Quarterly Theory ICT 01 [TradingFinder] XAMD + Q1-Q4 Sessions🔵 Introduction
The Quarterly Theory ICT indicator is an advanced analytical system based on the concepts of ICT (Inner Circle Trader) and fractal time. It divides time into quarterly periods and accurately determines entry and exit points for trades by using the True Open as the starting point of each cycle. This system is applicable across various time frames including annual, monthly, weekly, daily, and even 90-minute sessions.
Time is divided into four quarters: in the first quarter (Q1), which is dedicated to the Accumulation phase, the market is in a consolidation state, laying the groundwork for a new trend; in the second quarter (Q2), allocated to the Manipulation phase (also known as Judas Swing), sudden price changes and false moves occur, marking the true starting point of a trend change; the third quarter (Q3) is dedicated to the Distribution phase, during which prices are broadly distributed and price volatility peaks; and the fourth quarter (Q4), corresponding to the Continuation/Reversal phase, either continues or reverses the previous trend.
By leveraging smart algorithms and technical analysis, this system identifies optimal price patterns and trading positions through the precise detection of stop-run and liquidity zones.
With the division of time into Q1 through Q4 and by incorporating key terms such as Quarterly Theory ICT, True Open, Accumulation, Manipulation (Judas Swing), Distribution, Continuation/Reversal, ICT, fractal time, smart algorithms, technical analysis, price patterns, trading positions, stop-run, and liquidity, this system enables traders to identify market trends and make informed trading decisions using real data and precise analysis.
♦ Important Note :
This indicator and the "Quarterly Theory ICT" concept have been developed based on material published in primary sources, notably the articles on Daye( traderdaye ) and Joshuuu . All copyright rights are reserved.
🔵 How to Use
The Quarterly Theory ICT strategy is built on dividing time into four distinct periods across various time frames such as annual, monthly, weekly, daily, and even 90-minute sessions. In this approach, time is segmented into four quarters, during which the phases of Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal appear in a systematic and recurring manner.
The first segment (Q1) functions as the Accumulation phase, where the market consolidates and lays the foundation for future movement; the second segment (Q2) represents the Manipulation phase, during which prices experience sudden initial changes, and with the aid of the True Open concept, the real starting point of the market’s movement is determined; in the third segment (Q3), the Distribution phase takes place, where prices are widely dispersed and price volatility reaches its peak; and finally, the fourth segment (Q4) is recognized as the Continuation/Reversal phase, in which the previous trend either continues or reverses.
This strategy, by harnessing the concepts of fractal time and smart algorithms, enables precise analysis of price patterns across multiple time frames and, through the identification of key points such as stop-run and liquidity zones, assists traders in optimizing their trading positions. Utilizing real market data and dividing time into Q1 through Q4 allows for a comprehensive and multi-level technical analysis in which optimal entry and exit points are identified by comparing prices to the True Open.
Thus, by focusing on keywords like Quarterly Theory ICT, True Open, Accumulation, Manipulation, Distribution, Continuation/Reversal, ICT, fractal time, smart algorithms, technical analysis, price patterns, trading positions, stop-run, and liquidity, the Quarterly Theory ICT strategy acts as a coherent framework for predicting market trends and developing trading strategies.
🔵b]Settings
Cycle Display Mode: Determines whether the cycle is displayed on the chart or on the indicator panel.
Show Cycle: Enables or disables the display of the ranges corresponding to each quarter within the micro cycles (e.g., Q1/1, Q1/2, Q1/3, Q1/4, etc.).
Show Cycle Label: Toggles the display of textual labels for identifying the micro cycle phases (for example, Q1/1 or Q2/2).
Table Display Mode: Enables or disables the ability to display cycle information in a tabular format.
Show Table: Determines whether the table—which summarizes the phases (Q1 to Q4)—is displayed.
Show More Info: Adds additional details to the table, such as the name of the phase (Accumulation, Manipulation, Distribution, or Continuation/Reversal) or further specifics about each cycle.
🔵 Conclusion
Quarterly Theory ICT provides a fractal and recurring approach to analyzing price behavior by dividing time into four quarters (Q1, Q2, Q3, and Q4) and defining the True Open at the beginning of the second phase.
The Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal phases repeat in each cycle, allowing traders to identify price patterns with greater precision across annual, monthly, weekly, daily, and even micro-level time frames.
Focusing on the True Open as the primary reference point enables faster recognition of potential trend changes and facilitates optimal management of trading positions. In summary, this strategy, based on ICT principles and fractal time concepts, offers a powerful framework for predicting future market movements, identifying optimal entry and exit points, and managing risk in various trading conditions.
Cerca negli script per "accumulation"
WMA Trend and Growth Rate IndicatorThe "WMA Trend and Growth Rate Indicator" is a powerful tool for analyzing market trends and momentum. By understanding its components and how to configure it, traders of all levels can leverage this indicator to enhance their trading strategies. Experiment with the settings and integrate it into your analysis to gain valuable insights into market movements.
Indicator Components
WMA Length : The length of the WMA. This controls how many periods are included in the calculation.
Start : The starting value for accumulation levels.
End : The ending value for accumulation levels.
Key Concepts
Weighted Moving Average (WMA): A type of moving average that gives more weight to recent price data, making it more responsive to recent price changes.
Growth Rate : Measures how much the WMA has increased or decreased over a specified period, expressed as a percentage.
Accumulation and Distribution Levels : Zones where buying (accumulation) or selling (distribution) pressure is expected.
Configuring the Inputs
WMA Length : Adjust this value to change the sensitivity of the WMA. A smaller value makes the WMA more sensitive to recent price changes, while a larger value smooths out the data more.
Start and End : Adjust these values to define the range for accumulation and distribution levels. The indicator will automatically adjust the colors based on whether the Start value is higher or lower than the End value.
Interpreting the Plots
WMAT Line : The main trend line that shows the direction and strength of the trend.
Growth Index : Shows the growth rate of the WMAT.
Accumulation Levels : Indicated by lines and fill colors, showing potential zones to increase positions.
Distribution Levels : Indicated by lines and fill colors, showing potential zones to decrease positions.
The indicator checks if "Start" is greater than "End". Based on this check, it assigns colors to the accumulation and distribution levels. This color scheme helps traders visually distinguish between areas of potential buying and selling zones.
Wyckoff Range StrategyThe Wyckoff Range Strategy is a trading strategy that aims to identify potential accumulation and distribution phases in the market using the principles of Wyckoff analysis. It also incorporates the detection of spring and upthrust patterns.
Here's a step-by-step explanation of how to use this strategy:
Understanding Accumulation and Distribution Phases:
Accumulation Phase: This is a period where smart money (large institutional traders) accumulates a particular asset at lower prices. It is characterized by a sideways or consolidating price action.
Distribution Phase: This is a period where smart money distributes or sells a particular asset at higher prices. It is also characterized by a sideways or consolidating price action.
Input Variables:
crossOverLength: This variable determines the length of the moving average crossover used to identify accumulation and distribution phases. You can adjust this value based on the market you are trading and the time frame you are analyzing.
stopPercentage: This variable determines the percentage used to calculate the stop loss level. It helps you define a predefined level at which you would exit a trade if the price moves against your position.
Strategy Conditions:
Enter Long: The strategy looks for a crossover of the close price above the SMA of the close price with a length of crossOverLength and a crossover of the low price above the SMA of the low price with a length of 20. This combination suggests the start of an accumulation phase and a potential buying opportunity.
Exit Long: The strategy looks for a crossunder of the close price below the SMA of the close price with a length of crossOverLength or a crossunder of the high price below the SMA of the high price with a length of 20. This combination suggests the end of an accumulation phase and a potential exit signal for long positions.
Enter Short: The strategy looks for a crossunder of the close price below the SMA of the close price with a length of crossOverLength and a crossunder of the high price below the SMA of the high price with a length of 20. This combination suggests the start of a distribution phase and a potential selling opportunity.
Exit Short: The strategy looks for a crossover of the close price above the SMA of the close price with a length of crossOverLength or a crossover of the low price above the SMA of the low price with a length of 20. This combination suggests the end of a distribution phase and a potential exit signal for short positions.
Stop Loss:
The strategy sets a stop loss level for both long and short positions. The stop loss level is calculated based on the stopPercentage variable, which represents the percentage of the current close price. If the price reaches the stop loss level, the strategy will automatically exit the position.
Plotting Wyckoff Schematics:
The strategy plots different shapes on the chart to indicate the identified phases and patterns. Green and red labels indicate the accumulation and distribution phases, respectively. Blue triangles indicate spring patterns, and orange triangles indicate upthrust patterns.
To use this strategy, you can follow these steps:
Jim Forte — Anatomy of a Trading Range
robertbrain.com/Bull...+a+Trading+Range.pdf
PEAD ScreenerPEAD Screener - Post-Earnings Announcement Drift Scanner
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WHY EARNINGS ANNOUNCEMENTS CREATE OPPORTUNITY
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The days immediately following an earnings announcement are among the noisiest periods for any stock. Within hours, the market must digest new information about a company's profits, revenue, and future outlook. Analysts scramble to update their models. Institutions rebalance positions. Retail traders react to headlines.
This chaos creates a well-documented phenomenon called Post-Earnings Announcement Drift (PEAD): stocks that beat expectations tend to keep rising, while those that miss tend to keep falling - often for weeks after the initial announcement. Academic research has confirmed this pattern persists across decades and markets.
But not every earnings surprise is equal. A company that beats estimates by 5 cents might move very differently than one that beats by 5 cents with unusually high volume, or one where both earnings AND revenue exceeded expectations. Raw numbers alone don't tell the full story.
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HOW "STANDARDIZED UNEXPECTED" METRICS CUT THROUGH THE NOISE
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This screener uses a statistical technique to measure how "surprising" a result truly is - not just whether it beat or missed, but how unusual that beat or miss was compared to the company's own history.
The core idea: convert raw surprises into Z-scores.
A Z-score answers the question: "How many standard deviations away from normal is this result?"
- A Z-score of 0 means the result was exactly average
- A Z-score of +2 means the result was unusually high (better than ~95% of historical results)
- A Z-score of -2 means the result was unusually low
By standardizing surprises this way, we can compare apples to apples. A small-cap biotech's $0.02 beat might actually be more significant than a mega-cap's $0.50 beat, once we account for each company's typical variability.
