Auto-Adjusting Kalman Filter by TenozenNew year, new indicator! Auto-Adjusting Kalman Filter is an indicator designed to provide an adaptive approach to trend analysis. Using the Kalman Filter (a recursive algorithm used in signal processing), this algo dynamically adjusts to market conditions, offering traders a reliable way to identify trends and manage risk! In other words, it's a remaster of my previous indicator, Kalman Filter by Tenozen.
What's the difference with the previous indicator (Kalman Filter by Tenozen)?
The indicator adjusts its parameters (Q and R) in real-time using the Average True Range (ATR) as a measure of market volatility. This ensures the filter remains responsive during high-volatility periods and smooth during low-volatility conditions, optimizing its performance across different market environments.
The filter resets on a user-defined timeframe, aligning its calculations with dominant trends and reducing sensitivity to short-term noise. This helps maintain consistency with the broader market structure.
A confidence metric, derived from the deviation of price from the Kalman filter line (measured in ATR multiples), is visualized as a heatmap:
Green : Bullish confidence (higher values indicate stronger trends).
Red : Bearish confidence (higher values indicate stronger trends).
Gray : Neutral zone (low confidence, suggesting caution).
This provides a clear, objective measure of trend strength.
How it works?
The Kalman Filter estimates the "true" price by filtering out market noise. It operates in two steps, that is, prediction and update. Prediction is about projection the current state (price) forward. Update is about adjusting the prediction based on the latest price data. The filter's parameters (Q and R) are scaled using normalized ATR, ensuring adaptibility to changing market conditions. So it means that, Q (Process Noise) increases during high volatility, making the filter more responsive to price changes and R (Measurement Noise) increases during low volatility, smoothing out the filter to avoid overreacting to minor fluctuations. Also, the trend confidence is calculated based on the deviation of price from the Kalman filter line, measured in ATR multiples, this provides a quantifiable measure of trend strength, helping traders assess market conditions objectively.
How to use?
Use the Kalman Filter line to identify the prevailing trend direction. Trade in alignment with the filter's slope for higher-probability setups.
Look for pullbacks toward the Kalman Filter line during strong trends (high confidence zones)
Utilize the dynamic stop-loss and take-profit levels to manage risk and lock in profits
Confidence Heatmap provides an objective measure of market sentiment, helping traders avoid low-confidence (neutral) zones and focus on high-probability opportunities
Guess that's it! I hope this indicator helps! Let me know if you guys got some feedback! Ciao!
Quantitative
Z-Score RSI StrategyOverview
The Z-Score RSI Indicator is an experimental take on momentum analysis. By applying the Relative Strength Index (RSI) to a Z-score of price data, it measures how far prices deviate from their mean, scaled by standard deviation. This isn’t your traditional use of RSI, which is typically based on price data alone. Nevertheless, this unconventional approach can yield unique insights into market trends and potential reversals.
Theory and Interpretation
The RSI calculates the balance between average gains and losses over a set period, outputting values from 0 to 100. Typically, people look at the overbought or oversold levels to identify momentum extremes that might be likely to lead to a reversal. However, I’ve often found that RSI can be effective for trend-following when observing the crossover of its moving average with the midline or the crossover of the RSI with its own moving average. These crossovers can provide useful trend signals in various market conditions.
By combining RSI with a Z-score of price, this indicator estimates the relative strength of the price’s distance from its mean. Positive Z-score trends may signal a potential for higher-than-average prices in the near future (scaled by the standard deviation), while negative trends suggest the opposite. Essentially, when the Z-Score RSI indicates a trend, it reflects that the Z-score (the distance between the average and current price) is likely to continue moving in the trend’s direction. Generally, this signals a potential price movement, though it’s important to note that this could also occur if there’s a shift in the mean or standard deviation, rather than a meaningful change in price itself.
