Elliott's Quadratic Momentum - Strategy [presentTrading]█ Introduction and How It Is Different
The "Elliott's Quadratic Momentum - Strategy" is a unique and innovative approach in the realm of technical trading. This strategy is a fusion of multiple SuperTrend indicators combined with an Elliott Wave-like pattern analysis, offering a comprehensive and dynamic trading tool. It stands apart from conventional strategies by incorporating multiple layers of trend analysis, thereby providing a more robust and nuanced view of market movements.
*Although the script doesn't explicitly analyze Elliott Wave patterns, it employs a wave-like approach by considering multiple SuperTrend indicators. Elliott Wave theory is based on the premise that markets move in predictable wave patterns. While this script doesn't identify specific Elliott Wave structures like impulsive and corrective waves, the sequential checking of trend conditions across multiple SuperTrend indicators mimics a wave-like progression.
BTC 8hr Long/Short Performance
Local Detail
█ Strategy, How It Works: Detailed Explanation
The core of this strategy lies in its multi-tiered approach:
1. Multiple SuperTrend Indicators:
The strategy employs four different SuperTrend indicators, each with unique ATR lengths and multipliers. These indicators offer various perspectives on market trends, ranging from short to long-term views.
By analyzing the convergence of these indicators, the strategy can pinpoint robust entry signals for both long and short positions.
2. Elliott Wave-like Pattern Recognition:
While not directly applying Elliott Wave theory, the strategy takes inspiration from its pattern recognition approach. It looks for alignments in market movements that resemble the characteristic waves of Elliott's theory.
This pattern recognition aids in confirming the signals provided by the SuperTrend indicators, adding an extra layer of validation to the trading signals.
3. Comprehensive Market Analysis:
By combining multiple indicators and pattern analysis, the strategy offers a holistic view of the market. This allows for capturing potential trend reversals and significant market moves early.
█ Trade Direction
The strategy is designed with flexibility in mind, allowing traders to select their preferred trading direction – Long, Short, or Both. This adaptability is key for traders looking to tailor their approach to different market conditions or personal trading styles. The strategy automatically adjusts its logic based on the chosen direction, ensuring that traders are always aligned with their strategic objectives.
█ Usage
To utilize the "Elliott's Quadratic Momentum - Strategy" effectively:
Traders should first determine their trading direction and adjust the SuperTrend settings according to their market analysis and risk appetite.
The strategy is versatile and can be applied across various time frames and asset classes, making it suitable for a wide range of trading scenarios.
It's particularly effective in trending markets, where the alignment of multiple SuperTrend indicators can provide strong trade signals.
█ Default Settings
Trading Direction: Configurable (Long, Short, Both)
SuperTrend Settings:
SuperTrend 1: ATR Length 7, Multiplier 4.0
SuperTrend 2: ATR Length 14, Multiplier 3.618
SuperTrend 3: ATR Length 21, Multiplier 3.5
SuperTrend 4: ATR Length 28, Multiplier 3.382
Additional Settings: Gradient effect for trend visualization, customizable color schemes for upward and downward trends.
Search in scripts for "pattern"
Southern Star Shadows with AlertThe "Southern Star Shadows with Alert" indicator in Pine Script is designed to identify and visually represent a specific candlestick pattern known as the "Southern Star Shadows" pattern on a TradingView chart. This pattern can provide traders with potential signals for both bullish and bearish market conditions.
Here's a short description of how the indicator works:
Pattern Identification: The indicator scans price data to identify the conditions that constitute a "Southern Star Shadows" pattern. It checks for a combination of factors, including the relationship between the current and previous candle's high, low, open, and close prices.
Signal Generation: The indicator assigns a signal based on the identified pattern. It generates a "1" for a bullish signal and "-1" for a bearish signal. If the pattern conditions are not met, it assigns a "0," indicating no clear signal.
Visualization: The indicator visually represents the signals by coloring the price bars. Bullish signals are typically colored in blue, while bearish signals are colored in red.
Triangle Plots: Additionally, the indicator plots small triangle shapes above the respective candles to highlight where the pattern occurred. Green triangles are used for bullish signals, and red triangles are used for bearish signals.
Alerts: Traders can set up alerts based on the indicator. When the pattern is detected and a signal is generated, the indicator sends an alert message, providing traders with a timely notification of potential trading opportunities.
Overall, the "Southern Star Shadows with Alert" indicator helps traders identify and react to potential trend reversal or continuation opportunities in the market by recognizing specific candlestick patterns and providing visual and alert-based signals.
Sushi Trend [HG]🍣 The Sushi Roll, a trading concept conceived at a restaurant by Mark Fisher.
While the indicator itself goes by Sushi Trend, it is completely backed by the idea of Mark Fisher's Sushi Roll Reversal Pattern. No, it has nothing to do with raw fish, it just so happens that somebody was ordering sushi during the discussion of the idea, and that's how it got its name.
📝 Origin
First mentioned in his book, The Logical Trader --- the idea of the Sushi Roll is to serve as an early warning system to identify reversals in the market. Fisher defines the pattern as a series of 10 bars, split into two different sections, seen as 5 and 5. In order for the pattern to be emitted, the 5 bars to the right must completely engulf the 5 bars to the left. It's not a super complex system and is in fact extremely simple to grasp.
📈 Supertrend Similarities
Instead of displaying the pattern in the way Fisher meant for it to be portrayed (as seen in the photo above), I instead turned it into an indicator similar to that of Supertrend while also inheriting the same concepts from the pattern. I did this because the pattern itself has inconsistencies which can be quite noticeable when trading with it after a while. For example, these patterns can occur even during consolidating periods, and even though the pattern is meant to be recognized during trending markets, the engulfing bars can sometimes be left with indecisive directions.
➡️ The Result
Here is the result, visualized to be better in a trending format. (The indicator will not contain the boxes.)
While Fisher does mention the pattern to include 10 bars, you can actually use this pattern with any number of bars. At the end of the day, it's a concept derived from a discussion at a Japanese restaurant, and a pattern that has been around for years that has seen results. Due to this, I added an input option to control the series of bars for right-bar engulf detection.
To reassure the meaning of the pattern --> "A series of 10 bars" means 5 left bars and 5 right bars. So if you want to check if 5 right bars are engulfing the previous 5 bars (as seen in the photo above), you would want to select 5 in the input settings.
You can learn more about it from the following links
Market Reversals and the Sushi Roll Technique
The Logical Trader
Lex_3CR_Functions_Library2Library "Lex_3CR_Functions_Library2"
This is a source code for a technical analysis library in Pine Script language,
designed to identify and mark Bullish and Bearish Three Candle Reversal (3CR) chart patterns.
The library provides three functions to be used in a trading algorithm.
The first function, Bull_3crMarker, adds a dashed line and label to a Bullish 3CR chart pattern, indicating the 3CR point.
The second function, Bear_3crMarker, adds a dashed line and label to a Bearish 3CR chart pattern.
The third function, Bull_3CRlogicals, checks for a Bullish 3CR pattern where the first candle's low is greater than the second candle's low and the second candle's low is less than the third candle's low.
If found, creates a line at the breakout point and a label at the fail point,
if specified. All functions take parameters such as the chart pattern's characteristics and output colors, labels, and markers.
Bull_3crMarker(bulllinearray, barnum, breakpoint, failpointB, failpoint, linecolorbull, bulllabelarray, labelcolor, textcolor, labelon)
Bull_3crMarker Adds a 3CR marker to a Bullish 3CR chart pattern
@description Adds a dashed line and label to a 3CR up chart pattern, indicating the 3CR (3 Candle Reversal) point.
Parameters:
bulllinearray (line )
barnum (int)
breakpoint (float)
failpointB (float )
failpoint (float)
linecolorbull (color)
bulllabelarray (label )
labelcolor (color)
textcolor (color)
labelon (bool)
Bear_3crMarker(bearlinearray, barnum, breakpoint, failpointB, failpoint, linecolorbear, bearlabelarray, labelcolor, textcolor, labelon)
Bear_3crMarker Adds a 3CR marker to a Bearish 3CR chart pattern
@description Adds a dashed line and label to a 3CR down chart pattern, indicating the 3CR (3 Candle Reversal) point.
Parameters:
bearlinearray (line )
barnum (int)
breakpoint (float)
failpointB (float )
failpoint (float)
linecolorbear (color)
bearlabelarray (label )
labelcolor (color)
textcolor (color)
labelon (bool)
Bull_3CRlogicals(low1, low2, low3, bulllinearray, bulllabelarray, failpointB, linecolorbull, labelcolor, textcolor, labelon)
Checks for a bullish three candle reversal pattern and creates a line and label at the breakout point if found
@description Checks for a bullish three candle reversal pattern where the first candle's low is greater than the second candle's low and the second candle's low is less than the third candle's low. If found, creates a line at the breakout point and a label at the fail point, if specified.
Parameters:
low1 (float)
low2 (float)
low3 (float)
bulllinearray (line )
bulllabelarray (label )
failpointB (float )
linecolorbull (color)
labelcolor (color)
textcolor (color)
labelon (bool)
Bear_3CRlogicals(high1, high2, high3, bearlinearray, bearlabelarray, failpointB, linecolorbear, labelcolor, textcolor, labelon)
Checks for a Bearish 3CR pattern and draws a bearish marker on the chart at the appropriate location
@description This function checks for a Bearish 3CR (Three-Candle Reversal) pattern, which is defined as the second candle having a higher high than the first and third candles, and the third candle having a lower high than the first candle. If the pattern is detected, a bearish marker is drawn on the chart at the appropriate location, and an optional label can be added to the marker.
Parameters:
high1 (float)
high2 (float)
high3 (float)
bearlinearray (line )
bearlabelarray (label )
failpointB (float )
linecolorbear (color)
labelcolor (color)
textcolor (color)
labelon (bool)
bullLineDelete(i, bulllinearray, failarray, bulllabelarray, labelon)
Removes a bullish line from a specified position in a line array, and optionally removes a label associated with that line
@description Removes a bullish line from a specified position in a line array, and optionally removes a label associated with that line.
Parameters:
i (int)
bulllinearray (line )
failarray (float )
bulllabelarray (label )
labelon (bool)
bearLineDelete(i, bearlinearray, failarray, bearlabelarray, labelon)
Removes a bearish line from a specified position in a line array, and optionally removes a label associated with that line
@description Removes a bearish line from a specified position in a line array, and optionally removes a label associated with that line.
Parameters:
i (int)
bearlinearray (line )
failarray (float )
bearlabelarray (label )
labelon (bool)
bulloffsetdelete(i, bulllinearray, failarray, bulllabelarray, labelon)
Removes a bullish line from a specified position in a line array, and optionally removes a label associated with that line
@description Removes a bullish line from a specified position in a line array, and optionally removes a label associated with that line.
Parameters:
i (int)
bulllinearray (line )
failarray (float )
bulllabelarray (label )
labelon (bool)
bearoffsetdelete(i, bearlinearray, failarray, bearlabelarray, labelon)
Removes a bearish line from a specified position in a line array, and optionally removes a label associated with that line
@description Removes a bearish line from a specified position in a line array, and optionally removes a label associated with that line.
Parameters:
i (int)
bearlinearray (line )
failarray (float )
bearlabelarray (label )
labelon (bool)
BullEntry_setter(i, bulllinearray, failpointB, entrystopB, entryB, entryboolB)
Checks if the specified value is greater than the break point of any bullish line in an array, and removes that line if true
@description Checks if the s pecified value is greater than the break point of any bullish line in an array, and removes that line if true.
Parameters:
i (int)
bulllinearray (line )
failpointB (float )
entrystopB (float )
entryB (float )
entryboolB (bool )
Bull3CRchecker(close1, bulllinearray, FailpointB, rsiB, bulllabelarray, labelt, bullcolored, directionarray, rsi, secondbullline, entrystopB, entryB, entryboolB)
Parameters:
close1 (float)
bulllinearray (line )
FailpointB (float )
rsiB (float )
bulllabelarray (label )
labelt (bool)
bullcolored (color)
directionarray (label )
rsi (float)
secondbullline (line )
entrystopB (float )
entryB (float )
entryboolB (bool )
Bear3CRchecker(close1, bearlinearray, FailpointB, bearlabelarray, labelt, bearcolored, directionarray, rsi, secondbearline, rsiB)
Checks if the specified value is less than the break point of any bearish line in an array, and removes that line if true
@description Checks if the specified value is less than the break point of any bearish line in an array, and removes that line if true.
