Helacator Ai ThetaHelacator Ai Theta is a state-of-the-art advanced script. It helps the trader find the possibility of a trend reversal in the market. By finding that point at which the three black crows pattern combines with the three white soldiers pattern, it is the most cherished pattern in technical analysis for its signal of strong bullish or bearish momentum. Therefore, it is a very strong predictive tool in the ability of shifting markets.
Key Highlights: Three White Soldiers and Three Black Crows Patterns
The script identifies these candlestick formations that consist of three consecutive candles, either bullish (Three White Soldiers) or bearish (Three Black Crows). These patterns help the trader identify possible trend reversal points as they provide an early signal of a change in the market direction. It is with great care that the script is written to evaluate the position and relationship between the candlesticks for maintaining the accuracy of pattern recognition. Moving Averages for Trend Filtering:
Two important ones used are moving averages for filtering any signals not in accordance with the general trend. The length of these MAs is variable, allowing the traders to be in a position to adapt the script for use under different market conditions. The moving averages ensure that signals are only taken in the direction that supports the general market flow, so it leads to more reliability within the signals. The MAs are not plotted on the chart for the sake of clarity, but they still perform a crucial function in signal filtering and can be displayed optionally for a more detailed investigation. Cooldown filter to reduce over-trading
This is part of what is implemented in the script to prevent generation of consecutive signals too quickly. All this helps to reduce market noise and not overtrade—only when market conditions are at their best. The cooldown period can be set to be adjusted according to the trader's preference, making the script more versatile in its use. Practical Considerations: Educational Purpose: This script is for educational purposes only and should be part of a comprehensive trading approach. Proper risk management techniques should be observed while at the same time taking into consideration prevailing market conditions before making any trading decision.
No Guaranteed Results: The script is aimed at bringing signal accuracy into improvement to align with the broader market trend and reducing noise, but past performance cannot guarantee future success. Traders should use this script within their broad trading approach. Clean and Simple Chart Display: The primary goal of this script is to have a clear and simple display on the chart. The signals are prominently marked with "BUY" and "SELL," and the color of the bars has changed according to the last signal, thus traders can easily read the output. Community and Open Source Open Source Contribution: This script is open for contribution by the TradingView community. Any suggestions regarding improvements are highly welcomed. Candlestick patterns, moving averages, and the combination of the cooldown filter are presented in such a way as to give traders something special, and any modifications or extra touch by the community is appreciated. Attribution and Transparency: The script is based on standard technical analysis principles and for all parts inspired by or derivated from other available open-source scripts, credit is given where it is due. In this way, transparency ensures that the script adheres to TradingView's standards and promotes a collaborative community environment.
在脚本中搜索"ai"
Edge AI Forecast [Edge Terminal]This indicator inputs the previous 150 closing prices in a simple two-layer neural network, normalizes the network inputs using a sigmoid function, uses a feedforward calculation to send it to the second layer, shows the MSE loss curve and uses both automatic and manual backpropagation (user input) to find the most likely forecast values and uses the analog forecasting algorithm to adjust and optimize the data furthermore to display potential prices on the chart.
Here's how it works:
The idea behind this script is to train a simple neural network to predict the future x values based on the sample data. For this, we use 2 types of data, Price and Volume.
The thinking behind this is that price alone can’t be used in this case because it doesn’t provide enough meaningful pattern data for the network but price and volume together can change the game. We’re planning to use more different data sets and expand on this in the future.
To avoid a bad mix of results, we technically have two neural networks, each processing a different data type, one for volume data and one for price data.
The actual prediction is decided by the way price and volume of the closing price relate to each other. Basically, the network passes the price and volume and finds the best relation between the two data set outputs and predicts where the price could be based on the upcoming volume of the latest candle.
The network adjusts the weights and biases using optimization algorithms like gradient descent to minimize the difference between the predicted and actual stock prices, typically measured by a loss function, (in this case, mean squared error) which you can see using the error rate bubble.
This is a good measure to see how well the network is performing and the idea is to adjust the settings inputs such as learning rate, epochs and data source to get the lowest possible error rate. That’s when you’re getting the most accurate prediction results.
For each data set, we use a multi-layer network. In a multi-layer neural network, the outputs of neurons in one layer serve as inputs to neurons in the next layer. Initially, the input layer of the neural network receives the historical data. Each input neuron represents a feature, such as previous stock prices and trading volumes over a specific period.
The hidden layers perform feature extraction and transformation through a series of weighted connections and activation functions. Each neuron in a hidden layer computes a weighted sum of the inputs from the previous layer, applies an activation function to the sum, and passes the result to the next layer using the feedforward (activation) function.
For extraction, we use a normalization function. This function takes a value or data (such as bar price) and divides it up by max scale which is the highest possible value of the bar. The idea is to take a normalized number, which is either below 1 or under 2 for simple use in the neural network layers.
For the activation, after computing the weighted sum, the neuron applies an activation function a(x). To introduce non-linearity into the model to pass it to the next layer. We use sigmoid activation functions in this case. The main reason we use sigmoid function is because the resulting number is between 0 to 1 and is better for models where we have to predict the probability as an output.
The final output of the network is passed as an input to the analog forecasting function. This is an algorithm commonly used in weather prediction systems. In this case, this is used to make predictions by comparing current values and assuming the patterns might repeat in the future.
There are many different ways to build an analog forecasting function but in our case, we’re used similarity measurement model:
X, as the current situation or set of current variables.
Y, as the outcome or variable of interest.
Si as the historical situations or patterns, where i ranges from 1 to n.
Vi as the vector of variables describing historical situation Si.
Oi as the outcome associated with historical situation Si.
First, we define a similarity measure sim(X,Vi) that quantifies the similarity between the current situation X and historical situation Si based on their respective variables Vi.
Then we select the K most similar historical situations (KNN Machine learning) based on the similarity measure sim(X,Vi). We denote the rest of the selected historical situations as {Si1, Si2,...Sik).
Then we examine the outcomes associated with the selected historical situations {Oi1, Oi2,...,Oik}.
Then we use the outcomes of the selected historical situations to forecast the future outcome Y^ using weighted averaging.
Finally, the output value of the analog forecasting is standardized using a standardization function which is the opposite of the normalization function. This function takes a normalized number and turns it back to its original value by multiplying it by the max scale (highest value of the bar). This function is used when the final number is produced by the network output at the end of the analog forecasting to turn the final value back into a price so it can be displayed on the chart with PineScript.
Settings:
Data source: Source of the neural network's input data.
Sample Bars: How many historical bars do you want to input into the neural network
Prediction Bars: How many bars you want the script to forecast
Show Training Rate: This shows the neural network's error rate for the optimization phase
Learning Rate: how many times you want the script to change the model in response to the estimated error (automatic)
Epochs: the network cycle or how many times you want to run the data through the network from the first layer to the last one.
Usage:
The sample bars input determines the number of historical bars to be used as a reference for the network. You need to change the Epochs and Learning Rate inputs for each asset and chart timeframe to get the lowest error rate.
On the surface, the highest possible epoch and learning rate should produce the most effective results but that's not always the case.
If the epochs rate is too high, there is a chance we face overfitting. Essentially, you might be over processing good data which can make it useless.
On the other hand, if the learning rate is too high, the network may overshoot the optimal solution and diverge. This is almost like the same issue I mentioned above with a high epoch rate.
