Volume SuperTrend AI (Expo)█ Overview
The Volume SuperTrend AI is an advanced technical indicator used to predict trends in price movements by utilizing a combination of traditional SuperTrend calculation and AI techniques, particularly the k-nearest neighbors (KNN) algorithm.
The Volume SuperTrend AI is designed to provide traders with insights into potential market trends, using both volume-weighted moving averages (VWMA) and the k-nearest neighbors (KNN) algorithm. By combining these approaches, the indicator aims to offer more precise predictions of price trends, offering bullish and bearish signals.
█ How It Works
Volume Analysis: By utilizing volume-weighted moving averages (VWMA), the Volume SuperTrend AI emphasizes the importance of trading volume in the trend direction, allowing it to respond more accurately to market dynamics.
Artificial Intelligence Integration - k-Nearest Neighbors (k-NN) Algorithm: The k-NN algorithm is employed to intelligently examine historical data points, measuring distances between current parameters and previous data. The nearest neighbors are utilized to create predictive modeling, thus adapting to intricate market patterns.
█ How to use
Trend Identification
The Volume SuperTrend AI indicator considers not only price movement but also trading volume, introducing an extra dimension to trend analysis. By integrating volume data, the indicator offers a more nuanced and robust understanding of market trends. When trends are supported by high trading volumes, they tend to be more stable and reliable. In practice, a green line displayed beneath the price typically suggests an upward trend, reflecting a bullish market sentiment. Conversely, a red line positioned above the price signals a downward trend, indicative of bearish conditions.
Trend Continuation signals
The AI algorithm is the fundamental component in the coloring of the Volume SuperTrend. This integration serves as a means of predicting the trend while preserving the inherent characteristics of the SuperTrend. By maintaining these essential features, the AI-enhanced Volume SuperTrend allows traders to more accurately identify and capitalize on trend continuation signals.
TrailingStop
The Volume SuperTrend AI indicator serves as a dynamic trailing stop loss, adjusting with both price movement and trading volume. This approach protects profits while allowing the trade room to grow, taking into account volume for a more nuanced response to market changes.
█ Settings
AI Settings:
Neighbors (k):
This setting controls the number of nearest neighbors to consider in the k-Nearest Neighbors (k-NN) algorithm. By adjusting this parameter, you can directly influence the sensitivity of the model to local fluctuations in the data. A lower value of k may lead to predictions that closely follow short-term trends but may be prone to noise. A higher value of k can provide more stable predictions, considering the broader context of market trends, but might lag in responsiveness.
Data (n):
This setting refers to the number of data points to consider in the model. It allows the user to define the size of the dataset that will be analyzed. A larger value of n may provide more comprehensive insights by considering a wider historical context but can increase computational complexity. A smaller value of n focuses on more recent data, possibly providing quicker insights but might overlook longer-term trends.
AI Trend Settings:
Price Trend & Prediction Trend:
These settings allow you to adjust the lengths of the weighted moving averages that are used to calculate both the price trend and the prediction trend. Shorter lengths make the trends more responsive to recent price changes, capturing quick market movements. Longer lengths smooth out the trends, filtering out noise, and highlighting more persistent market directions.
AI Trend Signals:
This toggle option enables or disables the trend signals generated by the AI. Activating this function may assist traders in identifying key trend shifts and opportunities for entry or exit. Disabling it may be preferred when focusing on other aspects of the analysis.
Super Trend Settings:
Length:
This setting determines the length of the SuperTrend, affecting how it reacts to price changes. A shorter length will produce a more sensitive SuperTrend, reacting quickly to price fluctuations. A longer length will create a smoother SuperTrend, reducing false alarms but potentially lagging behind real market changes.
Factor:
This parameter is the multiplier for the Average True Range (ATR) in SuperTrend calculation. By adjusting the factor, you can control the distance of the SuperTrend from the price. A higher factor makes the SuperTrend further from the price, giving more room for price movement but possibly missing shorter-term signals. A lower factor brings the SuperTrend closer to the price, making it more reactive but possibly more prone to false signals.
