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.
Forecast
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.
Kaschko's Seasonal TrendThis script calculates the average price moves (using each bar's close minus the previous bar's close) for the trading days, weeks or months (depending on the timeframe it is applied to) of a number of past calendar years (up to 30) to construct a seasonal trend which is then drawn as a seasonal chart (overlay) onto the price chart. Supported are the 1D,1W,1M timeframes.
The seasonal chart is adjusted to the price chart (so that both occupy the same height on the overall chart) and it is also de-trended, which means that the seasonal chart's starting value is the same in each year and the progression during the year is adjusted so that no abrupt gap occurs between years and the highs and lows of consecutive years of the seasonal chart (if projected over more than one year) are also at the same level. Of course, this also means that the absolute value of the seasonal chart has no meaning at all.
You can configure the number of bars the seasonal chart is drawn into the future. This projection shows how price could move in the future if the market shows the same seasonal tendencies like in the past. On the daily chart, the trading week of year (TWOY), trading day of month (TDOM) and trading day of year (TDOY) are shown in the status line.
Caution is advised as seasonality is based on the past. It is not a reliable prediction of the future. But it can still be used as an additional confirmation or contradiction of an otherwise recognized possible impending trend.
I have used a virtually identical indicator for a long time in a commercial software package popular among futures traders, but have not found anything comparable here. Therefore I implemented it myself. I hope you find it useful.
SFC Valuation Model - Fair ValueValuation is the analytical process of determining the current (or projected) worth of an asset or a company. There are many techniques used for doing a valuation. An analyst placing a value on a company looks at the business's management, the composition of its capital structure, the prospect of future earnings, and the market value of its assets, among other metrics.
Fundamental analysis is often employed in valuation, although several other methods may be employed such as the capital asset pricing model (CAPM) or the dividend discount model (DDM), Discounted Cash Flow (DCF) and many others.
A valuation can be useful when trying to determine the fair value of a security, which is determined by what a buyer is willing to pay a seller, assuming both parties enter the transaction willingly. When a security trades on an exchange, buyers and sellers determine the market value of a stock or bond.
There is no universal standard for calculating the intrinsic value of a company or stock. Financial analysts attempt to determine an asset's intrinsic value by using fundamental and technical analyses to gauge its actual financial performance.
Intrinsic value is useful because it can help an investor understand whether a potential investment is overvalued or undervalued.
This indicator allows investors to simulate different scenarios depending on their view of the stock's value. It calculates different models automatically, but users can define the fair value manually by changing the settings.
For example: change the weight of the model; choose how conservatively want to evaluate the stock; use different growth rate or discount rate and so on.
The indicator shows other useful metrics in order to help investors to evaluate the stock.
This indicator can save users hours of searching financial data and calculating fair value.
There are few valuation methods/steps
- Macroeconomics - analyse the current economic;
- Define how the sector is performing;
- Relative valuation method - compare few stocks and find the Outlier;
- Absolute valuation method historically- define how the stock performed in the past;
- Absolute valuation method - define how the stock is performed now and find the fair value;
- Technical analysis
How to use:
1. Once you have completed the initial evaluation steps, simply load the indicator.
2. Check the default settings and see if they suit you.
3. Find the fair value and wait for the stock to reach it.
Machine Learning Regression Trend [LuxAlgo]The Machine Learning Regression Trend tool uses random sample consensus (RANSAC) to fit and extrapolate a linear model by discarding potential outliers, resulting in a more robust fit.
🔶 USAGE
The proposed tool can be used like a regular linear regression, providing support/resistance as well as forecasting an estimated underlying trend.
Using RANSAC allows filtering out outliers from the input data of our final fit, by outliers we are referring to values deviating from the underlying trend whose influence on a fitted model is undesired. For financial prices and under the assumptions of segmented linear trends, these outliers can be caused by volatile moves and/or periodic variations within an underlying trend.
Adjusting the "Allowed Error" numerical setting will determine how sensitive the model is to outliers, with higher values returning a more sensitive model. The blue margin displayed shows the allowed error area.
The number of outliers in the calculation window (represented by red dots) can also be indicative of the amount of noise added to an underlying linear trend in the price, with more outliers suggesting more noise.
