will help developers create interesting machine learning applications within the Tradingview ecosystem.
@tk · spectral█ OVERVIEW
This script is an indicator that helps traders to identify the price difference between spot and futures of the current crypto plotted into the chart. It works in both types of markets, when the chart is plotting the crypto in spot market, it will compare with its respective futures ticker and vice-versa. If the current asset isn't a crypt ticker, the indicator will not be plotted into the chart.
█ MOTIVATION
Since crypto's derivative market is based on spot market asset's price, to calculate the arbitrage mechanisms that attempts to balance the asset price, this indicator can help traders to identify some spot and futures price divergence that can create an anomaly of funding rate and can push it to an extreme negative — or positive — rate. So, easing to track the price difference between both markets will bring more evidences to identify an artificial price move, specially in crypto assets with low market cap.
█ CONCEPT
The trading concept to use this indicator is the concept of the arbitrage machamism created by exchanges that calculates the funding rate based on spot and futures price difference that will vary from exchange to exchange. This strategy don't works alone. It needs to be aligned together with others indicators like Exponential Moving Averages, Chart Patterns, Support and Resistance, and so on... Even more confluences that you have, bigger are your chances to increase the probability for a successful trade. So, don't use this indicator alone. Compose a trading strategy and use it to improve your analysis.
█ CUSTOMIZATION
This indicator allows the trader to customize the following settings:
GENERAL
Text size
Changes the font size of price difference table to improve accessibility.
Type: string
Options: `tiny`, `small`, `normal`, `large`.
Default: `small`
Position
Changes the position of price difference table.
Type: string
Options: `top_left`, `top_center`, `top_right`, `middle_left`, `middle_center`, `middle_right`, `bottom_left`, `bottom_center`, `bottom_right`.
Default: `bottom_right`
Pair Quote
The ticker quote symbol that will be used to base the ticker comparison from spot to futures (e.g. BTCUSDT which `USDT` is the quote. ETHBTC which `BTC` is the quote).
Type: string
Default: USDT
Spectrum Color
The color of the spectrum candles. Spectrum candles are the candles of the opposite market. If the current ticker is in the spot market, the spectrum candles will be the price of the futures market.
Type: color
Default: #434651
█ FUNCTIONS
The indicator contains the following functions:
stripStarts(src, str)
Strips a defined pattern from a string.
Parameters:
src: (string) Source string
str: (string) String pattern to be stripped from start of source string.
Returns: (string) Stripped string with matched regex pattern.
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.
Highlight BarHighlight bars in the past. I use this to show the start of moving average calculations - very helpful to anticipate the change in slope of moving averages. You can change color as well as how far back in time to highlight. The defaults are 20, 50 and 200.
I learned of the idea from Brian Shannon - thanks!
Seasonality and Presidential cycleAn incredibly useful indicator that shows seasonality and presidential cycles by indices, stocks and industries. Just type in a ticker and trade according to seasonal patterns
Blue line - seasonality excluding presidential cycles
Green line - seasonality taking into account presidential cycles
*Seasonal patterns over the last 10 years
This indicator uses the request.seed() function.
Requests data from a GitHub repository maintained by our team and returns it as a series.
Pine Seeds is a service to import custom data and access it via TradingView.
Use TradingView as frontend and use a GitHub repository as backend.
github.com
...
Rus: Невероятно полезный индикатор, который показывает сезонность и президентские циклы по индексам, акциям и отраслям. Просто вбейте тикер и торгуйте согласно сезонным паттернам
Синяя линия - сезонность без учета президентских циклов
Зеленая линия - сезонность с учетом президентских циклов
*Сезонные паттерны за последние 10 лет
Machine Learning: Gaussian Process Regression [LuxAlgo]We provide an implementation of the Gaussian Process Regression (GPR), a popular machine-learning method capable of estimating underlying trends in prices as well as forecasting them.
While this implementation is adapted to real-time usage, do remember that forecasting trends in the market is challenging, do not use this tool as a standalone for your trading decisions.
🔶 USAGE
The main goal of our implementation of GPR is to forecast trends. The method is applied to a subset of the most recent prices, with the Training Window determining the size of this subset.
Two user settings controlling the trend estimate are available, Smooth and Sigma . Smooth determines the smoothness of our estimate, with higher values returning smoother results suitable for longer-term trend estimates.
Sigma controls the amplitude of the forecast, with values closer to 0 returning results with a higher amplitude. Do note that due to the calculation of the method, lower values of sigma can return errors with higher values of the training window.