Moving Average Source:
This setting lets you choose the type of moving average used for the SuperTrend calculation, such as Simple Moving Average (SMA), Exponential Moving Average (EMA), etc.
Different types of moving averages provide various characteristics to the SuperTrend, enabling customization to align with individual trading strategies and market conditions.
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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!
Aisupertrend
SuperTrend AI (Clustering) [LuxAlgo]The SuperTrend AI indicator is a novel take on bridging the gap between the K-means clustering machine learning method & technical indicators. In this case, we apply K-Means clustering to the famous SuperTrend indicator.
🔶 USAGE
Users can interpret the SuperTrend AI trailing stop similarly to the regular SuperTrend indicator. Using higher minimum/maximum factors will return longer-term signals.
The displayed performance metrics displayed on each signal allow for a deeper interpretation of the indicator. Whereas higher values could indicate a higher potential for the market to be heading in the direction of the trend when compared to signals with lower values such as 1 or 0 potentially indicating retracements.
In the image above, we can notice more clear examples of the performance metrics on signals indicating trends, however, these performance metrics cannot perform or predict every signal reliably.
We can see in the image above that the trailing stop and its adaptive moving average can also act as support & resistance. Using higher values of the performance memory setting allows users to obtain a longer-term adaptive moving average of the returned trailing stop.
🔶 DETAILS
🔹 K-Means Clustering
When observing data points within a specific space, we can sometimes observe that some are closer to each other, forming groups, or "Clusters". At first sight, identifying those clusters and finding their associated data points can seem easy but doing so mathematically can be more challenging. This is where cluster analysis comes into play, where we seek to group data points into various clusters such that data points within one cluster are closer to each other. This is a common branch of AI/machine learning.
Various methods exist to find clusters within data, with the one used in this script being K-Means Clustering , a simple iterative unsupervised clustering method that finds a user-set amount of clusters.
A naive form of the K-Means algorithm would perform the following steps in order to find K clusters:
(1) Determine the amount (K) of clusters to detect.
(2) Initiate our K centroids (cluster centers) with random values.
(3) Loop over the data points, and determine which is the closest centroid from each data point, then associate that data point with the centroid.
(4) Update centroids by taking the average of the data points associated with a specific centroid.
Repeat steps 3 to 4 until convergence, that is until the centroids no longer change.
To explain how K-Means works graphically let's take the example of a one-dimensional dataset (which is the dimension used in our script) with two apparent clusters:
This is of course a simple scenario, as K will generally be higher, as well the amount of data points. Do note that this method can be very sensitive to the initialization of the centroids, this is why it is generally run multiple times, keeping the run returning the best centroids.
🔹 Adaptive SuperTrend Factor Using K-Means
The proposed indicator rationale is based on the following hypothesis:
Given multiple instances of an indicator using different settings, the optimal setting choice at time t is given by the best-performing instance with setting s(t) .
Performing the calculation of the indicator using the best setting at time t would return an indicator whose characteristics adapt based on its performance. However, what if the setting of the best-performing instance and second best-performing instance of the indicator have a high degree of disparity without a high difference in performance?
Even though this specific case is rare its however not uncommon to see that performance can be similar for a group of specific settings (this could be observed in a parameter optimization heatmap), then filtering out desirable settings to only use the best-performing one can seem too strict. We can as such reformulate our first hypothesis:
Given multiple instances of an indicator using different settings, an optimal setting choice at time t is given by the average of the best-performing instances with settings s(t) .
Finding this group of best-performing instances could be done using the previously described K-Means clustering method, assuming three groups of interest (K = 3) defined as worst performing, average performing, and best performing.