This screener applies this standardization to three dimensions: earnings (SUE), revenue (SURGE), and volume (SUV).
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THE 9 SCREENING CRITERIA
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1. SUE (Standardized Unexpected Earnings)
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WHAT IT IS:
SUE measures how surprising an earnings result was, adjusted for the company's historical forecast accuracy.
Calculation: Take the earnings surprise (actual EPS minus analyst estimate), then divide by the standard deviation of past forecast errors. This uses a rolling window of the last 8 quarters by default.
Formula: SUE = (Actual EPS - Estimated EPS) / Standard Deviation of Past Errors
HOW TO INTERPRET:
- SUE > +2.0: Strongly positive surprise - earnings beat expectations by an unusually large margin. These stocks often continue drifting higher.
- SUE between 0 and +2.0: Modest positive surprise - beat expectations, but within normal range.
- SUE between -2.0 and 0: Modest negative surprise - missed expectations, but within normal range.
- SUE < -2.0: Strongly negative surprise - significant miss. These stocks often continue drifting lower.
For long positions, look for SUE values above +2.0, ideally combined with positive SURGE.
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2. SURGE (Standardized Unexpected Revenue)
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WHAT IT IS:
SURGE applies the same standardization technique to revenue surprises. While earnings can be manipulated through accounting choices, revenue is harder to fake - it represents actual sales.
Calculation: Take the revenue surprise (actual revenue minus analyst estimate), then divide by the standard deviation of past revenue forecast errors.
Formula: SURGE = (Actual Revenue - Estimated Revenue) / Standard Deviation of Past Errors
HOW TO INTERPRET:
- SURGE > +1.5: Strongly positive revenue surprise - the company sold significantly more than expected.
- SURGE between 0 and +1.5: Modest positive surprise.
- SURGE < 0: Revenue missed expectations.
The most powerful signals occur when BOTH SUE and SURGE are positive and elevated (ideally SUE > 2.0 AND SURGE > 1.5). This indicates the company beat on both profitability AND top-line growth - a much stronger signal than either alone.
When SUE and SURGE diverge significantly (e.g., high SUE but negative SURGE), treat with caution - the earnings beat may have come from cost-cutting rather than genuine growth.
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3. SUV (Standardized Unexpected Volume)
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WHAT IT IS:
SUV detects unusual trading volume after accounting for how volatile the stock is. More volatile stocks naturally have higher volume, so raw volume comparisons can be misleading.
Calculation: This uses regression analysis to model the expected relationship between price volatility and volume. The "unexpected" volume is the residual - how much actual volume deviated from what the model predicted. This residual is then standardized into a Z-score.
In plain terms: SUV asks "Given how much this stock typically moves, is today's volume unusually high or low?"
HOW TO INTERPRET:
- SUV > +2.0: Exceptionally high volume relative to the stock's volatility. This often signals institutional activity - big players moving in or out.
- SUV between +1.0 and +2.0: Elevated volume - above normal interest.
- SUV between -1.0 and +1.0: Normal volume range.
- SUV < -1.0: Unusually quiet - less activity than expected.
High SUV combined with positive price movement suggests accumulation (buying). High SUV combined with negative price movement suggests distribution (selling).
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4. % From D0 Close
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WHAT IT IS:
This measures how far the current price has moved from the closing price on its initial earnings reaction day (D0). The "reaction day" is the first trading day that fully reflects the earnings news - typically the day after an after-hours announcement, or the announcement day itself for pre-market releases.
Calculation: ((Current Price - D0 Close) / D0 Close) × 100
HOW TO INTERPRET:
- Positive values: Stock has gained ground since earnings. The higher the percentage, the stronger the post-earnings drift.
- 0% to +5%: Modest positive drift - earnings were received well but momentum is limited.
- +5% to +15%: Strong drift - buyers continue accumulating.
- > +15%: Exceptional drift - significant institutional interest likely.
- Negative values: Stock has given back gains or extended losses since earnings. May indicate the initial reaction was overdone, or that sentiment is deteriorating.
This metric is most meaningful within the first 5-20 trading days after earnings. Extended drift (maintaining gains over 2+ weeks) is a stronger signal than a quick spike that fades.
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5. # Pocket Pivots
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WHAT IT IS:
Pocket Pivots are a volume-based pattern developed by Chris Kacher and Gil Morales. They identify days where institutional buyers are likely accumulating shares without causing obvious breakouts.
Calculation: A Pocket Pivot occurs when:
- The stock closes higher than it opened (up day)
- The stock closes higher than the previous day's close
- Today's volume exceeds the highest down-day volume of the prior 10 trading sessions
The screener counts how many Pocket Pivots have occurred since the earnings announcement.
HOW TO INTERPRET:
- 0 Pocket Pivots: No detected institutional accumulation patterns since earnings.
- 1-2 Pocket Pivots: Some institutional buying interest - worth monitoring.
- 3+ Pocket Pivots: Strong accumulation signal - institutions appear to be building positions.
Pocket Pivots are most significant when they occur:
- Immediately following earnings announcements
- Near moving average support (10-day, 21-day, or 50-day)
- On above-average volume
- After a period of price consolidation
Multiple Pocket Pivots in a short period suggest sustained institutional demand, not just a one-day event.
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6. ADX/DI (Trend Strength and Direction)
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WHAT IT IS:
ADX (Average Directional Index) measures trend strength regardless of direction. DI (Directional Indicator) shows whether the trend is bullish or bearish.
Calculation: ADX uses a 14-period lookback to measure how directional (trending) price movement is. Values range from 0 to 100. The +DI and -DI components compare upward and downward movement.
The screener shows:
- ADX value (trend strength)
- Direction indicator: "+" for bullish (price trending up), "-" for bearish (price trending down)
HOW TO INTERPRET:
- ADX < 20: Weak trend - the stock is moving sideways, choppy. Not ideal for momentum trading.
- ADX 20-25: Trend is emerging - potentially starting a directional move.
- ADX 25-40: Strong trend - clear directional movement. Good for momentum plays.
- ADX > 40: Very strong trend - powerful move in progress, but may be extended.
The direction indicator (+/-) tells you which way:
- "25+" means ADX of 25 with bullish direction (uptrend)
- "25-" means ADX of 25 with bearish direction (downtrend)
For post-earnings plays, ideal setups show ADX rising above 25 with positive direction, confirming the earnings reaction is developing into a sustained trend rather than a one-day spike.
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7. Institutional Buying PASS
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WHAT IT IS:
This proprietary composite indicator detects patterns consistent with institutional accumulation at three stages after earnings:
EARLY (Days 0-4): Looks for "large block" buying on the earnings reaction day (exceptionally high volume with a close in the upper half of the day's range) combined with follow-through buying on the next day.
MID (Days 5-9): Checks for sustained elevated volume (averaging 1.5x the 20-day average) combined with positive drift and consistent upward price movement (more up days than down days).
LATE (Days 10+): Detects either visible accumulation (positive drift with high volume) OR stealth accumulation (positive drift with unusually LOW volume - suggesting smart money is quietly building positions without attracting attention).
HOW TO INTERPRET:
- Check mark/value of '1': Institutional buying pattern detected. The stock shows characteristics consistent with large players accumulating shares.
- X mark/value of '0': No institutional buying pattern detected. This doesn't mean institutions aren't buying - just that the typical footprints aren't visible.
A passing grade here adds conviction to other bullish signals. Institutions have research teams, information advantages, and long time horizons. When their footprints appear in the data, it often precedes sustained moves.
Important: This is a pattern detection tool, not a guarantee. Always combine with other analysis.
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8. Strong ATR Drift PASS
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WHAT IT IS:
This measures whether the stock has drifted significantly relative to its own volatility. Instead of asking "did it move 10%?", it asks "did it move more than 1.5 ATRs?"
ATR (Average True Range) measures a stock's typical daily movement. A volatile stock might move 5% daily, while a stable stock might move 0.5%. Using ATR normalizes for this difference.
Calculation:
ATR Drift = (Current Close - D0 Close) / D0 ATR in dollars
The indicator passes when ATR Drift exceeds 1.5 AND at least 5 days have passed since earnings.
HOW TO INTERPRET:
- Check mark/value of '1': The stock has drifted more than 1.5 times its average daily range since earnings - a statistically significant move that suggests genuine momentum, not just noise.
- X mark/value of '0': The drift (if any) is within normal volatility bounds - could just be random fluctuation.
Why wait 5 days? The immediate post-earnings reaction (days 0-2) often includes gap fills and noise. By day 5, if the stock is still extended beyond 1.5 ATRs from the earnings close, it suggests real buying pressure, not just a reflexive gap.
A passing grade here helps filter out stocks that "beat earnings" but haven't actually moved meaningfully. It focuses attention on stocks where the market is voting with real capital.
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9. Days Since D0
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WHAT IT IS:
Simply counts the number of trading days since the earnings reaction day (D0).
HOW TO INTERPRET:
- Days 0-5 (Green): Fresh earnings - the information is new, institutional repositioning is active, and momentum trades are most potent. This is the "sweet spot" for PEAD strategies.
- Days 6-10 (Neutral): Mid-period - some edge remains but diminishing. Good for adding to winning positions, less ideal for new entries.
- Days 11+ (Red): Extended period - most of the post-earnings drift has typically played out. Higher risk that momentum fades or reverses.
Research shows PEAD effects are strongest in the first 5-10 days after earnings, then decay. Beyond 20-30 days, the informational advantage of the earnings surprise is largely priced in.
Use this to prioritize: focus on stocks with strong signals that are still in the early window, and be more selective about entries as days accumulate.
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PUTTING IT ALL TOGETHER
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You can use this screener in the chart view or in the Screener.
One combination of the above filters to develop a shortlist of positive drift candidates may be:
- SUE > 2.0 (significant earnings beat)
- SURGE > 1.5 (significant revenue beat)
- Positive % From D0 Close (price confirming the good news)
- Institutional Buying PASS (big players accumulating)
- Strong ATR Drift PASS (statistically significant movement)
- Days Since D0 < 10 (still in the active drift window)
No single indicator is sufficient. The power comes from convergence - when multiple independent measures all point the same direction.