While the Z-Score RSI could be an insightful addition to a comprehensive trading system, it should be interpreted carefully. Mean shifts may validate the indicator’s predictions without necessarily indicating any notable price change, meaning it’s best used in tandem with other indicators or strategies.
Recommendations
Before putting this indicator to use, conduct thorough backtesting and avoid overfitting. The added parameters allow fine-tuning to fit various assets, but be careful not to optimize purely for the highest historical returns. Doing so may create an overly tailored strategy that performs well in backtests but fails in live markets. Keep it balanced and look for robust performance across multiple scenarios, as overfitting is likely to lead to disappointing real-world results.
Trend Forecasting - The Quant Science🌏 Trend Forecasting | ENG 🌏
This plug-in acts as a statistical filter, adding new information to your chart that will allow you to quickly verify the direction of a trend and the probability with which the price will be above or below the average in the future, helping you to uncover probable market inefficiencies.
🧠 Model calculation
The model calculates the arithmetic mean in relation to positive and negative events within the available sample for the selected time series. Where a positive event is defined as a closing price greater than the average, and a negative event as a closing price less than the average. Once all events have been calculated, the probabilities are extrapolated by relating each event.
Example
Positive event A: 70
Negative event B: 30
Total events: 100
Probabilities A: (100 / 70) x 100 = 70%
Probabilities B: (100 / 30) x 100 = 30%
Event A has a 70% probability of occurring compared to Event B which has a 30% probability.
🔍 Information Filter
The data on the graph show the future probabilities of prices being above average (default in green) and the probabilities of prices being below average (default in red).
The information that can be quickly retrieved from this indicator is:
1. Trend: Above-average prices together with a constant of data in green greater than 50% + 1 indicate that the observed historical series shows a bullish trend. The probability is correlated proportionally to the value of the data; the higher and increasing the expected value, the greater the observed bullish trend. On the other hand, a below-average price together with a red-coloured data constant show quantitative data regarding the presence of a bearish trend.
2. Future Probability: By analysing the data, it is possible to find the probability with which the price will be above or below the average in the future. In green are classified the probabilities that the price will be higher than the average, in red are classified the probabilities that the price will be lower than the average.
🔫 Operational Filter .
The indicator can be used operationally in the search for investment or trading opportunities given its ability to identify an inefficiency within the observed data sample.
⬆ Bullish forecast
For bullish trades, the inefficiency will appear as a historical series with a bullish trend, with high probability of a bullish trend in the future that is currently below the average.
⬇ Bearish forecast
For short trades, the inefficiency will appear as a historical series with a bearish trend, with a high probability of a bearish trend in the future that is currently above the average.
📚 Settings
Input: via the Input user interface, it is possible to adjust the periods (1 to 500) with which the average is to be calculated. By default the periods are set to 200, which means that the average is calculated by taking the last 200 periods.
Style: via the Style user interface it is possible to adjust the colour and switch a specific output on or off.
🇮🇹Previsione Della Tendenza Futura | ITA 🇮🇹
Questo plug-in funge da filtro statistico, aggiungendo nuove informazioni al tuo grafico che ti permetteranno di verificare rapidamente tendenza di un trend, probabilità con la quale il prezzo si troverà sopra o sotto la media in futuro aiutandoti a scovare probabili inefficienze di mercato.
🧠 Calcolo del modello
Il modello calcola la media aritmetica in relazione con gli eventi positivi e negativi all'intero del campione disponibile per la serie storica selezionata. Dove per evento positivo si intende un prezzo alla chiusura maggiore della media, mentre per evento negativo si intende un prezzo alla chiusura minore della media. Calcolata la totalità degli eventi le probabilità vengono estrapolate rapportando ciascun evento.
Esempio
Evento positivo A: 70
Evento negativo B: 30
Totale eventi : 100
Formula A: (100 / 70) x 100 = 70%
Formula B: (100 / 30) x 100 = 30%
Evento A ha una probabilità del 70% di realizzarsi rispetto all' Evento B che ha una probabilità pari al 30%.
