MathSearchDijkstraLibrary "MathSearchDijkstra"
Shortest Path Tree Search Methods using Dijkstra Algorithm.
min_distance(distances, flagged_vertices) Find the lowest cost/distance.
Parameters:
distances : float array, data set with distance costs to start index.
flagged_vertices : bool array, data set with visited vertices flags.
Returns: int, lowest cost/distance index.
dijkstra(matrix_graph, dim_x, dim_y, start) Dijkstra Algorithm, perform a greedy tree search to calculate the cost/distance to selected start node at each vertex.
Parameters:
matrix_graph : int array, matrix holding the graph adjacency list and costs/distances.
dim_x : int, x dimension of matrix_graph.
dim_y : int, y dimension of matrix_graph.
start : int, the vertex index to start search.
Returns: int array, set with costs/distances to each vertex from start vertexs.
shortest_path(start, end, matrix_graph, dim_x, dim_y) Retrieves the shortest path between 2 vertices in a graph using Dijkstra Algorithm.
Parameters:
start : int, the vertex index to start search.
end : int, the vertex index to end search.
matrix_graph : int array, matrix holding the graph adjacency list and costs/distances.
dim_x : int, x dimension of matrix_graph.
dim_y : int, y dimension of matrix_graph.
Returns: int array, set with vertex indices to the shortest path.
在腳本中搜尋"algo"
P-Square - Estimation of the Nth percentile of a seriesEstimation of the Nth percentile of a series
When working with built-in functions in TradingView we have to limit our length parameters to max 4999. In case we want to use a function on the whole available series (bar 0 all the way to the current bar), we can usually not do this without manually creating these calculations in our code. For things like mean or standard deviation, this is quite trivial, but for things like percentiles, this is usually very costly. In more complex scripts, this becomes impossible because of resource restrictions from the Pine Script execution servers.
One solution to this is to use an estimation algorithm to get close to the true percentile value. Therefore, I have ported this implementation of the P-Square algorithm to Pine Script. P-Square is a fast algorithm that does a good job at estimating percentiles in data streams. Here's the algorithms original paper .
The chart
On the chart we see:
The returns of the series (blue scatter plot)
The mean of the returns of the series (orange line)
The standard deviation of the returns of the series (yellow line)
The actual 84.1th percentile of the returns (white line)
The estimatedl 84.1th percentile of the returns using the P-Square algorithm (green line)
Note: We can see that the returns are not normally distributed as we can see that one standard deviation is higher than the 84.1th percentile. One standard deviation should equal the 84.1th percentile if the data is normally distributed.
Machine Learning: Logistic RegressionMulti-timeframe Strategy based on Logistic Regression algorithm
Description:
This strategy uses a classic machine learning algorithm that came from statistics - Logistic Regression (LR).
The first and most important thing about logistic regression is that it is not a 'Regression' but a 'Classification' algorithm. The name itself is somewhat misleading. Regression gives a continuous numeric output but most of the time we need the output in classes (i.e. categorical, discrete). For example, we want to classify emails into “spam” or 'not spam', classify treatment into “success” or 'failure', classify statement into “right” or 'wrong', classify election data into 'fraudulent vote' or 'non-fraudulent vote', classify market move into 'long' or 'short' and so on. These are the examples of logistic regression having a binary output (also called dichotomous).
You can also think of logistic regression as a special case of linear regression when the outcome variable is categorical, where we are using log of odds as dependent variable. In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function.
Basically, the theory behind Logistic Regression is very similar to the one from Linear Regression, where we seek to draw a best-fitting line over data points, but in Logistic Regression, we don’t directly fit a straight line to our data like in linear regression. Instead, we fit a S shaped curve, called Sigmoid, to our observations, that best SEPARATES data points. Technically speaking, the main goal of building the model is to find the parameters (weights) using gradient descent.
In this script the LR algorithm is retrained on each new bar trying to classify it into one of the two categories. This is done via the logistic_regression function by updating the weights w in the loop that continues for iterations number of times. In the end the weights are passed through the sigmoid function, yielding a prediction.
Mind that some assets require to modify the script's input parameters. For instance, when used with BTCUSD and USDJPY, the 'Normalization Lookback' parameter should be set down to 4 (2,...,5..), and optionally the 'Use Price Data for Signal Generation?' parameter should be checked. The defaults were tested with EURUSD.
Note: TradingViews's playback feature helps to see this strategy in action.
Warning: Signals ARE repainting.
Style tags: Trend Following, Trend Analysis
Asset class: Equities, Futures, ETFs, Currencies and Commodities
Dataset: FX Minutes/Hours/Days
Price levelsThanks to the developers for adding arrays to TradingView. This gives you more freedom in Pine Script coding.
I have created an algorithm that draws support and resistance levels on a chart. The algorithm can be easily customized as you need.
This algorithm can help both intuitive and system traders. Intuitive traders just look at the drawn lines. For system traders, the "levels" array stores all level values. Thus, you can use these values for algorithmic trading.
[R&D] Moving CentroidThis script utilizes this concept. Instead of weighting by volume, it weights by amount of price action on every close price of the rolling window. I assume it can be used as an additional reference point for price mode and price antimode.
it is directly connected with Market (not volume) profile, or TPO charts.
The algorithm:
1) takes a rolling window of, for example, 50 data points of close prices:
2) for each of this closing prices, the algorithm will check how many bars touched this close price.
3) then: sum of datapoints * weights/sum of weights
Since the logic is implemented in pretty non-efficient way, the script sometimes can take time to make calculations. Moreover, it calculates the centroid taking into account only close prices, not every tick. of a given rolling window That's why it's still experimental.
RenkoNow you can plot a "Renko" chart on any timeframe for free! As with my previous algorithm, you can plot the "Linear Break" chart on any timeframe for free!
I again decided to help TradingView programmers and wrote code that converts a standard candles / bars to a "Renko" chart. The built-in renko() and security() functions for constructing a "Renko" chart are working wrong. Do not try to write strategies based on the built-in renko() function! The developers write in the manual: "Please note that you cannot plot Renko bricks from Pine script exactly as they look. You can only get a series of numbers similar to OHLC values for Renko bars and use them in your algorithms". However, it is possible to build a "Renko" chart exactly like the "Renko" chart built into TradingView. Personally, I had enough Pine Script functionality.
For a complete understanding of how such a chart is built, you can read to Steve Nison's book "BEYOND JAPANESE CANDLES" and see the instructions for creating a "Renko" chart:
Rule 1: one white brick (or series) is built when the price rises above the base price by a fixed threshold value or more.
Rule 2: one black brick (or series) is built when the price falls below the base price by a fixed threshold or more.
Rule 3: if the rise or fall of the price is less than the minimum fixed value, then new bricks are not drawn.
Rule 4: if today's closing price is higher than the maximum of the last brick (white or black) by a threshold or more, move to the column to the right and build one or more white bricks of equal height. A new brick begins with the maximum of the previous brick.
Rule 5: if today's closing price is below the minimum of the last brick (white or black) by a threshold or more, move to the column to the right and build one or more black bricks of equal height. A new brick begins with the minimum of the previous brick.
Rule 6: if the price is below the maximum or above the minimum, then new bricks are not drawn on the chart.
So my algorithm can to plot Traditional Renko with a fixed box size. I want to note that such a "Renko" chart is slightly different from the "Renko" chart built into TradingView, because as a base price I use (by default) close of first candle. How the developers of TradingView calculate the base price I don’t know. Personally, I do as written in the book of Steve Neeson.
The algorithm is very complicated and I do not want to explain it in detail. I will explain very briefly. The first part of the get_renko () function — // creating lists — creates two lists that record how many green bricks should be and how many red bricks. The second part of the get_renko () function — // creating open and close series — creates open and close series to plot bricks. So, this is a white box - study it!
As you understand, one green candle can create a condition under which it will be necessary to plot, for example, 10 green bricks. So the smaller the box size you make, the smaller the portion of the chart you will see.
I stuffed all the logic into a wrapper in the form of the get_renko() function, which returns a tuple of OHLC values. And these series with the help of the plotcandle() annotation can be converted to the "Renko" chart. I also want to note that with a large number of candles on the chart, outrages about the buffer size uncertainty are heard from the TradingView blackbox. Because of it, in the annotation study() set the value of the max_bars_back parameter.
In general, use this script (for example, to write strategies)!
Enhanced Instantaneous Cycle Period - Dr. John EhlersThis is my first public release of detector code entitled "Enhanced Instantaneous Cycle Period" for PSv4.0 I built many months ago. Be forewarned, this is not an indicator, this is a detector to be used by ADVANCED developers to build futuristic indicators in Pine. The origins of this script come from a document by Dr. John Ehlers entitled "SIGNAL ANALYSIS CONCEPTS". You may find this using the NSA's reverse search engine "goggles", as I call it. John Ehlers' MESA used this measurement to establish the data window for analysis for MESA Cycle computations. So... does any developer wish to emulate MESA Cycle now??
I decided to take instantaneous cycle period to another level of novel attainability in this public release of source code with the following methods, if you are curious how I ENHANCED it. Firstly I reduced the delay of accurate measurement from bar_index==0 by quite a few bars closer to IPO. Secondarily, I provided a limit of 6 for a minimum instantaneous cycle period. At bar_index==0, it would provide a period of 0 wrecking many algorithms from the start. I also increased the instantaneous cycle period's maximum value to 80 from 50, providing a window of 6-80 for the instantaneous cycle period value window limits. Thirdly, I replaced the internal EMA with another algorithm. It reduces the lag while extracting a floating point number, for algorithms that will accept that, compared to a sluggish ordinary EMA return. You will see the excessive EMA delay with adding plot(ema(ICP,7)) as it was originally designed. Lastly it's in one simple function for reusability in a nice little package comprising of less than 40 lines of code. I hope I explained that adequately enough and gave you the reader a glimpse of the "Power of Pine" combined with ingenuity.
Be forewarned again, that most of Pine's built-in functions will not accept a floating-point number or dynamic integers for the "length" of it's calculation. You will have to emulate the built-in functions by creating Pine based custom functions, and I assure you, this is very possible in many cases, but not all without array support. You may use int(ICP) to extract an integer from the smoothICP return variable, which may be favorable compared to the choppiness/ringing if ICP alone.
This is commonly what my dense intricate code looks like behind the veil. If you are wondering why there is barely any notation, that's because the notation is in the variable naming and this is intended primarily for ADVANCED developers too. It does contain lines of code that explore techniques in Pine that may be applicable in other Pine projects for those learning or wishing to excel with Pine.
Showcased in the chart below is my free to use "Enhanced Schaff Trend Cycle Indicator", having a common appeal to TV users frequently. If you do have any questions or comments regarding this indicator, I will consider your inquiries, thoughts, and ideas presented below in the comments section, when time provides it. As always, "Like" it if you simply just like it with a proper thumbs up, and also return to my scripts list occasionally for additional postings. Have a profitable future everyone!