Parameters:
close1 (float)
bearlinearray (line )
FailpointB (float )
bearlabelarray (label )
labelt (bool)
bearcolored (color)
directionarray (label )
rsi (float)
secondbearline (line )
rsiB (float )
Bulloffsetcheck(FailpointB, bulllabelarray, linearray, labelt, offset)
Checks the offset of bullish lines and deletes them if they are beyond a certain offset from the current bar index
@description Checks the offset of bullish lines and deletes them if they are beyond a certain offset from the current bar index
Parameters:
FailpointB (float )
bulllabelarray (label )
linearray (line )
labelt (bool)
offset (int)
Bearoffsetcheck(FailpointB, bearlabelarray, linearray, labelt, offset)
Checks the offset of bearish lines and deletes them if they are beyond a certain offset from the current bar index
@description Checks the offset of bearish lines and deletes them if they are beyond a certain offset from the current bar index
Parameters:
FailpointB (float )
bearlabelarray (label )
linearray (line )
labelt (bool)
offset (int)
Bullfailchecker(close1, FailpointB, bulllabelarray, linearray, labelt)
Checks if the current price has crossed above a bullish fail point and deletes the corresponding line and label
@description Checks if the current price has crossed above a bullish fail point and deletes the corresponding line and label
Parameters:
close1 (float)
FailpointB (float )
bulllabelarray (label )
linearray (line )
labelt (bool)
Bearfailchecker(close1, FailpointB, bearlabelarray, linearray, labelt)
Checks for bearish lines that have failed to trigger and removes them from the chart
@description This function checks for bearish lines that have failed to trigger (i.e., where the current price is above the fail point) and removes them from the chart along with any associated label.
Parameters:
close1 (float)
FailpointB (float )
bearlabelarray (label )
linearray (line )
labelt (bool)
rsibullchecker(rsiinput, rsiBull, secondbullline)
Checks for bullish RSI lines that have failed to trigger and removes them from the chart
@description This function checks for bullish RSI lines that have failed to trigger (i.e., where the current RSI value is below the line's trigger level) and removes them from the chart along with any associated line.
Parameters:
rsiinput (float)
rsiBull (float )
secondbullline (line )
rsibearchecker(rsiinput, rsiBear, secondbearline)
Checks for bearish RSI lines that have failed to trigger and removes them from the chart
@description This function checks for bearish RSI lines that have failed to trigger (i.e., where the current RSI value is above the line's trigger level) and removes them from the chart along with any associated line.
Parameters:
rsiinput (float)
rsiBear (float )
secondbearline (line )
Volume ChartVolume data can be interpreted in many different ways. This is a very basic script and novel idea to display volume as a chart. The purpose of this script is to visually help identify volume breakouts and other common chart patterns. While this indicator could be useful for finding big moves and early reversals it not reliable for determining the direction of the move.
Below is an example of a volume breakout:
Below is confirmation of the second ear in the batman pattern:
Lower highs and higher lows can give early signs of a reversal:
Below we can see retailers getting pumped and dumped on during the gaps while they sleep:
PivotBoss TriggersI have collected the four PivotBoss indicators into one big indicator. Eventually I will delete the individual ones, since you can just turn off the ones you don't need in the style controller. Cheers.
Wick Reversal
When the market has been trending lower then suddenly forms a reversal wick candlestick , the likelihood of
a reversal increases since buyers have finally begun to overwhelm the sellers. Selling pressure rules the decline,
but responsive buyers entered the market due to perceived undervaluation. For the reversal wick to open near the
high of the candle, sell off sharply intra-bar, and then rally back toward the open of the candle is bullish , as it
signifies that the bears no longer have control since they were not able to extend the decline of the candle, or the
trend. Instead, the bulls were able to rally price from the lows of the candle and close the bar near the top of its
range, which is bullish - at least for one bar, which hadn't been the case during the bearish trend.
Essentially, when a reversal wick forms at the extreme of a trend, the market is telling you that the trend
either has stalled or is on the verge of a reversal. Remember, the market auctions higher in search of sellers, and
lower in search of buyers. When the market over-extends itself in search of market participants, it will find itself
out of value, which means responsive market participants will look to enter the market to push price back toward
an area of perceived value. This will help price find a value area for two-sided trade to take place. When the
market finds itself too far out of value, responsive market participants will sometimes enter the market with
force, which aggressively pushes price in the opposite direction, essentially forming reversal wick candlesticks .
This pattern is perhaps the most telling and common reversal setup, but requires steadfast confirmation in order
to capitalize on its power. Understanding the psychology behind these formations and learning to identify them
quickly will allow you to enter positions well ahead of the crowd, especially if you've spotted these patterns at
potentially overvalued or undervalued areas.
Fade (Extreme) Reversal
The extreme reversal setup is a clever pattern that capitalizes on the ongoing psychological patterns of
investors, traders, and institutions. Basically, the setup looks for an extreme pattern of selling pressure and then
looks to fade this behavior to capture a bullish move higher (reverse for shorts). In essence, this setup is visually
pointing out oversold and overbought scenarios that forces responsive buyers and sellers to come out of the dark
and put their money to work-price has been over-extended and must be pushed back toward a fair area of value
so two-sided trade can take place.
This setup works because many normal investors, or casual traders, head for the exits once their trade
begins to move sharply against them. When this happens, price becomes extremely overbought or oversold,
creating value for responsive buyers and sellers. Therefore, savvy professionals will see that price is above or
below value and will seize the opportunity. When the scared money is selling, the smart money begins to buy, and
Vice versa.
Look at it this way, when the market sells off sharply in one giant candlestick , traders that were short
during the drop begin to cover their profitable positions by buying. Likewise, the traders that were on the
sidelines during the sell-off now see value in lower prices and begin to buy, thus doubling up on the buying
pressure. This helps to spark a sharp v-bottom reversal that pushes price in the opposite direction back toward
fair value.
Engulfing (Outside) Reversal
The power behind this pattern lies in the psychology behind the traders involved in this setup. If you have
ever participated in a breakout at support or resistance only to have the market reverse sharply against you, then
you are familiar with the market dynamics of this setup. What exactly is going on at these levels? To understand
this concept is to understand the outside reversal pattern. Basically, market participants are testing the waters
above resistance or below support to make sure there is no new business to be done at these levels. When no
initiative buyers or sellers participate in range extension, responsive participants have all the information they
need to reverse price back toward a new area of perceived value.
As you look at a bullish outside reversal pattern, you will notice that the current bar's low is lower than the
prior bar's low. Essentially, the market is testing the waters below recently established lows to see if a downside
follow-through will occur. When no additional selling pressure enters the market, the result is a flood of buying
pressure that causes a springboard effect, thereby shooting price above the prior bar's highs and creating the
beginning of a bullish advance.
If you recall the child on the trampoline for a moment, you'll realize that the child had to force the bounce
mat down before he could spring into the air. Also, remember Jennifer the cake baker? She initially pushed price
to $20 per cake, which sent a flood of orders into her shop. The flood of buying pressure eventually sent the price
of her cakes to $35 apiece. Basically, price had to test the $20 level before it could rise to $35.
Let's analyze the outside reversal setup in a different light for a moment. One of the reasons I like this setup
is because the two-bar pattern reduces into the wick reversal setup, which we covered earlier in the chapter. If
you are not familiar with candlestick reduction, the idea is simple. You are taking the price data over two or more
candlesticks and combining them to create a single candlestick . Therefore, you will be taking the open, high, low,
and close prices of the bars in question to create a single composite candlestick .
Doji Reversal
The doji candlestick is the epitome of indecision. The pattern illustrates a virtual stalemate between buyers
and sellers, which means the existing trend may be on the verge of a reversal. If buyers have been controlling a
bullish advance over a period of time, you will typically see full-bodied candlesticks that personify the bullish
nature of the move. However, if a doji candlestick suddenly appears, the indication is that buyers are suddenly
not as confident in upside price potential as they once were. This is clearly a point of indecision, as buyers are no
longer pushing price to higher valuation, and have allowed sellers to battle them to a draw-at least for this one
candlestick . This leads to profit taking, as buyers begin to sell their profitable long positions, which is heightened
by responsive sellers entering the market due to perceived overvaluation. This "double whammy" of selling
pressure essentially pushes price lower, as responsive sellers take control of the market and push price back
toward fair value.
Opposite Candle Zone Identifier (v6) - Extended🔍 Opposite Candle Zone Identifier (Extended)
Opposite Candle Zone Identifier is a price-action based indicator designed to identify potential reversal or absorption zones by detecting candles that move against the surrounding trend.
The indicator highlights a central opposite candle (or group of candles) that is surrounded by candles moving in the opposite direction, both before and after the central candle.
This structure often represents areas where institutional activity, absorption, or supply/demand imbalance may occur.
📌 How the Indicator Works
The indicator analyzes price action using three configurable blocks:
1️⃣ Candles Before (Backward)
A user-defined number of candles before the central candle(s) must follow a consistent trend:
Bullish candles for a bearish zone
Bearish candles for a bullish zone
2️⃣ Central Candle(s)
The core of the pattern:
Default: 1 opposite candle
Can be increased (up to 5) to adapt the indicator to lower timeframes or noisier markets
This central block must move against the previous trend, signaling a potential shift or absorption area.
3️⃣ Candles After (Forward)
A user-defined number of candles after the central candle(s) must resume the original trend, confirming the pattern.
⚠️ The signal is confirmed only after the “after” candles are completed.
This avoids repainting and ensures structural confirmation.
📐 Zone Concept
The highlighted central candle (or candles) can be used to define a price zone:
The high and low of the central candle(s) represent a potential supply or demand zone
These zones can be used for:
Reversal areas
Reaction zones
Entry refinement
Stop placement
⚙️ Inputs & Customization
Number of candles before
Controls how many candles must follow the initial trend.
Number of candles after
Defines how many candles are required for confirmation.
Central candles count
Default is 1, but can be increased (e.g. 2) for:
Lower timeframes
More reliable structure
Reduced noise
ATR-based offset
Labels are positioned using a dynamic ATR offset to improve chart readability across different markets and timeframes.
📈 Bullish & Bearish Zones
🟢 Bullish Zone
Bearish candles before
Bullish central candle(s)
Bearish candles after
Indicates potential demand or accumulation zone
🔴 Bearish Zone
Bullish candles before
Bearish central candle(s)
Bullish candles after
Indicates potential supply or distribution zone
🧠 Best Use Cases
Works best on 15m and higher timeframes
Effective on:
Indices
Forex majors
Liquid cryptocurrencies
Can be combined with:
Trend filters (EMA, VWAP)
Support & resistance
Market structure analysis
⚠️ Notes
This indicator is confirmation-based, not predictive
Signals appear only after pattern completion
It does not repaint
Best used as a confluence tool, not as a standalone trading system
🎯 Summary
Opposite Candle Zone Identifier helps traders:
Detect opposite-direction candles within strong trends
Identify potential supply and demand zones
Adapt the pattern to different timeframes
Improve price-action based decision making
SMC N-Gram Probability Matrix [PhenLabs]📊 SMC N-Gram Probability Matrix
Version: PineScript™ v6
📌 Description
The SMC N-Gram Probability Matrix applies computational linguistics methodology to Smart Money Concepts trading. By treating SMC patterns as a discrete “alphabet” and analyzing their sequential relationships through N-gram modeling, this indicator calculates the statistical probability of which pattern will appear next based on historical transitions.
Traditional SMC analysis is reactive—traders identify patterns after they form and then anticipate the next move. This indicator inverts that approach by building a transition probability matrix from up to 5,000 bars of pattern history, enabling traders to see which SMC formations most frequently follow their current market sequence.
The indicator detects and classifies 11 distinct SMC patterns including Fair Value Gaps, Order Blocks, Liquidity Sweeps, Break of Structure, and Change of Character in both bullish and bearish variants, then tracks how these patterns transition from one to another over time.