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SETTINGS
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Key adjustable parameters:
- SUE Method: "Analyst-based" uses consensus estimates; "Time-series" uses year-over-year comparison
- Window Size: Number of quarters used for standardization (default: 8)
- ATR Drift Threshold: Minimum ATR multiple for "strong" classification (default: 1.5)
- Institutional Buying thresholds: Adjustable volume and CLV parameters
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DISCLAIMER
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This screener is a research tool, not financial advice. Past patterns do not guarantee future results. Always conduct your own due diligence and manage risk appropriately. Post-earnings trading involves significant uncertainty and volatility. The 'SUE' in this indicator does not represent a real person; any similarity to actual Sue's (or Susans for that matter) living or dead is quite frankly ridiculous, not to mention coincidental.
Macros+AMD [NW]Macros + AMD - Daily & Weekly Time-Based Analysis
Multi-timeframe AMD (Accumulation, Manipulation, Distribution) visualization with ICT Macro timing windows for time-based market analysis.
Overview
This indicator visualizes the AMD (Accumulation, Manipulation, Distribution) framework on both daily and weekly timeframes, combined with ICT Macro timing windows. It is designed as an educational tool to help traders study time-based market structure and algorithmic price delivery concepts.
The AMD model is based on the idea that markets move through distinct phases within each trading period:
Accumulation (A) - Initial range formation, liquidity building
Manipulation (M) - False moves to trap traders, liquidity sweeps
Distribution (D) - True directional move, price delivery to targets
What This Indicator Displays
Daily AMD Phases
Displays the intraday AMD cycle based on New York trading hours:
A Phase (Blue): 4:00 AM - 8:35 AM EST — Morning accumulation, Asian/London overlap
M Phase (Red): 8:35 AM - 11:25 AM EST — NY session manipulation, news events
D Phase (Green): 11:25 AM - 4:00 PM EST — Afternoon distribution and price delivery
Weekly AMD Phases
Displays the weekly AMD cycle from Monday to Monday:
A Phase: Monday 00:00 - Tuesday 21:56 EST — Weekly high/low formation begins
M Phase: Tuesday 21:56 - Thursday 02:04 EST — Mid-week reversal zone
D Phase: Thursday 02:04 - Monday 00:00 EST — Weekly price delivery
Inner M Phase Fibs
When enabled, subdivides the M (Manipulation) phase using Fibonacci levels:
0.382 level — Inner accumulation ends
0.500 level — Mid-point of manipulation
0.618 level — Inner distribution begins
This helps identify potential reversal points within the manipulation phase.
ICT Macro Windows
Horizontal lines marking the XX:42 to XX:15 macro periods (33-minute windows):
2:42 - 3:15 AM
3:42 - 4:15 AM (London)
7:42 - 8:15 AM
8:42 - 9:15 AM
9:42 - 10:15 AM (Prime AM session)
10:42 - 11:15 AM
11:42 - 12:15 PM
12:42 - 1:15 PM
1:42 - 2:15 PM
2:42 - 3:15 PM
These windows represent times when algorithmic price delivery is more likely to occur.
How To Use
Understanding the AMD Framework
During the A Phase:
Observe range formation and initial liquidity pools
Note the high and low established during this phase
Wait for manipulation before committing to direction
During the M Phase:
Watch for false breakouts and stop hunts
Look for reversal patterns after liquidity sweeps
The inner fibs (0.382, 0.5, 0.618) can help time entries within this phase
Mid-week (Wednesday) often sees key reversals on weekly AMD
During the D Phase:
This is typically when the true move occurs
Price tends to deliver toward draw on liquidity targets
The direction is often opposite to the manipulation move
Using the Macro Windows
The XX:42 to XX:15 windows are times to pay attention to price action:
These 33-minute periods often see increased algorithmic activity
Look for displacement, fair value gaps, or order blocks forming
The 9:42-10:15 AM window is considered particularly significant for NY session
Weekly Day Labels
Monday/Tuesday: "H/L of Week" — Watch for weekly high or low formation
Wednesday: "Reversal Day" — Mid-week reversal probability increases
Thursday/Friday: "Reversal Day" — Continuation or secondary reversal
Settings Guide
Main Settings
Timezone: Set to your broker's timezone or preferred timezone
Macros On Top: Toggle macro lines above or below AMD boxes
Show All Text Labels: Master toggle for all text (turn off for clean charts on HTF)
Daily/Weekly AMD
Show: Enable/disable the AMD visualization
Opacity: Adjust transparency of the phase boxes (higher = more transparent)
AMD Colors
Customize colors for each phase (A, M, D)
Default: Blue (A), Red (M), Green (D)
Inner M Style
Customize the inner M phase fib lines and text colors
Default: Black lines for clean visibility
Macro Settings
Adjust macro line color and thickness
Toggle individual macro windows on/off
Important Notes
This indicator is for educational purposes and time-based analysis
It does not provide buy/sell signals
Always use in conjunction with proper price action analysis
Past price behavior during these time windows does not guarantee future results
The AMD framework is one lens for viewing market structure — use it as part of a complete methodology
Credits
This indicator is based on concepts taught by ICT (Inner Circle Trader) and the broader Smart Money Concepts community. The AMD framework, macro timing windows, and weekly profile concepts are derived from this educational methodology.
Timeframe Recommendations
Best viewed on 1-minute to 15-minute charts
Text labels automatically hide on 9-minute and higher timeframes for cleaner visualization
Indicator hides completely on 1-hour and higher timeframes
Changelog
v1.0 - Initial release
Daily AMD phases (4am-4pm EST)
Weekly AMD phases (Monday-Monday)
Inner M phase Fibonacci subdivisions
10 ICT Macro timing windows
Full customization options
Automatic 9-day cleanup
Low Volatility Profiles [BigBeluga]🔵 OVERVIEW
Low Volatility Profiles is a market compression and breakout-anticipation tool that identifies phases of low volatility using ADX and then builds a real-time volume profile inside the detected range.
This helps traders spot accumulation/distribution zones and prepare for explosive moves when volatility expands.
When volatility is low ➜ price coils ➜ volume organizes ➜ breakouts become highly actionable.
This tool visualizes that process with dynamic range boxes + volume bins + PoC extension.
🔵 CONCEPTS
Low-Volatility Detection — Uses ADX threshold & cross logic to define volatility contraction regimes.
Range Construction — Draws a price box that expands with highs/lows during the compression phase.
Micro Volume Profile — Builds a volume histogram inside the range using bins (micro volume nodes).
Delta Calculation — Tracks positive vs negative volume to gauge buyer/seller pressure within range.
Point of Control (PoC) — Highlights the price level with max traded volume inside the range.
PoC Extension — Optionally extends PoC into future bars to show potential reaction zone after breakout.
Breakout Validation — Ends the profile zone when price breaks above or below the modeled range.
Noise Removal — Automatically removes invalid or small ranges to prevent chart clutter.
This tool turns consolidation into actionable structure by exposing where smart money accumulates before trending moves.
🔵 FEATURES
ADX-Driven Range Detection — Identify when market transitions into low-volatility compression.
Configurable ADX Threshold — Set sensitivity for contraction zones.
Cross-Type Option — Detect low volatility via cross under / crossover logic.
Dynamic Range Box — Expands live with price as contraction unfolds.
Micro Volume Profile (Bins) — Distributes volume across bins inside range for micro POC mapping.
Volume Delta Visualization — Shows imbalance inside consolidation (accumulation vs distribution).
Real-Time PoC Highlight — Instantly shows most traded price inside the compression.
PoC Extension Mode — Extend PoC forward to project reaction levels post-breakout.
Clean Auto-Reset Logic — Removes boxes if range invalid or breakout occurs too fast.
Optional Filled Boxes — Heatmap-style profile visualization inside range body.
ADX Line + Threshold Plot — Visual assistance for volatility state monitoring.
🔵 HOW TO USE
Identify Accumulation Zones — When price enters low-volatility ADX condition and profile builds.
Watch the PoC — PoC acts as battle zone; move above/below can signal initiator strength.
Breakout Strategy — Trade break above/below the range after compression.
Mean Reversion Inside Range — Fade edges while price remains inside compression box.
Combine With Trend Tools — Use trend confirmation (MA/EMA/Flow indicators) after breakout.
Use Delta Clues — Positive delta tilt suggests accumulation; negative suggests distribution.
Monitor Range Size — Longer build + high PoC volume = stronger potential breakout energy.
🔵 CONCLUSION
Low Volatility Profiles isolates accumulation phases and maps volume concentration before volatility expansion.
By combining ADX compression, micro volume distribution, and PoC tracing, traders gain an edge in anticipating powerful breakout cycles and institutional positioning.
Trade the quiet moment before the storm — where smart money prepares the move, and the real opportunity emerges.
Oracle Protocol — Arch Public (Testing Clone) Oracle Protocol — Arch Public Series (testing clone)
This model implements the Arch Public Oracle structure: a systematic accumulation-and-distribution engine built around a dynamic Accumulation Cost Base (ACB), strict profit-gate exit logic, and a capital-bounded flywheel reinvestment system.
It is designed for transparent execution, deterministic behaviour, and rule-based position management.
Core Function Set
1. Accumulation Framework (ACB-Driven)
The accumulation engine evaluates market movement against defined entry conditions, including:
Percentage-based entry-drop triggers
Optional buy-below-ACB mode
Capital-gated entries tied to available ledger balance
Fixed-dollar and min-dollar entry rules (as seen in Arch public materials)
Automated sizing through flywheel capital
Range-bounded ledger for controlled backtesting input
Each qualifying buy updates the live ACB, maintains the internal ledger, and forms the next reference point for exit evaluation.
No forecasting mechanisms are included.
2. Profit-Gate Exit System
Exits are governed by the standard Arch public approach:
A sealed ACB reference for threshold evaluation
Optional live-ACB visibility
Profit-gate triggers defined per asset class
Candle-confirmation integration (“ProfitGate + Candle” mode)
Distribution only when the smallest active threshold is met
This provides a consistent cadence with published Arch diagrams and PDFs.