🔍 Filtro informativo
I dati sul grafico mostrano le probabilità future che i prezzi siano sopra la media (di default in verde) e le probabilità che i prezzi siano sotto la media (di default in rosso).
Le informazioni che si possono rapidamente reperire da questo indicatore sono:
1. Trend: I prezzi sopra la media insieme ad una costante di dati in verde maggiori al 50% + 1 indicano che la serie storica osservata presenta un trend rialzista. La probabilità è correlata proporzionalmente al valore del dato; tanto più sarà alto e crescente il valore atteso e maggiore sarà la tendenza rialzista osservata. Viceversa, un prezzo sotto la media insieme ad una costante di dati classificati in colore rosso mostrano dati quantitativi riguardo la presenza di una tendenza ribassista.
2. Probabilità future: analizzando i dati è possibile reperire la probabilità con cui il prezzo si troverà sopra o sotto la media in futuro. In verde vengono classificate le probabilità che il prezzo sarà maggiore alla media, in rosso vengono classificate le probabilità che il prezzo sarà minore della media.
🔫 Filtro operativo
L' indicatore può essere utilizzato a livello operativo nella ricerca di opportunità di investimento o di trading vista la capacità di identificare un inefficienza all'interno del campione di dati osservato.
⬆ Previsione rialzista
Per operatività di tipo rialzista l'inefficienza apparirà come una serie storica a tendenza rialzista, con alte probabilità di tendenza rialzista in futuro che attualmente si trova al di sotto della media.
⬇ Previsione ribassista
Per operatività di tipo short l'inefficienza apparirà come una serie storica a tendenza ribassista, con alte probabilità di tendenza ribassista in futuro che si trova attualmente sopra la media.
📚 Impostazioni
Input: tramite l'interfaccia utente Input è possibile regolare i periodi (da 1 a 500) con cui calcolare la media. Di default i periodi sono impostati sul valore di 200, questo significa che la media viene calcolata prendendo gli ultimi 200 periodi.
Style: tramite l'interfaccia utente Style è possibile regolare il colore e attivare o disattivare un specifico output.
Price Scenarios - The Quant ScienceGENERAL OVERVIEW
Price Scenarios - The Quant Science is a quantitative statistical indicator that provides a forecast probability about future prices moving using the mathematical-statistical formula of statistical probability and expected value.
HOW TO USE
The indicator displays arrow-shaped signals that represent the probable future price movement calculated by the indicator, including the current percentage probability. Additionally, the candlesticks are colored based on the predicted direction to facilitate visual analysis. By default, green is used for bullish movements and red for bearish movements. The trader can set the analysis period (default value is 200) and the percentage threshold of probability to consider (default value is greater than 0.50 or 50%) through the user interface.
USER INTERFACE
Lenght analysis: with this features you can handle the length of the dataset to be used for estimating statistical probabilities.
Expected value: with this feature you can handle the threshold of the expected value to filter, only probabilities greater than this threshold will be considered by the model. By default, it is set to 0.50, which is equivalent to 50%.
Design Settings: modify the colors of your indicator with just a few clicks by managing this function.
We recommend disabling 'Wick' and 'Border' from the settings panel for a smoother and more efficient user experience.
Kalman Filter by TenozenAnother useful indicator is here! Kalman Filter is a quantitative tool created by Rudolf E. Kalman. In the case of trading, it can help smooth out the price data that traders observe, making it easier to identify underlying trends. The Kalman Filter is particularly useful for handling price data that is noisy and unpredictable. As an adaptive-based algorithm, it can easily adjust to new data, which makes it a handy tool for traders operating in markets that are prone to change quickly.