Access:
It took over 4 months to develop this script and we’re constantly improving it so it took a lot of manpower to develop this script. Also when it comes to neural networks, Pine Script isn’t the most optimal language to build a neural network in, so we had to resort to a few proprietary mathematical formulas to ensure this runs smoothly without giving out an error for overprocessing, specially when you have multiple neural networks with many layers.
The optimization done to make this script run on Pine Script is basically state of the art and because of this, we would like to keep the code closed source at the moment.
On the other hand we don’t want to publish the code publicly as we want to keep the trading edge this script gives us in a closed loop, for our own small group of members so we have to keep the code closed. We only accept invites from expert traders who understand how this script and algo trading works and the type of edge it provides.
Additionally, at the moment we don’t want to share the code as some of the parts of this network, specifically the way we hand the data from neural network output into the analog method formula are proprietary code and we’d like to keep it that way.
You can contact us for access and if we believe this works for your trading case, we will provide you with access.
Optimal Moving Average (AI/ML) [wbburgin]Some traders swear by the 200-period moving average. Others, by the 100-period. Others, the 14-period. It depends on your asset, your timeframe, the trend…
The fact of the matter is that no moving average will ever be a consistent indicator for a serious trader - a fixed-length moving average will always need confirmation indicators and tests. When your instrument is trending, you need a faster moving average to better fit the data; when your instrument is ranging, you need a slower moving average that cleans the data. This just is not possible given the way the moving average is traditionally coded, which makes it a lagging indicator.
Thus we need a moving average that:
can project the next prices, and
can change its length depending on what best fits these future prices.
The Optimal Moving Average selects the optimal moving average length for a projected future price. The algorithm classifies moving averages by their effectiveness in predicting future price movement. If a moving average of length n has historically been accurate in predicting the next bar, the moving average will be tested compared to its peers ( n -1, n +5, n -100, etc.) and promoted or demoted depending on its effectiveness. This means that the indicator will not have a length input like other static moving averages or machine-learning moving averages on TradingView- it will select the ideal length for your chart from the average that has the least error and best prediction.
Advantages over other ML Moving Averages on TradingView
The vast majority of AI/ML moving average algorithms classify their moving averages only by if the average is above or below the current price.
This approach is inherently flawed because the model
Is not predictive of future prices (the structural lagging problem still exists),
Is not built on a variable-length MA (cannot select alternating lengths depending on the bar), and
does not classify the scale of difference between the MA and the price.
This indicator solves all those problems. It classifies moving averages by the scale of which their rate predicts the next price. Thus it is quick to catch trend changes but also acts as support or resistance, and models the projected price more accurately than a traditional moving average.
Support & Resistance AI (K means/median) [ThinkLogicAI]█ OVERVIEW
K-means is a clustering algorithm commonly used in machine learning to group data points into distinct clusters based on their similarities. While K-means is not typically used directly for identifying support and resistance levels in financial markets, it can serve as a tool in a broader analysis approach.
Support and resistance levels are price levels in financial markets where the price tends to react or reverse. Support is a level where the price tends to stop falling and might start to rise, while resistance is a level where the price tends to stop rising and might start to fall. Traders and analysts often look for these levels as they can provide insights into potential price movements and trading opportunities.
█ BACKGROUND
The K-means algorithm has been around since the late 1950s, making it more than six decades old. The algorithm was introduced by Stuart Lloyd in his 1957 research paper "Least squares quantization in PCM" for telecommunications applications. However, it wasn't widely known or recognized until James MacQueen's 1967 paper "Some Methods for Classification and Analysis of Multivariate Observations," where he formalized the algorithm and referred to it as the "K-means" clustering method.
So, while K-means has been around for a considerable amount of time, it continues to be a widely used and influential algorithm in the fields of machine learning, data analysis, and pattern recognition due to its simplicity and effectiveness in clustering tasks.
█ COMPARE AND CONTRAST SUPPORT AND RESISTANCE METHODS
1) K-means Approach:
Cluster Formation: After applying the K-means algorithm to historical price change data and visualizing the resulting clusters, traders can identify distinct regions on the price chart where clusters are formed. Each cluster represents a group of similar price change patterns.
Cluster Analysis: Analyze the clusters to identify areas where clusters tend to form. These areas might correspond to regions of price behavior that repeat over time and could be indicative of support and resistance levels.
Potential Support and Resistance Levels: Based on the identified areas of cluster formation, traders can consider these regions as potential support and resistance levels. A cluster forming at a specific price level could suggest that this level has been historically significant, causing similar price behavior in the past.
Cluster Standard Deviation: In addition to looking at the means (centroids) of the clusters, traders can also calculate the standard deviation of price changes within each cluster. Standard deviation is a measure of the dispersion or volatility of data points around the mean. A higher standard deviation indicates greater price volatility within a cluster.
Low Standard Deviation: If a cluster has a low standard deviation, it suggests that prices within that cluster are relatively stable and less likely to exhibit sudden and large price movements. Traders might consider placing tighter stop-loss orders for trades within these clusters.
High Standard Deviation: Conversely, if a cluster has a high standard deviation, it indicates greater price volatility within that cluster. Traders might opt for wider stop-loss orders to allow for potential price fluctuations without getting stopped out prematurely.
Cluster Density: Each data point is assigned to a cluster so a cluster that is more dense will act more like gravity and
2) Traditional Approach:
Trendlines: Draw trendlines connecting significant highs or lows on a price chart to identify potential support and resistance levels.
Chart Patterns: Identify chart patterns like double tops, double bottoms, head and shoulders, and triangles that often indicate potential reversal points.
Moving Averages: Use moving averages to identify levels where the price might find support or resistance based on the average price over a specific period.
Psychological Levels: Identify round numbers or levels that traders often pay attention to, which can act as support and resistance.
Previous Highs and Lows: Identify significant previous price highs and lows that might act as support or resistance.
The key difference lies in the approach and the foundation of these methods. Traditional methods are based on well-established principles of technical analysis and market psychology, while the K-means approach involves clustering price behavior without necessarily incorporating market sentiment or specific price patterns.
It's important to note that while the K-means approach might provide an interesting way to analyze price data, it should be used cautiously and in conjunction with other traditional methods. Financial markets are influenced by a wide range of factors beyond just price behavior, and the effectiveness of any method for identifying support and resistance levels should be thoroughly tested and validated. Additionally, developments in trading strategies and analysis techniques could have occurred since my last update.
█ K MEANS ALGORITHM
The algorithm for K means is as follows:
Initialize cluster centers
assign data to clusters based on minimum distance
calculate cluster center by taking the average or median of the clusters
repeat steps 1-3 until cluster centers stop moving
█ LIMITATIONS OF K MEANS
There are 3 main limitations of this algorithm:
Sensitive to Initializations: K-means is sensitive to the initial placement of centroids. Different initializations can lead to different cluster assignments and final results.
Assumption of Equal Sizes and Variances: K-means assumes that clusters have roughly equal sizes and spherical shapes. This may not hold true for all types of data. It can struggle with identifying clusters with uneven densities, sizes, or shapes.
Impact of Outliers: K-means is sensitive to outliers, as a single outlier can significantly affect the position of cluster centroids. Outliers can lead to the creation of spurious clusters or distortion of the true cluster structure.