We first obtain an analog of performance P(t, factor) described as:
P(t, factor) = P(t-1, factor) + α * (∆C(t) × S(t-1, factor) - P(t-1, factor))
where 1 > α > 0, which is the performance memory determining the degree to which older inputs affect the current output. C(t) is the closing price, and S(t, factor) is the SuperTrend signal generating function with multiplicative factor factor .
We run this performance function for multiple factor settings and perform K-Means clustering on the multiple obtained performances to obtain the best-performing cluster. We initiate our centroids using quartiles of the obtained performances for faster centroids convergence.
The average of the factors associated with the best-performing cluster is then used to obtain the final factor setting, which is used to compute the final SuperTrend output.
Do note that we give the liberty for the user to get the final factor from the best, average, or worst cluster for experimental purposes.
🔶 SETTINGS
ATR Length: ATR period used for the calculation of the SuperTrends.
Factor Range: Determine the minimum and maximum factor values for the calculation of the SuperTrends.
Step: Increments of the factor range.
Performance Memory: Determine the degree to which older inputs affect the current output, with higher values returning longer-term performance measurements.
From Cluster: Determine which cluster is used to obtain the final factor.
🔹 Optimization
This group of settings affects the runtime performances of the script.
Maximum Iteration Steps: Maximum number of iterations allowed for finding centroids. Excessively low values can return a better script load time but poor clustering.
Historical Bars Calculation: Calculation window of the script (in bars).
Kioseff Trading - AI-Optimized Supertrend
AI-Optimized Supertrend
Introducing AI-Optimized Supertrend: a streamlined solution for traders of any skill level seeking to rapidly test and optimize Supertrend. Capable of analyzing thousands of strategies, this tool cuts through the complexity to identify the most profitable, reliable, or efficient approaches.
Paired with TradingView's native backtesting capabilities, the AI-Optimized Supertrend learns from historical performance data. Set up is easy for all skill levels, and it makes fine-tuning trading alerts and Supertrend straightforward.
Features
Rapid Supertrend Strategy Testing : Quickly evaluate thousands of Supertrend strategies to find the most effective ones.
AI-Assisted Optimization : Leverage AI recommendations to fine-tune strategies for superior results.
Multi-Objective Optimization : Prioritize Supertrend based on your preference for the highest win rate, maximum profit, or efficiency.
Comprehensive Analytics : The strategy script provides an array of statistics such as profit factor, PnL, win rate, trade counts, max drawdown, and an equity curve to gauge performance accurately.
Alerts Setup : Conveniently set up alerts to be notified about critical trade signals or changes in performance metrics.
Versatile Stop Strategies : Experiment with profit targets, trailing stops, and fixed stop losses.
Binary Supertrend Exploration : Test binary Supertrend strategies.
Limit Orders : Analyze the impact of limit orders on your trading strategy.
Integration with External Indicators : Enhance strategy refinement by incorporating custom or publicly available indicators from TradingView into the optimization process.
Key Settings
The image above shows explanations for a list of key settings for the optimizer.
Set the Factor Range Limits : The AI suggests optimal upper and lower limits for the Factor range, defining the sensitivity of the Supertrend to price fluctuations. A wider range tests a greater variety, while a narrower range focuses on fine-tuning.
Adjust the ATR Range : Use the AI's recommendations to establish the upper and lower bounds for the Average True Range (ATR), which influences the Supertrend's volatility threshold.
ATR Flip : This option lets you interchange the order of ATR and Factor values to quicky test different sequences, giving you the flexibility to explore various combinations and their impact on the Supertrend indicator's performance.
Strategies Evaluated : Adjust this setting to determine how many Supertrend strategies you want to assess and compare.
Enable AI Mode : Turn this feature on to allow the AI to determine and employ the optimal Supertrend strategy with the desired performance metric, such as the highest win rate or maximum profitability.
Target Metric : Adjust this to direct the AI towards optimizing for maximum profit, top win rates, or the most efficient profits.