Compared to a regular linear regression which does not discriminate against any point in the calculation window, we see that the model using RANSAC is more conservative, giving more importance to detecting a higher number of inliners.
🔶 DETAILS
RANSAC is a general approach to fitting more robust models in the presence of outliers in a dataset and as such does not limit itself to a linear regression model.
This iterative approach can be summarized as follow for the case of our script:
Step 1: Obtain a subset of our dataset by randomly selecting 2 unique samples
Step 2: Fit a linear regression to our subset
Step 3: Get the error between the value within our dataset and the fitted model at time t , if the absolute error is lower than our tolerance threshold then that value is an inlier
Step 4: If the amount of detected inliers is greater than a user-set amount save the model
Repeat steps 1 to 4 until the set number of iterations is reached and use the model that maximizes the number of inliers
🔶 SETTINGS
Length: Calculation window of the linear regression.
Width: Linear regression channel width.
Source: Input data for the linear regression calculation.
🔹 RANSAC
Minimum Inliers: Minimum number of inliers required to return an appropriate model.
Allowed Error: Determine the tolerance threshold used to detect potential inliers. "Auto" will automatically determine the tolerance threshold and will allow the user to multiply it through the numerical input setting at the side. "Fixed" will use the user-set value as the tolerance threshold.
Maximum Iterations Steps: Maximum number of allowed iterations.
Gann Angles EnterpriseThe Gann Angles indicator is a tool based on the methods developed by William Delbert Gann. It is designed to analyze and forecast price movements in financial markets. The indicator automatically calculates the angle scale using Gann, Herzhik, Heliker, and Borovski methods. Additionally, users have the option to manually input their own angle scale.
The Gann methods and those of his followers are based on representing price movements as geometric shapes such as triangles, squares, and circles. Gann believed that price movements adhere to certain patterns and that future changes can be predicted based on these geometric forms.
The Gann Angle indicator allows users to identify the angles of trend and their strength. It plots template lines with different angles of inclination on the price chart, representing support and resistance levels. These levels can be used to determine entry and exit points in the market, as well as to set stop-loss and profit levels.
When automatically calculating the angle scale, the indicator takes into account various factors such as the current trend, market volatility, and the period of analyzed data. It applies relevant formulas and algorithms to determine optimal angles of inclination and create a fan-like pattern of angles.
However, the indicator also provides the option for users to manually input their own angle scale. This allows analysts or traders to customize the indicator according to their own preferences and strategies.
Overall, the Gann Angle indicator is a powerful tool for technical analysis in financial markets. It helps identify key support and resistance levels and provides information about the trend and its strength. Combining the automatic calculation of the angle scale with the option to input a manual scale gives users flexibility and adaptability in using the indicator. They can consider their own preferences, experience, and unique market conditions when determining angles of inclination and support/resistance levels.
It is important to note that the effectiveness of the Gann Angle indicator, whether using an automatic or manual scale, depends on proper analysis and interpretation of the results. Users should have knowledge and understanding of Gann's methods to make informed decisions based on the data provided by the indicator.
In conclusion, the Gann Angle indicator with automatic and manual angle scale calculation provides users with a powerful tool for analyzing and forecasting price movements in financial markets. It combines the fundamental principles of William Delbert Gann's methods with flexibility and customization to meet the needs of various traders and analysts.
The different methods of calculating the scale give traders the flexibility to choose the follower's school they prefer.
The features of the indicator include:
Mandatory knowledge of Gann's methods.
Use as a template for drawing angles and fan patterns.
Selection of scale calculation options:
Heliker
Herzhik
Gann
Borovski
Manual input of the scale
Working principle:
The indicator is used as a template.
After installing the indicator and configuring it, the trader needs to draw a trend line (or a pre-drawn fan) along the desired angle(s).
Without changing the inclination, the trader simply moves this line to the desired extreme for further analysis.
Autocorrelation - The Quant ScienceAutocorrelation - The Quant Science it is an indicator developed to quickly calculate the autocorrelation of a historical series. The objective of this indicator is to plot the autocorrelation values and highlight market moments where the value is positive and exceeds the attention threshold.