NOTICE: Copy pasting bandits who may be having nefarious thoughts, DO NOT attempt this, because this may violate Tradingview's terms, conditions and/or house rules. "WE" are always watching the TV community vigilantly for mischievous behaviors and actions that exploit well intended authors for the purpose of increasing brownie points in reputation scores. Hiding behind a "protected" wall may not protect you from investigation and account penalization by TV staff. Be respectful, and don't just throw an ma() in there branding it as "your" gizmo. Fair enough? Alrighty then... I firmly believe in "innovating" future state-of-the-art indicators, and please contact me if you wish to do so.
BOR + 08:28BOR + TIME: Precision 1-Minute Opening Range Analysis
METHODOLOGY OVERVIEW
This indicator implements a proprietary time-based trading methodology that combines opening range analysis with precision timing algorithms designed exclusively for 1-minute charts during the New York trading session.
CORE ALGORITHM COMPONENTS
1. Bond Opening Range (BOR) Identification
- Captures the complete price range during 08:00-09:00 NY time
- Establishes the foundational trading range for the session
- Uses high-precision minute-level data to define exact boundaries
2. Critical Time Level Analysis (08:28 Candle)
- Identifies the 08:28-08:29 minute candle as a key reference point
- This specific timing represents a critical juncture before market open
- Captures the exact high/low range of this precise minute
3. Directional Bias Determination (09:00 Analysis)
- At exactly 09:00, compares current price position relative to 08:28 boundaries
- Above 08:28 High: Activates support-seeking mode (bullish bias)
- Below 08:28 Low: Activates resistance-seeking mode (bearish bias)
- Inside 08:28 Range: No directional bias established
4. Dynamic Standard Deviation Projections
- Uses the 08:28 candle range as the mathematical basis for standard deviation calculations
- Support Mode: Projects levels below 08:28 low using range multipliers (-1σ, -2σ, -3σ, -4σ)
- Resistance Mode: Projects levels above 08:28 high using range multipliers (+1σ, +2σ, +3σ, +4σ)
- Levels are active only during 09:00-10:30 trading window
UNIQUE FEATURES
Conditional Logic Engine
- Real-time directional switching based on 09:00 price position
- No static levels - everything adapts to intraday price action
- Eliminates noise by focusing on specific time windows
Precision Timing Requirements
- Requires exact 1-minute timeframe for accurate calculations
- Time-sensitive algorithm that relies on minute-by-minute analysis
- Optimized for high-frequency intraday trading decisions
Mathematical Framework
- Standard deviations calculated using actual candle range data
- Dynamic level spacing based on market volatility (08:28 range)
- Four-tier projection system for multiple target/stop levels
TRADING APPLICATION
Best Used For:
- ES, NQ, YM and other liquid index futures
- Active day trading during NY session (07:00-12:00)
- Scalping and short-term reversal strategies
- Intraday support/resistance identification
Signal Interpretation:
- Red lines represent potential reversal zones
- Direction determined by 09:00 vs 08:28 relationship
- Multiple standard deviation levels provide layered entry/exit points
- Time-restricted plotting ensures relevance during active trading hours
IMPORTANT REQUIREMENTS
- ONLY works on 1-minute charts - precision timing is essential
- Designed for New York trading session (futures markets)
- Most effective during high-volume trading periods
CUSTOMIZATION OPTIONS
- Toggle BOR box visibility and transparency
- Enable/disable 08:28 candle highlighting
- Adjust visual elements (colors, transparency)
- Show/hide range information labels
RifleShooterLibLibrary "RifleShooterLib"
Provides a collection of helper functions in support of the Rifle Shooter Indicators.
Functions support the key components of the Rifle Trade algorithm including
* measuring momentum
* identifying paraboloic price action (to disable the algorthim during such time)
* determine the lookback criteria of X point movement in last N minutes
* processing and navigating between the 23/43/73 levels
* maintaining a status table of algorithm progress
toStrRnd(val, digits)
Parameters:
val (float)
digits (int)
_isValidTimeRange(startTimeInput, endTimeInput)
Parameters:
startTimeInput (string)
endTimeInput (string)
_normalize(_src, _min, _max)
_normalize Normalizes series with unknown min/max using historical min/max.
Parameters:
_src (float) : Source series to normalize
_min (float) : minimum value of the rescaled series
_max (float) : maximum value of the rescaled series
Returns: The series scaled with values between min and max
arrayToSeries(arrayInput)
arrayToSeries Return an array from the provided series.
Parameters:
arrayInput (array) : Source array to convert to a series
Returns: The array as a series datatype
f_parabolicFiltering(_activeCount, long, shooterRsi, shooterRsiLongThreshold, shooterRsiShortThreshold, fiveMinuteRsi, fiveMinRsiLongThreshold, fiveMinRsiShortThreshold, shooterRsiRoc, shooterRsiRocLongThreshold, shooterRsiRocShortThreshold, quickChangeLookbackBars, quckChangeThreshold, curBarChangeThreshold, changeFromPrevBarThreshold, maxBarsToholdParabolicMoveActive, generateLabels)
f_parabolicFiltering Return true when price action indicates a parabolic active movement based on the provided inputs and thresholds.
Parameters:
_activeCount (int)
long (bool)
shooterRsi (float)
shooterRsiLongThreshold (float)
shooterRsiShortThreshold (float)
fiveMinuteRsi (float)
fiveMinRsiLongThreshold (float)
fiveMinRsiShortThreshold (float)
shooterRsiRoc (float)
shooterRsiRocLongThreshold (float)
shooterRsiRocShortThreshold (float)
quickChangeLookbackBars (int)
quckChangeThreshold (int)
curBarChangeThreshold (int)
changeFromPrevBarThreshold (int)
maxBarsToholdParabolicMoveActive (int)
generateLabels (bool)
rsiValid(rsi, buyThreshold, sellThreshold)
rsiValid Returns true if the provided RSI value is withing the associated threshold. For the unused threshold set it to na
Parameters:
rsi (float)
buyThreshold (float)
sellThreshold (float)
squezeBands(source, length)
squezeBands Returns the squeeze bands momentum color of current source series input
Parameters:
source (float)
length (int)
f_momentumOscilator(source, length, transperency)
f_momentumOscilator Returns the squeeze pro momentum value and bar color states of the series input
Parameters:
source (float)
length (int)
transperency (int)
f_getLookbackExtreme(lowSeries, highSeries, lbBars, long)
f_getLookbackExtreme Return the highest high or lowest low over the look back window
Parameters:
lowSeries (float)
highSeries (float)
lbBars (int)
long (bool)
f_getInitialMoveTarget(lbExtreme, priveMoveOffset, long)
f_getInitialMoveTarget Return the point delta required to achieve an initial rifle move (X points over Y lookback)
Parameters:
lbExtreme (float)
priveMoveOffset (int)
long (bool)
isSymbolSupported(sym)
isSymbolSupported Return true if provided symbol is one of the supported DOW Rifle Indicator symbols
Parameters:
sym (string)
getBasePrice(price)
getBasePrice Returns integer portion of provided float
Parameters:
price (float)
getLastTwoDigitsOfPrice(price)
getBasePrice Returns last two integer numerals of provided float value
Parameters:
price (float)
getNextLevelDown(price, lowestLevel, middleLevel, highestLevel)
getNextLevelDown Returns the next level above the provided price value
Parameters:
price (float)
lowestLevel (float)
middleLevel (float)
highestLevel (float)
getNextLevelUp(price, lowestLevel, middleLevel, highestLevel)
getNextLevelUp Returns the next level below the provided price value
Parameters:
price (float)
lowestLevel (float)
middleLevel (float)
highestLevel (float)
isALevel(price, lowestLevel, middleLevel, highestLevel)
isALevel Returns true if the provided price is onve of the specified levels
Parameters:
price (float)
lowestLevel (float)
middleLevel (float)
highestLevel (float)
getClosestLevel(price, lowestLevel, middleLevel, highestLevel)
getClosestLevel Returns the level closest to the price value provided
Parameters:
price (float)
lowestLevel (float)
middleLevel (float)
highestLevel (float)
f_fillSetupTableCell(_table, _col, _row, _text, _bgcolor, _txtcolor, _text_size)
f_fillSetupTableCell Helper function to fill a setup table celll
Parameters:
_table (table)
_col (int)
_row (int)
_text (string)
_bgcolor (color)
_txtcolor (color)
_text_size (string)
f_fillSetupTableRow(_table, _row, _col0Str, _col1Str, _col2Str, _bgcolor, _textColor, _textSize)
f_fillSetupTableRow Helper function to fill a setup table row
Parameters:
_table (table)
_row (int)
_col0Str (string)
_col1Str (string)
_col2Str (string)
_bgcolor (color)
_textColor (color)
_textSize (string)
f_addBlankRow(_table, _row)
f_addBlankRow Helper function to fill a setup table row with empty values
Parameters:
_table (table)
_row (int)
f_updateVersionTable(versionTable, versionStr, versionDateStr)
f_updateVersionTable Helper function to fill the version table with provided values
Parameters:
versionTable (table)
versionStr (string)
versionDateStr (string)
f_updateSetupTable(_table, parabolicMoveActive, initialMoveTargetOffset, initialMoveAchieved, shooterRsi, shooterRsiValid, rsiRocEnterThreshold, shooterRsiRoc, fiveMinuteRsi, fiveMinuteRsiValid, requireValid5MinuteRsiForEntry, stallLevelOffset, stallLevelExceeded, stallTargetOffset, recoverStallLevelValid, curBarChangeValid, volumeRoc, volumeRocThreshold, enableVolumeRocForTrigger, tradeActive, entryPrice, curCloseOffset, curSymCashDelta, djiCashDelta, showDjiDelta, longIndicator, fontSize)
f_updateSetupTable Manages writing current data to the setup table
Parameters:
_table (table)
parabolicMoveActive (bool)
initialMoveTargetOffset (float)
initialMoveAchieved (bool)
shooterRsi (float)
shooterRsiValid (bool)
rsiRocEnterThreshold (float)
shooterRsiRoc (float)
fiveMinuteRsi (float)
fiveMinuteRsiValid (bool)
requireValid5MinuteRsiForEntry (bool)
stallLevelOffset (float)
stallLevelExceeded (bool)
stallTargetOffset (float)
recoverStallLevelValid (bool)
curBarChangeValid (bool)
volumeRoc (float)
volumeRocThreshold (float)
enableVolumeRocForTrigger (bool)
tradeActive (bool)
entryPrice (float)
curCloseOffset (float)
curSymCashDelta (float)
djiCashDelta (float)
showDjiDelta (bool)
longIndicator (bool)
fontSize (string)
Smarter Money Flow Divergence Detector [PhenLabs]📊 Smarter Money Flow Divergence Detector
Version: PineScript™ v6
📌 Description
SMFD was developed to help give you guys a better ability to “read” what is going on behind the scenes without directly having access to that level of data. SMFD is an enhanced divergence detection indicator that identifies money flow patterns from advanced volume analysis and price action correspondence. The detection portion of this indicator combines intelligent money flow calculations with multi timeframe volume analysis to help you see hidden accumulation and distribution phases before major price movements occur.