🔹 Updating Mechanisms
The script includes three methods to update a forecast. By default a forecast will not update for new bars (Lock Forecast).
The forecast can be re-estimated once the price reaches the end of the forecasting window when using the "Update Once Reached" method.
Finally "Continuously Update" will update the whole forecast on any new bar.
🔹 Estimating Trends
Gaussian Process Regression can be used to estimate past underlying local trends in the price, allowing for a noise-free interpretation of trends.
This can be useful for performing descriptive analysis, such as highlighting patterns more easily.
🔶 SETTINGS
Training Window: Number of most recent price observations used to fit the model
Forecasting Length: Forecasting horizon, determines how many bars in the future are forecasted.
Smooth: Controls the degree of smoothness of the model fit.
Sigma: Noise variance. Controls the amplitude of the forecast, lower values will make it more sensitive to outliers.
Update: Determines when the forecast is updated, by default the forecast is not updated for new bars.
Rug Pull DetectorOverview
Have you ever wondered why tickers have such erratic movements that seemingly come from nowhere? These "rug pull" events happen quite often and can catch even the most seasoned traders off-guard.
Unlike most other indicators which rely on historical data to make inferences about future price movements, the Rug Pull Detector (RPD) enables you to take a glimpse into market makers' delta-neutral hedging in real-time.
Market makers by nature must be delta-neutral which means that they cannot position themselves to profit from providing liquidity (either long or short). Liquidity provided to the short or long side must end up in a stock purchase or sale to neutralize the trade.
Volatile movements in a ticker's price movement most often result directly after a period of extremely low volatility. These volatile movements are very often "rug pulled" which ends up reverting the ticker back to the price at which the event first occurred. RPD shows these events in real-time. This knowledge can be used to help determine the most probable near-future direction a ticker will gravitate towards after a rug pull event occurs.
Usage
RPD works on any ticker and on any timeframe and can be used as a tool in determining an exit price for a trade. Vertical shading on the chart indicates a warning signal that a rug pull event may be about to kick-off. Once a rug pull event has occurred and is confirmed, a blue label will appear on the chart with a price. A line is then drawn from the bar at which the event occurred and is extended to each subsequent bar until the price is reached once more; thus concluding the event. Furthermore, red or green shading will be present to easily visually identify rug pull events on the chart and whether they are risks to the downside (red) or upside (green). RPD is broken down into 2 main types of events:
Active Event - These events are characterized by a red or green shading and a blue price line.
Dormant Event - These events do not have shading but are still identifiable via a blue price line. Active events that are superseded by newer events will become dormant.
Active events tend to have a higher chance to return to the initial price point and tend to arrive there quicker.
Dormant events have a slightly lower chance to return to the initial price point and may take longer to arrive there.
Please note:
This indicator has no way of telling the exact amount of time that will pass before the ticker returns to the identified price; however, in more cases than not - the ticker will return to that price within a reasonable amount of time relative to the timeframe you are viewing.
There is a small chance any single event will never conclude. These are anomalies and do occur on occasion.
Using RPD alongside tools such as the RSI, Anchored VWAP, or other trend-based indicators will help determine when the ticker's price might be about to pivot and head back towards the identified price point.
Seeing is Believing:
SPY 1D downside rug-pull
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AAPL 15s downside and upside rug-pulls
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AMD 2D downside rug-pull
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VIX 1h downside and upside rug-pulls
Want to see more? Check out my recent Ideas for more examples of the Rug Pull Detector in action.
Disclaimer:
Any information in relation to the Rug Pull Detector does not constitute any financial, investment, or trading advice. Trade or invest at your own risk.
Intraday Volatility Bands [Honestcowboy]The Intraday Volatility Bands aims to provide a better alternative to ATR in the calculation of targets or reversal points.
How are they different from ATR based bands?
While ATR and other measures of volatility base their calculations on the previous bars on the chart (for example bars 1954 to 1968). The volatility used in these bands measure expected volatility during that time of the day.
Why would you take this approach?
Markets behave different during certain times of the day, also called sessions.
Here are a couple examples.
Asian Session (generally low volatility)
London Session (bigger volatility starts)
New York Session (overlap of New York with London creates huge volatility)
Generally when using bands or channel type indicators intraday they do not account for the upcoming sessions. On London open price will quickly spike through a bollinger band and it will take some time for the bands to adjust to new volatility.
This script will show expected volatility targets at the start of each new bar and will not adjust during the bar. It already knows what price is expected to do at this time of day.