3. Once-Per-Rally Governance
After a distribution, the algorithm locks until price retraces below the most recent accumulation base.
Only after re-arming can the next profit gate activate.
This prevents over-frequency selling and aligns with the public-domain Oracle behaviour.
4. Quiet-Bars & Threshold Cluster Control
A volatility-stabilisation layer prevents multiple exits from micro-fluctuations or transient spikes.
This ensures clean execution during fast markets and high volatility.
5. Flywheel Reinvestment
Distribution proceeds automatically return to the capital pool where permitted, creating a closed system of:
Entry sizing
Exit proceeds
Ledger-managed capital state
All sizing respects capital boundaries and does not breach dollar floors or overrides.
6. Automation Hooks and Integration
The script exposes:
3Commas-compatible JSON sizing
Entry/exit signalling via alertcondition()
Deterministic event reporting suitable for external automation
This allows consistent deployment across automated execution environments.
7. Visual Tooling
Optional displays include:
Live ACB line
Exit-guide markers
Capital, state, and ledger panels
Realized/unrealized outcome tracking based on internal logic only
Visual components do not influence execution rules.
Operating Notes
This model is rule-based, deterministic, and non-predictive.
It executes only according to the explicit thresholds, capital limits, and state transitions defined within the script.
No discretionary or forward-looking logic is included.
Momentum Squeeze Candle [Darwinian]# Momentum Squeeze Candle
Professional squeeze detection indicator with Wyckoff accumulation/distribution analysis and multi-method momentum signals.
## Overview
Identifies volatility compression (squeeze) periods and provides intelligent momentum direction signals based on institutional accumulation/distribution patterns.
## Features
6 Squeeze Detection Methods:
• BB + KC (Classic) - John Carter's TTM Squeeze
• ATR Ratio - Volatility compression detection
• Choppiness Index - Ranging vs trending analysis
• BB Width - Bollinger Band contraction
• Volume Contraction - Drying volume detection
• Hybrid Multi-Method - Ensemble approach (3+ methods must agree)
Smart Momentum Direction:
• Priority 1: Wyckoff signals (ATR compression + volume analysis)
• Priority 2: RSI momentum (55/45 thresholds)
• Priority 3: Hybrid slope + momentum confirmation
Visual Indicators:
• Blue candle coloring during squeeze
• Green circles = Bullish momentum (accumulation detected)
• Red circles = Bearish momentum (distribution detected)
• Optional BB/KC band overlay
## How It Works
Wyckoff Accumulation (Bullish):
ATR compressing + volume drying + price holding above MA = Smart money accumulating
→ Green circle signals
Wyckoff Distribution (Bearish):
ATR expanding + volume surging + price failing below MA = Smart money distributing
→ Red circle signals
## Recommended Settings
Swing Trading (Daily/4H):
Method: BB + KC or Hybrid | Sensitivity: 1.2-1.5
Day Trading (15m-1H):
Method: ATR Ratio or BB Width | Sensitivity: 0.8-1.0
Scalping (1m-5m):
Method: Volume Contraction | Sensitivity: 0.7-0.9
High Probability:
Method: Hybrid Multi-Method | Min Score: 4/5 | Sensitivity: 1.5
## Key Advantages
✓ Multiple squeeze detection algorithms for different market conditions
✓ Wyckoff methodology for institutional activity detection
✓ Priority-based momentum system reduces false signals
✓ Clean, optimized code (70% faster than typical indicators)
✓ Fully customizable sensitivity and visual settings
## Usage
1. Choose squeeze detection method based on your trading style
2. Watch for blue candles (squeeze active)
3. Monitor momentum signals:
- Green circles below bars = Accumulation phase (bullish)
- Red circles below bars = Distribution phase (bearish)
4. Trade the breakout in the direction of momentum signals
## Notes
• All inputs hidden from status line by default for clean charts
• Works on all timeframes and asset classes
• Combine with your trading strategy for confirmation
• Best results when multiple priority signals align
Perfect for traders looking to identify consolidation periods and predict breakout direction using institutional accumulation/distribution patterns.
Volume BubblesVolume Bubbles Indicator
Introduction
The Volume Bubbles indicator is a powerful tool designed to visually highlight significant volume spikes on your TradingView charts. It helps traders identify potential areas of whale accumulation (large buying activity) or dumping (large selling activity) by displaying colored bubbles on candles where volume exceeds a customizable threshold. Green bubbles indicate bullish (buy) volume on up candles, suggesting possible accumulation, while red bubbles signal bearish (sell) volume on down candles, indicating potential dumping. The bubble size scales with the volume magnitude, making it easy to spot major market moves at a glance.
This indicator is particularly useful for crypto, forex, and stock traders looking to gauge market sentiment and large player involvement without cluttering the chart. It's built in Pine Script v5 and overlays directly on your price action.
How It Works
The indicator calculates a moving average of volume (default: 20-period SMA) and detects spikes when current volume exceeds this average by a multiplier (default: 2x).
Buy Bubbles (Green): Appear on bullish candles (close >= open) at the low wick, representing potential whale buying or accumulation zones.
Sell Bubbles (Red): Appear on bearish candles (close < open) at the high wick, indicating potential whale selling or dumping zones.
Bubble Size: Dynamically sized based on volume thresholds – huge for >1M, large for 500K-1M, normal for <500K.
Transparency: Increases with volume ratio for better visibility on extreme spikes.
Tooltip:
Hover over a bubble to see detailed info like total volume, average volume, and ratio.
By focusing on these high-volume events, traders can spot key support/resistance levels where whales might be active.
How to Use for Whale Accumulation and Dumping
Whales (large holders) often move markets with high-volume trades. This indicator helps spot them:
Accumulation (Buying): Look for clusters of large green bubbles at price lows or during consolidations. This suggests whales are buying dips, potentially signaling a reversal or uptrend start. Combine with support levels for confirmation.
Dumping (Selling): Watch for big red bubbles at price highs or after rallies. This indicates whales unloading positions, which could lead to downtrends or corrections. Pair with resistance levels.
Tips:
Use on higher timeframes (e.g., 1H+) for reliable signals.
Confirm with other indicators like RSI or MACD to avoid false positives.
In trending markets, buy bubbles in uptrends confirm strength; sell bubbles in downtrends signal continuation.
Credits and Disclaimer
Inspired by volume analysis techniques. This is free to use; feedback welcome! Not financial advice – trade at your own risk.
UDVR + OBV Combo — MTF (v6)The UDVR + OBV Combo is a multi-timeframe volume analysis tool that blends the Up/Down Volume Ratio with a normalized On-Balance Volume signal. It highlights when accumulation or distribution truly supports price action, adds higher-timeframe context, and shades the background when both indicators align. Use it to confirm breakouts, spot divergences, and filter trades with the backing of real volume flows.
1.Up/Down Volume Ratio (UDVR)
•Compares the rolling sum of up-volume (bars where price closed higher) vs down-volume (bars where price closed lower).
•A ratio > 1.0 = more accumulation (bullish pressure).
•A ratio < 1.0 = more distribution (bearish pressure).
•Optional histogram shows deviations from the 1.0 baseline.
•Customizable handling of equal closes (count as up, down, split, or ignore).
•Configurable lookback length and optional EMA smoothing.
2. On-Balance Volume (OBV)
•Classic cumulative OBV implemented natively (adds volume on up-bars, subtracts on down-bars).
•Normalized with a z-score so it can be compared across different symbols/timeframes.
•Includes an EMA signal line for slope detection.
•Alignment of OBV vs its EMA highlights rising or waning participation.
3. Multi-Timeframe Support
•Both UDVR and OBV can be plotted from a higher timeframe (HTF) (e.g. Daily UDVR shown on a 1h chart).
•Lets you see big-money accumulation/distribution while trading intraday.
•Shaded background when current TF and HTF agree (both bullish or both bearish).
How to read it
• Bullish confirmation = UDVR > 1 (accumulation) and OBV above EMA (rising participation).
• Bearish confirmation = UDVR < 1 (distribution) and OBV below EMA (falling participation).
• Mixed signals (e.g. UDVR > 1 but OBV falling) = caution; price may lack conviction.
• Divergences : If price makes a new high but OBV or UDVR does not, it’s a warning of weakening trend.
• Higher timeframe context : set HTF = Daily or Weekly and watch how short-term signals align with institutional flows. A long trade on the 15m chart is stronger when Daily UDVR is also above 1.
Inputs
•UDVR Lookback: number of bars for rolling volume sums.
•Smoothing EMA: smooths UDVR for stability.
•Equal Close Handling: decide how equal closes affect UDVR.
•Signal Band: optional UDVR extreme thresholds.
•Show Histogram: toggle UDVR histogram around baseline.
•Higher Timeframe UDVR: overlay Daily/Weekly UDVR on lower timeframe charts.
•OBV EMA length: slope proxy for normalized OBV.
•OBV Normalization window: controls z-score sensitivity.
•Higher Timeframe OBV: overlay higher timeframe OBV.
Alerts
•UDVR Bullish/Bearish cross at the 1.0 baseline.
•OBV slope up/down when OBV crosses its EMA.
•Alignment signals when UDVR and OBV agree (both confirm bullish or bearish conditions).
Why it’s useful
•Combines trend, momentum, and participation in one place.
•Helps avoid false breakouts by checking if volume supports the move.
•Lets you spot accumulation/distribution shifts before they show up in price.
•Gives a higher timeframe context so you’re not trading against the “big picture.”
Once applied, the indicator creates a dedicated pane below price with the following components:
UDVR Line (green/red)
• Green when UDVR > 1.0 (more up-volume than down-volume → accumulation).
• Red when UDVR < 1.0 (more down-volume → distribution).
UDVR Baseline and Bands
• Grey baseline at 1.0 = balance between buying and selling volume.
• Optional upper/lower bands (default 1.5 and 0.67) highlight extreme imbalances.
• Shaded areas between baseline and bands provide visual context for strength/weakness.
UDVR Histogram (optional)
• Columns around the baseline showing (UDVR – 1.0).
• Quick way to gauge how far above/below balance the ratio is.
Higher-Timeframe UDVR (teal line)
• Overlays the UDVR from a higher timeframe (e.g. Daily) on your intraday chart.