The indicator measures institutional trading activity by looking at volume surges, price volume dynamics, and the factors of momentum to construct an overall picture of market sentiment. It’s built to assist traders in identifying high probability entries by identifying if smart money is positioning against price action.
🚀 Points of Innovation
● Advanced Smart Money Flow algorithm with volume spike detection and large trade weighting
● Multi timeframe volume analysis for enhanced institutional activity detection
● Dynamic overbought/oversold zones that adapt to current market conditions
● Enhanced divergence detection with pivot confirmation and strength validation
● Color themes with customizable visual styling options
● Real time institutional bias tracking through accumulation/distribution analysis
🔧 Core Components
● Smart Money Flow Calculation: Combines price momentum, volume expansion, and VWAP analysis
● Institutional Bias Oscillator: Tracks accumulation/distribution patterns with volume pressure analysis
● Enhanced Divergence Engine: Detects bullish/bearish divergences with multiple confirmation factors
● Dynamic Zone Detection: Automatically adjusts overbought/oversold levels based on market volatility
● Volume Pressure Analysis: Measures buying vs selling pressure over configurable periods
● Multi factor Signal System: Generates entries with trend alignment and strength validation
🔥 Key Features
● Smart Money Flow Period: Configurable calculation period for institutional activity detection
● Volume Spike Threshold: Adjustable multiplier for detecting unusual institutional volume
● Large Trade Weight: Emphasis factor for high volume periods in flow calculations
● Pivot Detection: Customizable lookback period for accurate divergence identification
● Signal Sensitivity: Three tier system (Conservative/Medium/Aggressive) for signal generation
● Themes: Four color schemes optimized for different chart backgrounds
🎨 Visualization
● Main Oscillator: Line, Area, or Histogram display styles with dynamic color coding
● Institutional Bias Line: Real time tracking of accumulation/distribution phases
● Dynamic Zones: Adaptive overbought/oversold boundaries with gradient fills
● Divergence Lines: Automatic drawing of bullish/bearish divergence connections
● Entry Signals: Clear BUY/SELL labels with signal strength indicators
● Information Panel: Real time statistics and status updates in customizable positions
📖 Usage Guidelines
Algorithm Settings
● Smart Money Flow Period
○ Default: 20
○ Range: 5-100
○ Description: Controls the calculation period for institutional flow analysis.
Higher values provide smoother signals but reduce responsiveness to recent activity
● Volume Spike Threshold
○ Default: 1.8
○ Range: 1.0-5.0
○ Description: Multiplier for detecting unusual volume activity indicating institutional participation. Higher values require more extreme volume for detection
● Large Trade Weight
○ Default: 2.5
○ Range: 1.5-5.0
○ Description: Weight applied to high volume periods in smart money calculations. Increases emphasis on institutional sized transactions
Divergence Detection
● Pivot Detection Period
○ Default: 12
○ Range: 5-50
○ Description: Bars to analyze for pivot high/low identification.
Affects divergence accuracy and signal frequency
● Minimum Divergence Strength
○ Default: 0.25
○ Range: 0.1-1.0
○ Description: Required price change percentage for valid divergence patterns.
Higher values filter out weaker signals
✅ Best Use Cases
● Trading with intraday to daily timeframes for institutional position identification
● Confirming trend reversals when divergences align with support/resistance levels
● Entry timing in trending markets when institutional bias supports the direction
● Risk management by avoiding trades against strong institutional positioning
● Multi timeframe analysis combining short term signals with longer term bias
⚠️ Limitations
● Requires sufficient volume for accurate institutional detection in low volume markets
● Divergence signals may have false positives during highly volatile news events
● Best performance on liquid markets with consistent institutional participation
● Lagging nature of volume based calculations may delay signal generation
● Effectiveness reduced during low participation holiday periods
💡 What Makes This Unique
● Multi Factor Analysis: Combines volume, price, and momentum for comprehensive institutional detection
● Adaptive Zones: Dynamic overbought/oversold levels that adjust to market conditions
● Volume Intelligence: Advanced algorithms identify institutional sized transactions
● Professional Visualization: Multiple display styles with customizable themes
● Confirmation System: Multiple validation layers reduce false signal generation
🔬 How It Works
1. Volume Analysis Phase:
● Analyzes current volume against historical averages to identify institutional activity
● Applies multi timeframe analysis for enhanced detection accuracy
● Calculates volume pressure through buying vs selling momentum
2. Smart Money Flow Calculation:
● Combines typical price with volume weighted analysis
● Applies institutional trade weighting for high volume periods
● Generates directional flow based on price momentum and volume expansion
3. Divergence Detection Process:
● Identifies pivot highs/lows in both price and indicator values
● Validates divergence strength against minimum threshold requirements
● Confirms signals through multiple technical factors before generation
💡 Note: This indicator works best when combined with proper risk management and position sizing. The institutional bias component helps identify market sentiment shifts, while divergence signals provide specific entry opportunities. For optimal results, use on liquid markets with consistent institutional participation and combine with additional technical analysis methods.
Faster Heikin AshiFaster Heikin Ashi
The Faster Heikin Ashi improves traditional Heikin Ashi candles by introducing advanced weighting mechanisms and lag reduction techniques. While maintaining the price smoothing benefits of standard Heikin Ashi, this enhanced version delivers faster signals and responsiveness.
Key Features
Unified Responsiveness Control
Single parameter (0.1 - 1.0) controls all responsiveness aspects
Eliminates conflicting settings found in other enhanced HA indicators
Intuitive scaling from conservative (0.1) to highly responsive (1.0)
Advanced Weighted Calculations
Smart Close Weighting: Close prices receive 2-3x more influence for faster trend detection
Dynamic OHLC Processing: All price components are intelligently weighted based on responsiveness setting
Balanced High/Low Emphasis: Maintains price level accuracy while improving speed
Enhanced Open Calculation
Transition Speed: Open prices "catch up" to market movements faster
Lag Reduction Algorithm: Eliminates the typical delay in Heikin Ashi open calculations
Smooth Integration: Maintains visual continuity while improving responsiveness
Four-Color Scheme
- 🟢 **Lime**: Strong bullish momentum
- 🔴 **Red**: Strong bearish momentum
- 🟢 **Green**: Moderate bullish
- 🔴 **Maroon**: Moderate bearish
How It Works
Traditional Heikin Ashi smooths price action but often lags behind real market movements. This enhanced version:
1. Weights price components based on their predictive value
2. Accelerates trend transitions through advanced open calculations
3. Scales all enhancements through a single responsiveness parameter
4. Maintains smoothing benefits while reducing lag
Responsiveness (0.1 - 1.0)
0.1 - 0.3: Conservative, maximum smoothing
0.4 - 0.6: Balanced, good for swing trading and trend following
0.7 - 1.0: Aggressive, fast signals, suitable for scalping and active trading
Lorentzian Classification - Advanced Trading DashboardLorentzian Classification - Relativistic Market Analysis
A Journey from Theory to Trading Reality
What began as fascination with Einstein's relativity and Lorentzian geometry has evolved into a practical trading tool that bridges theoretical physics and market dynamics. This indicator represents months of wrestling with complex mathematical concepts, debugging intricate algorithms, and transforming abstract theory into actionable trading signals.
The Theoretical Foundation
Lorentzian Distance in Market Space
Traditional Euclidean distance treats all feature differences equally, but markets don't behave uniformly. Lorentzian distance, borrowed from spacetime geometry, provides a more nuanced similarity measure:
d(x,y) = Σ ln(1 + |xi - yi|)
This logarithmic formulation naturally handles:
Scale invariance: Large price moves don't overwhelm small but significant patterns
Outlier robustness: Extreme values are dampened rather than dominating
Non-linear relationships: Captures market behavior better than linear metrics
K-Nearest Neighbors with Relativistic Weighting
The algorithm searches historical market states for patterns similar to current conditions. Each neighbor receives weight inversely proportional to its Lorentzian distance:
w = 1 / (1 + distance)
This creates a "gravitational" effect where closer patterns have stronger influence on predictions.
The Implementation Challenge
Creating meaningful market features required extensive experimentation:
Price Features: Multi-timeframe momentum (1, 2, 3, 5, 8 bar lookbacks) Volume Features: Relative volume analysis against 20-period average
Volatility Features: ATR and Bollinger Band width normalization Momentum Features: RSI deviation from neutral and MACD/price ratio
Each feature undergoes min-max normalization to ensure equal weighting in distance calculations.
The Prediction Mechanism
For each current market state:
Feature Vector Construction: 12-dimensional representation of market conditions
Historical Search: Scan lookback period for similar patterns using Lorentzian distance
Neighbor Selection: Identify K nearest historical matches
Outcome Analysis: Examine what happened N bars after each match
Weighted Prediction: Combine outcomes using distance-based weights
Confidence Calculation: Measure agreement between neighbors
Technical Hurdles Overcome
Array Management: Complex indexing to prevent look-ahead bias
Distance Calculations: Optimizing nested loops for performance
Memory Constraints: Balancing lookback depth with computational limits
Signal Filtering: Preventing clustering of identical signals
Advanced Dashboard System
Main Control Panel
The primary dashboard provides real-time market intelligence:
Signal Status: Current prediction with confidence percentage
Neighbor Analysis: How many historical patterns match current conditions
Market Regime: Trend strength, volatility, and volume analysis
Temporal Context: Real-time updates with timestamp
Performance Analytics
Comprehensive tracking system monitors:
Win Rate: Percentage of successful predictions
Signal Count: Total predictions generated
Streak Analysis: Current winning/losing sequence
Drawdown Monitoring: Maximum equity decline
Sharpe Approximation: Risk-adjusted performance estimate
Risk Assessment Panel
Multi-dimensional risk analysis:
RSI Positioning: Overbought/oversold conditions
ATR Percentage: Current volatility relative to price
Bollinger Position: Price location within volatility bands
MACD Alignment: Momentum confirmation
Confidence Heatmap
Visual representation of prediction reliability:
Historical Confidence: Last 10 periods of prediction certainty
Strength Analysis: Magnitude of prediction values over time
Pattern Recognition: Color-coded confidence levels for quick assessment
Input Parameters Deep Dive
Core Algorithm Settings
K Nearest Neighbors (1-20): More neighbors create smoother but less responsive signals. Optimal range 5-8 for most markets.
Historical Lookback (50-500): Deeper history improves pattern recognition but reduces adaptability. 100-200 bars optimal for most timeframes.
Feature Window (5-30): Longer windows capture more context but reduce sensitivity. Match to your trading timeframe.
Feature Selection
Price Changes: Essential for momentum and reversal detection Volume Profile: Critical for institutional activity recognition Volatility Measures: Key for regime change detection Momentum Indicators: Vital for trend confirmation
Signal Generation
Prediction Horizon (1-20): How far ahead to predict. Shorter horizons for scalping, longer for swing trading.
Signal Threshold (0.5-0.9): Confidence required for signal generation. Higher values reduce false signals but may miss opportunities.