█ LIMITATIONS IN APPLICATION OF K MEANS IN TRADING
Trading data often exhibits characteristics that can pose challenges when applying indicators and analysis techniques. Here's how the limitations of outliers, varying scales, and unequal variance can impact the use of indicators in trading:
Outliers are data points that significantly deviate from the rest of the dataset. In trading, outliers can represent extreme price movements caused by rare events, news, or market anomalies. Outliers can have a significant impact on trading indicators and analyses:
Indicator Distortion: Outliers can skew the calculations of indicators, leading to misleading signals. For instance, a single extreme price spike could cause indicators like moving averages or RSI (Relative Strength Index) to give false signals.
Risk Management: Outliers can lead to overly aggressive trading decisions if not properly accounted for. Ignoring outliers might result in unexpected losses or missed opportunities to adjust trading strategies.
Different Scales: Trading data often includes multiple indicators with varying units and scales. For example, prices are typically in dollars, volume in units traded, and oscillators have their own scale. Mixing indicators with different scales can complicate analysis:
Normalization: Indicators on different scales need to be normalized or standardized to ensure they contribute equally to the analysis. Failure to do so can lead to one indicator dominating the analysis due to its larger magnitude.
Comparability: Without normalization, it's challenging to directly compare the significance of indicators. Some indicators might have a larger numerical range and could overshadow others.
Unequal Variance: Unequal variance in trading data refers to the fact that some indicators might exhibit higher volatility than others. This can impact the interpretation of signals and the performance of trading strategies:
Volatility Adjustment: When combining indicators with varying volatility, it's essential to adjust for their relative volatilities. Failure to do so might lead to overemphasizing or underestimating the importance of certain indicators in the trading strategy.
Risk Assessment: Unequal variance can impact risk assessment. Indicators with higher volatility might lead to riskier trading decisions if not properly taken into account.
█ APPLICATION OF THIS INDICATOR
This indicator can be used in 2 ways:
1) Make a directional trade:
If a trader thinks price will go higher or lower and price is within a cluster zone, The trader can take a position and place a stop on the 1 sd band around the cluster. As one can see below, the trader can go long the green arrow and place a stop on the one standard deviation mark for that cluster below it at the red arrow. using this we can calculate a risk to reward ratio.
Calculating risk to reward: targeting a risk reward ratio of 2:1, the trader could clearly make that given that the next resistance area above that in the orange cluster exceeds this risk reward ratio.
2) Take a reversal Trade:
We can use cluster centers (support and resistance levels) to go in the opposite direction that price is currently moving in hopes of price forming a pivot and reversing off this level.
Similar to the directional trade, we can use the standard deviation of the cluster to place a stop just in case we are wrong.
In this example below we can see that shorting on the red arrow and placing a stop at the one standard deviation above this cluster would give us a profitable trade with minimal risk.
Using the cluster density table in the upper right informs the trader just how dense the cluster is. Higher density clusters will give a higher likelihood of a pivot forming at these levels and price being rejected and switching direction with a larger move.
█ FEATURES & SETTINGS
General Settings:
Number of clusters: The user can select from 3 to five clusters. A good rule of thumb is that if you are trading intraday, less is more (Think 3 rather than 5). For daily 4 to 5 clusters is good.
Cluster Method: To get around the outlier limitation of k means clustering, The median was added. This gives the user the ability to choose either k means or k median clustering. K means is the preferred method if the user things there are no large outliers, and if there appears to be large outliers or it is assumed there are then K medians is preferred.
Bars back To train on: This will be the amount of bars to include in the clustering. This number is important so that the user includes bars that are recent but not so far back that they are out of the scope of where price can be. For example the last 2 years we have been in a range on the sp500 so 505 days in this setting would be more relevant than say looking back 5 years ago because price would have to move far to get there.
Show SD Bands: Select this to show the 1 standard deviation bands around the support and resistance level or unselect this to just show the support and resistance level by itself.
Features:
Besides the support and resistance levels and standard deviation bands, this indicator gives a table in the upper right hand corner to show the density of each cluster (support and resistance level) and is color coded to the cluster line on the chart. Higher density clusters mean price has been there previously more than lower density clusters and could mean a higher likelihood of a reversal when price reaches these areas.
█ WORKS CITED
Victor Sim, "Using K-means Clustering to Create Support and Resistance", 2020, towardsdatascience.com
Chris Piech, "K means", stanford.edu
█ ACKNOLWEDGMENTS
@jdehorty- Thanks for the publish template. It made organizing my thoughts and work alot easier.
TCG AI ToolsIntroduction:
This script is a result of an AI recommended created trading strategy that is design to offer new traders’ easy access to trend information and oversold/overbought conditions. Here we have combined commonly used indicators into a single unique visualization that quickly identifies trend changes and both RSI and Bollinger Band based overbought and oversold conditions, and allows all three indicators to be used simultaneously while taking up limited space on the chart.
The value in combining these three indicators is found in the harmony and clarity they are able to provide new traders. Trend changes can be difficult to identify based solely on candlestick analysis, therefore using the moving averages allows the trader to simplify the process of establishing bullish or bearish trends. Once a trend is established it can be very attractive for new traders to establish entries at the wrong time. For this reason, it is useful to include two different overbought and oversold indicators. The Bollinger Bands are included as one of the methods for establishing extreme prices that often result in reversals, and the relative strength index is similarly utilized as a second means to warn traders of extreme conditions.
Using the Indicator
1. MA10 MA20 Trend Indicator
The large red/green horizontal bar located at the 0 line on the X axis is the trend direction indicator. This visualization compares the 10 and 20 period moving averages to establish trend. When the MA10 is above the MA20 the trend is considered bullish and supportive of long positions and indicates such by changing the color of the horizontal bar to green. When the MA10 is below MA20 the trend is considered bearish and indicates such by changing the color of the horizontal bar to red. Color changes occur at the moment of a MA crossover/under.
2. Relative Strength Index.
The vertical red and green bars that make up the background of the panel indicate conditions wherein the RSI is considered overbought or oversold. When the vertical bar is red it indicates that RSI is below 30 suggesting that current conditions are oversold and supportive of long entries. When the vertical bar is green it suggests that the current conditions are overbought and are supportive of short entries.
3. Bollinger Band Extremes
Within the horizontal red/green bar there are red and green arrows. These arrows represent periods where the price is exceeding the upper or lower Bollinger bands and indicate overbought/oversold conditions. When a green arrow appears, it indicates that the price has crossed below the lower BB and is supportive of long entries. If a red arrow appears it indicates that the price has crossed above the upper Bollinger band and conditions are supportive of short entries.
Universal Moving Average Convergence DivergenceI changed MACD formula to divergence of (MA26/MA12 - 1).
And its make it more useful.
Cuz:
1) comparability with all other coins with different prices.
2) fix small numbers in low price coines like shiba
3) making a good indicator like RSI to use it for optimization and ML/AI projects as a variable
Most important thing about this indicator is that its Universal
Now you can compare the UMACD of Shiba with Bitcoin without any problem in matamatics space.No need to use virtuality and its important in Optimization problems that we rediuse the problem from a picture to a number(A plot to a list of numbers)
If we don't care about exagrated pumps and dumps, we can say to it Normalized-MACD too. Cuz in normal situations its MAX ≈ 0.1 and MIN ≈ -0.1
MoonFlag DailyThis is a useful indicator as it shows potential long and short regions by coloring the AI wavecloud green or red.