This indicator can be used for manual analysis when a trader needs to search for new price patterns within the historical series or to create complex formulas in estimating future prices.
What is autocorrelation?
Autocorrelation in trading is a statistical measure used to determine the presence of a relationship or pattern of dependence between values in a financial time series over time. It represents the correlation of past values in a series with its future values. In other words, autocorrelation in trading aims to identify if there are systematic relationships between the past prices or returns of a security or market and its future prices or returns. This analysis can be helpful in identifying patterns or trends that can be leveraged for informed trading decisions. The presence of autocorrelation may suggest that market prices or returns follow a certain pattern or trend over time.
Limitations of the model
It is important to note that autocorrelation does not necessarily imply a causal relationship between past and future values. Other variables or market factors may influence the dynamics of prices or returns, and therefore autocorrelation could be merely a random coincidence. Therefore, it is essential to carefully evaluate the results of autocorrelation analysis along with other information and trading strategies to make informed decisions.
How to use
The usage is very simple, you just need to add it to the current chart to activate the indicator.
From the user interface, you can manage two important features:
1. Lenght: the delay period applied to the historical series during the autocorrelation calculation can be managed from the user interface. By default, it is set to 20, which means that the autocorrelation ratio within the historical series is calculated with a delay of 20 bars.
2. Threshold: the threshold value that the autocorrelation level must meet can be managed from the user interface. By default, it is set to 0.50, which means that the autocorrelation value must be higher than this threshold to be considered valid and displayed on the chart.
3. Bar color: the color used to display the autocorrelation data and highlight the bars when autocorrelation is valid can be managed from the user interface.
To set up the chart
We recommend disabling the 'wick' and 'border' of the candlesticks from the chart settings for a high-quality user experience.
Gann Price LevelsGann Price Level is a powerful indicator based on the methods of the legendary trader William D. Gann. It provides traders with the ability to forecast future targets, both trending and retracement, based on just three anchor points and generates clear entry signals in the form of arrows. This indicator offers broad capabilities that assist traders in making informed decisions and optimizing their trading strategies. Here are a few key features of this indicator:
Calculation of future targets: Gann Price Level allows traders to determine potential price levels that may be reached in the future. It is based on the concept of geometric levels and numerical relationships, making it an effective tool for forecasting future price movements. Its algorithm incorporates geometry, mathematics, and Gann's angular relationships.
Three-point approach: One of the main advantages of Gann Price Level is its ability to work with only three anchor points. Traders need to specify three (ABC) points forming a triangle, and the indicator automatically calculates the target price levels. This simplifies the analysis process and makes it more intuitive.
Entry signals: In addition to forecasting target levels, Gann Price Level provides clear entry signals in the form of arrows. This helps traders identify optimal moments to enter positions, improving the accuracy of their trades.
Timeframes: Gann Price Level can be applied to various time intervals, including both short-term and long-term charts. This allows traders to adapt the indicator to their trading strategies and trade across different markets.
Versatility: Gann Price Level can be used to analyze various financial instruments, including stocks, forex, commodities, cryptocurrencies, and more. This makes it a versatile tool for traders operating in different market segments.
Another key feature of this indicator is the additional level calculation algorithm, which, when working with a trend, forms an optimal gray zone for forming point C, while when calculating retracement levels, it adds an additional magnetic target in the form of a gray zone.
Additionally, traders can combine this indicator with other indicators or chart patterns to obtain more accurate signals and confirmations. Moreover, Gann Price Level works effectively in both upward and downward trends, making it a flexible tool for traders of different trading styles. It can be used to determine potential support and resistance levels, as well as entry and exit points for positions.
Working with this indicator is straightforward. The user needs to select three (ABC) points forming a triangle, and the indicator will automatically calculate the future price targets. An entry arrow will also appear, enabling the user to enter the trade in a timely manner. The stop loss is placed slightly below point C (at the spread distance) for buy trades and above point C (at the spread distance) for sell trades. The first target is represented by a dashed line. Once this target is reached, a portion of the position (usually 50%) is closed, and the stop loss is moved to breakeven. The remaining part of the position is held until subsequent price levels based on personal preferences.
Construction rules:
When calculating targets in an upward trend, point A is below points BC, and point C is always between points AB.