🚀 Points of Innovation
First indicator to apply N-gram sequence modeling from computational linguistics to SMC pattern analysis
Dynamic transition matrix rebuilds every 50 bars for adaptive probability calculations
Supports bigram (2), trigram (3), and quadgram (4) sequence lengths for varying analysis depth
Priority-based pattern classification ensures higher-significance patterns (CHoCH, BOS) take precedence
Configurable minimum occurrence threshold filters out statistically insignificant predictions
Real-time probability visualization with graphical confidence bars
🔧 Core Components
Pattern Alphabet System: 11 discrete SMC patterns encoded as integers for efficient matrix indexing and transition tracking
Swing Point Detection: Uses ta.pivothigh/pivotlow with configurable sensitivity for non-repainting structure identification
Transition Count Matrix: Flattened array storing occurrence counts for all possible pattern sequence transitions
Context Encoder: Converts N-gram pattern sequences into unique integer IDs for matrix lookup
Probability Calculator: Transforms raw transition counts into percentage probabilities for each possible next pattern
🔥 Key Features
Multi-Pattern SMC Detection: Simultaneously identifies FVGs, Order Blocks, Liquidity Sweeps, BOS, and CHoCH formations
Adjustable N-Gram Length: Choose between 2-4 pattern sequences to balance specificity against sample size
Flexible Lookback Range: Analyze anywhere from 100 to 5,000 historical bars for matrix construction
Pattern Toggle Controls: Enable or disable individual SMC pattern types to customize analysis focus
Probability Threshold Filtering: Set minimum occurrence requirements to ensure prediction reliability
Alert Integration: Built-in alert conditions trigger when high-probability predictions emerge
🎨 Visualization
Probability Table: Displays current pattern, recent sequence, sample count, and top N predicted patterns with percentage probabilities
Graphical Probability Bars: Visual bar representation (█░) showing relative probability strength at a glance
Chart Pattern Markers: Color-coded labels placed directly on price bars identifying detected SMC formations
Pattern Short Codes: Compact notation (F+, F-, O+, O-, L↑, L↓, B+, B-, C+, C-) for quick pattern identification
Customizable Table Position: Place probability display in any corner of your chart
📖 Usage Guidelines
N-Gram Configuration
N-Gram Length: Default 2, Range 2-4. Lower values provide more samples but less specificity. Higher values capture complex sequences but require more historical data.
Matrix Lookback Bars: Default 500, Range 100-5000. More bars increase statistical significance but may include outdated market behavior.
Min Occurrences for Prediction: Default 2, Range 1-10. Higher values filter noise but may reduce prediction availability.
SMC Detection Settings
Swing Detection Length: Default 5, Range 2-20. Controls pivot sensitivity for structure analysis.
FVG Minimum Size: Default 0.1%, Range 0.01-2.0%. Filters insignificant gaps.
Order Block Lookback: Default 10, Range 3-30. Bars to search for OB formations.
Liquidity Sweep Threshold: Default 0.3%, Range 0.05-1.0%. Minimum wick extension beyond swing points.
Display Settings
Show Probability Table: Toggle the probability matrix display on/off.
Show Top N Probabilities: Default 5, Range 3-10. Number of predicted patterns to display.
Show SMC Markers: Toggle on-chart pattern labels.
✅ Best Use Cases
Anticipating continuation or reversal patterns after liquidity sweeps
Identifying high-probability BOS/CHoCH sequences for trend trading
Filtering FVG and Order Block signals based on historical follow-through rates
Building confluence by comparing predicted patterns with other technical analysis
Studying how SMC patterns typically sequence on specific instruments or timeframes
⚠️ Limitations
Predictions are based solely on historical pattern frequency and do not account for fundamental factors
Low sample counts produce unreliable probabilities—always check the Samples display
Market regime changes can invalidate historical transition patterns
The indicator requires sufficient historical data to build meaningful probability matrices
Pattern detection uses standardized parameters that may not capture all institutional activity
💡 What Makes This Unique
Linguistic Modeling Applied to Markets: Treats SMC patterns like words in a language, analyzing how they “flow” together
Quantified Pattern Relationships: Transforms subjective SMC analysis into objective probability percentages
Adaptive Learning: Matrix rebuilds periodically to incorporate recent pattern behavior
Comprehensive SMC Coverage: Tracks all major Smart Money Concepts in a unified probability framework
🔬 How It Works
1. Pattern Detection Phase
Each bar is analyzed for SMC formations using configurable detection parameters
A priority hierarchy assigns the most significant pattern when multiple detections occur
2. Sequence Encoding Phase
Detected patterns are stored in a rolling history buffer of recent classifications
The current N-gram context is encoded into a unique integer identifier
3. Matrix Construction Phase
Historical pattern sequences are iterated to count transition occurrences
Each context-to-next-pattern transition increments the appropriate matrix cell
4. Probability Calculation Phase
Current context ID retrieves corresponding transition counts from the matrix
Raw counts are converted to percentages based on total context occurrences
5. Visualization Phase
Probabilities are sorted and the top N predictions are displayed in the table
Chart markers identify the current detected pattern for visual reference
💡 Note:
This indicator performs best when used as a confluence tool alongside traditional SMC analysis. The probability predictions highlight statistically common pattern sequences but should not be used as standalone trading signals. Always verify predictions against price action context, higher timeframe structure, and your overall trading plan. Monitor the sample count to ensure predictions are based on adequate historical data.
Bitcoin Multibook v1.0 [Apollo Algo]Bitcoin Multibook v1.0 by Apollo Algo is an advanced market depth and order flow visualization tool that brings professional-grade multi-exchange order book analysis to TradingView. Inspired by Bookmap's multibook functionality and built upon LucF's original single "Tape" indicator concept, this tool aggregates real-time trading data from multiple Bitcoin exchanges into a unified tape display.
Credits & Attribution
This indicator is an evolution of the original "Tape" indicator created by LucF (TradingView: @LucF). The multibook enhancement and Bitcoin-specific optimizations were developed by Apollo Algo to provide traders with institutional-grade market microstructure visibility across major Bitcoin trading venues.
Purpose & Philosophy
Bitcoin leads the entire cryptocurrency market. By monitoring order flow across the primary Bitcoin exchanges simultaneously, traders gain crucial insights into:
Cross-exchange arbitrage opportunities
Institutional order flow patterns
Market maker positioning
True market sentiment beyond single-exchange data
Key Features
📊 Multi-Exchange Data Aggregation
Real-time tape from 3 major exchanges:
Binance (BTCUSDT)
Coinbase (BTCUSD)
Kraken (BTCUSD)
Customizable source inputs for any trading pair
Synchronized price and volume tracking
Exchange name identification in tape display
📈 Advanced Tape Display
Dynamic tape visualization with configurable line quantity (0-50 lines)
Directional flow indicators (＋/－ symbols for price changes)
Exchange identification for each trade
Volume precision control (0-16 decimal places)
Flexible positioning (9 screen positions available)
Real-time only operation for accurate order flow
🎯 Volume Delta Analysis
Real-time cumulative volume delta calculation
Divergence detection (price vs. volume direction)
Colored visual feedback for market sentiment
Total session delta displayed in footer
Cross-exchange delta aggregation
🚨 Smart Alert System
Marker 1: Volume Delta Bumps (⬆⬇)
Triggers on consecutive volume delta increases
Identifies momentum acceleration points
Filters out divergent movements
Marker 2: Volume Delta Thresholds (⇑⇓)
Fires when delta exceeds user-defined thresholds
Catches significant order imbalances
Excludes divergence conditions
Marker 3: Large Volume Detection (⤊⤋)
Highlights unusually large individual trades
Spots potential institutional activity
Direction-specific triggers
Configure Data Sources
Adjust exchange pairs if needed (e.g., for altcoin analysis)
Leave blank to disable specific exchanges
Use format: EXCHANGE:SYMBOL
Customize Display
Set tape line quantity based on screen size
Position the table for optimal visibility
Choose color scheme (text or background)
Adjust text size for readability
Configure Alerts
Enable desired markers (1, 2, or 3)
Set volume thresholds appropriate for your timeframe
Choose direction (Longs, Shorts, or Both)
Create TradingView alerts on marker signals
Trading Applications
Scalping (1-5 min)
Monitor tape speed for momentum shifts
Watch for cross-exchange divergences
Track large volume clusters
Use Marker 1 for quick momentum trades
Day Trading (5-60 min)
Identify accumulation/distribution phases
Spot institutional positioning
Confirm breakout validity with volume delta
Use Marker 2 for significant imbalances
Swing Trading (1H+)
Analyze volume delta trends
Detect smart money rotation
Time entries with order flow confirmation
Use Marker 3 for institutional footprints
Advanced Techniques
Cross-Exchange Arbitrage Detection
When price disparities appear between exchanges:
Immediate Opportunity: Price differences > 0.1%
Bot Activity: Rapid convergence patterns
Liquidity Vacuum: One exchange leading others
Divergence Trading Strategies
Volume delta diverging from price direction:
Absorption: Strong hands entering (price down, delta up)
Distribution: Smart money exiting (price up, delta down)
Reversal Setup: Sustained divergence over multiple bars
Institutional Footprint Recognition
Large volume characteristics:
Simultaneous Spikes: Same timestamp across exchanges
TWAP Patterns: Consistent volume over time
Iceberg Orders: Repeated same-size trades
Pine Script v6 Enhancements
Type Safety Improvements
Strict boolean type handling
Explicit type declarations
Enhanced error checking
Performance Optimizations
Improved request.security() function
Better memory management with arrays
Optimized table rendering
Modern Syntax Updates
indicator() instead of study()
Namespaced math functions (math.round())
Typed input functions (input.int(), input.float())
Performance Considerations
System Requirements
Real-time Data: Essential for tape operation
Multiple Security Calls: May impact performance
Array Operations: Memory intensive with high line counts
Table Rendering: CPU usage increases with tape size
Optimization Tips
Reduce tape lines for better performance
Increase volume filter to reduce noise
Disable unused markers
Use text-only coloring for faster rendering
Wick to Body Ratio TableHello, I'm Gomaa if don't know me and if you want to know more about me follow me on my social media accounts which my propose to teach people "How To Learn".
Use this link so you can find me: linktr.ee
Overview
The "Wick to Body Ratio Table" is a comprehensive analytical tool designed to provide traders with detailed insights into candle structure and price movement dynamics. This indicator breaks down each candle into its component parts and displays real-time statistics in an easy-to-read table format.
What It Does
This indicator analyzes the current candle and displays four key metrics for each component:
Ratio to Body - How large each wick is compared to the candle body
Percentage of Total - What portion of the entire candle each component represents
Move Percentage - The actual price movement as a percentage from the opening price
Component breakdown - Upper wick, body, lower wick, and totals
Key Features
Real-Time Analysis:
Updates automatically with every price tick on the current candle
Works seamlessly across ALL timeframes (1 second to monthly charts)
No lag or delay in calculations
Comprehensive Metrics:
Upper Wick: Shows rejection from higher prices and selling pressure
Closed Body: Displays the actual price change from open to close (bullish=green, bearish=red)
Lower Wick: Indicates rejection from lower prices and buying pressure
Total Wick: Combined wick analysis for overall volatility assessment
Whole Candle: Complete range from high to low with total movement percentage
Visual Design:
Color-coded rows for easy identification
Clear headers for each metric column
Positioned at top-right of chart (non-intrusive)
Professional table format with borders and proper spacing
How to Interpret the Data
Ratio to Body Column:
A ratio of 2.0x means that component is twice the size of the body
N/A appears for doji candles (when body = 0)
Higher ratios indicate stronger rejection or indecision
% of Total Column:
Shows what percentage each part contributes to the whole candle
All percentages always add up to 100%
Helps identify if price spent more time in wicks or body
Move % Column:
Calculated from the opening price
Shows actual volatility during the candle period
Example: 0.5% body with 3% total candle = high volatility but little net movement
Trading Applications
1. Rejection Analysis:
Long upper wicks at resistance = strong selling pressure
Long lower wicks at support = strong buying pressure
Wick-to-body ratios above 2:1 suggest significant rejection
2. Volatility Assessment:
Compare body move % to whole candle move %
Large difference indicates choppy price action
Small difference indicates trending movement
3. Candle Patterns:
Identify doji, hammer, shooting star patterns quantitatively
Measure strength of pin bars and rejection candles
Compare current candle structure to historical patterns
4. Market Sentiment:
Body % > 70% = strong directional movement
Wick % > 60% = indecision and rejection
Balanced distribution = consolidation
Settings & Customization
Table position can be modified in the code (top_right, top_left, bottom_right, bottom_left)
Colors can be adjusted for different components
Text size can be changed (size.small, size.normal, size.large)
Decimal precision can be modified in the str.tostring() functions
Best Practices
Use on higher timeframes (15m+) for more reliable signals
Combine with support/resistance levels for context
Look for extreme ratios (>3:1) for high-probability setups
Monitor the move % to gauge true volatility vs. net movement
Technical Details
Written in Pine Script v5
Zero division protection built-in
Handles all edge cases (gaps, doji, extreme wicks)
Lightweight and efficient (minimal CPU usage)
Auto 5-Wave Fixed Channel + Wave 5 Top / Wave 2-ABC BottomAuto 5-Wave Fixed Channel + Wave 5 Top / Wave 2-ABC Bottom
by Ron999
1. What this indicator does
This tool automatically hunts for bullish 5-wave impulse structures and then:
Labels the waves: W1, W2, W3, W4, W5
Draws a fixed “acceleration” channel based on the wave structure
Projects a Wave-5 target zone using a 1.618 extension
Marks the Wave-2 level as an ABC correction target
Triggers optional alerts when:
A new Wave-5 top completes
An ABC bottom forms back near the Wave-2 low
It’s designed as a mechanical, rule-based approximation of Elliott 5-wave impulses – built for traders who like the idea of wave structure but want something objective and programmable.