Script also plots arrows when price breaches either the top or bottom of the bands. You can also set alerts for when this occurs. These are non repainting as the script knows the level at start of the bar and does not change.
🔷 CALCULATION
Think of this script like an ATR but instead it uses past days data instead of previous bars data. Charts below should visualise this more clearly:
The scripts measure of volatility is based on a simple high-low.
The script also counts the number of bars that exist in a day on your current timeframe chart. After knowing that number it creates the matrix used in it's calculations and data storage.
See how it works perfectly on a lower timeframe chart below:
Getting this right was the hardest part, check the coding if you are interested in this type of stuff. I commented every step in the coding process.
🔷 SETTINGS
Every setting of the script has a tooltip but I provided a breakdown here:
Some more examples of different charts:
Machine Learning: Trend Lines [YinYangAlgorithms]Trend lines have always been a key indicator that may help predict many different types of price movements. They have been well known to create different types of formations such as: Pennants, Channels, Flags and Wedges. The type of formation they create is based on how the formation was created and the angle it was created. For instance, if there was a strong price increase and then there is a Wedge where both end points meet, this is considered a Bull Pennant. The formations Trend Lines create may be powerful tools that can help predict current Support and Resistance and also Future Momentum changes. However, not all Trend Lines will create formations, and alone they may stand as strong Support and Resistance locations on the Vertical.
The purpose of this Indicator is to apply Machine Learning logic to a Traditional Trend Line Calculation, and therefore allowing a new approach to a modern indicator of high usage. The results of such are quite interesting and goes to show the impacts a simple KNN Machine Learning model can have on Traditional Indicators.
Tutorial:
There are a few different settings within this Indicator. Many will greatly impact the results and if any are changed, lots will need ‘Fine Tuning’. So let's discuss the main toggles that have great effects and what they do before discussing the lengths. Currently in this example above we have the Indicator at its Default Settings. In this example, you can see how the Trend Lines act as key Support and Resistance locations. Due note, Support and Resistance are a relative term, as is their color. What starts off as Support or Resistance may change when the price crosses over / under them.
In the example above we have zoomed in and circled locations that exhibited markers of Support and Resistance along the Trend Lines. These Trend Lines are all created using the Default Settings. As you can see from the example above; just because it is a Green Upwards Trend Line, doesn’t mean it’s a Support Line. Support and Resistance is always shifting on Trend Lines based on the prices location relative to them.
We won’t go through all the Formations Trend Lines make, but the example above, we can see the Trend Lines formed a Downward Channel. Channels are when there are two parallel downwards Trend Lines that are at a relatively similar angle. This means that they won’t ever meet. What may happen when the price is within these channels, is it may bounce between the upper and lower bounds. These Channels may drive the price upwards or downwards, depending on if it is in an Upwards or Downwards Channel.
If you refer to the example above, you’ll notice that the Trend Lines are formed like traditional Trend Lines. They don’t stem from current Highs and Lows but rather Machine Learning Highs and Lows. More often than not, the Machine Learning approach to Trend Lines cause their start point and angle to be quite different than a Traditional Trend Line. Due to this, it may help predict Support and Resistance locations at are more uncommon and therefore can be quite useful.
In the example above we have turned off the toggle in Settings ‘Use Exponential Data Average’. This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN. By Default it is enabled, but as you can see when it is disabled it may create some pretty strong lasting Trend Lines. This is why we advise you ZOOM OUT AS FAR AS YOU CAN. Trend Lines are only displayed when you’ve zoomed out far enough that their Start Point is visible.
As you can see in this example above, there were 3 major Upward Trend Lines created in 2020 that have had a major impact on Support and Resistance Locations within the last year. Lets zoom in and get a closer look.
We have zoomed in for this example above, and circled some of the major Support and Resistance locations that these Upward Trend Lines may have had a major impact on.
Please note, these Machine Learning Trend Lines aren’t a ‘One Size Fits All’ kind of thing. They are completely customizable within the Settings, so that you can get a tailored experience based on what Pair and Time Frame you are trading on.
When any values are changed within the Settings, you’ll likely need to ‘Fine Tune’ the rest of the settings until your desired result is met. By default the modifiable lengths within the Settings are:
Machine Learning Length: 50
KNN Length:5
Fast ML Data Length: 5
Slow ML Data Length: 30
For example, let's toggle ‘Use Exponential Data Averages’ back on and change ‘Fast ML Data Length’ from 5 to 20 and ‘Slow ML Data Length’ from 30 to 50.