When calculating targets in a downward trend, point A is above points BC, and point C is always between points AB.
When calculating retracement targets in an upward trend, point B is above points AC, point A is always between points BC, and point C is below AB.
When calculating retracement targets in a downward trend, point B is below points AC, point A is always between points BC, and point C is above AB.
This indicator relies entirely on the manual construction of the ABC points by the user.
Inverted ProjectionThe "Inverted Projection" indicator calculates the Simple Moving Average (SMA) and draws lines representing an inverted projection. The indicator swaps the highs and lows of the projection to provide a unique perspective on price movement.
This indicator is a simple study that should not be taken seriously as a tool for predicting future price movements; it is purely intended for exploratory purposes.
Auto Trend ProjectionAuto Trend Projection is an indicator designed to automatically project the short-term trend based on historical price data. It utilizes a dynamic calculation method to determine the slope of the linear regression line, which represents the trend direction. The indicator takes into account multiple length inputs and calculates the deviation and Pearson's R values for each length.
Using the highest Pearson's R value, Auto Trend Projection identifies the optimal length for the trend projection. This ensures that the projected trend aligns closely with the historical price data.
The indicator visually displays the projected trend using trendlines. These trendlines extend into the future, providing a visual representation of the potential price movement in the short term. The color and style of the trendlines can be customized according to user preferences.
Auto Trend Projection simplifies the process of trend analysis by automating the projection of short-term trends. Traders and investors can use this indicator to gain insights into potential price movements and make informed trading decisions.
Please note that Auto Trend Projection is not a standalone trading strategy but a tool to assist in trend analysis. It is recommended to combine it with other technical analysis tools and indicators for comprehensive market analysis.
Overall, Auto Trend Projection offers a convenient and automated approach to projecting short-term trends, empowering traders with valuable insights into the potential price direction.
Ultimate Trend LineThe "Ultimate Trend Line" indicator, designed for overlay on financial charts, calculates and plots a global trend line. It works by first allowing users to input several parameters such as different lengths for up to 21 groups, a multiplier that defines the deviation from the linear regression line for calculating the upper and lower bands, and a color for the fill.
Using these inputs, it calculates the upper and lower bands for each length group based on a multiple of the standard deviation from the linear regression line. It then averages these bands to define the global trend line, which is plotted on the graph.
Although the code includes commented-out lines for plotting each individual upper and lower band, the indicator as it stands only displays the overall average trend line. The line's color and linewidth can be adjusted according to user preferences.
This indicator can be effectively used on both logarithmic and linear scales. This versatility allows it to be adaptable to various types of financial charts and trading styles, providing a flexible tool for users to assess and visualize trend patterns across different market conditions and time frames. It maintains its accuracy and relevance, regardless of the scale used, thus making it a comprehensive solution for trend line analysis in diverse scenarios.
It's important to note that the "Ultimate Trend Line" indicator requires a substantial amount of historical data to function properly. If insufficient historical data is available, the indicator may not display accurately or at all. This issue is particularly prevalent when using larger time units, such as weekly or monthly charts, where the available data may not stretch back far enough to satisfy the requirements of the indicator. As such, users should ensure they are operating on a time scale and data set that provides adequate historical depth for the reliable operation of this indicator.
TrueLevel BandsTrueLevel Bands is a powerful trading indicator that employs linear regression and standard deviation to create dynamic, envelope-style bands around the price action of a financial instrument. These bands are designed to help traders identify potential support and resistance levels, trend direction, and volatility.
The TrueLevel Bands indicator consists of multiple envelope bands, each constructed using different timeframes or lengths, and a multiple (mult) factor. The multiple factor determines the width of the bands by adjusting the number of standard deviations from the linear regression line.
Key Features of TrueLevel Bands
1. Multi-Timeframe Analysis: Unlike traditional moving average-based indicators, TrueLevel Bands allow traders to incorporate multiple timeframes into their analysis. This helps traders capture both short-term and long-term market dynamics, offering a more comprehensive understanding of price behavior.
2. Customization: The TrueLevel Bands indicator offers a high level of customization, allowing traders to adjust the lengths and multiple factors to suit their trading style and preferences. This flexibility enables traders to fine-tune the indicator to work optimally with various instruments and market conditions.