AI Mode Aggressiveness : Set how assertively the AI pursues the chosen performance goal, such as highest profit or win rate.
Strategy Direction : Choose to focus the AI's testing and optimization on either long or short Supertrend strategies.
Stop Loss Type : Specify the stop loss approach for optimization—fixed value, a trailing stop, or Supertrend direction changes.
Limit Order : Decide if you want to execute trades using limit orders for setting your profit targets, stop losses, or apply them to both.
Profit Target : Define your desired profit level when using either a fixed stop loss or a trailing stop.
Stop Loss : Define your desired stop loss when using either a fixed stop loss or a trailing stop.
How to: Find the best Supertrend for trading
It's important to remember that merely having the AI-Optimized Supertrend on your chart doesn't automatically provide you with the best strategy. You need to follow the AI's guidance through an iterative process to discover the optimal Supertrend settings and strategy.
Optimizing Supertrend involves adjusting two key parameters: the Factor and the Average True Range (ATR). These parameters significantly influence the Supertrend indicator's sensitivity and responsiveness to price movements.
Factor : This parameter multiplies the ATR to determine the distance of the Supertrend line from the price. Higher values will create a wider band, potentially leading to fewer trade signals, while lower values create a narrower band, which may result in more signals but also more noise.
ATR (Average True Range) : ATR measures market volatility. By using the ATR, the Supertrend adapts to changing market volatility; a higher ATR value means a more volatile market, so the Supertrend adjusts accordingly.
During the optimization process, these parameters are systematically varied to determine the combination that yields the best performance based on predefined criteria such as profitability, win rate, or risk management efficiency. The optimization aims to find the optimal Factor and ATR settings.
1.Starting Your Strategy Setup
Begin by deciding your goals for each trade: your profit target and stop loss, or if all trades exit when Supertrend changes direction. You'll also choose how to manage your stops – whether they stay put (fixed) or move with the price (trailing), and whether you want to exit trades at a specific price (limit orders). Keep the initial settings for Supertrend Factor Range and Supertrend ATR Range at their default to give the tool a broad testing field. The AI's guidance will refine these settings to pinpoint the most effective ones through a process of comprehensive testing.
Demonstration Start: We'll begin with the settings outlined in the key settings section, using Supertrend's direction change to the downside as our exit signal for all trades.
2. Continue applying the AI’s suggestions
Keep updating your optimization settings based on the AI's recommendations. Proceed with this iterative optimization until the "Best Found" message is displayed, signaling that the most effective strategy has been identified.
While following the AI's suggestions, we've been prompted with a new suggestion: increase the
number of strategies evaluated. Keep following the AI's new suggestions to evaluate more strategies. Do this until the "Best Found" message shows up.
Success! We continued to follow the AI’s suggestions until “Best Found” was indicated!
AI Mode
AI Mode incorporates Heuristic-Based Adaptive Learning to fine-tune trading strategies in a continuous manner. This feature consists of two main components:
Heuristic-Based Decision Making: The algorithm evaluates multiple Supertrend-based trading strategies using metrics such as Profit and Loss (PNL), Win Rate, and Most Efficient Profit. These metrics act as heuristics to assist the algorithm in identifying suitable strategies for trade execution.
Online Learning: The algorithm updates the performance evaluations of each strategy based on incoming market data. This enables the system to adapt to current market conditions.
Incorporating both heuristic-based decision-making and online learning, this feature aims to provide a framework for trading strategy optimization.
AI Mode Settings
AI Mode Aggressiveness:
Description: The "AI Mode Aggressiveness" setting allows you to fine-tune the AI's trading behavior. This setting ranges from “Low” to “High”, with “High” indicating a more assertive trading approach.
Functionality: This feature filters trading strategies based on a proprietary evaluation method. A higher setting narrows down the strategies that the AI will consider, leaning towards more aggressive trading. Conversely, a lower setting allows for a more conservative approach by broadening the pool of potential strategies.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