Many people may assume that the Kalman Filter is the same as a Moving Average, but that is not the case. While both tools aim to smooth data and find trends, they serve different purposes and have their own sets of advantages and disadvantages. The Kalman Filter provides a more dynamic and adaptive approach, making it suitable for real-time analysis and predictive capabilities, but it is also more complex. On the other hand, Moving Averages offer a simpler and more intuitive way to visualize trends, which makes them a popular choice among traders for technical analysis. However, the Moving Average is a lagging indicator and less adaptive to market change, if it's adjusted it may result in overfitting. In this case, the Kalman Filter would be a better choice for smoothing the price up.
I hope you find this indicator useful! It's been an exciting and extensive journey since I began diving into the world of finance and trading. I'll keep you all updated on any new indicators I discover that could benefit the community in the future. Until then, take care, and happy trading! Ciao.
Hulk Grid Algorithm V2 - The Quant ScienceIt's the latest proprietary grid algorithm developed by our team. This software represents a clearer and more comprehensive modernization of the deprecated Hulk Grid Algorithm. In this new release, we have optimized the source code architecture and investment logic, which we will describe in detail below.
Overview
Hulk Grid Algorithm V2 is designed to optimize returns in sideways market conditions. In this scenario, the algorithm divides purchases with long orders at each level of the grid. Unlike a typical grid algorithm, this version applies an anti-martingale model to mitigate volatility and optimize the average entry price. Starting from the lower level, the purchase quantity is increased at each new subsequent level until reaching the upper level. The initial quantity of the first order is fixed at 0.50% of the initial capital. With each new order, the initial quantity is multiplied by a value equal to the current grid level (where 1 is the lower level and 10 is the upper level).
Example: Let's say we have an initial capital of $10,000. The initial capital for the first order would be $50 * 1 = $50, for the second order $50 * 2 = $100, for the third order $50 * 3 = $150, and so on until reaching the upper level.
All previously opened orders are closed using a percentage-based stop-loss and take-profit, calculated based on the extremes of the grid.
Set Up
As mentioned earlier, the user's goal is to analyze this strategy in markets with a lack of trend, also known as sideways markets. After identifying a price range within which the asset tends to move, the user can choose to create the grid by placing the starting price at the center of the range. This way, they can consider trading the asset, if the backtesting generates a return greater than the Buy & Hold return.
Grid Configuration
To create the grid, it's sufficient to choose the starting price during the launch phase. This level will be the center of the grid from which the upper and lower levels will be calculated. The grid levels are computed using an arithmetic method, adding and subtracting a configurable fixed amount from the user interface (Grid Step $).
Example: Let's imagine choosing 1000 as the starting price and 50 as the Grid Step ($). The upper levels will be 1000, 1050, 1100, 1150, 1200. The lower levels will be 950, 900, 850, 800, and 750.
Markets
This software can be used in all markets: stocks, indices, commodities, cryptocurrencies, ETFs, Forex, etc.
Application
With this backtesting software, is possible to analyze the strategy and search for markets where it can generate better performance than Buy & Hold returns. There are no alerts or automatic investment mechanisms, and currently, the strategy can only be executed manually.
Design
Is possible to modify the grid style and customize colors by accessing the Properties section of the user interface.
Returns Model by TenozenHey there! I've been diving into the book "Paul Wilmott on Quantitative Finance," and I stumbled upon this cool model for calculating and modeling returns. Basically, it helps us figure out how much a price has changed over a set number of periods—I like to use 20 periods as a default. Once we get that rate of change value, we crunch some numbers to find the standard deviation and mean using all the historical data we have. That's the foundation of this model.
Now, let's talk about how to use it. This model shows us how returns and price behavior are connected. When returns hang out in the +1 to +2 standard deviation range, it usually means returns are about to drop, and vice versa. Often, this leads to corresponding price moves. But here's the thing: sometimes prices don't do what we expect. Why? It's because there's another hidden factor at play—I like to call it "power."