3. Adaptive Volatility: By incorporating standard deviation, TrueLevel Bands can automatically adjust to changing market volatility. This feature enables the bands to expand during periods of high volatility and contract during periods of low volatility, providing traders with a more accurate representation of market dynamics.
4. Dynamic Support and Resistance Levels: TrueLevel Bands can help traders identify dynamic support and resistance levels, as the bands adjust in real-time according to price action. This can be particularly useful for traders looking to enter or exit positions based on support and resistance levels.
5. The "Global Trend Line" refers to the average of the bands used to indicate the overall trend.
Why TrueLevel Bands are Different from Classic Moving Averages
TrueLevel Bands differ from conventional moving averages in several ways:
1. Linear Regression: While moving averages are based on simple arithmetic means, TrueLevel Bands use linear regression to determine the centerline. This offers a more accurate representation of the trend and helps traders better assess potential entry and exit points.
2. Envelope Style Bands: Unlike moving averages, which are single lines, TrueLevel Bands form envelope-style bands around the price action. This provides traders with a visual representation of potential support and resistance levels, trend direction, and volatility.
3. Multi-Timeframe Analysis: Classic moving averages typically focus on a single timeframe. In contrast, TrueLevel Bands incorporate multiple timeframes, enabling traders to capture a broader understanding of market dynamics.
4. Adaptive Volatility: Traditional moving averages do not account for changing market volatility, whereas TrueLevel Bands automatically adjust to volatility shifts through the use of standard deviation.
The TrueLevel Bands indicator is a powerful, versatile tool that offers traders a unique approach to technical analysis. With its ability to adapt to changing market conditions, provide multi-timeframe analysis, and dynamic support and resistance levels, TrueLevel Bands can serve as an invaluable asset to both novice and experienced traders looking to gain an edge in the markets.
Price Action Color Forecast (Expo)█ Overview
The Price Action Color Forecast Indicator , is an innovative trading tool that uses the power of historical price action and candlestick patterns to predict potential future market movements. By analyzing the colors of the candlesticks and identifying specific price action events, this indicator provides traders with valuable insights into future market behavior based on past performance.
█ Calculations
The Price Action Color Forecast Indicator systematically analyzes historical price action events based on the colors of the candlesticks. Upon identifying a current price action coloring event, the indicator searches through its past data to find similar patterns that have happened before. By examining these past events and their outcomes, the indicator projects potential future price movements, offering traders valuable insights into how the market might react to the current price action event.
The indicator prioritizes the analysis of the most recent candlesticks before methodically progressing toward earlier data. This approach ensures that the generated candle forecast is based on the latest market dynamics.
The core functionality of the Price Action Color Forecast Indicator:
Analyzing historical price action events based on the colors of the candlesticks.
Identifying similar events from the past that correspond to the current price action coloring event.
Projecting potential future price action based on the outcomes of past similar events.
█ Example
In this example, we can see that the current price action pattern matches with a similar historical price action pattern that shares the same characteristics regarding candle coloring. The historical outcome is then projected into the future. This helps traders to understand how the past pattern evolved over time.
█ How to use
The indicator provides traders with valuable insights into how the market might react to the current price action event by examining similar historical patterns and projecting potential future price movements.
█ Settings
Candle series
The candle lookback length refers to the number of bars, starting from the current one, that will be examined in order to find a similar event in the past.
Forecast Candles
Number of candles to project into the future.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Gamma Bands v. 7.0Gamma Bands are based on previous day data of base intrument, Volatility , Options flow (imported from external source Quandl via TradingView API as TV is not supporting Options as instruments) and few other additional factors to calculate intraday levels. Those levels in correlation with even pure Price Action works like a charm what is confirmed by big orders often placed exactly on those levels on Futures Contracts. We have levels +/- 0.25, 0.5 and 1.0 that are calculated from Pivot Point and are working like Support and Resistance. Higher the number of Gamma, stronger the level. Passing Gamma +1/-1 would be good entry point for trades as almost everytime it is equal to Trend Day. Levels are calculated by Machine Learning algorithm written in Python which downloads data from Options and Darkpool markets, process and calculate levels, export to Quandl and then in PineScript I import the data to indicator. Levels are refreshed each day and are valid for particular trading day.