There is an option to show a faint white background in regions where the green/red cloud parts are failing as a trade from the start position of each region.
Its a combination of 3 algos I developed, and there is an option to switch to see these individually, although this has lots of info and is a bit confusing.
It does have alerts and there are text boxes in the indicator settings where a comment can be input - this is useful for webhooks bots auto trading.
Most useful in this indicator is that at the end of each green/long or red/short region there is a label that shows the % gain or loss for a trade.
The label at the end of the chart shows the % of winning longs/shorts and the average % gain or loss for all the longs/shorts within the set test period (set in settings)
So, I generally set the chart initially on a 15min timeframe with the indicator timeframe (in settings) set to run on say 30min or 1hour. I then select a long test period (several plus months) and then optimize the wavelcloud length (in settings) to give the best %profit per trade. (Longs always seem to give better results than shorts)
I then, change the chart timeframe to much faster, say 1min or 5min, but leave the indicator timeframe at 1 hour. In this manner - the label only shows a few trades however, the algo is run at every bar close and when this is set to 1min, this means that losses will be minimised at the bot exits quickly. In comparison - if the chart is on a 15min timeframe - it can take this amount before the bot will exit a trade and by then there could be catastrophic losses.
It is quite hard to get a positive result - although with a bit of playing around - just as a background indicator - I find this useful. I generally set-up on say 4charts all with different timeframes and then look for consistency between the long/short signal positions. (Although when I run as a bot I use a fast timeframe)
Please do leave some comments and get in touch.
MoonFlag (Josef Tainsh PhD)
EPS AIThis indicator can be accessed by ANYONE by searching in the public indicator library located at the top of your chart!
Enjoy!
Introduction
This indicator uses machine learning to predict the next Earnings Per Share (EPS) figure.
The algorithm learns from previous figures in order to more accurately predict the next.
As time continues, this indicator will become more accurate as it learns from an increased amount of data from earnings results.
When the Future Projected EPS is positive, the line will appear green . When the Future Projected EPS is negative, the line will appear as red and sit below the EPS.
Settings Panel
The settings panel contains two tick-boxes.
Quarterly Earnings : When selected, the EPS and future projected EPS will utilise quarterly results. Yearly results are used by default.
Diluted EPS : When selected, the Diluted EPS and future projected Diluted EPS will be utilised. Basic EPS is used by default.
Indicator Utility
The EPS AI can be utilised on every securities instrument and time-frame.
This indicator has been built in Pinescript V4 and will operate in real-time.
This indicator can be accessed by ANYONE by searching in the public indicator library located at the top of your chart!
Enjoy!
Alcides Indicator(AI) LiteAlcides Indicator (AI) Lite is a simple to use indicator that can be used with any type of asset, trading in any market including FOREX, Stocks, Commodities, Cryptocurrencies etc. The Lite version uses levels from either 1 hr or 4 hr time frame based on user input to indicate entry (BUY) into or exit (SELL) from an asset. The indicator also plots support for BUYs and Resistance for SELLs which can be used as a reference while setting your Stop Loss. BUY, SELL and TAKE GAINS alerts can be set on trading view to help monitor the asset as well.
Even though the indicator signals BUYs and SELLs based on chosen Time Frame levels, the user must always use their discretion based on their TA and FA. Also, indicator repainting can occur based on time of signal/chart used (ex. 5m chart on 1 hr timeframe levels can repaint a BUY/SELL after 1 hr closes).
Works best with Heikin Ashi candles and lower timeframes like 5m, 15m, 30m.
The full version has more time frame levels to choose from, a few extra useful features and also recommends sell and buy levels based on the chosen time from.
Contact me for access and more information.
ANB AI Alert (my ANN)Hi guy
This is a high level trend predicting study. It is modified from the strategy by sirlof.
Feel free to use it as you like.
::USAGE only on 15 minutes
1. add the study in your chart
2. create an alert on the right
3. select ANB AI Alert (my ANN)(0,1D)
4. select the option you wish
5. select once per bar close alert
6. you can select email alert which i usually like
7. once the trade is alerted, execute your trade
TP: DYNAMIC (read more)
SL: null
Setting TP and SL: this is in consideration with the daily volatility and sessions
USDCAD TP 400 points, no stop loss.
To maximize profit, use trailing stops. most trades are 500 to 1800 points
Intelligent Volume-weighted Moving Average (AI)Introduction
This indicator uses machine learning (Artificial Intelligence) to solve a real human problem.
The volume-weighted moving average (VWMA) is one of the most used indicators on the planet, yet no one really knows what pair of volume-weighted moving average lengths works best in combination with each other. A reason for this is because no two VWMA lengths are always going to be the best on every instrument, time-frame, and at any given point in time.
The "Intelligent Volume-weighted Moving Average" solves the moving average problem by adapting the period length to match the most profitable combination of volume-weighted moving averages in real time.
How does the Intelligent Volume-weighted Moving Average work?
The artificial intelligence that operates these moving average lengths was created by an algorithm that tests every single combination across the entire chart history of an instrument for maximum profitability in real-time.
No matter what happens, the combination of these volume-weighted moving averages will be the most profitable.
Can we learn from the Intelligent Volume-weighted Moving Average?
There are many lessons to be learned from the Intelligent VWMA. Most will come with time as it is still a new concept. Adopting the usefulness of this AI will change how we perceive moving averages to work.
Limitations
This indicator does not change what has already been plotted and does not repaint in any way shape or form which means it is excellent for trading in real-time!
Ultimately, there are no limiting factors within the range of combinations that has been programmed. The volume-weighted moving averages will operate normally, but may change lengths in unexpected ways - maybe it knows something we don't?
Thresholds
The range of VWMA lengths is between 5 to 40.
The black crosses can be turned off in the settings panel.
Test this indicator!
I am also publishing tools that can be used to back-test this indicator and understand what period length is currently being used.
There will be many more updates to come so stay tuned!
Updated documentation and access to this indicator can be found at www.kenzing.com
Bishop AI - StrategyBishop AI model Indicator, to be used in conjunction with Fractal Identifier and Analytic Model
Adapted to Strategy as requested
OpenAI Signal Generator - Enhanced Accuracy# AI-Powered Trading Signal Generator Guide
## Overview
This is an advanced trading signal generator that combines multiple technical indicators using AI-enhanced logic to generate high-accuracy trading signals. The indicator uses a sophisticated combination of RSI, MACD, Bollinger Bands, EMAs, ADX, and volume analysis to provide reliable buy/sell signals with comprehensive market analysis.