As you can in the example above, all of the lines have changed. Although there are still some strong Support Locations created by the Upwards Trend Lines.
We will conclude our Tutorial here. Hopefully you’ve learned how to use Machine Learning Trend Lines and will be able to now see some more unorthodox Support and Resistance locations on the Vertical.
Settings:
Use Machine Learning Sources: If disabled Traditional Trend line sources (High and Low) will be used rather than Rational Quadratics.
Use KNN Distance Sorting: You can disable this if you wish to not have the Machine Learning Data sorted using KNN. If disabled trend line logic will be Traditional.
Use Exponential Data Average: This Settings uses a custom Exponential Data Average of the KNN rather than simply averaging the KNN.
Machine Learning Length: How strong is our Machine Learning Memory? Please note, when this value is too high the data is almost 'too' much and can lead to poor results.
K-Nearest Neighbour (KNN) Length: How many K-Nearest Neighbours are allowed with our Distance Clustering? Please note, too high or too low may lead to poor results.
Fast ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 3/5/7 all seem to work well for Fast.
Slow ML Data Length: Fast and Slow speed needs to be adjusted properly to see results. 20 - 50 all seem to work well for Slow.
If you have any questions, comments, ideas or concerns please don't hesitate to contact us.
HAPPY TRADING!
RVI_HTFThe "RVI_HTF" indicator is a tool designed to assist traders in analyzing market trends using the Relative Vigor Index (RVI) across different timeframes. It enables users to customize various aspects of the indicator's appearance and behavior. By monitoring the RVI on different timeframes, tracking its relationship with the moving average, and paying attention to extreme arrows above the 80 or below the 20 line, traders can anticipate potential reversals, trends, or changes in market momentum.
Above 80 Line: When the RVI moves above the 80 line, it suggests that the market may be overbought. Extreme upward arrows (indicating potential sell signals) can be a sign that a bullish trend might be reaching an exhaustion point. Traders may anticipate a possible trend reversal or pullback.
Below 20 Line: When the RVI dips below the 20 line, it implies that the market might be oversold. Extreme downward arrows (indicating potential buy signals) can be an early signal of a potential bullish reversal. Traders may anticipate an upcoming uptrend or bounce.
Crossing Above Moving Average: When the RVI crosses above its moving average on the selected timeframe, it can serve as an early indication of potential bullish strength in the market. This suggests that buying pressure may be increasing.
Crossing Below Moving Average: Conversely, when the RVI crosses below its moving average, it can signal potential bearish momentum. This indicates that selling pressure may be gaining strength.
Variables:
Timeframe (TF) Selection:
The indicator allows you to select the timeframe for the RVI calculation. You can choose from various options such as 1 minute (1), 5 minutes (5), 15 minutes (15), 30 minutes (30), 60 minutes (60), 240 minutes (240), Daily (D), Weekly (W), Monthly (M), or use "Auto" to automatically select a higher timeframe based on your current chart's timeframe.
Moving Average Type (MA_Type):
Function: Allows users to select the type of moving average used in RVI calculations.
Options: You can select from various moving average types, including:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
SMMA (Smoothed Moving Average, also known as RMA)
WMA (Weighted Moving Average)
VWMA (Volume Weighted Moving Average)
DEMA (Double Exponential Moving Average)
Moving Average Length (MA_Length):
Function: Permits users to set the number of periods for the selected moving average type.
Purpose: Controls the sensitivity of the RVI indicator. Longer lengths provide smoother results, while shorter lengths react more quickly to price changes.
Up Arrow Color (upArrowColor):
Function: Enables users to customize the color of arrows that indicate potential Overbought areas. (Only shown when the TF is same as or lower than the chart TF)
Down Arrow Color (downArrowColor):
Function: Allows users to specify the color of downward-pointing arrows signaling potential Oversold areas. (Only shown when the TF is same as or lower than the chart TF)
RVI Up Color (firstColor):
Function: Defines the color of the RVI line when it indicates a bullish condition on the higher timeframe.
RVI Down Color (secondColor):
Function: Specifies the color of the RVI line when it suggests a bearish condition on the higher timeframe.
RVI-Based Moving Average Up Color (firstColorMA):
Function: Customizes the color of the RVI-based moving average line when it indicates a bullish condition.
RVI-Based Moving Average Down Color (secondColorMA):
Function: Defines the color of the RVI-based moving average line when it suggests a bearish condition.