This "power" isn't something we can see directly, but it's there. Basically, when returns are within that standard deviation range, the market faces resistance when trying to move in its preferred direction, whether bullish or bearish. The strength of this "power" determines if the market will snap back to the average or go for a wild ride. It can show up as small price wiggles, big price jumps, or lightning-fast moves. By understanding this "power," we can get a better handle on what the market might do next and avoid getting blindsided. In the meantime, I couldn't explain "power" yet, but In the future, when I've learned enough, I'd love to share the model with you guys!
So... I'm planning to explore and share more models from this book as I learn, even if those pesky math formulas can be tough to crack. I hope you find this indicator as helpful as I do, and if you've got any suggestions or feedback, please feel free to share! Ciao!
Ratio To Average - The Quant ScienceRatio To Average - The Quant Science is a quantitative indicator that calculates the percentage ratio of the market price in relation to a reference average. The indicator allows the calculation of the ratio using four different types of averages: SMA, EMA, WMA, and HMA. The ratio is represented by a series of histograms that highlight periods when the ratio is positive (in green) and periods when the ratio is negative (in red).
What is the Ratio to Average?
The Ratio to Average is a measure that tracks the price movements with one of its averages, calculating how much the price is above or below its own average, in percentage terms.
USER INTERFACE
Lenght: it adjusts the number of bars to include in the calculation of the average.
Moving Average: it allows you to choose the type of average to use.
Color Up/Color Down : it allows you to choose the color of the indicator for positive and negative ratios.
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)
Premium Linear Regression - The Quant ScienceThis script calculates the average deviation of the source data from the linear regression. When used with the indicator, it can plot the data line and display various pieces of information, including the maximum average dispersion around the linear regression.
The code includes various user configurations, allowing for the specification of the start and end dates of the period for which to calculate linear regression, the length of the period to use for the calculation, and the data source to use.
The indicator is designed for multi-timeframe use and to facilitate analysis for traders who use regression models in their analysis. It displays a green linear regression line when the price is above the line and a red line when the price is below. The indicator also highlights areas of dispersion around the regression using circles, with bullish areas shown in green and bearish areas shown in red.
Quantitative Price Forecasting - The Quant ScienceThis script is a quantitative price forecasting indicator that forecasts price changes for a given asset.
The model aims to forecast future prices by analyzing past data within a selected time period. Mathematical probability is used to calculate whether starting from time X can lead to reaching prices Y1 and Y2. In this context, X represents the current selected time period, Y1 represents the selected percentage decrease, and Y2 represents the selected percentage increase. The probabilities are estimated using the simple average.
The simple average is displayed on the chart, showing in red the periods where the price is below the average and in green the periods where the price is above the average.
This powerful tool not only provides forecasts of future prices but also calculates the distribution of variations around the average. It then takes this information and creates an estimate of the average price variation around the simple average.
Using a mean-reverting logic, buying and selling opportunities are highlighted.
We recommend turning off the display of bars on your chart for a better experience when using this indicator.
Unlock the full potential of your trading strategy with our powerful indicator. By analyzing past price data, it provides accurate forecasts and calculates the probability of reaching specific price targets. Its mean-reverting logic highlights buying and selling opportunities, while the simple moving average displayed on the chart shows periods where the price is above or below the average. Additionally, it estimates the average variation of price around the simple average, giving you valuable insights into price movements. Don't miss out on this valuable tool that can take your trading to the next level
Probabilities Module - The Quant Science This module can be integrate in your code strategy or indicator and will help you to calculate the percentage probability on specific event inside your strategy. The main goal is improve and simplify the workflow if you are trying to build a quantitative strategy or indicator based on statistics or reinforcement model.
Logic
The script made a simulation inside your code based on a single event. For single event mean a trading logic composed by three different objects: entry, take profit, stop loss.
The script scrape in the past through a look back function and return the positive percentage probability about the positive event inside the data sample. In this way you are able to understand and calculate how many time (in percentage term) the conditions inside the single event are positive, helping to create your statistical edge.
You can adjust the look back period in you user interface.