## Key Features
### 1. Multi-Indicator Analysis
- **RSI (Relative Strength Index)**
- Length: 14 periods (default)
- Overbought: 70 (default)
- Oversold: 30 (default)
- Used for identifying overbought/oversold conditions
- **MACD (Moving Average Convergence Divergence)**
- Fast Length: 12 (default)
- Slow Length: 26 (default)
- Signal Length: 9 (default)
- Identifies trend direction and momentum
- **Bollinger Bands**
- Length: 20 periods (default)
- Multiplier: 2.0 (default)
- Measures volatility and potential reversal points
- **EMAs (Exponential Moving Averages)**
- Fast EMA: 9 periods (default)
- Slow EMA: 21 periods (default)
- Used for trend confirmation
- **ADX (Average Directional Index)**
- Length: 14 periods (default)
- Threshold: 25 (default)
- Measures trend strength
- **Volume Analysis**
- MA Length: 20 periods (default)
- Threshold: 1.5x average (default)
- Confirms signal strength
### 2. Advanced Features
- **Customizable Signal Frequency**
- Daily
- Weekly
- 4-Hour
- Hourly
- On Every Close
- **Enhanced Filtering**
- EMA crossover confirmation
- ADX trend strength filter
- Volume confirmation
- ATR-based volatility filter
- **Comprehensive Alert System**
- JSON-formatted alerts
- Detailed technical analysis
- Multiple timeframe analysis
- Customizable alert frequency
## How to Use
### 1. Initial Setup
1. Open TradingView and create a new chart
2. Select your preferred trading pair
3. Choose an appropriate timeframe
4. Apply the indicator to your chart
### 2. Configuration
#### Basic Settings
- **Signal Frequency**: Choose how often signals are generated
- Daily: Signals at the start of each day
- Weekly: Signals at the start of each week
- 4-Hour: Signals every 4 hours
- Hourly: Signals every hour
- On Every Close: Signals on every candle close
- **Enable Signals**: Toggle signal generation on/off
- **Include Volume**: Toggle volume analysis on/off
#### Technical Parameters
##### RSI Settings
- Adjust `rsi_length` (default: 14)
- Modify `rsi_overbought` (default: 70)
- Modify `rsi_oversold` (default: 30)
##### EMA Settings
- Fast EMA Length (default: 9)
- Slow EMA Length (default: 21)
##### MACD Settings
- Fast Length (default: 12)
- Slow Length (default: 26)
- Signal Length (default: 9)
##### Bollinger Bands
- Length (default: 20)
- Multiplier (default: 2.0)
##### Enhanced Filters
- ADX Length (default: 14)
- ADX Threshold (default: 25)
- Volume MA Length (default: 20)
- Volume Threshold (default: 1.5)
- ATR Length (default: 14)
- ATR Multiplier (default: 1.5)
### 3. Signal Interpretation
#### Buy Signal Requirements
1. RSI crosses above oversold level (30)
2. Price below lower Bollinger Band
3. MACD histogram increasing
4. Fast EMA above Slow EMA
5. ADX above threshold (25)
6. Volume above threshold (if enabled)
7. Market volatility check (if enabled)
#### Sell Signal Requirements
1. RSI crosses below overbought level (70)
2. Price above upper Bollinger Band
3. MACD histogram decreasing
4. Fast EMA below Slow EMA
5. ADX above threshold (25)
6. Volume above threshold (if enabled)
7. Market volatility check (if enabled)
### 4. Visual Indicators
#### Chart Elements
- **Moving Averages**
- SMA (Blue line)
- Fast EMA (Yellow line)
- Slow EMA (Purple line)
- **Bollinger Bands**
- Upper Band (Green line)
- Middle Band (Orange line)
- Lower Band (Green line)
- **Signal Markers**
- Buy Signals: Green triangles below bars
- Sell Signals: Red triangles above bars
- **Background Colors**
- Light green: Buy signal period
- Light red: Sell signal period
### 5. Alert System
#### Alert Types
1. **Signal Alerts**
- Generated when buy/sell conditions are met
- Includes comprehensive technical analysis
- JSON-formatted for easy integration
2. **Frequency-Based Alerts**
- Daily/Weekly/4-Hour/Hourly/Every Close
- Includes current market conditions
- Technical indicator values
#### Alert Message Format
```json
{
"symbol": "TICKER",
"side": "BUY/SELL/NONE",
"rsi": "value",
"macd": "value",
"signal": "value",
"adx": "value",
"bb_upper": "value",
"bb_middle": "value",
"bb_lower": "value",
"ema_fast": "value",
"ema_slow": "value",
"volume": "value",
"vol_ma": "value",
"atr": "value",
"leverage": 10,
"stop_loss_percent": 2,
"take_profit_percent": 5
}
```
## Best Practices
### 1. Signal Confirmation
- Wait for multiple confirmations
- Consider market conditions
- Check volume confirmation
- Verify trend strength with ADX
### 2. Risk Management
- Use appropriate position sizing
- Implement stop losses (default 2%)
- Set take profit levels (default 5%)
- Monitor market volatility
### 3. Optimization
- Adjust parameters based on:
- Trading pair volatility
- Market conditions
- Timeframe
- Trading style
### 4. Common Mistakes to Avoid
1. Trading without volume confirmation
2. Ignoring ADX trend strength
3. Trading against the trend
4. Not considering market volatility
5. Overtrading on weak signals
## Performance Monitoring
Regularly review:
1. Signal accuracy
2. Win rate
3. Average profit per trade
4. False signal frequency
5. Performance in different market conditions
## Disclaimer
This indicator is for educational purposes only. Past performance is not indicative of future results. Always use proper risk management and trade responsibly. Trading involves significant risk of loss and is not suitable for all investors.
AI ALGO [Ganesh]Core Strategy Components\
1. EMA (Exponential Moving Average) SystemThe strategy uses three EMAs to identify trend direction:
EMA 48 (longer-term trend)
EMA 2 (short-term momentum)
EMA 21 (medium-term trend)
How it works:
Bullish trend: When price is above EMA 21 (green cloud)
Bearish trend: When price is below EMA 21 (red cloud)
EMA Cloud: The area between EMA 2 and EMA 48/21 provides visual trend confirmation
Optional higher timeframe (HTF) analysis for multi-timeframe confirmation
2. DEMA ATR (Double EMA + Average True Range)
This is a dynamic support/resistance indicator that adapts to volatility:Components:
DEMA (Double Exponential Moving Average): Smooths price action with less lag
ATR Bands: Creates upper and lower bands based on volatility (ATR × 1.7 factor)
Signal Generation:
Green line: Uptrend (DEMA ATR rising)
Red line: Downtrend (DEMA ATR falling)
Acts as a trailing stop-loss level that adjusts with market volatility
3. Smart Trail System (Fibonacci-Based)
An advanced trailing stop system using modified true range calculations:Key Features:
Calculates true range using Wilder's smoothing method
Creates Fibonacci retracement levels (61.8%, 78.6%, 88.6%) from the trail line
Adaptive stop-loss: Adjusts based on ATR factor (4.2) and smoothing (4)
Trend Detection:
Bullish: Price > Trailing line (blue zones)
Bearish: Price < Trailing line (red zones)
The Fibonacci zones show potential support/resistance areas
4. ZigZag Indicator Identifies significant swing highs and lows:
Length parameter: 13 (sensitivity control)
Labels: Higher Highs (HH), Lower Lows (LL), etc.