2. How the wave logic works
The script continuously scans for pivot highs and lows using a user-defined Pivot Length.
It only keeps the last 5 alternating pivots (high → low → high → low → high).
When those last 5 pivots form this pattern:
Pivot 1 → High (W1)
Pivot 2 → Low (W2)
Pivot 3 → High (W3)
Pivot 4 → Low (W4)
Pivot 5 → High (W5)
…the indicator treats this as a bullish 5-wave impulse.
When such a structure is detected, it “locks in” the wave prices and bars and draws the channels and labels.
Note: Pivots are only confirmed after Pivot Length bars, so swings are slightly delayed by design (standard pivot logic).
3. Channels & levels
Once a valid bullish 5-wave structure is found, the script builds three key pieces:
a) Base Acceleration Channel (Blue)
Anchored from Wave-2 low toward Wave-3 high.
This forms a rising acceleration channel that represents the impulse leg.
The channel extends to the right, so you can see how price interacts with it after W3–W5.
b) Wave-5 Target Line (Red, dashed)
Uses the height from Wave-2 low to Wave-3 high.
Projects a 1.618 extension of that height above Wave-3.
This line acts as a potential Wave-5 exhaustion zone (take-profit / reversal watch area).
c) Wave-2 / ABC Bottom Level (Green, dotted)
Horizontal line drawn at the Wave-2 low.
This acts as a retest / corrective target for the ABC correction after the impulse completes.
When price later revisits this area (within a tolerance), the script can mark it as a potential ABC bottom.
4. Labels & signals
If labels are enabled:
W1, W2, W3, W4, W5 are plotted directly on their corresponding pivot bars.
When an ABC-style retest is detected near the Wave-2 level, an “ABC” label is printed at that low.
Wave-5 Top Event
Triggered when a new valid bullish 5-wave structure is completed.
The last pivot high in the pattern is flagged as Wave-5.
ABC Bottom Event
After a Wave-5 impulse, the script watches for new low pivots.
If a new low forms within ABC Bottom Proximity (%) of the Wave-2 price, it is treated as an ABC bottom near Wave-2 and marked on the chart.
5. Inputs & customization
Show Fixed Channels
Toggle all channel drawing on/off.
Label Waves
Toggle plotting of W1–W5 and ABC labels.
Alerts: Wave-5 Top & ABC Bottom
Master switch for enabling the script’s alert conditions.
Pivot Length
Controls how “swingy” the detection is.
Smaller values → more frequent, smaller waves
Larger values → fewer, larger structural waves
ABC Bottom Proximity (%)
Allowed percentage distance between the ABC low and the Wave-2 price.
Example: 5% means any ABC low within ±5% of Wave-2 is considered valid.
6. Alerts (how to use them)
The script exposes two alertcondition() events:
Wave-5 Top (Bullish Impulse)
Fires when a new 5-wave bullish structure completes.
Use this to watch for potential exhaustion tops or to tighten stops.
ABC Bottom near Wave-2 Low
Fires when an ABC-style correction prints a low near the Wave-2 level.
Use this to stalk potential end-of-correction entries in the direction of the original impulse.
On TradingView, add an alert to the script and choose the desired condition from the dropdown.
7. How to use it in your trading
This tool is best used as a structural context layer, not a standalone system:
Identify bullish impulsive trends when a Wave-5 structure completes.
Use the Wave-5 target line as a potential area for:
Scaling out
Watching for exhaustion / divergences / reversal patterns
Use the Wave-2/ABC level and ABC Bottom signal:
To look for end of correction entries back in the trend direction
To align with your own confluence (support/resistance, volume, RSI, etc.)
It works well on crypto, FX, indices, and stocks, especially on higher timeframes where structure is cleaner.
8. Limitations & notes
This is a mechanical approximation of Elliott 5-wave theory — it will not match every analyst’s discretionary count.
Pivots are confirmed after Pivot Length bars, so signals are not instant; they’re based on completed swings.
The indicator currently focuses on bullish impulses (upward 5-wave structures).
As always, this is not financial advice. Combine it with your own strategy, risk management, and confirmation tools.
Created & coded by: Ron999
Built for traders who want wave structure + fixed channels, without the subjective Elliott argument on every chart. files.catbox.moe
Predicta Futures – Scalping Predictor with Confidence FilterPredicta Futures is an advanced short-term forecasting indicator that combines historical pattern similarity analysis with weighted technical signals to predict price movements 1–10 minutes ahead.
**Core Functionality**
The script scans up to 5,000 historical bars to identify structurally similar price patterns. It aggregates forward outcomes from matched patterns and integrates real-time signals from RSI, MACD, Bollinger Bands, volume momentum, and volatility. A composite confidence score filters signals, displaying only those meeting the user-defined threshold (default ≥68%).
**Key Outputs**
- Buy/sell triangles with text labels
- Dashed projection line to predicted price
- Dotted target and ATR-based stop lines
- Info panel showing forecast direction, confidence %, expected move %, pattern count, order book status, and data access details
**Customization & Performance**
- Execution modes: Fast, Balanced, Accurate
- Adaptive sampling with recency bias option
- Filters for volatility and market hours
- Adjustable weights, lookback period, and prediction horizon
**Use Cases**
Scalping, intraday trading, futures, cryptocurrencies, equities.
*Order book metrics are simulated (platform limitation). Technical analysis tool; not financial advice.*
Machine Learning Moving Average [BackQuant]Machine Learning Moving Average
A powerful tool combining clustering, pseudo-machine learning, and adaptive prediction, enabling traders to understand and react to price behavior across multiple market regimes (Bullish, Neutral, Bearish). This script uses a dynamic clustering approach based on percentile thresholds and calculates an adaptive moving average, ideal for forecasting price movements with enhanced confidence levels.
What is Percentile Clustering?
Percentile clustering is a method that sorts and categorizes data into distinct groups based on its statistical distribution. In this script, the clustering process relies on the percentile values of a composite feature (based on technical indicators like RSI, CCI, ATR, etc.). By identifying key thresholds (lower and upper percentiles), the script assigns each data point (price movement) to a cluster (Bullish, Neutral, or Bearish), based on its proximity to these thresholds.
This approach mimics aspects of machine learning, where we “train” the model on past price behavior to predict future movements. The key difference is that this is not true machine learning; rather, it uses data-driven statistical techniques to "cluster" the market into patterns.
Why Percentile Clustering is Useful
Clustering price data into meaningful patterns (Bullish, Neutral, Bearish) helps traders visualize how price behavior can be grouped over time.
By leveraging past price behavior and technical indicators, percentile clustering adapts dynamically to evolving market conditions.
It helps you understand whether price behavior today aligns with past bullish or bearish trends, improving market context.
Clusters can be used to predict upcoming market conditions by identifying regimes with high confidence, improving entry/exit timing.
What This Script Does
Clustering Based on Percentiles : The script uses historical price data and various technical features to compute a "composite feature" for each bar. This feature is then sorted and clustered based on predefined percentile thresholds (e.g., 10th percentile for lower, 90th percentile for upper).
Cluster-Based Prediction : Once clustered, the script uses a weighted average, cluster momentum, or regime transition model to predict future price behavior over a specified number of bars.
Dynamic Moving Average : The script calculates a machine-learning-inspired moving average (MLMA) based on the current cluster, adjusting its behavior according to the cluster regime (Bullish, Neutral, Bearish).
Adaptive Confidence Levels : Confidence in the predicted return is calculated based on the distance between the current value and the other clusters. The further it is from the next closest cluster, the higher the confidence.
Visual Cluster Mapping : The script visually highlights different clusters on the chart with distinct colors for Bullish, Neutral, and Bearish regimes, and plots the MLMA line.
Prediction Output : It projects the predicted price based on the selected method and shows both predicted price and confidence percentage for each prediction horizon.
Trend Identification : Using the clustering output, the script colors the bars based on the current cluster to reflect whether the market is trending Bullish (green), Bearish (red), or is Neutral (gray).
How Traders Use It
Predicting Price Movements : The script provides traders with an idea of where prices might go based on past market behavior. Traders can use this forecast for short-term and long-term predictions, guiding their trades.
Clustering for Regime Analysis : Traders can identify whether the market is in a Bullish, Neutral, or Bearish regime, using that information to adjust trading strategies.
Adaptive Moving Average for Trend Following : The adaptive moving average can be used as a trend-following indicator, helping traders stay in the market when it’s aligned with the current trend (Bullish or Bearish).
Entry/Exit Strategy : By understanding the current cluster and its associated trend, traders can time entries and exits with higher precision, taking advantage of favorable conditions when the confidence in the predicted price is high.
Confidence for Risk Management : The confidence level associated with the predicted returns allows traders to manage risk better. Higher confidence levels indicate stronger market conditions, which can lead to higher position sizes.
Pseudo Machine Learning Aspect
While the script does not use conventional machine learning models (e.g., neural networks or decision trees), it mimics certain aspects of machine learning in its approach. By using clustering and the dynamic adjustment of a moving average, the model learns from historical data to adjust predictions for future price behavior. The "learning" comes from how the script uses past price data (and technical indicators) to create patterns (clusters) and predict future market movements based on those patterns.
Why This Is Important for Traders
Understanding market regimes helps to adjust trading strategies in a way that adapts to current market conditions.
Forecasting price behavior provides an additional edge, enabling traders to time entries and exits based on predicted price movements.
By leveraging the clustering technique, traders can separate noise from signal, improving the reliability of trading signals.
The combination of clustering and predictive modeling in one tool reduces the complexity for traders, allowing them to focus on actionable insights rather than manual analysis.
How to Interpret the Output
Bullish (Green) Zone : When the price behavior clusters into the Bullish zone, expect upward price movement. The MLMA line will help confirm if the trend remains upward.
Bearish (Red) Zone : When the price behavior clusters into the Bearish zone, expect downward price movement. The MLMA line will assist in tracking any downward trends.
Neutral (Gray) Zone : A neutral market condition signals indecision or range-bound behavior. The MLMA line can help track any potential breakouts or trend reversals.
Predicted Price : The projected price is shown on the chart, based on the cluster's predicted behavior. This provides a useful reference for where the price might move in the near future.
Prediction Confidence : The confidence percentage helps you gauge the reliability of the predicted price. A higher percentage indicates stronger market confidence in the forecasted move.
Tips for Use
Combining with Other Indicators : Use the output of this indicator in combination with your existing strategy (e.g., RSI, MACD, or moving averages) to enhance signal accuracy.
Position Sizing with Confidence : Increase position size when the prediction confidence is high, and decrease size when it’s low, based on the confidence interval.
Regime-Based Strategy : Consider developing a multi-strategy approach where you use this tool for Bullish or Bearish regimes and a separate strategy for Neutral markets.
Optimization : Adjust the lookback period and percentile settings to optimize the clustering algorithm based on your asset’s characteristics.
Conclusion
The Machine Learning Moving Average offers a novel approach to price prediction by leveraging percentile clustering and a dynamically adapting moving average. While not a traditional machine learning model, this tool mimics the adaptive behavior of machine learning by adjusting to evolving market conditions, helping traders predict price movements and identify trends with improved confidence and accuracy.
Squeeze Weekday Frequency [CHE] Squeeze Weekday Frequency — Tracks historical frequency of low-volatility squeezes by weekday to inform timing of low-risk setups.
Summary
This indicator monitors periods of unusually low volatility, defined as when the average true range falls below a percentile threshold, and tallies their occurrences across each weekday. By aggregating these counts over the chart's history, it reveals patterns in squeeze frequency, helping traders avoid or target specific days for reduced noise. The approach uses persistent counters to ensure accurate daily tallies without duplicates, providing a robust view of weekday biases in volatility regimes.
Motivation: Why this design?