How can set up the module for your use case
At the top of the script you can find:
1. entry_condition : replace the default condition with your specific entry condition.
2. TPcondition_exit : replace the default condition with your specific take profit condition.
3. SLcondition_exit : replace the default condition with your specific stop loss condition.
Volume Variation Index IndicatorThis tool is a quantitative tip for analysts who study volumes or create volume based trading strategies.
Like all our projects, we start with a statistical logic to which we add coding logic.
This indicator can save a huge amount of time in calculating the variation of volume between sessions .
How it work
The indicator calculates the difference between the volume of the last closing bar and the volume of the previous closing bar. It shows the difference between the trading volumes.
The session in which the trading volume is up are represented in green.
Red session represent trading volume down.
We have added a third function.
Through the User Interface the trader can activate or deactivate the variation average.
The indicator is able to calculate the average of the volume changes by representing it with a blue line.
To activate the average, simply set it to ON in the User Interface.
By default, the indicator calculates the average of the last 10 periods, but you are free to set this parameter in the User Interface.
Data access
To access the data, simply move the cursor. When you move the cursor over the green bars, the increase data will be displayed in green. By hovering the cursor over the red bars you will see the decrease data in red. By hovering the cursor over the average will show you the average data in blue.
The data is displayed in the top left corner of the indicator dashboard.
If you found this indicator helpful, please like our script.
Prime Distance Frame Quant Model for Risk Reward & Pivot PointsIn this script we take all of the prime numbers up to 100 and plot them as olive lines and then consider the distance between two adjacent plots and color code these distances with the fill function. This allows us to find higher and lower prime gaps allowing us to make much more informed decisions on our risk reward for a given trade and the levels where we should consider taking profit.
The Script includes scaling for all assets and is intended to be used for crypto trading.
ALMA PhysicsA super simple yet elegant indicator, "ALMA Physics" calculates the derivatives of the Arnaud Legoux Moving Average (ALMA) with respect to Time. Both the ALMA parameters and the time variable can be modified in the indicator's settings.
derivatives "physics":
Blue - ALMA Velocity (dALMA / dt)
Magenta - ALMA Acceleration (d_Velocity / dt)
White - ALMA Jerk (d_Acceleration / dt)
The indicator may be used to gauge market momentum. The simple code can also be used for pedagogical purposes for anyone interested in developing indicators in TradingView.
Mathematical Derivatives of PriceThis indicator is meant to show the Velocity (1st order derivative), Acceleration (2nd order derivative), Jerk (3rd order derivative), Snap (4th order derivative), Crackle (5th order derivative), & Pop (6th order derivative) of price. The values at the top of the indicator window are in this order from left to right. I don't particularly know how this would be used in a trading strategy, but if you're ever curious about how quickly price is moving and how much it is accelerating, then you could use this tool.
*If you only care about velocity and acceleration, and don't like how squished the window is because of the long decimal numbers then edit the "precision" value in the first line of the script to a smaller number of your choosing.*
DVDIQQE [DW]This is an experimental study inspired by the Quantitative Qualitative Estimation indicator designed to identify trend and wave activity.
In this study, rather than using RSI for the calculation, the Dual Volume Divergence Index oscillator is utilized.
First, the DVDI oscillator is calculated by taking the difference between PVI and its EMA, and NVI and its EMA, then taking the difference between the two results.
Optional parameters for DVDI calculation are included within this script:
- An option to use tick volume rather than real volume for the volume source
- An option to use cumulative data, which sums the movements of the oscillator from the beginning to the end of TradingView's maximum window to give a more broad picture of market sentiment
Next, two trailing levels are calculated using the average true range of the oscillator. The levels are then used to determine wave direction.
Lastly, rather than using 0 as the center line, it is instead calculated by taking a cumulative average of the oscillator.
Custom bar colors are included.
Note: For charts that have no real volume component, use tick volume as the volume source.