There's possibility also to enable display of Initial Balance range (High and Low range of bars/candles from 1st hour of regular cash session). Breaking one of extremes of Initial Balance is very often driving sentiment for rest of the session.
Volatility Reversal Levels
They're calculated taking into account Options flow imported to TV (Strikes, Call/Put types & Expiration dates) in combination with Volatility, Volume flow. Based on that we calculate on daily basis Significant Close level and "Stop and Reversal level".
Very often reaching area close to those levels either trigger immediate reversal of previous trend or at least push price into consolidation range.
[TTI] ToS MarketForecast Indicator––––HISTORY & CREDITS 🏦
The ThinkorSwim Market Forecast indicator is an adaptation of the Market Forecast indicator originally created for the ThinkorSwim trading platform. This version has been adapted for use in TradingView, replicating the functionality of the original indicator to assist traders in their market analysis.
––––WHAT IT DOES 💡
The ThinkorSwim Market Forecast is a technical indicator designed to identify potential buying and selling opportunities based on market analysis techniques applied to multiple timeframes. It consists of three plots: Momentum (red line), NearTerm (blue line), and Intermediate (green line). These plots tend to cycle on daily, weekly, and monthly basis, respectively. The indicator also includes static lines representing the top, bottom, and reversal zones.
Calculations:
The ThinkorSwim Market Forecast indicator is a technical analysis tool that calculates three separate lines – Momentum, NearTerm, and Intermediate – to help traders identify potential buying and selling opportunities. The calculations are based on market data from multiple timeframes and involve measuring price movements in relation to their recent high and low values. The indicator highlights areas of potential reversals in the upper and lower zones, allowing traders to make more informed decisions on when to enter or exit a position.
––––HOW TO USE IT 🔧
To use the ThinkorSwim Market Forecast indicator, look for simultaneous reversals of the three lines in the upper or lower zones. A Buy signal is generated when all three lines go through a reversal at the same (or almost the same) time in the bottom zone (green cloud). Conversely, a simultaneous reversal in the upper zone (red cloud) suggests a Sell signal.
To add this indicator to your TradingView chart, copy the provided script and paste it into the Pine editor. Save and add the script to your chart, and the indicator will be displayed, allowing you to analyze the market based on the Momentum, NearTerm, and Intermediate lines, as well as the upper and lower reversal zones.
Trend forecasting by c00l75----------- ITALIANO -----------
Questo codice è uno script di previsione del trend creato solo a scopo didattico. Utilizza una media mobile esponenziale (EMA) e una media mobile di Hull (HMA) per calcolare il trend attuale e prevedere il trend futuro. Il codice utilizza anche una regressione lineare per calcolare il trend attuale e un fattore di smorzamento per regolare l’effetto della regressione lineare sulla previsione del trend. Infine il codice disegna due linee tratteggiate per mostrare la previsione del trend per i periodi futuri specificati dall’utente. Se ti piace l'idea mettimi un boost e lascia un commento!
----------- ENGLISH -----------
This code is a trend forecasting script created for educational purposes only. It uses an exponential moving average (EMA) and a Hull moving average (HMA) to calculate the current trend and forecast the future trend. The code also uses a linear regression to calculate the current trend and a damping factor to adjust the effect of the linear regression on the trend prediction. Finally, the code draws two dashed lines to show the trend prediction for future periods specified by the user. If you like the idea please put a boost and leave a comment!
Volume Forecasting [LuxAlgo]The Volume Forecasting indicator provides a forecast of volume by capturing and extrapolating periodic fluctuations. Historical forecasts are also provided to compare the method against volume at time t .
This script will not work on tickers that do not have volume data.
🔶 SETTINGS
Median Memory: Number of days used to compute the median and first/third quartiles.
Forecast Window: Number of bars forecasted in the future.
Auto Forecast Window: Set the forecast window so that the forecast length completes an interval.
🔶 USAGE
The periodic nature of volume on certain securities allows users to more easily forecast using historical volume. The forecast can highlight intervals where volume tends to be more important, that is where most trading activity takes place.