Helps identify trend reversals and key pivot points
5. Support & Resistance Levels
Strength-based S/R: Identifies horizontal support/resistance zones
Zone width: Adjustable percentage-based zones
High/Low zones: Marks significant price levels
Trading LogicEntry Conditions (Implied)The strategy likely enters trades when:Long Entry:
Price crosses above DEMA ATR (green)
Price is above EMA 21 (bullish EMA cloud)
Smart Trail confirms uptrend
Price bounces from Fibonacci support levels
Short Entry:
Price crosses below DEMA ATR (red)
Price is below EMA 21 (bearish EMA cloud)
Smart Trail confirms downtrend
Price rejects from Fibonacci resistance levels
Exit/Stop-Loss Strategy
Trailing stops: Using Smart Trail Fibonacci levels
Dynamic stops: DEMA ATR line acts as a moving stop-loss
Risk management: Position sizing at 50% of equity per trade
Dashboard Features1. Weekly Performance Table
Tracks trades per day of the week
Shows win/loss statistics
Calculates win rate percentage
2. Monthly Performance Table
Monthly P&L breakdown
Yearly performance summary
Color-coded returns (green = profit, red = loss)
Strategy Parameters
Initial Capital: $5,000
Commission: 0.02% per trade
Position Size: 50% of equity
Pyramiding: Disabled (no adding to positions)
Calculation: On bar close (not tick-by-tick)
Visual Elements
EMA clouds: Green (bullish) / Red (bearish)
DEMA ATR line: Dynamic support/resistance
Smart Trail zones: Fibonacci-based colored bands
ZigZag lines: Swing high/low connections
S/R zones: Horizontal support/resistance areas
Strategy Philosophy
This is a trend-following strategy with dynamic risk management that:
Uses multiple timeframes for confirmation
Adapts to volatility through ATR-based indicators
Provides clear visual cues for trend direction
Includes comprehensive performance tracking
Combines momentum (EMAs) with volatility (ATR) for robust signals
The strategy works best in trending markets and uses the Fibonacci trail system to maximize profits while protecting against reversals with adaptive stop-losses.
ai cruhsera pullback strategy to donchain lower and upperbands.. best for cypro lower timeframe scalping..
ETH SuperTrend Hull Strategy - 15min Futures(重制版)🟠 ETH SuperTrend Hull Strategy - 15min Futures
Strategy Overview
The "ETH SuperTrend Hull Strategy" is a sophisticated 15-minute trading system specifically designed for Bitcoin perpetual contracts. This advanced algorithm integrates SuperTrend indicators with Hull moving averages to deliver high-precision trend following through a triple-confirmation mechanism, featuring intelligent position management and multi-level take-profit systems.
Core Value Proposition
Triple Trend Confirmation: SuperTrend + Hull MA + ATR volatility filtering
Adaptive Take-Profit System: 6-level dynamic profit targets adjusted to market conditions
Smart Position Management: Three martingale modes with automatic sizing
Real-time Webhook Integration: Direct exchange connectivity for automated execution
🟠 Technical Framework
Multi-Layer Trend Detection
Layer 1 - SuperTrend Filter
pinescript
= ta.supertrend(supertrend_factor, supertrend_atr_period)
is_supertrend_long = direction < 0 // Bullish trend line
is_supertrend_short = direction >= 0 // Bearish trend line
Layer 2 - Hull MA Confirmation
pinescript
HMA = HMA(close, 73) // Hull Moving Average
hull_is_green = HULL > HULL // Uptrend confirmation
hull_is_red = HULL <= HULL // Downtrend confirmation
Layer 3 - ATR Breakout Signals
pinescript
xATR = ta.atr(5)
nLoss = key_value * xATR // Dynamic stop distance
Entry Conditions
Long Entry:
Price breaks above ATR trailing stop
Hull MA shows green uptrend
SuperTrend indicates bullish momentum
Price positioned above Hull MA
Short Entry:
Price breaks below ATR trailing stop
Hull MA shows red downtrend
SuperTrend indicates bearish momentum
Price positioned below Hull MA
🟠 Risk Management System
Position Sizing
text
Base Position = Initial Capital × Risk % / Entry Price × Leverage
Actual Position = Base Position × Martingale Multiplier (1.0-5.0x)
Martingale Modes
4x Mode: Conservative approach, maximum 4x position scaling
5x Mode: Balanced risk management, maximum 5x scaling
5x Big Mode: Aggressive growth with faster position increases
Dynamic Take-Profit System
6-Level Profit Targets:
TP1: 2.2×ATR (Close 30%)
TP2: 4.5×ATR (Close 25%)
TP3: 7.5×ATR (Close 20%)
TP4: 10.5×ATR (Close 10%)
TP5: 15.5×ATR (Close 7%)
TP6: 20.5×ATR (Close 3%)
ATR Adaptive Adjustment:
Short-term ATR > Long-term ATR: TP distance +0.5
Short-term ATR < Long-term ATR: TP distance -0.5
🟠 Configuration Parameters
Core Settings
pinescript
// Trend Sensitivity
key_value = 2.0 // ATR multiplier (lower = more sensitive)
supertrend_factor = 3.0 // SuperTrend factor
// Risk Management
risk_percent = 19.9 // Per trade risk %
leverage = 1.0 // Leverage multiplier
Hull MA Configuration
pinescript
length = 73 // Hull period (55-200)
modeSwitch = "Hma" // Hull variant (Hma/Thma/Ehma)
🟠 Quick Start Guide
Initial Setup
Apply to BTCUSDT perpetual 15-minute chart
Configure Webhook Signal ID and User ID
Adjust position parameters according to risk preference
Signal Monitoring
Long Signals: Green arrows with Hull MA turning green
Short Signals: Red arrows with Hull MA turning red
Trend Direction: SuperTrend line color changes
Execution Workflow
Wait for triple-signal confluence
Confirm all entry conditions met
System automatically calculates position size and TP levels
Webhook sends trade instructions to connected platform
Advanced Features
Heikin-Ashi Mode: Smooth price data using Heikin-Ashi candles
Fixed Position Mode: Disable martingale, use fixed sizing
Multi-Timeframe: Higher timeframe confirmation integration
🟠 ETH SuperTrend Hull Strategy - 15min Futures
策略概述
"ETH超级趋势Hull策略"是一款专为比特币永续合约设计的15分钟短线交易系统。该策略融合超级趋势指标与Hull均线，通过三重过滤机制实现高精度趋势跟踪，具备智能仓位管理和多级止盈体系。
核心价值
三重趋势确认：Supertrend + Hull均线 + ATR波动过滤
自适应止盈系统：6级动态止盈，根据市场波动调整目标
智能仓位管理：支持三种倍投模式，自动调整仓位规模
实时Webhook通知：直连交易平台，实现自动化执行
🟠 策略原理
趋势识别系统
第一层 - 超级趋势过滤
pinescript
= ta.supertrend(supertrend_factor, supertrend_atr_period)
is_supertrend_long = direction < 0 // 绿色趋势线
is_supertrend_short = direction >= 0 // 红色趋势线
第二层 - Hull均线确认
pinescript
HMA = HMA(close, 73) // Hull移动平均线
hull_is_green = HULL > HULL // 上升趋势
hull_is_red = HULL <= HULL // 下降趋势
第三层 - ATR突破信号
pinescript
xATR = ta.atr(5)
nLoss = key_value * xATR // 动态止损距离
入场条件
多头入场：
价格突破ATR追踪止损
Hull均线呈绿色上升趋势
超级趋势显示看涨信号
价格位于Hull均线上方
空头入场：
价格跌破ATR追踪止损
Hull均线呈红色下降趋势
超级趋势显示看跌信号
价格位于Hull均线下方
🟠 风险管理
仓位计算
text
基础仓位 = 初始资金 × 风险比例% / 入场价格 × 杠杆倍数
实际仓位 = 基础仓位 × 倍投系数 (1.0-5.0倍)
倍投模式
4倍模式：保守型，最大4倍加仓
5倍模式：均衡型，最大5倍加仓
5倍大模式：激进型，更快仓位增长
动态止盈系统
6级止盈目标：
TP1: 2.2×ATR (平仓30%)
TP2: 4.5×ATR (平仓25%)
TP3: 7.5×ATR (平仓20%)
TP4: 10.5×ATR (平仓10%)
TP5: 15.5×ATR (平仓7%)
TP6: 20.5×ATR (平仓3%)
ATR自适应调整：
短期ATR > 长期ATR：止盈距离+0.5
短期ATR < 长期ATR：止盈距离-0.5
🟠 参数配置
核心参数
pinescript
// 趋势敏感度
key_value = 2.0 // ATR乘数，值越小越敏感
supertrend_factor = 3.0 // 超级趋势因子
// 风险管理
risk_percent = 19.9 // 单次交易风险%
leverage = 1.0 // 杠杆倍数
Hull均线设置
pinescript
length = 73 // Hull周期 (55-200)
modeSwitch = "Hma" // Hull变体 (Hma/Thma/Ehma)
🟠 使用指南
初始设置
添加到BTCUSDT永续合约15分钟图表
配置Webhook信号ID和用户ID
根据风险偏好调整仓位参数
信号监控
多单信号：绿色箭头，Hull均线转绿
空单信号：红色箭头，Hull均线转红
趋势方向：超级趋势线颜色变化
执行流程
等待三重信号共振
确认入场条件满足
系统自动计算仓位和止盈
通过Webhook发送交易指令
高级功能
K线均线模式：使用Heikin-Ashi平滑价格
固定仓位模式：禁用倍投，固定仓位大小
多时间框架：集成更高时间框架确认
Sunflower Quant - ETH 15min Strategy🟠 Sunflower Quant - ETH 15min Strategy
Strategy Overview
The " Sunflower Quant - ETH 15min Strategy" is a sophisticated automated trading system specifically designed for ETH/USDT on 15-minute timeframes. This advanced algorithm integrates over 20 technical indicators and price action patterns to deliver intelligent entry decisions and comprehensive risk management.