Traders often face inconsistent signal quality due to varying volatility patterns tied to the trading calendar, such as quieter mid-week sessions or busier Mondays. This indicator addresses that by binning low-volatility events into weekday buckets, allowing users to spot recurring low-activity days where trends may develop with less whipsaw. It focuses on historical aggregation rather than real-time alerts, emphasizing pattern recognition over prediction.
What’s different vs. standard approaches?
- Reference baseline: Traditional volatility trackers like simple moving averages of range or standalone Bollinger Band squeezes, which ignore temporal distribution.
- Architecture differences:
- Employs array-based persistent counters for each weekday to accumulate events without recounting.
- Includes duplicate prevention via day-key tracking to handle sparse data.
- Features on-demand sorting and conditional display modes for focused insights.
- Practical effect: Charts show a persistent table of ranked weekdays instead of transient plots, making it easier to glance at biases like higher squeezes on Fridays, which reduces the need for manual logging and highlights calendar-driven edges.
How it works (technical)
The indicator first computes the average true range over a specified lookback period to gauge recent volatility. It then ranks this value against its own history within a sliding window to identify squeezes when the rank drops below the threshold. Each bar's timestamp is resolved to a weekday using the selected timezone, and a unique day identifier is generated from the date components.
On detecting a squeeze and valid price data, it checks against a stored last-marked day for that weekday to avoid multiple counts per day. If it's a new occurrence, the corresponding weekday counter in an array increments. Total days and data-valid days are tracked separately for context.
At the chart's last bar, it sums all counters to compute shares, sorts weekdays by their squeeze proportions, and populates a table with the selected subset. The table alternates row colors and highlights the peak weekday. An info label above the final bar summarizes totals and the top day. Background shading applies a faint red to squeeze bars for visual confirmation. State persists via variable arrays initialized once, ensuring counts build incrementally without resets.
Parameter Guide
ATR Length — Sets the lookback for measuring average true range, influencing squeeze sensitivity to short-term swings. Default: 14. Trade-offs/Tips: Shorter values increase responsiveness but raise false positives in chop; longer smooths for stability, potentially missing early squeezes.
Percentile Window (bars) — Defines the history length for ranking the current ATR, balancing recent relevance with sample size. Default: 252. Trade-offs/Tips: Narrower windows adapt faster to regime shifts but amplify noise; wider ones stabilize ranks yet lag in fast markets—aim for 100-500 bars on daily charts.
Squeeze threshold (PR < x) — Determines the cutoff for low-volatility classification; lower values flag rarer, tighter squeezes. Default: 10.0. Trade-offs/Tips: Tighter thresholds (under 5) yield fewer but higher-quality signals, reducing clutter; looser (over 20) captures more events at the cost of relevance.
Timezone — Selects the reference for weekday assignment; exchange default aligns with asset's session. Default: Exchange. Trade-offs/Tips: Use custom for cross-market analysis, but verify alignment to avoid offset errors in global pairs.
Show — Toggles the results table visibility for quick on/off of the display. Default: true. Trade-offs/Tips: Disable in multi-indicator setups to save screen space; re-enable for periodic reviews.
Pos — Positions the table on the chart pane for optimal viewing. Default: Top Right. Trade-offs/Tips: Bottom options suit long-term charts; test placements to avoid overlapping price action.
Font — Adjusts text size in the table for readability at different zooms. Default: normal. Trade-offs/Tips: Smaller fonts fit more data but strain eyes on small screens; larger for presentations.
Dark — Applies a dark color scheme to the table for contrast against chart backgrounds. Default: true. Trade-offs/Tips: Toggle false for light themes; ensures legibility without manual recoloring.
Display — Filters table rows to show all, top three, or bottom three weekdays by squeeze share. Default: All. Trade-offs/Tips: Use "Top 3" for focus on high-frequency days in active trading; "All" for full audits.
Reading & Interpretation
Red-tinted backgrounds mark individual squeeze bars, indicating current low-volatility conditions. The table's summary row shows the highest squeeze count, its percentage of total events, and the associated weekday in teal. Detail rows list selected weekdays with their absolute counts, proportional shares, and a left arrow for the peak day—higher percentages signal days where squeezes cluster, suggesting potential for calmer trend development. The info label reports overall days observed, valid data days, and reiterates the top weekday with its count. Drifting counts toward zero on a weekday imply rarity, while elevated ones point to habitual low-activity sessions.
Practical Workflows & Combinations
- Trend following: Scan for squeezes on high-frequency weekdays as entry filters, confirming with higher highs or lower lows in the structure; pair with momentum oscillators to time breaks.
- Exits/Stops: On low-squeeze days, widen stops for breathing room, tightening them during peak squeeze periods to guard against false breaks—use the table's percentages as a regime proxy.
- Multi-asset/Multi-TF: Defaults work across forex and indices on hourly or daily frames; for stocks, adjust percentile window to 100 for shorter histories. Scale thresholds up by 5-10 points for high-vol assets like crypto to maintain signal sparsity.
Behavior, Constraints & Performance
- Repaint/confirmation: Counts update only on confirmed bars via day-key changes, with no future references—live bars may shade red tentatively but tallies finalize at session close.
- security()/HTF: Not used, so no higher-timeframe repaint risks; all computations stay in the chart's resolution.
- Resources: Relies on a fixed-size array of seven elements and small loops for sorting and table fills, capped at 5000 bars back—efficient for most charts but may slow on very long intraday histories.
- Known limits: Ignores weekends and holidays implicitly via data presence; early chart bars lack full percentile context, leading to initial undercounting; assumes continuous sessions, so gaps in data (e.g., news halts) skew totals.
Sensible Defaults & Quick Tuning
Start with the built-in values for broad-market daily charts: ATR at 14, window at 252, threshold at 10. For noisier environments, lower the threshold to 5 and shorten the window to 100 to prioritize rare squeezes. If too few events appear, raise the threshold to 15 and extend ATR to 20 for broader capture. To combat overcounting in sparse data, widen the window to 500 while keeping others stock—monitor the info label's data-days count before trusting patterns.
What this indicator is—and isn’t
This serves as a statistical overlay for spotting calendar-based volatility biases, aiding in session selection and filter design. It is not a standalone signal generator, predictive model, or risk manager—integrate it with price action, volume, and broader strategy rules for decisions.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
SMC Structures and Multi-Timeframe FVG PYSMC Structures and Multi-Timeframe FVG Indicator
Tip: For optimal performance, adjust the number of FVGs displayed per timeframe in the settings. On high-performance devices, up to 8 FVGs per timeframe can be used without issues. If you experience slowdowns, reduce to 3 or 4 FVGs per timeframe. If the chart flashes, disable indicators one by one to identify conflicts, or try using the TradingView Mobile or Windows App for a smoother experience.
Overview
This Pine Script indicator enhances market analysis by integrating Smart Money Concepts (SMC) with Fair Value Gaps (FVG) across multiple timeframes. It identifies trend continuations (Break of Structure, BOS) and trend reversals (Change of Character, CHoCH) while highlighting liquidity zones through FVG detection. The indicator includes eight customizable Moving Average (MA) curve templates, disabled by default, to complement SMC and FVG analysis. Its originality lies in combining multi-timeframe FVG detection with SMC structure analysis, providing traders with a cohesive tool to visualize price action patterns and liquidity zones efficiently.
Features and Functionality
1. Fair Value Gaps (FVG)
The indicator detects and displays bullish, bearish, and mitigated FVGs, representing liquidity zones where price inefficiencies occur. These gaps are dynamically updated based on price action:
Bullish FVG: Displayed in green when unmitigated, indicating potential upward liquidity zones.
Bearish FVG: Displayed in red when unmitigated, signaling potential downward liquidity zones.
Mitigated FVG: Shown in gray once the gap is partially filled by price action.
Fully Mitigated FVG: Automatically removed from the chart when the gap is fully filled, reducing visual clutter.
Users can customize the number of historical FVGs displayed via the settings, allowing focus on recent liquidity zones for targeted analysis.
2. SMC Structures
The indicator identifies key SMC price action patterns:
Break of Structure (BOS): Marked with gray lines, indicating trend continuation when price breaks a significant high or low.
Change of Character (CHoCH): Highlighted with yellow lines, signaling potential trend reversals when price fails to maintain the current structure.
High/Low Values: Blue lines denote the highest high and lowest low of the current structure, providing reference points for market context.
3. Multi-Timeframe FVG Analysis
A standout feature is the ability to analyze FVGs across multiple timeframes simultaneously. This allows traders to align higher-timeframe liquidity zones with lower-timeframe entries, improving trade precision. The indicator fetches FVG data from user-selected timeframes, displaying them cohesively on the chart.
4. Moving Average (MA) Templates
The indicator includes eight customizable MA curve templates in the Settings > Template section, disabled by default. These templates allow users to overlay MAs (e.g., SMA, EMA, WMA) to complement SMC and FVG analysis. Each template is pre-configured with different periods and types, enabling quick adaptation to various trading strategies, such as trend confirmation or dynamic support/resistance.
How It Works
The script processes price action to detect FVGs by analyzing three-candle patterns where a gap forms between the high/low of the first and third candles. Multi-timeframe data is retrieved using Pine Script’s request.security() function, ensuring accurate FVG plotting across user-defined timeframes. BOS and CHoCH are identified by tracking swing highs and lows, with logic to differentiate trend continuation from reversals. The MA templates are computed using standard Pine Script TA functions, with user inputs controlling visibility and parameters.
How to Use
Add to Chart: Apply the indicator to any TradingView chart.
Configure Settings:
FVG Settings: Adjust the number of historical FVGs to display (default: 10). Enable/disable specific FVG types (bullish, bearish, mitigated).
Timeframe Selection: Choose up to three timeframes for FVG analysis (e.g., 1H, 4H, 1D) to align with your trading strategy.
Structure Settings: Toggle BOS (gray lines) and CHoCH (yellow lines) visibility. Adjust sensitivity for structure detection if needed.
MA Templates: Enable MA curves via the Template section. Select from eight pre-configured MA types and periods to suit your analysis.
Interpret Signals:
Use green/red FVGs for potential entry points targeting liquidity zones.
Monitor gray lines (BOS) for trend continuation and yellow lines (CHoCH) for reversal signals.
Align multi-timeframe FVGs with BOS/CHoCH for high-probability setups.
Optionally, use MA curves for trend confirmation or dynamic levels.
Clean Chart Usage: The indicator is designed to work standalone. Ensure no conflicting scripts are applied unless explicitly needed for your strategy.
Why This Indicator Is Unique
Unlike standalone FVG or SMC indicators, this script combines both concepts with multi-timeframe analysis, offering a comprehensive view of market structure and liquidity. The addition of customizable MA templates enhances flexibility, while the dynamic removal of mitigated FVGs keeps the chart clean. This mashup is purposeful, as it integrates complementary tools to streamline decision-making for traders using SMC strategies.
Credits
This indicator builds on foundational SMC and FVG concepts from the TradingView community. Some open-source code was reused, and do performance enhancement as you guys can read the code. This type of indicators has inspiration was drawn from public domain SMC methodologies. All code is partly original with manual work on performance optimization in Pine Script.
Notes
Ensure your chart is clean (no unnecessary drawings or indicators) to maximize clarity.
The indicator is open-source, and traders are encouraged to review the code for deeper understanding.
For optimal use, test the indicator on a demo account to familiarize yourself with its signals.
Crypto Mean Reversion System (Pullback & Bounce)Mean Reversion Theory
The indicator operates on the principle that extreme price movements in crypto markets tend to revert toward their mean over time.
Consider this a valuable aid for your dollar-cost averaging strategy, effectively identifying periods ripe for accumulating or divesting from the market.