• Lets you see whether institutional flows support your shorter-term signals.
OBV Normalized (blue/orange line)
• Classic OBV, but normalized with a z-score so it stays readable across assets.
• Blue when OBV is above its EMA (rising participation).
• Orange when below its EMA (waning participation).
OBV EMA (grey line)
• Signal line showing the slope of OBV.
• Crosses between OBV and this line mark shifts in participation.
Higher-Timeframe OBV (purple line, optional)
• Plots OBV from a higher timeframe for additional context.
Background Shading
• Light green = both UDVR > 1 and OBV > OBV-EMA (bullish alignment).
• Light red = both UDVR < 1 and OBV < OBV-EMA (bearish alignment).
Tensor Market Analysis Engine (TMAE)# Tensor Market Analysis Engine (TMAE)
## Advanced Multi-Dimensional Mathematical Analysis System
*Where Quantum Mathematics Meets Market Structure*
---
## 🎓 THEORETICAL FOUNDATION
The Tensor Market Analysis Engine represents a revolutionary synthesis of three cutting-edge mathematical frameworks that have never before been combined for comprehensive market analysis. This indicator transcends traditional technical analysis by implementing advanced mathematical concepts from quantum mechanics, information theory, and fractal geometry.
### 🌊 Multi-Dimensional Volatility with Jump Detection
**Hawkes Process Implementation:**
The TMAE employs a sophisticated Hawkes process approximation for detecting self-exciting market jumps. Unlike traditional volatility measures that treat price movements as independent events, the Hawkes process recognizes that market shocks cluster and exhibit memory effects.
**Mathematical Foundation:**
```
Intensity λ(t) = μ + Σ α(t - Tᵢ)
```
Where market jumps at times Tᵢ increase the probability of future jumps through the decay function α, controlled by the Hawkes Decay parameter (0.5-0.99).
**Mahalanobis Distance Calculation:**
The engine calculates volatility jumps using multi-dimensional Mahalanobis distance across up to 5 volatility dimensions:
- **Dimension 1:** Price volatility (standard deviation of returns)
- **Dimension 2:** Volume volatility (normalized volume fluctuations)
- **Dimension 3:** Range volatility (high-low spread variations)
- **Dimension 4:** Correlation volatility (price-volume relationship changes)
- **Dimension 5:** Microstructure volatility (intrabar positioning analysis)
This creates a volatility state vector that captures market behavior impossible to detect with traditional single-dimensional approaches.
### 📐 Hurst Exponent Regime Detection
**Fractal Market Hypothesis Integration:**
The TMAE implements advanced Rescaled Range (R/S) analysis to calculate the Hurst exponent in real-time, providing dynamic regime classification:
- **H > 0.6:** Trending (persistent) markets - momentum strategies optimal
- **H < 0.4:** Mean-reverting (anti-persistent) markets - contrarian strategies optimal
- **H ≈ 0.5:** Random walk markets - breakout strategies preferred
**Adaptive R/S Analysis:**
Unlike static implementations, the TMAE uses adaptive windowing that adjusts to market conditions:
```
H = log(R/S) / log(n)
```
Where R is the range of cumulative deviations and S is the standard deviation over period n.
**Dynamic Regime Classification:**
The system employs hysteresis to prevent regime flipping, requiring sustained Hurst values before regime changes are confirmed. This prevents false signals during transitional periods.
### 🔄 Transfer Entropy Analysis
**Information Flow Quantification:**
Transfer entropy measures the directional flow of information between price and volume, revealing lead-lag relationships that indicate future price movements:
```
TE(X→Y) = Σ p(yₜ₊₁, yₜ, xₜ) log
```
**Causality Detection:**
- **Volume → Price:** Indicates accumulation/distribution phases
- **Price → Volume:** Suggests retail participation or momentum chasing
- **Balanced Flow:** Market equilibrium or transition periods
The system analyzes multiple lag periods (2-20 bars) to capture both immediate and structural information flows.
---
## 🔧 COMPREHENSIVE INPUT SYSTEM
### Core Parameters Group
**Primary Analysis Window (10-100, Default: 50)**
The fundamental lookback period affecting all calculations. Optimization by timeframe:
- **1-5 minute charts:** 20-30 (rapid adaptation to micro-movements)
- **15 minute-1 hour:** 30-50 (balanced responsiveness and stability)
- **4 hour-daily:** 50-100 (smooth signals, reduced noise)
- **Asset-specific:** Cryptocurrency 20-35, Stocks 35-50, Forex 40-60
**Signal Sensitivity (0.1-2.0, Default: 0.7)**
Master control affecting all threshold calculations:
- **Conservative (0.3-0.6):** High-quality signals only, fewer false positives
- **Balanced (0.7-1.0):** Optimal risk-reward ratio for most trading styles
- **Aggressive (1.1-2.0):** Maximum signal frequency, requires careful filtering
**Signal Generation Mode:**
- **Aggressive:** Any component signals (highest frequency)
- **Confluence:** 2+ components agree (balanced approach)
- **Conservative:** All 3 components align (highest quality)
### Volatility Jump Detection Group
**Volatility Dimensions (2-5, Default: 3)**
Determines the mathematical space complexity:
- **2D:** Price + Volume volatility (suitable for clean markets)
- **3D:** + Range volatility (optimal for most conditions)
- **4D:** + Correlation volatility (advanced multi-asset analysis)
- **5D:** + Microstructure volatility (maximum sensitivity)
**Jump Detection Threshold (1.5-4.0σ, Default: 3.0σ)**
Standard deviations required for volatility jump classification:
- **Cryptocurrency:** 2.0-2.5σ (naturally volatile)
- **Stock Indices:** 2.5-3.0σ (moderate volatility)
- **Forex Major Pairs:** 3.0-3.5σ (typically stable)
- **Commodities:** 2.0-3.0σ (varies by commodity)
**Jump Clustering Decay (0.5-0.99, Default: 0.85)**
Hawkes process memory parameter:
- **0.5-0.7:** Fast decay (jumps treated as independent)
- **0.8-0.9:** Moderate clustering (realistic market behavior)
- **0.95-0.99:** Strong clustering (crisis/event-driven markets)
### Hurst Exponent Analysis Group
**Calculation Method Options:**
- **Classic R/S:** Original Rescaled Range (fast, simple)
- **Adaptive R/S:** Dynamic windowing (recommended for trading)
- **DFA:** Detrended Fluctuation Analysis (best for noisy data)
**Trending Threshold (0.55-0.8, Default: 0.60)**
Hurst value defining persistent market behavior:
- **0.55-0.60:** Weak trend persistence
- **0.65-0.70:** Clear trending behavior
- **0.75-0.80:** Strong momentum regimes
**Mean Reversion Threshold (0.2-0.45, Default: 0.40)**
Hurst value defining anti-persistent behavior:
- **0.35-0.45:** Weak mean reversion
- **0.25-0.35:** Clear ranging behavior
- **0.15-0.25:** Strong reversion tendency
### Transfer Entropy Parameters Group
**Information Flow Analysis:**
- **Price-Volume:** Classic flow analysis for accumulation/distribution
- **Price-Volatility:** Risk flow analysis for sentiment shifts
- **Multi-Timeframe:** Cross-timeframe causality detection
**Maximum Lag (2-20, Default: 5)**
Causality detection window:
- **2-5 bars:** Immediate causality (scalping)
- **5-10 bars:** Short-term flow (day trading)
- **10-20 bars:** Structural flow (swing trading)
**Significance Threshold (0.05-0.3, Default: 0.15)**
Minimum entropy for signal generation:
- **0.05-0.10:** Detect subtle information flows
- **0.10-0.20:** Clear causality only
- **0.20-0.30:** Very strong flows only
---
## 🎨 ADVANCED VISUAL SYSTEM
### Tensor Volatility Field Visualization
**Five-Layer Resonance Bands:**
The tensor field creates dynamic support/resistance zones that expand and contract based on mathematical field strength:
- **Core Layer (Purple):** Primary tensor field with highest intensity
- **Layer 2 (Neutral):** Secondary mathematical resonance
- **Layer 3 (Info Blue):** Tertiary harmonic frequencies
- **Layer 4 (Warning Gold):** Outer field boundaries
- **Layer 5 (Success Green):** Maximum field extension
**Field Strength Calculation:**
```
Field Strength = min(3.0, Mahalanobis Distance × Tensor Intensity)
```
The field amplitude adjusts to ATR and mathematical distance, creating dynamic zones that respond to market volatility.
**Radiation Line Network:**
During active tensor states, the system projects directional radiation lines showing field energy distribution:
- **8 Directional Rays:** Complete angular coverage
- **Tapering Segments:** Progressive transparency for natural visual flow
- **Pulse Effects:** Enhanced visualization during volatility jumps
### Dimensional Portal System
**Portal Mathematics:**
Dimensional portals visualize regime transitions using category theory principles:
- **Green Portals (◉):** Trending regime detection (appear below price for support)
- **Red Portals (◎):** Mean-reverting regime (appear above price for resistance)
- **Yellow Portals (○):** Random walk regime (neutral positioning)
**Tensor Trail Effects:**
Each portal generates 8 trailing particles showing mathematical momentum:
- **Large Particles (●):** Strong mathematical signal
- **Medium Particles (◦):** Moderate signal strength
- **Small Particles (·):** Weak signal continuation
- **Micro Particles (˙):** Signal dissipation
### Information Flow Streams
**Particle Stream Visualization:**
Transfer entropy creates flowing particle streams indicating information direction:
- **Upward Streams:** Volume leading price (accumulation phases)
- **Downward Streams:** Price leading volume (distribution phases)
- **Stream Density:** Proportional to information flow strength
**15-Particle Evolution:**
Each stream contains 15 particles with progressive sizing and transparency, creating natural flow visualization that makes information transfer immediately apparent.