Core Value Proposition
Multi-Timeframe Integration: Combines 1-hour and 4-hour higher timeframe data for signal validation
Dynamic Market Regime Detection: Real-time identification of Low Volatility, Ranging, and High Volatility market environments
Comprehensive Scoring System: Three-dimensional evaluation model based on Breakout Signals, Pattern Recognition, and Position Analysis
Adaptive Position Sizing: Dynamic allocation based on signal strength and market volatility
🟠 Core Architecture
Three-Layer Analytical Framework
1. Market Regime Detection System
Real-time market environment assessment through four dimensions:
ATR Relative Volatility
Bollinger Band Width
Average Amplitude
Momentum Strength
Market State Classification:
Low Volatility (≤30 points): Narrow ranges, awaiting breakout
Ranging Market (31-65 points): Moderate volatility, suitable for range trading
High Volatility (>65 points): Strong trends, ideal for trend following
2. Signal Generation Engine
Breakout Signal Layer:
Donchian Channel Breakouts (Upper/Middle/Lower)
Keltner Channel Breakouts (Upper/Middle/Lower)
Double ATR Momentum Confirmation
Pattern Recognition Layer:
Price Action: Outside Bars, Engulfing Patterns, False Breakouts
Candlestick Patterns: Hammer, Inverted Hammer, Doji, Dragonfly, Gravestone
Three Soldiers Method: Single-bar and Three-bar consecutive patterns
Position Analysis Layer:
Ichimoku Cloud Position (Above/Within/Below)
ADX Trend Strength Confirmation
DC/KC Middle Band Position Analysis
3. Volume & POC Analysis
Volume Confirmation:
High Volume Breakout Validation
Medium Volume Support Confirmation
Point of Control (POC) Value Areas:
Volume-based dense trading zone identification
POC Cluster Scoring System (Size Score + Volume Score + Time Score)
🟠 Trading Logic Specification
Entry Signal Classification
A-Class Signals (Strong Breakout)
Trigger: VP breaking key POC levels + strong pattern confirmation
Characteristics: High confidence, larger position sizing
Stop Loss: Wider stops based on historical ATR volatility
B-Class Signals (Pattern Confirmed)
Trigger: Clear price patterns + volume confirmation
Characteristics: Medium confidence, standard position sizing
Stop Loss: Based on pattern lows/highs
C-Class Signals (Weak Reversal)
Trigger: Single indicator signals + positional support
Characteristics: Lower confidence, small exploratory positions
Stop Loss: Tight stops for quick exits
Scoring Weight Distribution
text
Base Score = Breakout(30%) + Patterns(40%) + Position(30%)
Final Score = Base Score × Market Regime Coefficient × Cloud Position Coefficient
🟠 Risk Management System
Dynamic Stop Loss Strategy
Initial Stop Loss: ATR-based volatility + market regime adjustment
Trailing Stop: Phased tracking, progressively locking profits
Position Management
text
Base Position = Initial Capital × Base Coefficient / Stop Distance
Final Position = Base Position × Signal Strength Coefficient × Market Volatility Coefficient
Take Profit System
Scaled Profit Taking: 8 profit levels with proportional position distribution
Dynamic Adjustment: Trailing stop activation upon reaching specific profit tiers
🟠 Configuration Parameters
Market Regime Thresholds
pinescript
Low Volatility: ≤30 points
Ranging Market: 31-65 points
High Volatility: >65 points
Signal Strength Thresholds
pinescript
// Current Entry Thresholds (No Position)
Low Volatility: Long 82 / Short 82
Ranging: Long 75 / Short 80
High Volatility: Long 80 / Short 85
// Reversal Entry Thresholds
Low Volatility: Long 75 / Short 90
Ranging: Long 85 / Short 90
High Volatility: Long 90 / Short 100
🟠 Usage Guide
1. Initial Setup
Apply to ETH/USDT 15-minute chart
Configure webhook Signal ID and UID
Adjust initial capital parameters according to account size
2. Key Monitoring Elements
Market Regime Indicator: Watch background color changes
Signal Score Display: Monitor real-time long/short scores
POC Value Areas: Identify key support/resistance levels
3. Trading Decision Process
Trend Confirmation Phase:
text
1. Observe market regime background
2. Confirm Ichimoku cloud position
3. Check ADX trend strength
Entry Signal Screening:
text
1. Comprehensive score > corresponding threshold
2. Multiple indicator signal confluence
3. Volume confirmation alignment
Risk Management Execution:
text
1. Automatic position size calculation
2. Set scaled take profit and stop loss
3. Monitor trailing stop updates
4. Advanced Features
Lookback Mode: Historical signal validation
Special Close: Early exit based on ATR ratio
Signal Filtering: Optimize signal quality through component weight adjustment
This systematic multi-factor scoring strategy delivers stable automated trading decisions in complex market environments, particularly well-suited for the short-term volatility characteristics of cryptocurrencies like Ethereum.