More pronounced periodicity will tend to return more accurate forecasts.
The historical forecast can also highlight intervals where high/low volume is not expected.
The interquartile range is also highlighted, giving an area where we can expect the volume to lie.
🔶 DETAILS
This forecasting method is similar to the time series decomposition method used to obtain the seasonal component.
We first segment the chart over equidistant intervals. Each interval is delimited by a change in the daily timeframe.
To forecast volume at time t+1 we see where the current bar lies in the interval, if the bar is the 78th in interval then the forecast on the next bar is made by taking the median of the 79th bar over N intervals, where N is the median memory.
This method ensures capturing the periodic fluctuation of volume.
Momentum Covariance Oscillator by TenozenWell, guess what? A new indicator is here! Again it's a coincidence, as I experiment with my formula. So far it's less noisy than Autoregressive Covariance Oscillator, so possibly this one is better. The formula is much simpler, care me to explain.
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Yt = close - previous average
Val = Yt/close
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Welp that's the formula lol. Funny thing is that it's so simple, but it's good! What matters is the use of it haha.
So how to use this Oscillator? If the value is above 0, we expect a bullish response, if the value is below 0 we expect a bearish response. That simple. Ciao.
(Any questions and suggestions? feel free to comment!)
Autoregressive Covariance Oscillator by TenozenWell to be honest I don't know what to name this indicator lol. But anyway, here is my another original work! Gonna give some background of why I create this indicator, it's all pretty much a coincidence when I'm learning about time series analysis.
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Well, the formula of Auto-covariance is:
E{(X(t)-(t) * (X(t-s)-(t-s))}= Y_s
But I don't multiply both values but rather subtract them:
E{(X(t)-(t) - (X(t-s)-(t-s))}= Y_s?
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For arm_vald, the equation is as follows:
arm_vald = val_mu + mu_plus_lsm + et
val_mu --> mean of time series
mu_plus_lsm --> val_mu + LSM
et --> error term
As you can see, val_mu^2. I did this so the oscillator is much smoother.
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After I get the value, I normalize them:
aco = Y_s? / arm_vald
So by this calculation, I get something like an oscillator!
(more details in the code)
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So how to use this indicator? It's so easy! If the value is above 0, we gonna expect a bullish response, if the value is below 0, we gonna expect a bearish response; that simple. Be aware that you should wait for the price to be closed before executing a trade.
Well, try it out! So far this is the most powerful indicator that I've created, hope it's useful. Ciao.
(more updates for the indicator if needed)
Fed Projected Interest RatesThis script shows you the current interest rates by the FED (see ZQ symbol nearest expiration)
and the next expirations (see ZQ further expiration dates).
It is important to keep your expiration and descriptions up to date, to do that to the indicator inputs and change as you please.
Trendgetter: Trend Detection, Regime Change, Bias Filter by [CR]Trendgetter: Trend Detection, Regime Change, Bias Filter by Cryptorhythms
“If you are not a trend setter, at least be able to exploit the ones you see.”
― Jeffrey Fry
Intro
Cryptorhythms back again with a members only indicator for trend capture this time! Trendgetter is not crypto specific and can be applied to a variety of timeframes, markets, and tickers. Its meant to be a general purpose trading aid and bias filter, providing reliable trend, bias and regime change information.
Introduction
This indicator relies upon various methods related to probabilities/statistics, digital signal processing and data science to predict optimal fair local price given any financial time series data. The goal was to create a tool that isolates trends and captures their bias, making it easier to follow a noisy market. The focus is making high hit rate uncorrelated returns to your base market. The way in which this indicator is constructed is not based upon any previous public work, and was researched and refined over a period of 6 months of trading and testing based on my own personal trading experiences and observations of the market. I use novel techniques I developed in house to denoise the data and determine a local fair price.
Description
The parameters in this indicator are mostly fixed and do not lend themselves to overfitting. So when you find good settings, its probably legit and not a false positive. They were pre-determined based on my own testing and research to handle almost all possible combinations of price action for determining trends. By fixing some parameters, you automatically reduce the chances of overfitting to historical data. The pre determined levels were carefully chosen after many options were considered.