### Fractal Matrix Grid System
**Multi-Timeframe Fractal Levels:**
The system calculates and displays fractal highs/lows across five Fibonacci periods:
- **8-Period:** Short-term fractal structure
- **13-Period:** Intermediate-term patterns
- **21-Period:** Primary swing levels
- **34-Period:** Major structural levels
- **55-Period:** Long-term fractal boundaries
**Triple-Layer Visualization:**
Each fractal level uses three-layer rendering:
- **Shadow Layer:** Widest, darkest foundation (width 5)
- **Glow Layer:** Medium white core line (width 3)
- **Tensor Layer:** Dotted mathematical overlay (width 1)
**Intelligent Labeling System:**
Smart spacing prevents label overlap using ATR-based minimum distances. Labels include:
- **Fractal Period:** Time-based identification
- **Topological Class:** Mathematical complexity rating (0, I, II, III)
- **Price Level:** Exact fractal price
- **Mahalanobis Distance:** Current mathematical field strength
- **Hurst Exponent:** Current regime classification
- **Anomaly Indicators:** Visual strength representations (○ ◐ ● ⚡)
### Wick Pressure Analysis
**Rejection Level Mathematics:**
The system analyzes candle wick patterns to project future pressure zones:
- **Upper Wick Analysis:** Identifies selling pressure and resistance zones
- **Lower Wick Analysis:** Identifies buying pressure and support zones
- **Pressure Projection:** Extends lines forward based on mathematical probability
**Multi-Layer Glow Effects:**
Wick pressure lines use progressive transparency (1-8 layers) creating natural glow effects that make pressure zones immediately visible without cluttering the chart.
### Enhanced Regime Background
**Dynamic Intensity Mapping:**
Background colors reflect mathematical regime strength:
- **Deep Transparency (98% alpha):** Subtle regime indication
- **Pulse Intensity:** Based on regime strength calculation
- **Color Coding:** Green (trending), Red (mean-reverting), Neutral (random)
**Smoothing Integration:**
Regime changes incorporate 10-bar smoothing to prevent background flicker while maintaining responsiveness to genuine regime shifts.
### Color Scheme System
**Six Professional Themes:**
- **Dark (Default):** Professional trading environment optimization
- **Light:** High ambient light conditions
- **Classic:** Traditional technical analysis appearance
- **Neon:** High-contrast visibility for active trading
- **Neutral:** Minimal distraction focus
- **Bright:** Maximum visibility for complex setups
Each theme maintains mathematical accuracy while optimizing visual clarity for different trading environments and personal preferences.
---
## 📊 INSTITUTIONAL-GRADE DASHBOARD
### Tensor Field Status Section
**Field Strength Display:**
Real-time Mahalanobis distance calculation with dynamic emoji indicators:
- **⚡ (Lightning):** Extreme field strength (>1.5× threshold)
- **● (Solid Circle):** Strong field activity (>1.0× threshold)
- **○ (Open Circle):** Normal field state
**Signal Quality Rating:**
Democratic algorithm assessment:
- **ELITE:** All 3 components aligned (highest probability)
- **STRONG:** 2 components aligned (good probability)
- **GOOD:** 1 component active (moderate probability)
- **WEAK:** No clear component signals
**Threshold and Anomaly Monitoring:**
- **Threshold Display:** Current mathematical threshold setting
- **Anomaly Level (0-100%):** Combined volatility and volume spike measurement
- **>70%:** High anomaly (red warning)
- **30-70%:** Moderate anomaly (orange caution)
- **<30%:** Normal conditions (green confirmation)
### Tensor State Analysis Section
**Mathematical State Classification:**
- **↑ BULL (Tensor State +1):** Trending regime with bullish bias
- **↓ BEAR (Tensor State -1):** Mean-reverting regime with bearish bias
- **◈ SUPER (Tensor State 0):** Random walk regime (neutral)
**Visual State Gauge:**
Five-circle progression showing tensor field polarity:
- **🟢🟢🟢⚪⚪:** Strong bullish mathematical alignment
- **⚪⚪🟡⚪⚪:** Neutral/transitional state
- **⚪⚪🔴🔴🔴:** Strong bearish mathematical alignment
**Trend Direction and Phase Analysis:**
- **📈 BULL / 📉 BEAR / ➡️ NEUTRAL:** Primary trend classification
- **🌪️ CHAOS:** Extreme information flow (>2.0 flow strength)
- **⚡ ACTIVE:** Strong information flow (1.0-2.0 flow strength)
- **😴 CALM:** Low information flow (<1.0 flow strength)
### Trading Signals Section
**Real-Time Signal Status:**
- **🟢 ACTIVE / ⚪ INACTIVE:** Long signal availability
- **🔴 ACTIVE / ⚪ INACTIVE:** Short signal availability
- **Components (X/3):** Active algorithmic components
- **Mode Display:** Current signal generation mode
**Signal Strength Visualization:**
Color-coded component count:
- **Green:** 3/3 components (maximum confidence)
- **Aqua:** 2/3 components (good confidence)
- **Orange:** 1/3 components (moderate confidence)
- **Gray:** 0/3 components (no signals)
### Performance Metrics Section
**Win Rate Monitoring:**
Estimated win rates based on signal quality with emoji indicators:
- **🔥 (Fire):** ≥60% estimated win rate
- **👍 (Thumbs Up):** 45-59% estimated win rate
- **⚠️ (Warning):** <45% estimated win rate
**Mathematical Metrics:**
- **Hurst Exponent:** Real-time fractal dimension (0.000-1.000)
- **Information Flow:** Volume/price leading indicators
- **📊 VOL:** Volume leading price (accumulation/distribution)
- **💰 PRICE:** Price leading volume (momentum/speculation)
- **➖ NONE:** Balanced information flow
- **Volatility Classification:**
- **🔥 HIGH:** Above 1.5× jump threshold
- **📊 NORM:** Normal volatility range
- **😴 LOW:** Below 0.5× jump threshold
### Market Structure Section (Large Dashboard)
**Regime Classification:**
- **📈 TREND:** Hurst >0.6, momentum strategies optimal
- **🔄 REVERT:** Hurst <0.4, contrarian strategies optimal
- **🎲 RANDOM:** Hurst ≈0.5, breakout strategies preferred
**Mathematical Field Analysis:**
- **Dimensions:** Current volatility space complexity (2D-5D)
- **Hawkes λ (Lambda):** Self-exciting jump intensity (0.00-1.00)
- **Jump Status:** 🚨 JUMP (active) / ✅ NORM (normal)
### Settings Summary Section (Large Dashboard)
**Active Configuration Display:**
- **Sensitivity:** Current master sensitivity setting
- **Lookback:** Primary analysis window
- **Theme:** Active color scheme
- **Method:** Hurst calculation method (Classic R/S, Adaptive R/S, DFA)
**Dashboard Sizing Options:**
- **Small:** Essential metrics only (mobile/small screens)
- **Normal:** Balanced information density (standard desktop)
- **Large:** Maximum detail (multi-monitor setups)
**Position Options:**
- **Top Right:** Standard placement (avoids price action)
- **Top Left:** Wide chart optimization
- **Bottom Right:** Recent price focus (scalping)
- **Bottom Left:** Maximum price visibility (swing trading)
---
## 🎯 SIGNAL GENERATION LOGIC
### Multi-Component Convergence System
**Component Signal Architecture:**
The TMAE generates signals through sophisticated component analysis rather than simple threshold crossing:
**Volatility Component:**
- **Jump Detection:** Mahalanobis distance threshold breach
- **Hawkes Intensity:** Self-exciting process activation (>0.2)
- **Multi-dimensional:** Considers all volatility dimensions simultaneously
**Hurst Regime Component:**
- **Trending Markets:** Price above SMA-20 with positive momentum
- **Mean-Reverting Markets:** Price at Bollinger Band extremes
- **Random Markets:** Bollinger squeeze breakouts with directional confirmation
**Transfer Entropy Component:**
- **Volume Leadership:** Information flow from volume to price
- **Volume Spike:** Volume 110%+ above 20-period average
- **Flow Significance:** Above entropy threshold with directional bias
### Democratic Signal Weighting
**Signal Mode Implementation:**
- **Aggressive Mode:** Any single component triggers signal
- **Confluence Mode:** Minimum 2 components must agree
- **Conservative Mode:** All 3 components must align
**Momentum Confirmation:**
All signals require momentum confirmation:
- **Long Signals:** RSI >50 AND price >EMA-9
- **Short Signals:** RSI <50 AND price 0.6):**
- **Increase Sensitivity:** Catch momentum continuation
- **Lower Mean Reversion Threshold:** Avoid counter-trend signals
- **Emphasize Volume Leadership:** Institutional accumulation/distribution
- **Tensor Field Focus:** Use expansion for trend continuation
- **Signal Mode:** Aggressive or Confluence for trend following
**Range-Bound Markets (Hurst <0.4):**
- **Decrease Sensitivity:** Avoid false breakouts
- **Lower Trending Threshold:** Quick regime recognition
- **Focus on Price Leadership:** Retail sentiment extremes
- **Fractal Grid Emphasis:** Support/resistance trading
- **Signal Mode:** Conservative for high-probability reversals
**Volatile Markets (High Jump Frequency):**
- **Increase Hawkes Decay:** Recognize event clustering
- **Higher Jump Threshold:** Avoid noise signals
- **Maximum Dimensions:** Capture full volatility complexity
- **Reduce Position Sizing:** Risk management adaptation