Smoothing (1-10): EMA applied to raw predictions. More smoothing reduces noise but increases lag.
Visual Design Philosophy
Color Themes
Professional: Corporate blue/red for institutional environments Neon: Cyberpunk cyan/magenta for modern aesthetics
Matrix: Green/red hacker-inspired palette Classic: Traditional trading colors
Information Hierarchy
The dashboard system prioritizes information by importance:
Primary Signals: Largest, most prominent display
Confidence Metrics: Secondary but clearly visible
Supporting Data: Detailed but unobtrusive
Historical Context: Available but not distracting
Trading Applications
Signal Interpretation
Long Signals: Prediction > threshold with high confidence
Look for volume confirmation
- Check trend alignment
- Verify support levels
Short Signals: Prediction < -threshold with high confidence
Confirm with resistance levels
- Check for distribution patterns
- Verify momentum divergence
- Market Regime Adaptation
Trending Markets: Higher confidence in directional signals
Ranging Markets: Focus on reversal signals at extremes
Volatile Markets: Require higher confidence thresholds
Low Volume: Reduce position sizes, increase caution
Risk Management Integration
Confidence-Based Sizing: Larger positions for higher confidence signals
Regime-Aware Stops: Wider stops in volatile regimes
Multi-Timeframe Confirmation: Align signals across timeframes
Volume Confirmation: Require volume support for major signals
Originality and Innovation
This indicator represents genuine innovation in several areas:
Mathematical Approach
First application of Lorentzian geometry to market pattern recognition. Unlike Euclidean-based systems, this naturally handles market non-linearities.
Feature Engineering
Sophisticated multi-dimensional feature space combining price, volume, volatility, and momentum in normalized form.
Visualization System
Professional-grade dashboard system providing comprehensive market intelligence in intuitive format.
Performance Tracking
Real-time performance analytics typically found only in institutional trading systems.
Development Journey
Creating this indicator involved overcoming numerous technical challenges:
Mathematical Complexity: Translating theoretical concepts into practical code
Performance Optimization: Balancing accuracy with computational efficiency
User Interface Design: Making complex data accessible and actionable
Signal Quality: Filtering noise while maintaining responsiveness
The result is a tool that brings institutional-grade analytics to individual traders while maintaining the theoretical rigor of its mathematical foundation.
Best Practices
- Parameter Optimization
- Start with default settings and adjust based on:
Market Characteristics: Volatile vs. stable
Trading Timeframe: Scalping vs. swing trading
Risk Tolerance: Conservative vs. aggressive
Signal Confirmation
Never trade on Lorentzian signals alone:
Price Action: Confirm with support/resistance
Volume: Verify with volume analysis
Multiple Timeframes: Check higher timeframe alignment
Market Context: Consider overall market conditions
Risk Management
Position Sizing: Scale with confidence levels
Stop Losses: Adapt to market volatility
Profit Targets: Based on historical performance
Maximum Risk: Never exceed 2-3% per trade
Disclaimer
This indicator is for educational and research purposes only. It does not constitute financial advice or guarantee profitable trading results. The Lorentzian classification system reveals market patterns but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose.
Market dynamics are inherently uncertain, and past performance does not guarantee future results. This tool should be used as part of a comprehensive trading strategy, not as a standalone solution.
Bringing the elegance of relativistic geometry to market analysis through sophisticated pattern recognition and intuitive visualization.
Thank you for sharing the idea. You're more than a follower, you're a leader!
@vasanthgautham1221
Trade with precision. Trade with insight.
— Dskyz , for DAFE Trading Systems
PhenLabs - Market Fluid Dynamics📊 Market Fluid Dynamics -
Version: PineScript™ v6
📌 Description
The Market Fluid Dynamics - Phen indicator is a new thinking regarding market analysis by modeling price action, volume, and volatility using a fluid system. It attempts to offer traders control over more profound market forces, such as momentum (speed), resistance (thickness), and buying/selling pressure. By visualizing such dynamics, the script allows the traders to decide on the prevailing market flow, its power, likely continuations, and zones of calmness and chaos, and thereby allows improved decision-making.
This measure avoids the usual difficulty of reconciling multiple, often contradictory, market indications by including them within a single overarching model. It moves beyond traditional binary indicators by providing a multi-dimensional view of market behavior, employing fluid dynamic analogs to describe complex interactions in an accessible manner.
🚀 Points of Innovation
Integrated Fluid Dynamics Model: Combines velocity, viscosity, pressure, and turbulence into a single indicator.
Normalized Metrics: Uses ATR and other normalization techniques for consistent readings across different assets and timeframes.
Dynamic Flow Visualization: Main flow line changes color and intensity based on direction and strength.
Turbulence Background: Visually represents market stability with a gradient background, from calm to turbulent.
Comprehensive Dashboard: Provides an at-a-glance summary of key fluid dynamic metrics.
Multi-Layer Smoothing: Employs several layers of EMA smoothing for a clearer, more responsive main flow line.
🔧 Core Components
Velocity Component: Measures price momentum (first derivative of price), normalized by ATR. It indicates the speed and direction of price changes.
Viscosity Component: Represents market resistance to price changes, derived from ATR relative to its historical average. Higher viscosity suggests it’s harder for prices to move.
Pressure Component: Quantifies the force created by volume and price range (close - open), normalized by ATR. It reflects buying or selling pressure.
Turbulence Detection: Calculates a Reynolds number equivalent to identify market stability, ranging from laminar (stable) to turbulent (chaotic).
Main Flow Indicator: Combines the above components, applying sensitivity and smoothing, to generate a primary signal of market direction and strength.
🔥 Key Features
Advanced Smoothing Algorithm: Utilizes multiple EMA layers on the raw flow calculation for a fluid and responsive main flow line, reducing noise while maintaining sensitivity.
Gradient Flow Coloring: The main flow line dynamically changes color from light to deep blue for bullish flow and light to deep red for bearish flow, with intensity reflecting flow strength. This provides an immediate visual cue of market sentiment and momentum.
Turbulence Level Background: The chart background changes color based on calculated turbulence (from calm gray to vibrant orange), offering an intuitive understanding of market stability and potential for erratic price action.
Informative Dashboard: A customizable on-screen table displays critical metrics like Flow State, Flow Strength, Market Viscosity, Turbulence, Pressure Force, Flow Acceleration, and Flow Continuity, allowing traders to quickly assess current market conditions.
Configurable Lookback and Sensitivity: Users can adjust the base lookback period for calculations and the sensitivity of the flow to viscosity, tailoring the indicator to different trading styles and market conditions.
Alert Conditions: Pre-defined alerts for flow direction changes (positive/negative crossover of zero line) and detection of high turbulence states.
🎨 Visualization
Main Flow Line: A smoothed line plotted below the main chart, colored blue for bullish flow and red for bearish flow. The intensity of the color (light to dark) indicates the strength of the flow. This line crossing the zero line can signal a change in market direction.
Zero Line: A dotted horizontal line at the zero level, serving as a baseline to gauge whether the market flow is positive (bullish) or negative (bearish).
Turbulence Background: The indicator pane’s background color changes based on the calculated turbulence level. A calm, almost transparent gray indicates low turbulence (laminar flow), while a more vibrant, semi-transparent orange signifies high turbulence. This helps traders visually assess market stability.
Dashboard Table: An optional table displayed on the chart, showing key metrics like ‘Flow State’, ‘Flow Strength’, ‘Market Viscosity’, ‘Turbulence’, ‘Pressure Force’, ‘Flow Acceleration’, and ‘Flow Continuity’ with their current values and qualitative descriptions (e.g., ‘Bullish Flow’, ‘Laminar (Stable)’).
📖 Usage Guidelines
Setting Categories
Show Dashboard - Default: true; Range: true/false; Description: Toggles the visibility of the Market Fluid Dynamics dashboard on the chart. Enable to see key metrics at a glance.
Base Lookback Period - Default: 14; Range: 5 - (no upper limit, practical limits apply); Description: Sets the primary lookback period for core calculations like velocity, ATR, and volume SMA. Shorter periods make the indicator more sensitive to recent price action, while longer periods provide a smoother, slower signal.
Flow Sensitivity - Default: 0.5; Range: 0.1 - 1.0 (step 0.1); Description: Adjusts how much the market viscosity dampens the raw flow. A lower value means viscosity has less impact (flow is more sensitive to raw velocity/pressure), while a higher value means viscosity has a greater dampening effect.
Flow Smoothing - Default: 5; Range: 1 - 20; Description: Controls the length of the EMA smoothing applied to the main flow line. Higher values result in a smoother flow line but with more lag; lower values make it more responsive but potentially noisier.
Dashboard Position - Default: ‘Top Right’; Range: ‘Top Right’, ‘Top Left’, ‘Bottom Right’, ‘Bottom Left’, ‘Middle Right’, ‘Middle Left’; Description: Determines the placement of the dashboard on the chart.
Header Size - Default: ‘Normal’; Range: ‘Tiny’, ‘Small’, ‘Normal’, ‘Large’, ‘Huge’; Description: Sets the text size for the dashboard header.
Values Size - Default: ‘Small’; Range: ‘Tiny’, ‘Small’, ‘Normal’, ‘Large’; Description: Sets the text size for the metric values in the dashboard.
✅ Best Use Cases
Trend Identification: Identifying the dominant market flow (bullish or bearish) and its strength to trade in the direction of the prevailing trend.
Momentum Confirmation: Using the flow strength and acceleration to confirm the conviction behind price movements.
Volatility Assessment: Utilizing the turbulence metric to gauge market stability, helping to adjust position sizing or avoid choppy conditions.
Reversal Spotting: Watching for divergences between price and flow, or crossovers of the main flow line above/below the zero line, as potential reversal signals, especially when combined with changes in pressure or viscosity.
Swing Trading: Leveraging the smoothed flow line to capture medium-term market swings, entering when flow aligns with the desired trade direction and exiting when flow weakens or reverses.
Intraday Scalping: Using shorter lookback periods and higher sensitivity to identify quick shifts in flow and turbulence for short-term trading opportunities, particularly in liquid markets.
⚠️ Limitations
Lagging Nature: Like many indicators based on moving averages and lookback periods, the main flow line can lag behind rapid price changes, potentially leading to delayed signals.
Whipsaws in Ranging Markets: During periods of low volatility or sideways price action (high viscosity, low flow strength), the indicator might produce frequent buy/sell signals (whipsaws) as the flow oscillates around the zero line.
Not a Standalone System: While comprehensive, it should be used in conjunction with other forms of analysis (e.g., price action, support/resistance levels, other indicators) and not as a sole basis for trading decisions.
Subjectivity in Interpretation: While the dashboard provides quantitative values, the interpretation of “strong” flow, “high” turbulence, or “significant” acceleration can still have a subjective element depending on the trader’s strategy and risk tolerance.
💡 What Makes This Unique
Fluid Dynamics Analogy: Its core strength lies in translating complex market interactions into an intuitive fluid dynamics framework, making concepts like momentum, resistance, and pressure easier to visualize and understand.