Research shows that:
Short-term momentum often persists briefly after surges, but extreme moves trigger mean reversion
Sharp drops exhibit strong bounce patterns, especially after capitulation events
Longer timeframes (7-day) show stronger mean reversion tendencies than shorter ones (1-day)
Timeframe Analysis
1-Day Timeframe
Pullback probabilities: 45-85% depending on surge magnitude
Bounce probabilities: 55-95% depending on drop severity
Captures immediate overextension and panic selling
More volatile but faster signal generation
7-Day Timeframe
Pullback probabilities: 50-90% (higher confidence)
Bounce probabilities: 50-90% (slightly moderated)
Filters out noise and identifies sustained trends
Stronger mean reversion signals due to extended moves
Probability Tiers
Pullback Risk (After Surges)
Moderate (45-60%): 5-10% surge → Expected -3% to -12% pullback
High (55-70%): 10-15% surge → Expected -5% to -18% pullback
Very High (65-80%): 15-25% surge → Expected -10% to -25% pullback
Extreme (75-90%): 25%+ surge → Expected -15% to -40% pullback
Bounce Probability (After Drops)
Moderate (55-65%): -5% to -10% drop → Expected +3% to +10% bounce
High (65-75%): -10% to -15% drop → Expected +6% to +18% bounce
Very High (75-85%): -15% to -25% drop → Expected +10% to +30% bounce
Extreme (85-95%): -25%+ drop → Expected +18% to +45% bounce
The probability ranges are derived from:
Crypto volatility patterns: Higher volatility than traditional assets creates stronger mean reversion
Behavioral finance: Extreme moves trigger emotional trading (FOMO/panic) that reverses
Historical backtesting: Probability estimates based on typical reversion patterns in crypto markets
Timeframe correlation: Longer timeframes show increased reversion probability due to reduced noise
Key Features
Dual-direction signals: Identifies both overbought (pullback) and oversold (bounce) conditions
Multi-timeframe confirmation: 1D and 7D analysis for different trading styles
Customizable thresholds: Adjust sensitivity based on asset volatility
Visual alerts: Color-coded labels and table for quick assessment
Risk categorization: Clear severity levels for position sizing
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
References
Ang, A. (2014) *Asset Management: A Systematic Approach to Factor Investing*. Oxford: Oxford University Press.
Ang, A., Piazzesi, M. and Wei, M. (2006) 'What does the yield curve tell us about GDP growth?', *Journal of Econometrics*, 131(1-2), pp. 359-403.
Asness, C.S. (2003) 'Fight the Fed Model', *The Journal of Portfolio Management*, 30(1), pp. 11-24.
Asness, C.S., Moskowitz, T.J. and Pedersen, L.H. (2013) 'Value and Momentum Everywhere', *The Journal of Finance*, 68(3), pp. 929-985.
Baker, M. and Wurgler, J. (2006) 'Investor Sentiment and the Cross-Section of Stock Returns', *The Journal of Finance*, 61(4), pp. 1645-1680.
Baker, M. and Wurgler, J. (2007) 'Investor Sentiment in the Stock Market', *Journal of Economic Perspectives*, 21(2), pp. 129-152.
Baur, D.G. and Lucey, B.M. (2010) 'Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold', *Financial Review*, 45(2), pp. 217-229.
Bollerslev, T. (1986) 'Generalized Autoregressive Conditional Heteroskedasticity', *Journal of Econometrics*, 31(3), pp. 307-327.
Boudoukh, J., Michaely, R., Richardson, M. and Roberts, M.R. (2007) 'On the Importance of Measuring Payout Yield: Implications for Empirical Asset Pricing', *The Journal of Finance*, 62(2), pp. 877-915.
Brinson, G.P., Hood, L.R. and Beebower, G.L. (1986) 'Determinants of Portfolio Performance', *Financial Analysts Journal*, 42(4), pp. 39-44.
Brock, W., Lakonishok, J. and LeBaron, B. (1992) 'Simple Technical Trading Rules and the Stochastic Properties of Stock Returns', *The Journal of Finance*, 47(5), pp. 1731-1764.
Calmar, T.W. (1991) 'The Calmar Ratio', *Futures*, October issue.
Campbell, J.Y. and Shiller, R.J. (1988) 'The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors', *Review of Financial Studies*, 1(3), pp. 195-228.
Cochrane, J.H. (2011) 'Presidential Address: Discount Rates', *The Journal of Finance*, 66(4), pp. 1047-1108.
Damodaran, A. (2012) *Equity Risk Premiums: Determinants, Estimation and Implications*. Working Paper, Stern School of Business.
Engle, R.F. (1982) 'Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of United Kingdom Inflation', *Econometrica*, 50(4), pp. 987-1007.
Estrella, A. and Hardouvelis, G.A. (1991) 'The Term Structure as a Predictor of Real Economic Activity', *The Journal of Finance*, 46(2), pp. 555-576.
Estrella, A. and Mishkin, F.S. (1998) 'Predicting U.S. Recessions: Financial Variables as Leading Indicators', *Review of Economics and Statistics*, 80(1), pp. 45-61.
Faber, M.T. (2007) 'A Quantitative Approach to Tactical Asset Allocation', *The Journal of Wealth Management*, 9(4), pp. 69-79.
Fama, E.F. and French, K.R. (1989) 'Business Conditions and Expected Returns on Stocks and Bonds', *Journal of Financial Economics*, 25(1), pp. 23-49.
Fama, E.F. and French, K.R. (1992) 'The Cross-Section of Expected Stock Returns', *The Journal of Finance*, 47(2), pp. 427-465.
Garman, M.B. and Klass, M.J. (1980) 'On the Estimation of Security Price Volatilities from Historical Data', *Journal of Business*, 53(1), pp. 67-78.
Gilchrist, S. and Zakrajšek, E. (2012) 'Credit Spreads and Business Cycle Fluctuations', *American Economic Review*, 102(4), pp. 1692-1720.
Gordon, M.J. (1962) *The Investment, Financing, and Valuation of the Corporation*. Homewood: Irwin.
Graham, B. and Dodd, D.L. (1934) *Security Analysis*. New York: McGraw-Hill.
Hamilton, J.D. (1989) 'A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle', *Econometrica*, 57(2), pp. 357-384.
Ilmanen, A. (2011) *Expected Returns: An Investor's Guide to Harvesting Market Rewards*. Chichester: Wiley.
Jaconetti, C.M., Kinniry, F.M. and Zilbering, Y. (2010) 'Best Practices for Portfolio Rebalancing', *Vanguard Research Paper*.
Jegadeesh, N. and Titman, S. (1993) 'Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency', *The Journal of Finance*, 48(1), pp. 65-91.
Kahneman, D. and Tversky, A. (1979) 'Prospect Theory: An Analysis of Decision under Risk', *Econometrica*, 47(2), pp. 263-292.
Korteweg, A. (2010) 'The Net Benefits to Leverage', *The Journal of Finance*, 65(6), pp. 2137-2170.
Lo, A.W. and MacKinlay, A.C. (1990) 'Data-Snooping Biases in Tests of Financial Asset Pricing Models', *Review of Financial Studies*, 3(3), pp. 431-467.
Longin, F. and Solnik, B. (2001) 'Extreme Correlation of International Equity Markets', *The Journal of Finance*, 56(2), pp. 649-676.
Mandelbrot, B. (1963) 'The Variation of Certain Speculative Prices', *The Journal of Business*, 36(4), pp. 394-419.
Markowitz, H. (1952) 'Portfolio Selection', *The Journal of Finance*, 7(1), pp. 77-91.
Modigliani, F. and Miller, M.H. (1961) 'Dividend Policy, Growth, and the Valuation of Shares', *The Journal of Business*, 34(4), pp. 411-433.
Moreira, A. and Muir, T. (2017) 'Volatility-Managed Portfolios', *The Journal of Finance*, 72(4), pp. 1611-1644.
Moskowitz, T.J., Ooi, Y.H. and Pedersen, L.H. (2012) 'Time Series Momentum', *Journal of Financial Economics*, 104(2), pp. 228-250.
Parkinson, M. (1980) 'The Extreme Value Method for Estimating the Variance of the Rate of Return', *Journal of Business*, 53(1), pp. 61-65.
Piotroski, J.D. (2000) 'Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers', *Journal of Accounting Research*, 38, pp. 1-41.
Reinhart, C.M. and Rogoff, K.S. (2009) *This Time Is Different: Eight Centuries of Financial Folly*. Princeton: Princeton University Press.
Ross, S.A. (1976) 'The Arbitrage Theory of Capital Asset Pricing', *Journal of Economic Theory*, 13(3), pp. 341-360.
Roy, A.D. (1952) 'Safety First and the Holding of Assets', *Econometrica*, 20(3), pp. 431-449.
Schwert, G.W. (1989) 'Why Does Stock Market Volatility Change Over Time?', *The Journal of Finance*, 44(5), pp. 1115-1153.
Sharpe, W.F. (1966) 'Mutual Fund Performance', *The Journal of Business*, 39(1), pp. 119-138.
Sharpe, W.F. (1994) 'The Sharpe Ratio', *The Journal of Portfolio Management*, 21(1), pp. 49-58.
Simon, D.P. and Wiggins, R.A. (2001) 'S&P Futures Returns and Contrary Sentiment Indicators', *Journal of Futures Markets*, 21(5), pp. 447-462.
Taleb, N.N. (2007) *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Whaley, R.E. (2000) 'The Investor Fear Gauge', *The Journal of Portfolio Management*, 26(3), pp. 12-17.
Whaley, R.E. (2009) 'Understanding the VIX', *The Journal of Portfolio Management*, 35(3), pp. 98-105.
Yardeni, E. (2003) 'Stock Valuation Models', *Topical Study*, 51, Yardeni Research.
Zweig, M.E. (1973) 'An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums', *The Journal of Finance*, 28(1), pp. 67-78.
Smarter Money Concepts Dashboard [PhenLabs]📊Smarter Money Concepts Dashboard
Version: PineScript™v6
📌Description
The Smarter Money Concepts Dashboard is a comprehensive institutional trading analysis tool that combines six of our most powerful smarter money concepts indicators into one unified suite. This advanced system automatically detects and visualizes Fair Value Gaps, Inverted FVGs, Order Blocks, Wyckoff Springs/Upthrusts, Wick Rejection patterns, and ICT Market Structure analysis.
Built for serious traders who need institutional-grade market analysis, this dashboard eliminates subjective interpretation by automatically identifying where smart money is likely positioned. The integrated real-time dashboard provides instant status updates on all active patterns, making it easy to monitor market conditions at a glance.
🚀Points of Innovation
● Multi-Module Integration: Six different SMC concepts unified in one comprehensive system
● Real-Time Dashboard Display: Live tracking of all active patterns with customizable positioning
● Advanced Volume Filtering: Institutional volume confirmation across all pattern types
● Automated Pattern Management: Smart memory system prevents chart clutter while maintaining relevant zones
● Probability-Based Wyckoff Detection: Mathematical probability calculations for spring/upthrust patterns
● Dual FVG System: Both standard and inverted Fair Value Gap detection with equilibrium analysis
🔧Core Components
● Fair Value Gap Engine: Detects standard FVGs with volume confirmation and equilibrium line analysis
● Inverted FVG Module: Advanced IFVG detection using RVI momentum filtering for inversion confirmation
● Order Block System: Institutional order block identification with customizable mitigation methods
● Wyckoff Pattern Recognition: Automated spring and upthrust detection with probability scoring
● Wick Rejection Analysis: High-probability reversal patterns based on wick-to-body ratios
● ICT Market Structure: Simplified institutional concepts with commitment tracking
🔥Key Features
● Comprehensive Pattern Detection: All major SMC concepts in one indicator with automatic identification
● Volume-Confirmed Signals: Multiple volume filters ensure only institutional-grade patterns are highlighted
● Interactive Dashboard: Real-time status display with active pattern counts and module status
● Smart Memory Management: Automatic cleanup of old patterns while preserving relevant market zones
● Full Alert System: Complete notification coverage for all pattern types and signal generations
● Customizable Display Options: Adjustable colors, transparency, and positioning for all visual elements
🎨Visualization
● Color-Coded Zones: Distinct color schemes for bullish/bearish patterns across all modules
● Dynamic Box Extensions: Automatically extending zones until mitigation or invalidation
● Equilibrium Lines: Fair Value Gap midpoint analysis with dotted line visualization
● Signal Markers: Clear spring/upthrust signals with directional arrows and probability indicators
● Dashboard Table: Professional-grade status panel with module activation and pattern counts
● Candle Coloring: Wick rejection highlighting with transparency-based visual emphasis
📖Usage Guidelines
Fair Value Gap Settings
● Days to Analyze: Default 15, Range 1-100 - Controls historical FVG detection period
● Volume Filter: Enables institutional volume confirmation for gap validity
● Min Volume Ratio: Default 1.5 - Minimum volume spike required for gap recognition
● Show Equilibrium Lines: Displays FVG midpoint analysis for precise entry targeting
Order Block Configuration
● Scan Range: Default 25 bars - Lookback period for structure break identification
● Volume Filter: Institutional volume confirmation for order block validation
● Mitigation Method: Wick or Close-based invalidation for different trading styles
● Min Volume Ratio: Default 1.5 - Volume threshold for significant order block formation
Wyckoff Analysis Parameters
● S/R Lookback: Default 20 - Support/resistance calculation period for spring/upthrust detection
● Volume Spike Multiplier: Default 1.5 - Required volume increase for pattern confirmation
● Probability Threshold: Default 0.7 - Minimum probability score for signal generation
● ATR Recovery Period: Default 5 - Price recovery calculation for pattern strength assessment
Market Structure Settings
● Auto-Detect Zones: Automatic identification of high-volume thin zones
● Proximity Threshold: Default 0.20% - Price proximity requirements for zone interaction
● Test Window: Default 20 bars - Time period for zone commitment calculation
Display Customization
● Dashboard Position: Four corner options for optimal chart layout
● Text Size: Scalable from Tiny to Large for different screen configurations
● Pattern Colors: Full customization of all bullish and bearish zone colors
✅Best Use Cases
● Swing Trading: Identify major institutional zones for multi-day position entries
● Day Trading: Precise intraday entries at Fair Value Gaps and Order Block boundaries
● Trend Analysis: Market structure confirmation for directional bias establishment
● Risk Management: Clear invalidation levels provided by all pattern boundaries
● Multi-Timeframe Analysis: Works across all timeframes from 1-minute to monthly charts
⚠️Limitations
● Market Condition Dependency: Performance varies between trending and ranging market environments
● Volume Data Requirements: Requires accurate volume data for optimal pattern confirmation
● Lagging Nature: Some patterns confirmed after initial price movement has begun
● Pattern Density: High-volatility markets may generate excessive pattern signals
● Educational Tool: Requires understanding of smart money concepts for effective application
💡What Makes This Unique
● Complete SMC Integration: First indicator to combine all major smart money concepts comprehensively
● Real-Time Dashboard: Instant visual feedback on all active institutional patterns
● Advanced Volume Analysis: Multi-layered volume confirmation across all detection modules
● Probability-Based Signals: Mathematical approach to Wyckoff pattern recognition accuracy
● Professional Memory Management: Sophisticated pattern cleanup without losing market relevance
🔬How It Works
1. Pattern Detection Phase:
● Multi-timeframe scanning for institutional footprints across all enabled modules
● Volume analysis integration confirms patterns meet institutional trading criteria
● Real-time pattern validation ensures only high-probability setups are displayed
2. Signal Generation Process:
● Automated zone creation with precise boundary definitions for each pattern type
● Dynamic extension system maintains relevance until mitigation or invalidation occurs
● Alert system activation provides immediate notification of new pattern formations
3. Dashboard Update Cycle:
● Live status monitoring tracks all active patterns and module states continuously
● Pattern count updates provide instant feedback on current market condition density
● Commitment tracking for market structure analysis shows institutional engagement levels
💡Note:
This indicator represents institutional trading concepts and should be used as part of a comprehensive trading strategy. Pattern recognition accuracy improves with understanding of smart money principles. Combine with proper risk management and multiple confirmation methods for optimal results.