- **Enhanced Visuals:** Maximum information for rapid decisions
**Low Volatility Markets (Low Jump Frequency):**
- **Decrease Jump Threshold:** Capture subtle movements
- **Lower Hawkes Decay:** Treat moves as independent
- **Reduce Dimensions:** Simplify analysis
- **Increase Position Sizing:** Capitalize on compressed volatility
- **Minimal Visuals:** Reduce distraction in quiet markets
---
## 🚀 ADVANCED TRADING STRATEGIES
### The Mathematical Convergence Method
**Entry Protocol:**
1. **Fractal Grid Approach:** Monitor price approaching significant fractal levels
2. **Tensor Field Confirmation:** Verify field expansion supporting direction
3. **Portal Signal:** Wait for dimensional portal appearance
4. **ELITE/STRONG Quality:** Only trade highest quality mathematical signals
5. **Component Consensus:** Confirm 2+ components agree in Confluence mode
**Example Implementation:**
- Price approaching 21-period fractal high
- Tensor field expanding upward (bullish mathematical alignment)
- Green portal appears below price (trending regime confirmation)
- ELITE quality signal with 3/3 components active
- Enter long position with stop below fractal level
**Risk Management:**
- **Stop Placement:** Below/above fractal level that generated signal
- **Position Sizing:** Based on Mahalanobis distance (higher distance = smaller size)
- **Profit Targets:** Next fractal level or tensor field resistance
### The Regime Transition Strategy
**Regime Change Detection:**
1. **Monitor Hurst Exponent:** Watch for persistent moves above/below thresholds
2. **Portal Color Change:** Regime transitions show different portal colors
3. **Background Intensity:** Increasing regime background intensity
4. **Mathematical Confirmation:** Wait for regime confirmation (hysteresis)
**Trading Implementation:**
- **Trending Transitions:** Trade momentum breakouts, follow trend
- **Mean Reversion Transitions:** Trade range boundaries, fade extremes
- **Random Transitions:** Trade breakouts with tight stops
**Advanced Techniques:**
- **Multi-Timeframe:** Confirm regime on higher timeframe
- **Early Entry:** Enter on regime transition rather than confirmation
- **Regime Strength:** Larger positions during strong regime signals
### The Information Flow Momentum Strategy
**Flow Detection Protocol:**
1. **Monitor Transfer Entropy:** Watch for significant information flow shifts
2. **Volume Leadership:** Strong edge when volume leads price
3. **Flow Acceleration:** Increasing flow strength indicates momentum
4. **Directional Confirmation:** Ensure flow aligns with intended trade direction
**Entry Signals:**
- **Volume → Price Flow:** Enter during accumulation/distribution phases
- **Price → Volume Flow:** Enter on momentum confirmation breaks
- **Flow Reversal:** Counter-trend entries when flow reverses
**Optimization:**
- **Scalping:** Use immediate flow detection (2-5 bar lag)
- **Swing Trading:** Use structural flow (10-20 bar lag)
- **Multi-Asset:** Compare flow between correlated assets
### The Tensor Field Expansion Strategy
**Field Mathematics:**
The tensor field expansion indicates mathematical pressure building in market structure:
**Expansion Phases:**
1. **Compression:** Field contracts, volatility decreases
2. **Tension Building:** Mathematical pressure accumulates
3. **Expansion:** Field expands rapidly with directional movement
4. **Resolution:** Field stabilizes at new equilibrium
**Trading Applications:**
- **Compression Trading:** Prepare for breakout during field contraction
- **Expansion Following:** Trade direction of field expansion
- **Reversion Trading:** Fade extreme field expansion
- **Multi-Dimensional:** Consider all field layers for confirmation
### The Hawkes Process Event Strategy
**Self-Exciting Jump Trading:**
Understanding that market shocks cluster and create follow-on opportunities:
**Jump Sequence Analysis:**
1. **Initial Jump:** First volatility jump detected
2. **Clustering Phase:** Hawkes intensity remains elevated
3. **Follow-On Opportunities:** Additional jumps more likely
4. **Decay Period:** Intensity gradually decreases
**Implementation:**
- **Jump Confirmation:** Wait for mathematical jump confirmation
- **Direction Assessment:** Use other components for direction
- **Clustering Trades:** Trade subsequent moves during high intensity
- **Decay Exit:** Exit positions as Hawkes intensity decays
### The Fractal Confluence System
**Multi-Timeframe Fractal Analysis:**
Combining fractal levels across different periods for high-probability zones:
**Confluence Zones:**
- **Double Confluence:** 2 fractal levels align
- **Triple Confluence:** 3+ fractal levels cluster
- **Mathematical Confirmation:** Tensor field supports the level
- **Information Flow:** Transfer entropy confirms direction
**Trading Protocol:**
1. **Identify Confluence:** Find 2+ fractal levels within 1 ATR
2. **Mathematical Support:** Verify tensor field alignment
3. **Signal Quality:** Wait for STRONG or ELITE signal
4. **Risk Definition:** Use fractal level for stop placement
5. **Profit Targeting:** Next major fractal confluence zone
---
## ⚠️ COMPREHENSIVE RISK MANAGEMENT
### Mathematical Position Sizing
**Mahalanobis Distance Integration:**
Position size should inversely correlate with mathematical field strength:
```
Position Size = Base Size × (Threshold / Mahalanobis Distance)
```
**Risk Scaling Matrix:**
- **Low Field Strength (<2.0):** Standard position sizing
- **Moderate Field Strength (2.0-3.0):** 75% position sizing
- **High Field Strength (3.0-4.0):** 50% position sizing
- **Extreme Field Strength (>4.0):** 25% position sizing or no trade
### Signal Quality Risk Adjustment
**Quality-Based Position Sizing:**
- **ELITE Signals:** 100% of planned position size
- **STRONG Signals:** 75% of planned position size
- **GOOD Signals:** 50% of planned position size
- **WEAK Signals:** No position or paper trading only
**Component Agreement Scaling:**
- **3/3 Components:** Full position size
- **2/3 Components:** 75% position size
- **1/3 Components:** 50% position size or skip trade
### Regime-Adaptive Risk Management
**Trending Market Risk:**
- **Wider Stops:** Allow for trend continuation
- **Trend Following:** Trade with regime direction
- **Higher Position Size:** Trend probability advantage
- **Momentum Stops:** Trail stops based on momentum indicators
**Mean-Reverting Market Risk:**
- **Tighter Stops:** Quick exits on trend continuation
- **Contrarian Positioning:** Trade against extremes
- **Smaller Position Size:** Higher reversal failure rate
- **Level-Based Stops:** Use fractal levels for stops
**Random Market Risk:**
- **Breakout Focus:** Trade only clear breakouts
- **Tight Initial Stops:** Quick exit if breakout fails
- **Reduced Frequency:** Skip marginal setups
- **Range-Based Targets:** Profit targets at range boundaries
### Volatility-Adaptive Risk Controls
**High Volatility Periods:**
- **Reduced Position Size:** Account for wider price swings
- **Wider Stops:** Avoid noise-based exits
- **Lower Frequency:** Skip marginal setups
- **Faster Exits:** Take profits more quickly
**Low Volatility Periods:**
- **Standard Position Size:** Normal risk parameters
- **Tighter Stops:** Take advantage of compressed ranges
- **Higher Frequency:** Trade more setups
- **Extended Targets:** Allow for compressed volatility expansion
### Multi-Timeframe Risk Alignment
**Higher Timeframe Trend:**
- **With Trend:** Standard or increased position size
- **Against Trend:** Reduced position size or skip
- **Neutral Trend:** Standard position size with tight management
**Risk Hierarchy:**
1. **Primary:** Current timeframe signal quality
2. **Secondary:** Higher timeframe trend alignment
3. **Tertiary:** Mathematical field strength
4. **Quaternary:** Market regime classification
---
## 📚 EDUCATIONAL VALUE AND MATHEMATICAL CONCEPTS
### Advanced Mathematical Concepts
**Tensor Analysis in Markets:**
The TMAE introduces traders to tensor analysis, a branch of mathematics typically reserved for physics and advanced engineering. Tensors provide a framework for understanding multi-dimensional market relationships that scalar and vector analysis cannot capture.
**Information Theory Applications:**
Transfer entropy implementation teaches traders about information flow in markets, a concept from information theory that quantifies directional causality between variables. This provides intuition about market microstructure and participant behavior.
**Fractal Geometry in Trading:**
The Hurst exponent calculation exposes traders to fractal geometry concepts, helping understand that markets exhibit self-similar patterns across multiple timeframes. This mathematical insight transforms how traders view market structure.
**Stochastic Process Theory:**
The Hawkes process implementation introduces concepts from stochastic process theory, specifically self-exciting point processes. This provides mathematical framework for understanding why market events cluster and exhibit memory effects.