Market View: Instead of focusing on a single aspect (like just momentum or just volatility), it integrates multiple factors (velocity, viscosity, pressure, turbulence) to provide a more comprehensive picture of market conditions.
Adaptive Visualization: The dynamic coloring of the flow line and the turbulence background provide immediate, adaptive visual feedback that changes with market conditions.
🔬 How It Works
Price Velocity Calculation: The indicator first calculates price velocity by measuring the rate of change of the closing price over a given ‘lookback’ period. The raw velocity is then normalized by the Average True Range (ATR) of the same lookback period. Normalization enables comparison of momentum between assets or timeframes by scaling for volatility. This is the direction and speed of initial price movement.
Viscosity Calculation: Market ‘viscosity’ or resistance to price movement is determined by looking at the current ATR relative to its longer-term average (SMA of ATR over lookback * 2). The further the current ATR is above its average, the lower the viscosity (less resistance to price movement), and vice-versa. The script inverts this relationship and bounds it so that rising viscosity means more resistance.
Pressure Force Measurement: A ‘pressure’ variable is calculated as a function of the ratio of current volume to its simple moving average, multiplied by the price range (close - open) and normalized by ATR. This is designed to measure the force behind price movement created by volume and intraday price thrusts. This pressure is smoothed by an EMA.
Turbulence State Evaluation: A equivalent ‘Reynolds number’ is calculated by dividing the absolute normalized velocity by the viscosity. This is the proclivity of the market to move in a chaotic or orderly fashion. This ‘reynoldsValue’ is smoothed with an EMA to get the ‘turbulenceState’, which indicates if the market is laminar (stable), transitional, or turbulent.
Main Flow Derivation: The ‘rawFlow’ is calculated by taking the normalized velocity, dampening its impact based on the ‘viscosity’ and user-input ‘sensitivity’, and orienting it by the sign of the smoothed ‘pressureSmooth’. The ‘rawFlow’ is then put through multiple layers of exponential moving average (EMA) smoothing (with ‘smoothingLength’ and derived values) to reach the final ‘mainFlow’ line. The extensive smoothing is designed to give a smooth and clear visualization of the overall market direction and magnitude.
Dashboard Metrics Compilation: Additional metrics like flow acceleration (derivative of mainFlow), and flow continuity (correlation between close and volume) are calculated. All primary components (Flow State, Strength, Viscosity, Turbulence, Pressure, Acceleration, Continuity) are then presented in a user-configurable dashboard for ease of monitoring.
💡 Note:
The “Market Fluid Dynamics - Phen” indicator is designed to offer a unique perspective on market behavior by applying principles from fluid dynamics. It’s most effective when used to understand the underlying forces driving price rather than as a direct buy/sell signal generator in isolation. Experiment with the settings, particularly the ‘Base Lookback Period’, ‘Flow Sensitivity’, and ‘Flow Smoothing’, to find what best suits your trading style and the specific asset you are analyzing. Always combine its insights with robust risk management practices.
[blackcat] L1 Net Volume DifferenceOVERVIEW
The L1 Net Volume Difference indicator serves as an advanced analytical tool designed to provide traders with deep insights into market sentiment by examining the differential between buying and selling volumes over precise timeframes. By leveraging these volume dynamics, it helps identify trends and potential reversal points more accurately, thereby supporting well-informed decision-making processes. The key focus lies in dissecting intraday changes that reflect short-term market behavior, offering critical input for both swing and day traders alike. 📊
Key benefits encompass:
• Precise calculation of net volume differences grounded in real-time data.
• Interactive visualization elements enhancing interpretability effortlessly.
• Real-time generation of buy/sell signals driven by dynamic volume shifts.
TECHNICAL ANALYSIS COMPONENTS
📉 Volume Accumulation Mechanisms:
Monitors cumulative buy/sell volumes derived from comparative closing prices.
Periodically resets accumulation counters aligning with predefined intervals (e.g., 5-minute bars).
Facilitates identification of directional biases reflecting underlying market forces accurately.
🕵️♂️ Sentiment Detection Algorithms:
Employs proprietary logic distinguishing between bullish/bearish sentiments dynamically.
Ensures consistent adherence to predefined statistical protocols maintaining accuracy.
Supports adaptive thresholds adjusting sensitivities based on changing market conditions flexibly.
🎯 Dynamic Signal Generation:
Detects transitions indicating dominance shifts between buyers/sellers promptly.
Triggers timely alerts enabling swift reactions to evolving market dynamics effectively.
Integrates conditional logic reinforcing signal validity minimizing erroneous activations.
INDICATOR FUNCTIONALITY
🔢 Core Algorithms:
Utilizes moving averages along with standardized deviation formulas generating precise net volume measurements.
Implements Arithmetic Mean Line Algorithm (AMLA) smoothing techniques improving interpretability.
Ensures consistent alignment with established statistical principles preserving fidelity.
🖱️ User Interface Elements:
Dedicated plots displaying real-time net volume markers facilitating swift decision-making.
Context-sensitive color coding distinguishing positive/negative deviations intuitively.
Background shading highlighting proximity to key threshold activations enhancing visibility.
STRATEGY IMPLEMENTATION
✅ Entry Conditions:
Confirm bullish/bearish setups validated through multiple confirmatory signals.
Validate entry decisions considering concurrent market sentiment factors.
Assess alignment between net volume readings and broader trend directions ensuring coherence.
🚫 Exit Mechanisms:
Trigger exits upon hitting predetermined thresholds derived from historical analyses.
Monitor continuous breaches signifying potential trend reversals promptly executing closures.
Execute partial/total closes contingent upon cumulative loss limits preserving capital efficiently.
PARAMETER CONFIGURATIONS
🎯 Optimization Guidelines:
Reset Interval: Governs responsiveness versus stability balancing sensitivity/stability.
Price Source: Dictates primary data series driving volume calculations selecting relevant inputs accurately.
💬 Customization Recommendations:
Commence with baseline defaults; iteratively refine parameters isolating individual impacts.
Evaluate adjustments independently prior to combined modifications minimizing disruptions.
Prioritize minimizing erroneous trigger occurrences first optimizing signal fidelity.
Sustain balanced risk-reward profiles irrespective of chosen settings upholding disciplined approaches.
ADVANCED RISK MANAGEMENT
🛡️ Proactive Risk Mitigation Techniques:
Enforce strict compliance with pre-defined maximum leverage constraints adhering strictly to guidelines.
Mandatorily apply trailing stop-loss orders conforming to script outputs reinforcing discipline.
Allocate positions proportionately relative to available capital reserves managing exposures prudently.
Conduct periodic reviews gauging strategy effectiveness rigorously identifying areas needing refinement.
⚠️ Potential Pitfalls & Solutions:
Address frequent violations arising during heightened volatility phases necessitating manual interventions judiciously.
Manage false alerts warranting immediate attention avoiding adverse consequences systematically.
Prepare contingency plans mitigating margin call possibilities preparing proactive responses effectively.
Continuously assess automated system reliability amidst fluctuating conditions ensuring seamless functionality.
PERFORMANCE AUDITS & REFINEMENTS
🔍 Critical Evaluation Metrics:
Assess win percentages consistently across diverse trading instruments gauging reliability.
Calculate average profit ratios per successful execution measuring profitability efficiency accurately.
Measure peak drawdown durations alongside associated magnitudes evaluating downside risks comprehensively.
Analyze signal generation frequencies revealing hidden patterns potentially skewing outcomes uncovering systematic biases.
📈 Historical Data Analysis Tools:
Maintain comprehensive records capturing every triggered event meticulously documenting results.
Compare realized profits/losses against backtested simulations benchmarking actual vs expected performances accurately.
Identify recurrent systematic errors demanding corrective actions implementing iterative refinements steadily.
Document evolving performance metrics tracking progress dynamically addressing identified shortcomings proactively.
PROBLEM SOLVING ADVICE
🔧 Frequent Encountered Challenges:
Unpredictable behaviors emerging within thinly traded markets requiring filtration processes.
Latency issues manifesting during abrupt price fluctuations causing missed opportunities.
Overfitted models yielding suboptimal results post-extensive tuning demanding recalibrations.
Inaccuracies stemming from incomplete/inaccurate data feeds necessitating verification procedures.
💡 Effective Resolution Pathways:
Exclude low-liquidity assets prone to erratic movements enhancing signal integrity.
Introduce buffer intervals safeguarding major news/event impacts mitigating distortions effectively.
Limit ongoing optimization attempts preventing model degradation maintaining optimal performance levels consistently.
Verify reliable connections ensuring uninterrupted data flows guaranteeing accurate interpretations reliably.
USER ENGAGEMENT SEGMENT
🤝 Community Contributions Welcome
Highly encourage active participation sharing experiences & recommendations!
THANKS
Heartfelt acknowledgment extends to all developers contributing invaluable insights about volume-based trading methodologies! ✨
BollingerBands MTF | AlchimistOfCrypto🌌 Bollinger Bands – Unveiling Market Volatility Fields 🌌
"The Bollinger Bands, reimagined through quantum mechanics principles, visualizes the probabilistic distribution of price movements within a multi-dimensional volatility field. This indicator employs principles from wave function mathematics where standard deviation creates probabilistic boundaries, similar to electron cloud models in quantum physics. Our implementation features algorithmically enhanced visualization derived from extensive mathematical modeling, creating a dynamic representation of volatility compression and expansion cycles with adaptive glow effects that highlight the critical moments where volatility phase transitions occur."
📊 Professional Trading Application
The Bollinger Bands Quantum transcends traditional volatility measurement with a sophisticated gradient illumination system that reveals the underlying structure of market volatility fields. Scientifically calibrated for multiple timeframes and featuring eight distinct visual themes, it enables traders to perceive volatility contractions and expansions with unprecedented clarity.
⚙️ Indicator Configuration
- Volatility Field Parameters 📏
Python-optimized settings for specific market conditions:
- Period: 20 (default) - The quantum time window for volatility calculation
- StdDev Multiplier: 2.0 - The probabilistic boundary coefficient
- MA Type: SMA/EMA/VWMA/WMA/RMA - The quantum field smoothing algorithm
- Visual Theming 🎨
Eight scientifically designed visual palettes optimized for volatility pattern recognition:
- Neon (default): High-contrast green/red scheme enhancing volatility transition visibility
- Cyan-Magenta: Vibrant palette for maximum volatility boundary distinction
- Yellow-Purple: Complementary colors for enhanced compression/expansion detection
- Specialized themes (Green-Red, Forest Green, Blue Ocean, Orange-Red, Grayscale): Each calibrated for different market environments
- Opacity Control 🔍
- Variable transparency system (0-100) allowing seamless integration with price action
- Adaptive glow effect that intensifies during volatility phase transitions
- Quantum field visualization that reveals the probabilistic nature of price movements
🚀 How to Use
1. Select Visualization Parameters ⏰: Adjust period and standard deviation to match market conditions
2. Choose MA Type 🎚️: Select the appropriate smoothing algorithm for your trading strategy
3. Select Visual Theme 🌈: Choose a color scheme that enhances your personal pattern recognition
4. Adjust Opacity 🔎: Fine-tune visualization intensity to complement your chart analysis
5. Identify Volatility Phases ✅: Monitor band width to detect compression (pre-breakout) and expansion (trend)
6. Trade with Precision 🛡️: Enter during band contraction for breakouts, or trade mean reversion using band boundaries
7. Manage Risk Dynamically 🔐: Use band width as volatility-based position sizing parameter
Machine Learning RSI ║ BullVisionOverview:
Introducing the Machine Learning RSI with KNN Adaptation – a cutting-edge momentum indicator that blends the classic Relative Strength Index (RSI) with machine learning principles. By leveraging K-Nearest Neighbors (KNN), this indicator aims at identifying historical patterns that resemble current market behavior and uses this context to refine RSI readings with enhanced sensitivity and responsiveness.