Not just a bias filtooor, fair price predictooor and regime change detectoooor though! TG also provides a price envelope feature which shows a likely fair price range that price will distribute itself upon. Above or below the envelope indicates the presence of a very strong trend . Within the envelope indicates consolidation , but still conforming to the bias. TG then uses a statistics-based approach to display a likely range that price could potentially travel over the near term which we called a price envelope.
An additional option provides background coloration when there is the potential for a regime change on the trend bias. This can be used as a feature to help you manage your trades risk. This is simply measured by an internal (non exposed) script value returning to a mean which triggers the color to appear.
Further Explanation of Settings
-Timeframe : Change the timeframe the indicator is calculated on allowing you to for instance use the 15m Trendgetter output while remaining on the 5 minute chart.
-Trend Capture : This is the "type" of trend you are trying to follow. The different options will attempt to find the trends at various levels of noise cancellation within the lookback period you specify. "Reactive" means it will quickly change its bias and capture smaller trends. "Slow" means it will filter more noise and capture larger trends. "Adaptive" is completely in its own class of behavior and was my attempt to mix both a slow and reactive profile into one setting, it uses a few market metrics like volume and volatility to adjust parameters on the fly.
-Sample Length : Bars to consider in the calculation. Using large numbers here is not going to help, but rather hurt your results. Generally 10-100 is the range you should use for the best results. The exact value will depend on the timeframe, volatility and market/asset you are trading, and you should experiment to find it. (There is no "one size fits all" for potential trading situations)
-Source : Data series used for calculation. I recommend hlcc4 or hl2 or hlc3 instead of just "close." This will help to pre process a noisy data series for the rest of the algo.
-Certainty Level : This setting effects how easily the indicator will confirm a new trend and change its bias. " Reactive" does just as it says and will confirm new regimes faster, but can also lead to false signals or "flip flop" in certain types of price action. "Slow" will change biases less frequently or in conjunction with large moves - but this level of certainty requires the sacrifice of reactivity meaning its a bit laggy (but thats ok when you are following a larger trend). "Medium" is as you would expect the middle ground between reactive and slow. Lastly "Adaptive" tends to fall between reactive and medium in its behavior typically, but it will somewhat adjust itself to suit the variability of market conditions.
-Price Envelope :
-----My own personally created price distribution spread (not monte carlo based)
-----Above or below the envelope indicates the presence of a very strong trend. You should not be fading a trend when its in this position!
-----Within the envelope indicates consolidation, but still conforming to the bias.
User Requests :
Of course we also listen to the needs of our members and added these features upon request.
-Added dark mode and light mode themes.
----Dark Mode is for dark/black charts and uses lighter colorations
----Light mode is for light/white charts and uses darker colorations
-More updates to display and color selection options such as background colors and fill colors.
BB Mod + ForecastThis is a combination of two previous indicators; ALMA stdev band with fibs and Vector MACD.
Bollinger Band Mod fits the standard deviation on both sides of the center moving average ( ALMA +/- stdev / 2 ) and calculates Fibonacci ratios from stdev on both sides.
It is more averaging and more responsive at the same time compared to Bollinger Band.
Forecast is calculated from difference between origin ma ( ALMA from hl2 ) and six different period Hull moving averages averaged together and added to the center ma on both sides.
Fibonacci levels for 0.618 1.618 and 2.618 are added.
The dashed lines point towards the trend. Gives you a better idea of the current trend and momentum in the band.
Faytterro Oscillatorwhat is Faytterro oscillator?
An oscillator that perfectly identifies overbought and oversold zones.
what it does?
this places the price between 0 and 100 perfectly but with a little delay. To eliminate this delay, it predicts the price to come, and the indicator becomes clearer as the probability of its prediction increases.
how it does it?
This indicator is obtained with "faytterro bands", another indicator I designed. For more information about faytterro bands:
A kind of stochastic function is applied to the faytterro bands indicator, and then another transformation formula that I have designed and explained in detail in the link above is applied. These formulas are also applied again to calculate the prediction parts.
how to use it?
Use this indicator to see past overbought and oversold zones and to see future ones.
The input named source is used to change the source of the indicator.
The length serves to change the signal frequency of the indicator.