Bollinger Bands (SMA 21, 2.618σ)Indicator Description: Bollinger Bands (2.618σ, 21 SMA) + RSI with Fibonacci
This custom indicator combines Bollinger Bands and Relative Strength Index (RSI), enhanced with Fibonacci-based configurations, to provide confluence signals for rejection candles, reversal setups, and continuation patterns.
Bollinger Bands Settings (Customized)
Middle Band → 21-period Simple Moving Average (SMA)
Upper Band → SMA + 2.618 standard deviations
Lower Band → SMA − 2.618 standard deviations
These parameters expand the bands compared to the traditional (20, 2.0) settings, making them better suited for volatility extremes and higher timeframe swing analysis.
Color Scheme
Middle Band = Orange
Upper Band = Red
Lower Band = Green
This color-coding emphasizes key rejection levels visually.
Candle Rejection Logic
The indicator is designed to highlight potential rejection candles when price interacts with the outer Bollinger Bands:
At the Upper Band, rejection signals suggest overextension and potential downside reaction.
At the Lower Band, rejection signals suggest oversold conditions and potential upside reaction.
Rejection Candle Types Tracked
Hammer (bullish reversal, lower rejection wick at bottom band)
Inverted Hammer (bearish reversal, upper rejection wick at top band)
Doji candles (indecision at band extremes)
Double Top formations near the upper band
Double Bottom formations near the lower band
Relative Strength Index (RSI) Settings
RSI is configured with Fibonacci retracement levels instead of traditional 30/70 thresholds.
Fibonacci sequence levels used include:
23.6% (0.236)
38.2% (0.382)
50% (0.5)
61.8% (0.618)
78.6% (0.786)
This alignment with Fibonacci ratios provides deeper market structure insights into momentum strength and exhaustion points.
Trading Confluence Zones
Upper Band + RSI at 0.618–0.786 zone → High probability bearish rejection.
Lower Band + RSI at 0.236–0.382 zone → High probability bullish reversal.
Band interaction + Doji or Hammer candles → Stronger signal confirmation.
Use Cases
Identifying trend exhaustion when price repeatedly fails to break above the upper band.
Spotting accumulation or distribution phases when price consolidates around Fibonacci-based RSI zones.
Detecting false breakouts when candle patterns (like Doji or Inverted Hammer) occur beyond the bands.
Why 2.618 Deviation & 21 SMA?
Standard Bollinger Bands (20, 2.0) capture ~95% of price action.
By widening to 2.618σ, we target extreme volatility outliers — areas where reversals are statistically more likely.
A 21-period SMA aligns better with common cycle lengths (3 trading weeks on daily charts) and Fibonacci-related time cycles.
Practical Strategy
Step 1: Watch when price touches or pierces the upper/lower band.
Step 2: Check for candle rejection patterns (Hammer, Inverted Hammer, Doji, Double Top/Bottom).
Step 3: Confirm with RSI Fibonacci levels for confluence.
Step 4: Trade with the prevailing trend or look for reversal setups if multiple confluence factors align.
Cautions
Not all touches of the bands signal reversals — strong trends can ride along the bands for extended periods.
Always combine with price action structure, volume, and higher timeframe trend bias.
📌 Summary
This indicator blends volatility-based bands with Fibonacci momentum analysis and classical candle rejection patterns. The combination of Bollinger Bands (21, 2.618σ) and RSI Fibonacci levels helps traders detect high-probability rejection zones, reversal opportunities, and overextended conditions with improved accuracy over traditional default settings.
Open Interest OverlayOpen Interest Overlay
Overview
This indicator displays Open Interest (OI) data directly on your price chart as an overlay, eliminating the need for separate panes while preserving authentic OI movement patterns. Perfect for traders who want to analyze OI correlations without sacrificing chart real estate.
Key Features
📊 Smart Price Scaling
• Automatically maps Open Interest values to fit within your chart's price range
• Preserves all directional movements, timing, and relative magnitude relationships
• Uses official TradingView Open Interest feed for accuracy
🎨 Full Customization
• Custom Colors: Choose your own colors for rising/falling OI (defaults: teal/red)
• Line Style: Toggle between step-line (traditional) or smooth line display
• Optional Fill: Shade area between OI line and mid-price for better visual reference
• Smoothing Options: Apply moving average smoothing to reduce noise
⚙️ Intelligent Settings
• Normalization Window: 300-bar lookback (customizable) for scaling calculations
• Auto Timeframe: Uses daily data for intraday charts on traditional assets, chart timeframe for crypto
• Real Value Display: Shows actual (unscaled) OI value on the last bar
How It Works
The indicator performs proportional mapping of Open Interest data:
1. Calculates OI range (high/low) over the lookback period
2. Maps this range to your chart's price range during the same period
3. Displays OI movements that maintain authentic patterns and timing
Perfect For
✅ Correlation Analysis - See how OI moves with price in real-time
✅ Divergence Spotting - Identify when OI and price trends diverge
✅ Clean Charts - No need for separate panes or window splitting
✅ Pattern Recognition - Spot OI building/declining during key price levels
✅ Cross-Market Analysis - View any symbol's OI overlay on your current chart (e.g., Bitcoin OI while viewing Ethereum prices)
What You Get vs Traditional OI Indicators
Advantages:
• Authentic OI movement patterns preserved
• Direct visual correlation with price action
• No chart real estate sacrifice
• Immediate trend and divergence recognition
Trade-offs:
• Shows relative OI changes rather than absolute values
• Scaling is relative to the selected lookback period
Ideal For
• Day traders monitoring intraday OI flow
• Swing traders analyzing OI trends with price movements
• Futures traders tracking institutional interest
• Anyone wanting clean, correlation-focused OI analysis
Compatible With
• Futures contracts with Open Interest data
• Any timeframe (auto-adjusts for optimal data)
• All TradingView-supported OI symbols
Smart Bar Coloring: Tight Closes & Volume BreakoutsAdvanced Bar Coloring Indicator for Price Action and Volume Analysis
This sophisticated indicator automatically colors price bars based on two key market conditions: tight closing ranges and significant volume activity, helping traders quickly identify consolidation periods and potential breakout setups.
Key Features:
Tight Close Detection:
ATR-Based Analysis: Uses 14-period ATR to define "tight" price movement
Dual-Bar Confirmation: Requires both current and previous bar to have closing ranges ≤ 20% of ATR
Consolidation Identification: Highlights periods of reduced volatility that often precede significant moves
Customizable Color: Default amber/orange highlighting for easy visual identification
Volume Breakout Detection:
Multi-Criteria Volume Analysis: Triggers when volume exceeds any of three thresholds:
150% of 20-period volume SMA
150% of recent 3-bar average volume
150% of 50-period volume SMA
Price Action Filter: Requires bullish price action (close > previous close OR close in upper 75% of range)
Smart Volume Handling: Automatically detects and works only with instruments that have volume data
Customizable Color: Default teal highlighting for volume-driven moves
Technical Analysis Applications:
Consolidation Patterns: Identify tight trading ranges before potential breakouts
Volume Confirmation: Spot high-volume moves with supportive price action
Entry Timing: Use tight closes to identify potential accumulation zones
Breakout Validation: Volume-colored bars confirm legitimate breakout attempts
Risk Management: Tight closes often indicate lower immediate volatility
How to Use:
Amber/Orange Bars: Indicate tight closing ranges - potential accumulation or consolidation
Teal Bars: Show significant volume with bullish price action - potential breakout confirmation
Normal Bars: Standard market conditions without special highlighting
Pattern Recognition: Look for clusters of tight closes followed by volume breakouts
Technical Requirements:
Works on any timeframe
Automatically adapts to instruments with or without volume data
Compatible with all chart types and drawing tools
Seasonality Monte Carlo Forecaster [BackQuant]Seasonality Monte Carlo Forecaster
Plain-English overview
This tool projects a cone of plausible future prices by combining two ideas that traders already use intuitively: seasonality and uncertainty. It watches how your market typically behaves around this calendar date, turns that seasonal tendency into a small daily “drift,” then runs many randomized price paths forward to estimate where price could land tomorrow, next week, or a month from now. The result is a probability cone with a clear expected path, plus optional overlays that show how past years tended to move from this point on the calendar. It is a planning tool, not a crystal ball: the goal is to quantify ranges and odds so you can size, place stops, set targets, and time entries with more realism.
What Monte Carlo is and why quants rely on it
• Definition . Monte Carlo simulation is a way to answer “what might happen next?” when there is randomness in the system. Instead of producing a single forecast, it generates thousands of alternate futures by repeatedly sampling random shocks and adding them to a model of how prices evolve.
• Why it is used . Markets are noisy. A single point forecast hides risk. Monte Carlo gives a distribution of outcomes so you can reason in probabilities: the median path, the 68% band, the 95% band, tail risks, and the chance of hitting a specific level within a horizon.
• Core strengths in quant finance .
– Path-dependent questions : “What is the probability we touch a stop before a target?” “What is the expected drawdown on the way to my objective?”
– Pricing and risk : Useful for path-dependent options, Value-at-Risk (VaR), expected shortfall (CVaR), stress paths, and scenario analysis when closed-form formulas are unrealistic.
– Planning under uncertainty : Portfolio construction and rebalancing rules can be tested against a cloud of plausible futures rather than a single guess.
• Why it fits trading workflows . It turns gut feel like “seasonality is supportive here” into quantitative ranges: “median path suggests +X% with a 68% band of ±Y%; stop at Z has only ~16% odds of being tagged in N days.”
How this indicator builds its probability cone
1) Seasonal pattern discovery
The script builds two day-of-year maps as new data arrives:
• A return map where each calendar day stores an exponentially smoothed average of that day’s log return (yesterday→today). The smoothing (90% old, 10% new) behaves like an EWMA, letting older seasons matter while adapting to new information.