Unlike traditional RSI models, which treat every market environment the same, this version adapts in real-time based on how similar past conditions evolved, offering an analytical edge without relying on predictive assumptions.
Key Features:
🔁 KNN-Based RSI Refinement
This indicator uses a machine learning algorithm (K-Nearest Neighbors) to compare current RSI and price action characteristics to similar historical conditions. The resulting RSI is weighted accordingly, producing a dynamically adjusted value that reflects historical context.
📈 Multi-Feature Similarity Analysis
Pattern similarity is calculated using up to five customizable features:
RSI level
RSI momentum
Volatility
Linear regression slope
Price momentum
Users can adjust how many features are used to tailor the behavior of the KNN logic.
🧠 Machine Learning Weight Control
The influence of the machine learning model on the final RSI output can be fine-tuned using a simple slider. This lets you blend traditional RSI and machine learning-enhanced RSI to suit your preferred level of adaptation.
🎛️ Adaptive Filtering
Additional smoothing options (Kalman Filter, ALMA, Double EMA) can be applied to the RSI, offering better visual clarity and helping to reduce noise in high-frequency environments.
🎨 Visual & Accessibility Settings
Custom color palettes, including support for color vision deficiencies, ensure that trend coloring remains readable for all users. A built-in neon mode adds high-contrast visuals to improve RSI visibility across dark or light themes.
How It Works:
Similarity Matching with KNN:
At each candle, the current RSI and optional market characteristics are compared to historical bars using a KNN search. The algorithm selects the closest matches and averages their RSI values, weighted by similarity. The more similar the pattern, the greater its influence.
Feature-Based Weighting:
Similarity is determined using normalized values of the selected features, which gives a more refined result than RSI alone. You can choose to use only 1 (RSI) or up to all 5 features for deeper analysis.
Filtering & Blending:
After the machine learning-enhanced RSI is calculated, it can be optionally smoothed using advanced filters to suppress short-term noise or sharp spikes. This makes it easier to evaluate RSI signals in different volatility regimes.
Parameters Explained:
📊 RSI Settings:
Set the base RSI length and select your preferred smoothing method from 10+ moving average types (e.g., EMA, ALMA, TEMA).
🧠 Machine Learning Controls:
Enable or disable the KNN engine
Select how many nearest neighbors to compare (K)
Choose the number of features used in similarity detection
Control how much the machine learning engine affects the RSI calculation
🔍 Filtering Options:
Enable one of several advanced smoothing techniques (Kalman Filter, ALMA, Double EMA) to adjust the indicator’s reactivity and stability.
📏 Threshold Levels:
Define static overbought/oversold boundaries or reference dynamically adjusted thresholds based on historical context identified by the KNN algorithm.
🎨 Visual Enhancements:
Select between trend-following or impulse coloring styles. Customize color palettes to accommodate different types of color blindness. Enable neon-style effects for visual clarity.
Use Cases:
Swing & Trend Traders
Can use the indicator to explore how current RSI readings compare to similar market phases, helping to assess trend strength or potential turning points.
Intraday Traders
Benefit from adjustable filters and fast-reacting smoothing to reduce noise in shorter timeframes while retaining contextual relevance.
Discretionary Analysts
Use the adaptive OB/OS thresholds and visual cues to supplement broader confluence zones or market structure analysis.
Customization Tips:
Higher Volatility Periods: Use more neighbors and enable filtering to reduce noise.
Lower Volatility Markets: Use fewer features and disable filtering for quicker RSI adaptation.
Deeper Contextual Analysis: Increase KNN lookback and raise the feature count to refine pattern recognition.
Accessibility Needs: Switch to Deuteranopia or Monochrome mode for clearer visuals in specific color vision conditions.
Final Thoughts:
The Machine Learning RSI combines familiar momentum logic with statistical context derived from historical similarity analysis. It does not attempt to predict price action but rather contextualizes RSI behavior with added nuance. This makes it a valuable tool for those looking to elevate traditional RSI workflows with adaptive, research-driven enhancements.
Wall Street Ai**Wall Street Ai – Advanced Technical Indicator for Market Analysis**
**Overview**
Wall Street Ai is an advanced, AI-powered technical indicator meticulously engineered to provide traders with in-depth market analysis and insight. By leveraging state-of-the-art artificial intelligence algorithms and comprehensive historical price data, Wall Street Ai is designed to identify significant market turning points and key price levels. Its sophisticated analytical framework enables traders to uncover potential shifts in market momentum, assisting in the formulation of strategic trading decisions while maintaining the highest standards of objectivity and reliability.
**Key Features**
- **Intelligent Pattern Recognition:**
Wall Street Ai employs advanced machine learning techniques to analyze historical price movements and detect recurring patterns. This capability allows it to differentiate between typical market noise and meaningful signals indicative of potential trend reversals.
- **Robust Noise Reduction:**
The indicator incorporates a refined volatility filtering system that minimizes the impact of minor price fluctuations. By isolating significant price movements, it ensures that the analytical output focuses on substantial market shifts rather than ephemeral variations.
- **Customizable Analytical Parameters:**
With a wide range of adjustable settings, Wall Street Ai can be fine-tuned to align with diverse trading strategies and risk appetites. Traders can modify sensitivity, threshold levels, and other critical parameters to optimize the indicator’s performance under various market conditions.
- **Comprehensive Data Analysis:**
By harnessing the power of artificial intelligence, Wall Street Ai performs a deep analysis of historical data, identifying statistically significant highs and lows. This analysis not only reflects past market behavior but also provides valuable insights into potential future turning points, thereby enhancing the predictive aspect of your trading strategy.
- **Adaptive Market Insights:**
The indicator’s dynamic algorithm continuously adjusts to current market conditions, adapting its analysis based on real-time data inputs. This adaptive quality ensures that the indicator remains relevant and effective across different market environments, whether the market is trending strongly, consolidating, or experiencing volatility.
- **Objective and Reliable Analysis:**
Wall Street Ai is built on a foundation of robust statistical methods and rigorous data validation. Its outputs are designed to be objective and free from any exaggerated claims, ensuring that traders receive a clear, unbiased view of market conditions.
**How It Works**
Wall Street Ai integrates advanced AI and deep learning methodologies to analyze a vast array of historical price data. Its core algorithm identifies and evaluates critical market levels by detecting patterns that have historically preceded significant market movements. By filtering out non-essential fluctuations, the indicator emphasizes key price extremes and trend changes that are likely to impact market behavior. The system’s adaptive nature allows it to recalibrate its analytical parameters in response to evolving market dynamics, providing a consistently reliable framework for market analysis.
**Usage Recommendations**
- **Optimal Timeframes:**
For the most effective application, it is recommended to utilize Wall Street Ai on higher timeframe charts, such as hourly (H1) or higher. This approach enhances the clarity of the detected patterns and provides a more comprehensive view of long-term market trends.
- **Market Versatility:**
Wall Street Ai is versatile and can be applied across a broad range of financial markets, including Forex, indices, commodities, cryptocurrencies, and equities. Its adaptable design ensures consistent performance regardless of the asset class being analyzed.
- **Complementary Analytical Tools:**
While Wall Street Ai provides profound insights into market behavior, it is best utilized in combination with other analytical tools and techniques. Integrating its analysis with additional indicators—such as trend lines, support/resistance levels, or momentum oscillators—can further refine your trading strategy and enhance decision-making.
- **Strategy Testing and Optimization:**
Traders are encouraged to test Wall Street Ai extensively in a simulated trading environment before deploying it in live markets. This allows for thorough calibration of its settings according to individual trading styles and risk management strategies, ensuring optimal performance across diverse market conditions.
**Risk Management and Best Practices**
Wall Street Ai is intended to serve as an analytical tool that supports informed trading decisions. However, as with any technical indicator, its outputs should be interpreted as part of a comprehensive trading strategy that includes robust risk management practices. Traders should continuously validate the indicator’s findings with additional analysis and maintain a disciplined approach to position sizing and risk control. Regular review and adjustment of trading strategies in response to market changes are essential to mitigate potential losses.
**Conclusion**
Wall Street Ai offers a cutting-edge, AI-driven approach to technical analysis, empowering traders with detailed market insights and the ability to identify potential turning points with precision. Its intelligent pattern recognition, adaptive analytical capabilities, and extensive noise reduction make it a valuable asset for both experienced traders and those new to market analysis. By integrating Wall Street Ai into your trading toolkit, you can enhance your understanding of market dynamics and develop a more robust, data-driven trading strategy—all while adhering to the highest standards of analytical integrity and performance.
Lowess Channel + (RSI) [ChartPrime]The Lowess Channel + (RSI) indicator applies the LOWESS (Locally Weighted Scatterplot Smoothing) algorithm to filter price fluctuations and construct a dynamic channel. LOWESS is a non-parametric regression method that smooths noisy data by fitting weighted linear regressions at localized segments. This technique is widely used in statistical analysis to reveal trends while preserving data structure.
In this indicator, the LOWESS algorithm is used to create a central trend line and deviation-based bands. The midline changes color based on trend direction, and diamonds are plotted when a trend shift occurs. Additionally, an RSI gauge is positioned at the end of the channel to display the current RSI level in relation to the price bands.
lowess_smooth(src, length, bandwidth) =>
sum_weights = 0.0
sum_weighted_y = 0.0
sum_weighted_xy = 0.0
sum_weighted_x2 = 0.0
sum_weighted_x = 0.0
for i = 0 to length - 1
x = float(i)
weight = math.exp(-0.5 * (x / bandwidth) * (x / bandwidth))
y = nz(src , 0)
sum_weights := sum_weights + weight
sum_weighted_x := sum_weighted_x + weight * x
sum_weighted_y := sum_weighted_y + weight * y
sum_weighted_xy := sum_weighted_xy + weight * x * y
sum_weighted_x2 := sum_weighted_x2 + weight * x * x
mean_x = sum_weighted_x / sum_weights
mean_y = sum_weighted_y / sum_weights
beta = (sum_weighted_xy - mean_x * mean_y * sum_weights) / (sum_weighted_x2 - mean_x * mean_x * sum_weights)
alpha = mean_y - beta * mean_x
alpha + beta * float(length / 2) // Centered smoothing
⯁ KEY FEATURES
LOWESS Price Filtering – Smooths price fluctuations to reveal the underlying trend with minimal lag.