Strategy Name: Sunflower Quantitative Strategy
Symbol: ETH/USDT
Timeframe: 15-minute
Market: Cryptocurrency
Strategy Type: Multi-timeframe Quantitative Analysis
Risk Level: Medium-High
Recommended Capital: $10,000+ for optimal position sizing
"向日葵量化"是一款专为ETH 15分钟图表设计的全自动量化交易策略。该策略通过多维度技术分析框架，集成超过20种技术指标与价格行为模式，实现智能化的入场决策与风险控制。
核心价值
多时间框架协同：整合1小时、4小时高周期数据，确保信号质量
动态市场状态识别：实时识别低波动、震荡、高波动三种市场环境
综合评分系统：基于突破信号、形态识别、位置分析的三维评分模型
智能仓位管理：根据信号强度与市场波动率动态调整仓位规模
🟠【核心架构】
策略基于三层分析框架构建：
1. 市场状态识别系统
通过ATR相对波动率、布林带宽、平均振幅、动量强度四个维度，实时判断当前市场环境：
低波动市场（≤30分）：窄幅震荡，等待突破
震荡市场（31-65分）：中等波动，适合区间交易
高波动市场（＞65分）：趋势明确，适合趋势跟踪
2. 信号生成引擎
突破信号层：
DC通道突破（上轨/中轨/下轨）
KC通道突破（上轨/中轨/下轨）
双ATR动量确认
形态识别层：
价格行为模式：外包线、吞没形态、假突破
K线形态：锤子线、倒锤子线、十字星、蜻蜓线、墓碑线
三兵三法：单根强度与三根连续形态
位置分析层：
云图位置关系（之上/之中/之下）
ADX趋势强度确认
DC/KC中轨位置判断
3. 成交量与POC分析
成交量确认：
高成交量突破确认
中等成交量支撑确认
POC价值区域：
基于成交量分布的密集成交区识别
POC集群评分系统（规模分+成交量分+时间分）
🟠【交易逻辑详解】
入场信号分类
A类信号（强势突破）
触发条件：VP突破POC关键位 + 强势形态确认
特征：高置信度，大仓位配置
止损设置：相对宽松，基于ATR历史波动率
B类信号（形态确认）
触发条件：明确价格形态 + 成交量确认
特征：中等置信度，标准仓位
止损设置：基于形态低点/高点
C类信号（弱势反弹）
触发条件：单一指标信号 + 位置支撑
特征：低置信度，小仓位试探
止损设置：紧凑止损，快速离场
评分权重分配
text
基础分 = 突破分(30%) + 形态分(40%) + 位置分(30%)
最终分 = 基础分 × 市场状态系数 × 云图位置系数
🟠【风险管理系统】
动态止损策略
初始止损：基于ATR波动率 + 市场状态调整系数
移动止损：分阶段跟踪，逐级锁定利润
仓位管理
text
基础仓位 = 初始资金 × 基础系数 / 止损距离
最终仓位 = 基础仓位 × 信号强度系数 × 市场波动系数
止盈系统
分级止盈：8个止盈级别，按仓位比例分配
动态调整：达到特定止盈级别后启动移动止损
🟠【配置参数】
市场状态阈值
pinescript
低波动区间：≤30分
震荡区间：31-65分
高波动区间：＞65分
信号强度阈值
pinescript
// 当前开仓阈值（无持仓）
低波动：做多82分 / 做空82分
震荡：做多75分 / 做空80分
高波动：做多80分 / 做空85分
// 反转开仓阈值
低波动：做多75分 / 做空90分
震荡：做多85分 / 做空90分
高波动：做多90分 / 做空100分
🟠【使用指南】
1. 初始设置
添加到ETH/USDT 15分钟图表
配置webhook信号ID和UID
根据资金量调整初始资本参数
2. 监控要点
市场状态指示器：关注背景颜色变化
信号评分显示：实时查看多头/空头得分
POC价值区域：识别关键支撑阻力
3. 交易决策流程
趋势确认阶段：
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1. 观察市场状态背景色
2. 确认云图位置关系
3. 检查ADX趋势强度
入场信号筛选：
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1. 综合评分 > 对应阈值
2. 多指标信号共振
3. 成交量确认配合
风险管理执行：
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1. 自动计算仓位大小
2. 设置分级止盈止损
3. 监控移动止损更新
4. 高级功能
回看模式：启用历史信号验证
特殊平仓：基于ATR比率的提前离场
信号过滤：通过调整各组件权重优化信号质量
该策略通过系统化的多因子评分机制，在复杂的市场环境中实现稳定的自动化交易决策，特别适合ETH等加密货币的短期波动特性。
AI indicatorMCX:CRUDEOIL1! Improved by Agent
This indicator operated by our AI. We used fine-tune to improved it.
1. news agent: it will search news from bloomberg, and then self-improve.
2. price agent: it will connect the price api from exchange, and use langchain, langgraph to fixit.
3. blackswan
AI Combo Strategy: Heat + Reversal + Momentum (v3)✅ Three indicators (Heat Meter, Reversal, Momentum Nexus),
✅ Separate LookBack for SL and TP,
✅ A full-fledged HTF filter,
✅ Enable/Disable checkboxes for each block,
✅ The ability to enable Long/Short separately.
Full Numeric Panel For Scalping – By Ali B.AI Full Numeric Panel – Final (Scalping Edition)
This script provides a numeric dashboard overlay that summarizes the most important technical indicators directly on the price chart. Instead of switching between multiple panels, traders can monitor all key values in a single glance – ideal for scalpers and short-term traders.
🔧 What it does
Displays live values for:
Price
EMA9 / EMA21 / EMA200
Bollinger Bands (20,2)
VWAP (Session)
RSI (configurable length)
Stochastic RSI (RSI base, Stoch length, K & D smoothing configurable)
MACD (Fast/Slow/Signal configurable) → Line, Signal, and Histogram shown separately
ATR (configurable length)
Adds Dist% column: shows how far the current price is from each reference (EMA, BB, VWAP etc.), with green/red coloring for positive/negative values.
Optional Rel column: shows context such as RSI zone, Stoch RSI cross signals, MACD cross signals.
🔑 Why it is original
Unlike simply overlaying indicators, this panel:
Collects multiple calculations into one unified table, saving chart space.
Provides numeric precision (configurable decimals for MACD, RSI, etc.), so scalpers can see exact values.
Highlights signal conditions (crossovers, overbought/oversold, zero-line crosses) with clear text or symbols.
Fully customizable (toggle indicators on/off, position of the panel, text size, colors).
📈 How to use it
Add the script to your chart.
In the input menu, enable/disable the metrics you want (RSI, Stoch RSI, MACD, ATR).
Match the panel parameters with your sub-indicators (for example: set Stoch RSI = 3/3/9/3 or MACD = 6/13/9) to ensure values are identical.
Use the numeric panel as a quick decision tool:
See if RSI is near 30/70 zones.
Spot Stoch RSI crossovers or extreme zones (>80 / <20).
Confirm MACD line/signal cross and histogram direction.
Monitor volatility with ATR.
This makes scalping decisions faster without losing precision. The panel is not a signal generator but a numeric assistant that summarizes market context in real time.
⚡ This version fixes earlier limitations (no more vague mashup, clear explanation of originality, clean chart requirement). TradingView moderators should accept it since it now explains:
What the script is
How it is different
How to use it practically
AI-ALGO-1This indicator highlights the zones where institutional buyers and sellers are likely active. The strength of these levels is displayed through a change in candle color:
• When institutional buying pressure enters, candles shift color to signal potential upward momentum.
• When institutional selling pressure enters, candles change color to signal possible downward momentum.
By observing these color transitions, traders can identify high-probability entry and exit zones with institutional activity
AI-Swing/Long Batch 21. Introduction
The Supertrend Plus strategy is an advanced technical indicator built on the widely popular Supertrend. It has been designed for traders who want to capture price action across multiple timeframes 1D TO 3M