• A volatility map that tracks the typical absolute return for the same calendar day.
It calculates the day-of-year carefully (with leap-year adjustment) and indexes into a 365-slot seasonal array so “March 18” is compared with past March 18ths. This becomes the seasonal bias that gently nudges simulations up or down on each forecast day.
2) Choice of randomness engine
You can pick how the future shocks are generated:
• Daily mode uses a Gaussian draw with the seasonal bias as the mean and a volatility that comes from realized returns, scaled down to avoid over-fitting. It relies on the Box–Muller transform internally to turn two uniform random numbers into one normal shock.
• Weekly mode uses bootstrap sampling from the seasonal return history (resampling actual historical daily drifts and then blending in a fraction of the seasonal bias). Bootstrapping is robust when the empirical distribution has asymmetry or fatter tails than a normal distribution.
Both modes seed their random draws deterministically per path and day, which makes plots reproducible bar-to-bar and avoids flickering bands.
3) Volatility scaling to current conditions
Markets do not always live in average volatility. The engine computes a simple volatility factor from ATR(20)/price and scales the simulated shocks up or down within sensible bounds (clamped between 0.5× and 2.0×). When the current regime is quiet, the cone narrows; when ranges expand, the cone widens. This prevents the classic mistake of projecting calm markets into a storm or vice versa.
4) Many futures, summarized by percentiles
The model generates a matrix of price paths (capped at 100 runs for performance inside TradingView), each path stepping forward for your selected horizon. For each forecast day it sorts the simulated prices and pulls key percentiles:
• 5th and 95th → approximate 95% band (outer cone).
• 16th and 84th → approximate 68% band (inner cone).
• 50th → the median or “expected path.”
These are drawn as polylines so you can immediately see central tendency and dispersion.
5) A historical overlay (optional)
Turn on the overlay to sketch a dotted path of what a purely seasonal projection would look like for the next ~30 days using only the return map, no randomness. This is not a forecast; it is a visual reminder of the seasonal drift you are biasing toward.
Inputs you control and how to think about them
Monte Carlo Simulation
• Price Series for Calculation . The source series, typically close.
• Enable Probability Forecasts . Master switch for simulation and drawing.
• Simulation Iterations . Requested number of paths to run. Internally capped at 100 to protect performance, which is generally enough to estimate the percentiles for a trading chart. If you need ultra-smooth bands, shorten the horizon.
• Forecast Days Ahead . The length of the cone. Longer horizons dilute seasonal signal and widen uncertainty.
• Probability Bands . Draw all bands, just 95%, just 68%, or a custom level (display logic remains 68/95 internally; the custom number is for labeling and color choice).
• Pattern Resolution . Daily leans on day-of-year effects like “turn-of-month” or holiday patterns. Weekly biases toward day-of-week tendencies and bootstraps from history.
• Volatility Scaling . On by default so the cone respects today’s range context.
Plotting & UI
• Probability Cone . Plots the outer and inner percentile envelopes.
• Expected Path . Plots the median line through the cone.
• Historical Overlay . Dotted seasonal-only projection for context.
• Band Transparency/Colors . Customize primary (outer) and secondary (inner) band colors and the mean path color. Use higher transparency for cleaner charts.
What appears on your chart
• A cone starting at the most recent bar, fanning outward. The outer lines are the ~95% band; the inner lines are the ~68% band.
• A median path (default blue) running through the center of the cone.
• An info panel on the final historical bar that summarizes simulation count, forecast days, number of seasonal patterns learned, the current day-of-year, expected percentage return to the median, and the approximate 95% half-range in percent.
• Optional historical seasonal path drawn as dotted segments for the next 30 bars.
How to use it in trading
1) Position sizing and stop logic
The cone translates “volatility plus seasonality” into distances.
• Put stops outside the inner band if you want only ~16% odds of a stop-out due to noise before your thesis can play.
• Size positions so that a test of the inner band is survivable and a test of the outer band is rare but acceptable.
• If your target sits inside the 68% band at your horizon, the payoff is likely modest; outside the 68% but inside the 95% can justify “one-good-push” trades; beyond the 95% band is a low-probability flyer—consider scaling plans or optionality.
2) Entry timing with seasonal bias
When the median path slopes up from this calendar date and the cone is relatively narrow, a pullback toward the lower inner band can be a high-quality entry with a tight invalidation. If the median slopes down, fade rallies toward the upper band or step aside if it clashes with your system.
3) Target selection
Project your time horizon to N bars ahead, then pick targets around the median or the opposite inner band depending on your style. You can also anchor dynamic take-profits to the moving median as new bars arrive.
4) Scenario planning & “what-ifs”
Before events, glance at the cone: if the 95% band already spans a huge range, trade smaller, expect whips, and avoid placing stops at obvious band edges. If the cone is unusually tight, consider breakout tactics and be ready to add if volatility expands beyond the inner band with follow-through.
5) Options and vol tactics
• When the cone is tight : Prefer long gamma structures (debit spreads) only if you expect a regime shift; otherwise premium selling may dominate.
• When the cone is wide : Debit structures benefit from range; credit spreads need wider wings or smaller size. Align with your separate IV metrics.
Reading the probability cone like a pro
• Cone slope = seasonal drift. Upward slope means the calendar has historically favored positive drift from this date, downward slope the opposite.
• Cone width = regime volatility. A widening fan tells you that uncertainty grows fast; a narrow cone says the market typically stays contained.
• Mean vs. price gap . If spot trades well above the median path and the upper band, mean-reversion risk is high. If spot presses the lower inner band in an up-sloping cone, you are in the “buy fear” zone.
• Touches and pierces . Touching the inner band is common noise; piercing it with momentum signals potential regime change; the outer band should be rare and often brings snap-backs unless there is a structural catalyst.
Methodological notes (what the code actually does)
• Log returns are used for additivity and better statistical behavior: sim_ret is applied via exp(sim_ret) to evolve price.
• Seasonal arrays are updated online with EWMA (90/10) so the model keeps learning as each bar arrives.
• Leap years are handled; indexing still normalizes into a 365-slot map so the seasonal pattern remains stable.
• Gaussian engine (Daily mode) centers shocks on the seasonal bias with a conservative standard deviation.
• Bootstrap engine (Weekly mode) resamples from observed seasonal returns and adds a fraction of the bias, which captures skew and fat tails better.
• Volatility adjustment multiplies each daily shock by a factor derived from ATR(20)/price, clamped between 0.5 and 2.0 to avoid extreme cones.
• Performance guardrails : simulations are capped at 100 paths; the probability cone uses polylines (no heavy fills) and only draws on the last confirmed bar to keep charts responsive.
• Prerequisite data : at least ~30 seasonal entries are required before the model will draw a cone; otherwise it waits for more history.
Strengths and limitations
• Strengths :
– Probabilistic thinking replaces single-point guessing.
– Seasonality adds a small but meaningful directional bias that many markets exhibit.
– Volatility scaling adapts to the current regime so the cone stays realistic.
• Limitations :
– Seasonality can break around structural changes, policy shifts, or one-off events.
– The number of paths is performance-limited; percentile estimates are good for trading, not for academic precision.
– The model assumes tomorrow’s randomness resembles recent randomness; if regime shifts violently, the cone will lag until the EWMA adapts.
– Holidays and missing sessions can thin the seasonal sample for some assets; be cautious with very short histories.
Tuning guide
• Horizon : 10–20 bars for tactical trades; 30+ for swing planning when you care more about broad ranges than precise targets.
• Iterations : The default 100 is enough for stable 5/16/50/84/95 percentiles. If you crave smoother lines, shorten the horizon or run on higher timeframes.
• Daily vs. Weekly : Daily for equities and crypto where month-end and turn-of-month effects matter; Weekly for futures and FX where day-of-week behavior is strong.
• Volatility scaling : Keep it on. Turn off only when you intentionally want a “pure seasonality” cone unaffected by current turbulence.
Workflow examples
• Swing continuation : Cone slopes up, price pulls into the lower inner band, your system fires. Enter near the band, stop just outside the outer line for the next 3–5 bars, target near the median or the opposite inner band.
• Fade extremes : Cone is flat or down, price gaps to the upper outer band on news, then stalls. Favor mean-reversion toward the median, size small if volatility scaling is elevated.
• Event play : Before CPI or earnings on a proxy index, check cone width. If the inner band is already wide, cut size or prefer options structures that benefit from range.
Good habits
• Pair the cone with your entry engine (breakout, pullback, order flow). Let Monte Carlo do range math; let your system do signal quality.
• Do not anchor blindly to the median; recalc after each bar. When the cone’s slope flips or width jumps, the plan should adapt.
• Validate seasonality for your symbol and timeframe; not every market has strong calendar effects.
Summary
The Seasonality Monte Carlo Forecaster wraps institutional risk planning into a single overlay: a data-driven seasonal drift, realistic volatility scaling, and a probabilistic cone that answers “where could we be, with what odds?” within your trading horizon. Use it to place stops where randomness is less likely to take you out, to set targets aligned with realistic travel, and to size positions with confidence born from distributions rather than hunches. It will not predict the future, but it will keep your decisions anchored to probabilities—the language markets actually speak.
Smart Money Proxy IndexOverview
The Smart Money Proxy Index (SMPI) is an educational tool that attempts to identify potential institutional-style behavior patterns using publicly available market data. This comprehensive tool combines multiple institutional analysis techniques into a single, easy-to-read 0-100 oscillator.
Important Disclaimer
This is an educational proxy indicator that analyzes volume and price patterns. It cannot identify actual institutional trading activity and should not be interpreted as tracking real "smart money." Use for educational purposes and combine with other analysis methods.
Inspiration & Methodology
This indicator is inspired by MAPsignals' Big Money Index (BMI) methodology but uses publicly available price and volume data with original calculations. This is an independent educational interpretation designed to teach smart money concepts to retail traders.
What It Analyzes
SMPI tracks potential "smart money" activity by combining:
Block Trading Detection - Identifies unusual volume surges with significant price impact
Money Flow Analysis - Volume-weighted price pressure using Money Flow Index
Accumulation/Distribution Patterns - Modified On-Balance Volume signals
Institutional Control Proxy - End-of-day positioning and control analysis
Key Features
– Multi-Component Analysis - Combines 4 different institutional detection methods
– BMI-Style 0-100 Scale - Familiar oscillator range with clear extreme levels
– Professional Visualization - Dynamic colors, gradient fills, and clean data table
– Comprehensive Alerts - Buy/sell signals plus divergence detection
– Fully Customizable - Adjust all parameters, colors, and display options
– Non-Repainting Signals - All alerts use confirmed data for reliability
– Educational Focus - Designed to teach institutional flow concepts
How to Interpret
Above 80: Potential smart money distribution phase (bearish pressure)
Below 20: Potential smart money accumulation phase (bullish opportunity)
Signal Generation: Buy signals when crossing above 20, sell signals when crossing below 80
Divergences: Price vs SMPI divergences can signal potential trend changes
Volume Confirmation: Higher volume ratios strengthen signal reliability
Best Practices
Timeframes: Works best on higher timeframes for institutional behavior analysis
Confirmation: Combine with other technical analysis tools and market context
Volume: Pay attention to volume confirmation in the data table
Context: Consider overall market conditions and fundamental factors
Risk Management: Not recommended as standalone trading system
Customizable Parameters
Block Volume Threshold: Sensitivity for unusual volume detection (default: 2.5x average)
SMPI Smoothing Period: Index calculation smoothing (default: 25 bars)
Extreme Levels: Overbought/oversold thresholds (default: 80/20)
Money Flow Length: MFI calculation period (default: 14)
Visual Options: Colors, signals, and display preferences
Available Alerts
Buy Signal: SMPI crosses above oversold level (20)
Sell Signal: SMPI crosses below overbought level (80)
Extreme Levels: Alerts when reaching overbought/oversold zones
Divergence Detection: Bullish and bearish price vs SMPI divergences
Educational Purpose & Limitations
This indicator is designed as an educational proxy for understanding institutional flow concepts. It analyzes publicly available price and volume data to identify potential smart money behavior patterns.
Cannot access actual institutional transaction data
Signals may be slower than day-trading indicators (intentionally designed for institutional timeframes)
Should be used in conjunction with other analysis methods
Past performance does not guarantee future results
What Makes This Different
Unlike simple volume or momentum indicators, SMPI combines multiple institutional analysis techniques into one comprehensive tool. The multi-component approach provides a more robust view of potential smart money activity.