Dynamic Trend Coloring – The midline changes color based on trend direction (e.g., bullish or bearish).
Trend Shift Diamonds – Marks points where the midline color changes, indicating a possible trend shift.
Deviation-Based Bands – Expands above and below the midline using ATR-based multipliers for volatility tracking.
RSI Gauge Display – A vertical gauge at the right side of the chart shows the current RSI level relative to the price channel.
Fully Customizable – Users can adjust LOWESS length, band width, colors, and enable or disable the RSI gauge and adjust RSIlength.
⯁ HOW TO USE
Use the LOWESS midline as a trend filter —bullish when green, bearish when purple.
Watch for trend shift diamonds as potential entry or exit signals.
Utilize the price bands to gauge overbought and oversold zones based on volatility.
Monitor the RSI gauge to confirm trend strength—high RSI near upper bands suggests overbought conditions, while low RSI near lower bands indicates oversold conditions.
⯁ CONCLUSION
The Lowess Channel + (RSI) indicator offers a powerful way to analyze market trends by applying a statistically robust smoothing algorithm. Unlike traditional moving averages, LOWESS filtering provides a flexible, responsive trendline that adapts to price movements. The integrated RSI gauge enhances decision-making by displaying momentum conditions alongside trend dynamics. Whether used for trend-following or mean reversion strategies, this indicator provides traders with a well-rounded perspective on market behavior.
*Auto Backtest & Optimize EngineFull-featured Engine for Automatic Backtesting and parameter optimization. Allows you to test millions of different combinations of stop-loss and take profit parameters, including on any connected indicators.
⭕️ Key Futures
Quickly identify the optimal parameters for your strategy.
Automatically generate and test thousands of parameter combinations.
A simple Genetic Algorithm for result selection.
Saves time on manual testing of multiple parameters.
Detailed analysis, sorting, filtering and statistics of results.
Detailed control panel with many tooltips.
Display of key metrics: Profit, Win Rate, etc..
Comprehensive Strategy Score calculation.
In-depth analysis of the performance of different types of stop-losses.
Possibility to use to calculate the best Stop-Take parameters for your position.
Ability to test your own functions and signals.
Customizable visualization of results.
Flexible Stop-Loss Settings:
• Auto ━ Allows you to test all types of Stop Losses at once(listed below).
• S.VOLATY ━ Static stop based on volatility (Fixed, ATR, STDEV).
• Trailing ━ Classic trailing stop following the price.
• Fast Trail ━ Accelerated trailing stop that reacts faster to price movements.
• Volatility ━ Dynamic stop based on volatility indicators.
• Chandelier ━ Stop based on price extremes.
• Activator ━ Dynamic stop based on SAR.
• MA ━ Stop based on moving averages (9 different types).
• SAR ━ Parabolic SAR (Stop and Reverse).
Advanced Take-Profit Options:
• R:R: Risk/Reward ━ sets TP based on SL size.
• T.VOLATY ━ Calculation based on volatility indicators (Fixed, ATR, STDEV).
Testing Modes:
• Stops ━ Cyclical stop-loss testing
• Pivot Point Example ━ Example of using pivot points
• External Example ━ Built-in example how test functions with different parameters
• External Signal ━ Using external signals
⭕️ Usage
━ First Steps:
When opening, select any point on the chart. It will not affect anything until you turn on Manual Start mode (more on this below).
The chart will immediately show the best results of the default Auto mode. You can switch Part's to try to find even better results in the table.
Now you can display any result from the table on the chart by entering its ID in the settings.
Repeat steps 3-4 until you determine which type of Stop Loss you like best. Then set it in the settings instead of Auto mode.
* Example: I flipped through 14 parts before I liked the first result and entered its ID so I could visually evaluate it on the chart.
Then select the stop loss type, choose it in place of Auto mode and repeat steps 3-4 or immediately follow the recommendations of the algorithm.
Now the Genetic Algorithm at the bottom right will prompt you to enter the Parameters you need to search for and select even better results.
Parameters must be entered All at once before they are updated. Enter recommendations strictly in fields with the same names.
Repeat steps 5-6 until there are approximately 10 Part's left or as you like. And after that, easily pour through the remaining Parts and select the best parameters.
━ Example of the finished result.
━ Example of use with Takes
You can also test at the same time along with Take Profit. In this example, I simply enabled Risk/Reward mode and immediately specified in the TP field Maximum RR, Minimum RR and Step. So in this example I can test (3-1) / 0.1 = 20 Takes of different sizes. There are additional tips in the settings.
━
* Soon you will start to understand how the system works and things will become much easier.
* If something doesn't work, just reset the engine settings and start over again.
* Use the tips I have left in the settings and on the Panel.
━ Details:
Sort ━ Sorting results by Score, Profit, Trades, etc..
Filter ━ Filtring results by Score, Profit, Trades, etc..
Trade Type ━ Ability to disable Long\Short but only from statistics.
BackWin ━ Backtest Window Number of Candle the script can test.
Manual Start ━ Enabling it will allow you to call a Stop from a selected point. which you selected when you started the engine.
* If you have a real open position then this mode can help to save good Stop\Take for it.
1 - 9 Сheckboxs ━ Allow you to disable any stop from Auto mode.
Ex Source - Allow you to test Stops/Takes from connected indicators.
Connection guide:
//@version=6
indicator("My script")
rsi = ta.rsi(close, 14)
buy = not na(rsi) and ta.crossover (rsi, 40) // OS = 40
sell = not na(rsi) and ta.crossunder(rsi, 60) // OB = 60
Signal = buy ? +1 : sell ? -1 : 0
plot(Signal, "🔌Connector🔌", display = display.none)
* Format the signal for your indicator in a similar style and then select it in Ex Source.
⭕️ How it Works
Hypothesis of Uniform Distribution of Rare Elements After Mixing.
'This hypothesis states that if an array of N elements contains K valid elements, then after mixing, these valid elements will be approximately uniformly distributed.'
'This means that in a random sample of k elements, the proportion of valid elements should closely match their proportion in the original array, with some random variation.'
'According to the central limit theorem, repeated sampling will result in an average count of valid elements following a normal distribution.'
'This supports the assumption that the valid elements are evenly spread across the array.'
'To test this hypothesis, we can conduct an experiment:'
'Create an array of 1,000,000 elements.'
'Select 1,000 random elements (1%) for validation.'
'Shuffle the array and divide it into groups of 1,000 elements.'
'If the hypothesis holds, each group should contain, on average, 1~ valid element, with minor variations.'
* I'd like to attach more details to My hypothesis but it won't be very relevant here. Since this is a whole separate topic, I will leave the minimum part for understanding the engine.
Practical Application
To apply this hypothesis, I needed a way to generate and thoroughly mix numerous possible combinations. Within Pine, generating over 100,000 combinations presents significant challenges, and storing millions of combinations requires excessive resources.
I developed an efficient mechanism that generates combinations in random order to address these limitations. While conventional methods often produce duplicates or require generating a complete list first, my approach guarantees that the first 10% of possible combinations are both unique and well-distributed. Based on my hypothesis, this sampling is sufficient to determine optimal testing parameters.
Most generators and randomizers fail to accommodate both my hypothesis and Pine's constraints. My solution utilizes a simple Linear Congruential Generator (LCG) for pseudo-randomization, enhanced with prime numbers to increase entropy during generation. I pre-generate the entire parameter range and then apply systematic mixing. This approach, combined with a hybrid combinatorial array-filling technique with linear distribution, delivers excellent generation quality.
My engine can efficiently generate and verify 300 unique combinations per batch. Based on the above, to determine optimal values, only 10-20 Parts need to be manually scrolled through to find the appropriate value or range, eliminating the need for exhaustive testing of millions of parameter combinations.
For the Score statistic I applied all the same, generated a range of Weights, distributed them randomly for each type of statistic to avoid manual distribution.
Score ━ based on Trade, Profit, WinRate, Profit Factor, Drawdown, Sharpe & Sortino & Omega & Calmar Ratio.
⭕️ Notes
For attentive users, a little tricks :)
To save time, switch parts every 3 seconds without waiting for it to load. After 10-20 parts, stop and wait for loading. If the pause is correct, you can switch between the rest of the parts without loading, as they will be cached. This used to work without having to wait for a pause, but now it does slower. This will save a lot of time if you are going to do a deeper backtest.
Sometimes you'll get the error “The scripts take too long to execute.”
For a quick fix you just need to switch the TF or Ticker back and forth and most likely everything will load.
The error appears because of problems on the side of the site because the engine is very heavy. It can also appear if you set too long a period for testing in BackWin or use a heavy indicator for testing.
Manual Start - Allow you to Start you Result from any point. Which in turn can help you choose a good stop-stick for your real position.
* It took me half a year from idea to current realization. This seems to be one of the few ways to build something automatic in backtest format and in this particular Pine environment. There are already better projects in other languages, and they are created much easier and faster because there are no limitations except for personal PC. If you see solutions to improve this system I would be glad if you share the code. At the moment I am tired and will continue him not soon.
Also You can use my previosly big Backtest project with more manual settings(updated soon)
Clustering & Divergences (RSI-Stoch-CCI) [Sam SDF-Solutions]The Clustering & Divergences (RSI-Stoch-CCI) indicator is a comprehensive technical analysis tool that consolidates three popular oscillators—Relative Strength Index (RSI), Stochastic, and Commodity Channel Index (CCI)—into one unified metric called the Score. This Score offers traders an aggregated view of market conditions, allowing them to quickly identify whether the market is oversold, balanced, or overbought.
Functionality:
Oscillator Clustering: The indicator calculates the values of RSI, Stochastic, and CCI using user-defined periods. These oscillator values are then normalized using one of three available methods: MinMax, Z-Score, or Z-Bins.
Score Calculation: Each normalized oscillator value is multiplied by its respective weight (which the user can adjust), and the weighted values are summed to generate an overall Score. This Score serves as a single, interpretable metric representing the combined oscillator behavior.
Market Clustering: The indicator performs clustering on the Score over a configurable window. By dividing the Score range into a set number of clusters (also configurable), the tool visually represents the market’s state. Each cluster is assigned a unique color so that traders can quickly see if the market is trending toward oversold, balanced, or overbought conditions.
Divergence Detection: The script automatically identifies both Regular and Hidden divergences between the price action and the Score. By using pivot detection on both price and Score data, the indicator marks potential reversal signals on the chart with labels and connecting lines. This helps in pinpointing moments when the price and the underlying oscillator dynamics diverge.
Customization Options: Users have full control over the indicator’s behavior. They can adjust:
The periods for each oscillator (RSI, Stochastic, CCI).
The weights applied to each oscillator in the Score calculation.
The normalization method and its manual boundaries.
The number of clusters and whether to invert the cluster order.
Parameters for divergence detection (such as pivot sensitivity and the minimum/maximum bar distance between pivots).
Visual Enhancements:
Depending on the user’s preference, either the Score or the Cluster Index (derived from the clustering process) is plotted on the chart. Additionally, the script changes the color of the price bars based on the identified cluster, providing an at-a-glance visual cue of the current market regime.
Logic & Methodology:
Input Parameters: The script starts by accepting user inputs for clustering settings, oscillator periods, weights, divergence detection, and manual boundary definitions for normalization.
Oscillator Calculation & Normalization: It computes RSI, Stochastic, and CCI values from the price data. These values are then normalized using either the MinMax method (scaling between a lower and upper band) or the Z-Score method (standardizing based on mean and standard deviation), or using Z-Bins for an alternative scaling approach.
Score Computation: Each normalized oscillator is multiplied by its corresponding weight. The sum of these products results in the overall Score that represents the combined oscillator behavior.
Clustering Algorithm: The Score is evaluated over a moving window to determine its minimum and maximum values. Using these values, the script calculates a cluster index that divides the Score into a predefined number of clusters. An option to invert the cluster calculation is provided to adjust the interpretation of the clustering.
Divergence Analysis: The indicator employs pivot detection (using left and right bar parameters) on both the price and the Score. It then compares recent pivot values to detect regular and hidden divergences. When a divergence is found, the script plots labels and optional connecting lines to highlight these key moments on the chart.
Plotting: Finally, based on the user’s selection, the indicator plots either the Score or the Cluster Index. It also overlays manual boundary lines (for the chosen normalization method) and adjusts the bar colors according to the cluster to provide clear visual feedback on market conditions.
_________
By integrating multiple oscillator signals into one cohesive tool, the Clustering & Divergences (RSI-Stoch-CCI) indicator helps traders minimize subjective analysis. Its dynamic clustering and automated divergence detection provide a streamlined method for assessing market conditions and potentially enhancing the accuracy of trading decisions.
For further details on using this indicator, please refer to the guide available at:
Percentage Based ZigZag█ OVERVIEW
The Percentage-Based ZigZag indicator is a custom technical analysis tool designed to highlight significant price reversals while filtering out market noise. Unlike many standard zigzag tools that rely solely on fixed price moves or generic trend-following methods, this indicator uses a configurable percentage threshold to dynamically determine meaningful pivot points. This approach not only adapts to different market conditions but also helps traders distinguish between minor fluctuations and truly significant trend shifts—whether scalping on shorter timeframes or analyzing longer-term trends.
█ KEY FEATURES & ORIGINALITY
Dynamic Pivot Detection
The indicator identifies pivot points by measuring the percentage change from the previous extreme (high or low). Only when this change exceeds a user-defined threshold is a new pivot recognized. This method ensures that only substantial moves are considered, making the indicator robust in volatile or noisy markets.
Enhanced ZigZag Visualization
By connecting significant highs and lows with a continuous line, the indicator creates a clear visual map of price swings. Each pivot point is labelled with the corresponding price and the percentage change from the previous pivot, providing immediate quantitative insight into the magnitude of the move.
Trend Reversal Projections
In addition to marking completed reversals, the script computes and displays potential future reversal points based on the current trend’s momentum. This forecasting element gives traders an advanced look at possible turning points, which can be particularly useful for short-term scalping strategies.
Customizable Visual Settings
Users can tailor the appearance by:
• Setting the percentage threshold to control sensitivity.
• Customizing colors for bullish (e.g., green) and bearish (e.g., red) reversals.
• Enabling optional background color changes that visually indicate the prevailing trend.
█ UNDERLYING METHODOLOGY & CALCULATIONS
Percentage-Based Filtering
The script continuously monitors price action and calculates the relative percentage change from the last identified pivot. A new pivot is confirmed only when the price moves a preset percentage away from this pivot, ensuring that minor fluctuations do not trigger false signals.
Pivot Point Logic
The indicator tracks the highest high and the lowest low since the last pivot. When the price reverses by the required percentage from these extremes, the algorithm:
1 — Labels the point as a significant high or low.
2 — Draws a connecting line from the previous pivot to the current one.
3 — Resets the extreme-tracking for detecting the next move.
Real-Time Reversal Estimation
Building on traditional zigzag methods, the script incorporates a projection calculation. By analyzing the current trend’s strength and recent percentage moves, it estimates where a future reversal might occur, offering traders actionable foresight.
█ HOW TO USE THE INDICATOR
1 — Apply the Indicator
• Add the Percentage-Based ZigZag indicator to your trading chart.
2 — Adjust Settings for Your Market
• Percentage Move – Set a threshold that matches your trading style:
- Lower values for sensitive, high-frequency analysis (ideal for scalping).
- Higher values for filtering out noise on longer timeframes.
• Visual Customization – Choose your preferred colors for bullish and bearish signals and enable background color changes for visual trend cues.
• Reversal Projection – Enable or disable the projection feature to display potential upcoming reversal points.
3 — Interpret the Signals
• ZigZag Lines – White lines trace significant high-to-low or low-to-high movements, visually connecting key swing points.
• Pivot Labels – Each pivot is annotated with the exact price level and percentage change, providing quantitative insight into market momentum.
• Trend Projections – When enabled, projected reversal levels offer insight into where the current trend might change.
4 — Integrate with Your Trading Strategy
• Use the indicator to identify support and resistance zones derived from significant pivots.
• Combine the quantitative data (percentage changes) with your risk management strategy to set optimal stop-loss and take-profit levels.
• Experiment with different threshold settings to adapt the indicator for various instruments or market conditions.
█ CONCLUSION
The Percentage-Based ZigZag indicator goes beyond traditional trend-following tools by filtering out market noise and providing clear, quantifiable insights into price action. With its percentage threshold for pivot detection and real-time reversal projections, this original methodology and customizable feature set offer traders a versatile edge for making informed trading decisions.
Cluster Reversal Zones📌 Cluster Reversal Zones – Smart Market Turning Point Detector
📌 Category : Public (Restricted/Closed-Source) Indicator
📌 Designed for : Traders looking for high-accuracy reversal zones based on price clustering & liquidity shifts.
🔍 Overview
The Cluster Reversal Zones Indicator is an advanced market reversal detection tool that helps traders identify key turning points using a combination of price clustering, order flow analysis, and liquidity tracking. Instead of relying on static support and resistance levels, this tool dynamically adjusts to live market conditions, ensuring traders get the most accurate reversal signals possible.
📊 Core Features:
✅ Real-Time Reversal Zone Mapping – Detects high-probability market turning points using price clustering & order flow imbalance.
✅ Liquidity-Based Support/Resistance Detection – Identifies strong rejection zones based on real-time liquidity shifts.
✅ Order Flow Sensitivity for Smart Filtering – Filters out weak reversals by detecting real market participation behind price movements.
✅ Momentum Divergence for Confirmation – Aligns reversal zones with momentum divergences to increase accuracy.
✅ Adaptive Risk Management System – Adjusts risk parameters dynamically based on volatility and trend state.
🔒 Justification for Mashup
The Cluster Reversal Zones Indicator contains custom-built methodologies that extend beyond traditional support/resistance indicators:
✔ Smart Price Clustering Algorithm: Instead of plotting fixed support/resistance lines, this system analyzes historical price clustering to detect active reversal areas.
✔ Order Flow Delta & Liquidity Shift Sensitivity: The tool tracks real-time order flow data, identifying price zones with the highest accumulation or distribution levels.
✔ Momentum-Based Reversal Validation: Unlike traditional indicators, this tool requires a momentum shift confirmation before validating a potential reversal.
✔ Adaptive Reversal Filtering Mechanism: Uses a combination of historical confluence detection + live market validation to improve accuracy.
🛠️ How to Use:
• Works well for reversal traders, scalpers, and swing traders seeking precise turning points.
• Best combined with VWAP, Market Profile, and Delta Volume indicators for confirmation.
• Suitable for Forex, Indices, Commodities, Crypto, and Stock markets.
🚨 Important Note:
For educational & analytical purposes only.
Ehlers Maclaurin Ultimate Smoother [CT]Ehlers Maclaurin Ultimate Smoother
Introduction
The Ehlers Maclaurin Ultimate Smoother is an innovative enhancement of the classic Ehlers SuperSmoother. By leveraging advanced Maclaurin series approximations, this indicator offers superior market analysis and signal generation.
The indicator combines Ehlers' Ultimate Smoother with Maclaurin series approximations to create a more efficient and accurate smoothing mechanism:
Input price data passes through the initial smoothing phase
Maclaurin series approximates trigonometric functions
Enhanced high-pass filter removes market noise
Final smoothing phase produces the output signal
Why the Maclaurin Approach?
The Maclaurin series is a special form of the Taylor series, centered around 0. It provides an efficient way to approximate complex functions using polynomial terms. In this indicator, we use the Maclaurin approach to improve the sine and cosine functions, resulting in:
Faster Calculations: By using polynomial approximations, we significantly reduce computational complexity.
Improved Stability: The approximation provides a more stable numerical basis for calculations.
Preservation of Precision: Despite the approximation, we maintain the precision needed for price smoothing.
Calculations
The indicator employs several key mathematical components:
Maclaurin Series Approximation:
sin(x) ≈ x - x³/3! + x⁵/5! - x⁷/7! + x⁹/9!
cos(x) ≈ 1 - x²/2! + x⁴/4! - x⁶/6! + x⁸/8!
Smoothing Algorithm:
Uses exponential smoothing with optimized coefficients
Implements high-pass filtering for noise reduction
Applies dynamic weighting based on market conditions
Mathematical Foundation
Utilizes Maclaurin series for trigonometric approximation
Implements Ehlers' smoothing principles
Incorporates advanced filtering techniques
Technical Advantages
Signal Processing:
Lag Reduction: Faster signal detection with less delay.
Noise Filtration: Effective elimination of high-frequency noise.
Precision Enhancement: Preservation of critical price movements.
Adaptive Processing: Dynamic response to market volatility.
Visual Enhancements:
Smart color intensity mapping.
Real-time visualization of trend strength.
Adaptive opacity based on movement significance.
Implementation
Core Configuration:
Plot Type: Choose between the original and the Maclaurin enhanced version.
Length: Default set to 30, optimal for daily timeframes.
hpLength: Default set to 10 for enhanced noise reduction.
Advanced Parameters:
The indicator offers advanced control with:
Dual processing modes (Original/Maclaurin).
Dynamic color intensity system.
Customizable smoothing parameters.
Professional Analysis Tools:
Accurate trend reversal identification.
Advanced support/resistance detection.
Superior performance in volatile markets.
Technical Specifications
Maclaurin Series Implementation:
The indicator employs a 5-term Maclaurin series approximation for both sine and cosine, ensuring efficient and accurate computation.
Performance Metrics
Improved processing efficiency.
Reduced memory utilization.
Increased signal accuracy.
Licensing & Attribution
© 2024 Mupsje aka CasaTropical
Professional Credits
Original Ultimate and SuperSmoother concept: John F. Ehlers
Maclaurin enhancement: Casa Tropical (CT)
www.mathsisfun.com