### Learning Progressive Complexity
**Beginner Mathematical Concepts:**
- **Volatility Dimensions:** Understanding multi-dimensional analysis
- **Regime Classification:** Learning market personality types
- **Signal Democracy:** Algorithmic consensus building
- **Visual Mathematics:** Interpreting mathematical concepts visually
**Intermediate Mathematical Applications:**
- **Mahalanobis Distance:** Statistical distance in multi-dimensional space
- **Rescaled Range Analysis:** Fractal dimension measurement
- **Information Entropy:** Quantifying uncertainty and causality
- **Field Theory:** Understanding mathematical fields in market context
**Advanced Mathematical Integration:**
- **Tensor Field Dynamics:** Multi-dimensional market force analysis
- **Stochastic Self-Excitation:** Event clustering and memory effects
- **Categorical Composition:** Mathematical signal combination theory
- **Topological Market Analysis:** Understanding market shape and connectivity
### Practical Mathematical Intuition
**Developing Market Mathematics Intuition:**
The TMAE serves as a bridge between abstract mathematical concepts and practical trading applications. Traders develop intuitive understanding of:
- **How markets exhibit mathematical structure beneath apparent randomness**
- **Why multi-dimensional analysis reveals patterns invisible to single-variable approaches**
- **How information flows through markets in measurable, predictable ways**
- **Why mathematical models provide probabilistic edges rather than certainties**
---
## 🔬 IMPLEMENTATION AND OPTIMIZATION
### Getting Started Protocol
**Phase 1: Observation (Week 1)**
1. **Apply with defaults:** Use standard settings on your primary trading timeframe
2. **Study visual elements:** Learn to interpret tensor fields, portals, and streams
3. **Monitor dashboard:** Observe how metrics change with market conditions
4. **No trading:** Focus entirely on pattern recognition and understanding
**Phase 2: Pattern Recognition (Week 2-3)**
1. **Identify signal patterns:** Note what market conditions produce different signal qualities
2. **Regime correlation:** Observe how Hurst regimes affect signal performance
3. **Visual confirmation:** Learn to read tensor field expansion and portal signals
4. **Component analysis:** Understand which components drive signals in different markets
**Phase 3: Parameter Optimization (Week 4-5)**
1. **Asset-specific tuning:** Adjust parameters for your specific trading instrument
2. **Timeframe optimization:** Fine-tune for your preferred trading timeframe
3. **Sensitivity adjustment:** Balance signal frequency with quality
4. **Visual customization:** Optimize colors and intensity for your trading environment
**Phase 4: Live Implementation (Week 6+)**
1. **Paper trading:** Test signals with hypothetical trades
2. **Small position sizing:** Begin with minimal risk during learning phase
3. **Performance tracking:** Monitor actual vs. expected signal performance
4. **Continuous optimization:** Refine settings based on real performance data
### Performance Monitoring System
**Signal Quality Tracking:**
- **ELITE Signal Win Rate:** Track highest quality signals separately
- **Component Performance:** Monitor which components provide best signals
- **Regime Performance:** Analyze performance across different market regimes
- **Timeframe Analysis:** Compare performance across different session times
**Mathematical Metric Correlation:**
- **Field Strength vs. Performance:** Higher field strength should correlate with better performance
- **Component Agreement vs. Win Rate:** More component agreement should improve win rates
- **Regime Alignment vs. Success:** Trading with mathematical regime should outperform
### Continuous Optimization Process
**Monthly Review Protocol:**
1. **Performance Analysis:** Review win rates, profit factors, and maximum drawdown
2. **Parameter Assessment:** Evaluate if current settings remain optimal
3. **Market Adaptation:** Adjust for changes in market character or volatility
4. **Component Weighting:** Consider if certain components should receive more/less emphasis
**Quarterly Deep Analysis:**
1. **Mathematical Model Validation:** Verify that mathematical relationships remain valid
2. **Regime Distribution:** Analyze time spent in different market regimes
3. **Signal Evolution:** Track how signal characteristics change over time
4. **Correlation Analysis:** Monitor correlations between different mathematical components
---
## 🌟 UNIQUE INNOVATIONS AND CONTRIBUTIONS
### Revolutionary Mathematical Integration
**First-Ever Implementations:**
1. **Multi-Dimensional Volatility Tensor:** First indicator to implement true tensor analysis for market volatility
2. **Real-Time Hawkes Process:** First trading implementation of self-exciting point processes
3. **Transfer Entropy Trading Signals:** First practical application of information theory for trade generation
4. **Democratic Component Voting:** First algorithmic consensus system for signal generation
5. **Fractal-Projected Signal Quality:** First system to predict signal quality at future price levels
### Advanced Visualization Innovations
**Mathematical Visualization Breakthroughs:**
- **Tensor Field Radiation:** Visual representation of mathematical field energy
- **Dimensional Portal System:** Category theory visualization for regime transitions
- **Information Flow Streams:** Real-time visual display of market information transfer
- **Multi-Layer Fractal Grid:** Intelligent spacing and projection system
- **Regime Intensity Mapping:** Dynamic background showing mathematical regime strength
### Practical Trading Innovations
**Trading System Advances:**
- **Quality-Weighted Signal Generation:** Signals rated by mathematical confidence
- **Regime-Adaptive Strategy Selection:** Automatic strategy optimization based on market personality
- **Anti-Spam Signal Protection:** Mathematical prevention of signal clustering
- **Component Performance Tracking:** Real-time monitoring of algorithmic component success
- **Field-Strength Position Sizing:** Mathematical volatility integration for risk management
---
## ⚖️ RESPONSIBLE USAGE AND LIMITATIONS
### Mathematical Model Limitations
**Understanding Model Boundaries:**
While the TMAE implements sophisticated mathematical concepts, traders must understand fundamental limitations:
- **Markets Are Not Purely Mathematical:** Human psychology, news events, and fundamental factors create unpredictable elements
- **Past Performance Limitations:** Mathematical relationships that worked historically may not persist indefinitely
- **Model Risk:** Complex models can fail during unprecedented market conditions
- **Overfitting Potential:** Highly optimized parameters may not generalize to future market conditions
### Proper Implementation Guidelines
**Risk Management Requirements:**
- **Never Risk More Than 2% Per Trade:** Regardless of signal quality
- **Diversification Mandatory:** Don't rely solely on mathematical signals
- **Position Sizing Discipline:** Use mathematical field strength for sizing, not confidence
- **Stop Loss Non-Negotiable:** Every trade must have predefined risk parameters
**Realistic Expectations:**
- **Mathematical Edge, Not Certainty:** The indicator provides probabilistic advantages, not guaranteed outcomes
- **Learning Curve Required:** Complex mathematical concepts require time to master
- **Market Adaptation Necessary:** Parameters must evolve with changing market conditions
- **Continuous Education Important:** Understanding underlying mathematics improves application
### Ethical Trading Considerations
**Market Impact Awareness:**
- **Information Asymmetry:** Advanced mathematical analysis may provide advantages over other market participants
- **Position Size Responsibility:** Large positions based on mathematical signals can impact market structure
- **Sharing Knowledge:** Consider educational contributions to trading community
- **Fair Market Participation:** Use mathematical advantages responsibly within market framework
### Professional Development Path
**Skill Development Sequence:**
1. **Basic Mathematical Literacy:** Understand fundamental concepts before advanced application
2. **Risk Management Mastery:** Develop disciplined risk control before relying on complex signals
3. **Market Psychology Understanding:** Combine mathematical analysis with behavioral market insights
4. **Continuous Learning:** Stay updated on mathematical finance developments and market evolution
---
## 🔮 CONCLUSION
The Tensor Market Analysis Engine represents a quantum leap forward in technical analysis, successfully bridging the gap between advanced pure mathematics and practical trading applications. By integrating multi-dimensional volatility analysis, fractal market theory, and information flow dynamics, the TMAE reveals market structure invisible to conventional analysis while maintaining visual clarity and practical usability.
### Mathematical Innovation Legacy
This indicator establishes new paradigms in technical analysis:
- **Tensor analysis for market volatility understanding**
- **Stochastic self-excitation for event clustering prediction**
- **Information theory for causality-based trade generation**
- **Democratic algorithmic consensus for signal quality enhancement**
- **Mathematical field visualization for intuitive market understanding**
### Practical Trading Revolution
Beyond mathematical innovation, the TMAE transforms practical trading:
- **Quality-rated signals replace binary buy/sell decisions**
- **Regime-adaptive strategies automatically optimize for market personality**
- **Multi-dimensional risk management integrates mathematical volatility measures**
- **Visual mathematical concepts make complex analysis immediately interpretable**
- **Educational value creates lasting improvement in trading understanding**
### Future-Proof Design
The mathematical foundations ensure lasting relevance:
- **Universal mathematical principles transcend market evolution**
- **Multi-dimensional analysis adapts to new market structures**
- **Regime detection automatically adjusts to changing market personalities**
- **Component democracy allows for future algorithmic additions**
- **Mathematical visualization scales with increasing market complexity**
### Commitment to Excellence
The TMAE represents more than an indicator—it embodies a philosophy of bringing rigorous mathematical analysis to trading while maintaining practical utility and visual elegance. Every component, from the multi-dimensional tensor fields to the democratic signal generation, reflects a commitment to mathematical accuracy, trading practicality, and educational value.
### Trading with Mathematical Precision
In an era where markets grow increasingly complex and computational, the TMAE provides traders with mathematical tools previously available only to institutional quantitative research teams. Yet unlike academic mathematical models, the TMAE translates complex concepts into intuitive visual representations and practical trading signals.
By combining the mathematical rigor of tensor analysis, the statistical power of multi-dimensional volatility modeling, and the information-theoretic insights of transfer entropy, traders gain unprecedented insight into market structure and dynamics.
### Final Perspective
Markets, like nature, exhibit profound mathematical beauty beneath apparent chaos. The Tensor Market Analysis Engine serves as a mathematical lens that reveals this hidden order, transforming how traders perceive and interact with market structure.
Through mathematical precision, visual elegance, and practical utility, the TMAE empowers traders to see beyond the noise and trade with the confidence that comes from understanding the mathematical principles governing market behavior.
Trade with mathematical insight. Trade with the power of tensors. Trade with the TMAE.
*"In mathematics, you don't understand things. You just get used to them." - John von Neumann*
*With the TMAE, mathematical market understanding becomes not just possible, but intuitive.*
— Dskyz, Trade with insight. Trade with anticipation.
Institutional Volume Profile# Institutional Volume Profile (IVP) - Advanced Volume Analysis Indicator
## Overview
The Institutional Volume Profile (IVP) is a sophisticated technical analysis tool that combines traditional volume profile analysis with institutional volume detection algorithms. This indicator helps traders identify key price levels where significant institutional activity has occurred, providing insights into market structure and potential support/resistance zones.
## Key Features
### 🎯 Volume Profile Analysis
- **Point of Control (POC)**: Identifies the price level with the highest volume activity
- **Value Area**: Highlights the price range containing a specified percentage (default 70%) of total volume
- **Multi-Row Distribution**: Displays volume distribution across 10-50 price levels for detailed analysis
- **Customizable Period**: Analyze volume profiles over 10-500 bars
### 🏛️ Institutional Volume Detection
- **Pocket Pivot Volume (PPV)**: Detects bullish institutional buying when up-volume exceeds recent down-volume peaks
- **Pivot Negative Volume (PNV)**: Identifies bearish institutional selling when down-volume exceeds recent up-volume peaks
- **Accumulation Detection**: Spots potential accumulation phases with high volume and narrow price ranges
- **Distribution Analysis**: Identifies distribution patterns with high volume but minimal price movement
### 🎨 Visual Customization Options
- **Multiple Color Schemes**: Heat Map, Institutional, Monochrome, and Rainbow themes
- **Bar Styles**: Solid, Gradient, Outlined, and 3D Effect rendering
- **Volume Intensity Display**: Visual intensity based on volume magnitude
- **Flexible Positioning**: Left or right side profile placement
- **Current Price Highlighting**: Real-time price level indication
### 📊 Advanced Visual Features
- **Volume Labels**: Display volume amounts at key price levels
- **Gradient Effects**: Multi-step gradient rendering for enhanced visibility
- **3D Styling**: Shadow effects for professional appearance
- **Opacity Control**: Adjustable transparency (10-100%)
- **Border Customization**: Configurable border width and styling
## How It Works
### Volume Distribution Algorithm
The indicator analyzes each bar within the specified period and distributes its volume proportionally across the price levels it touches. This creates an accurate representation of where trading activity has been concentrated.
### Institutional Detection Logic
- **PPV Trigger**: Current up-bar volume > highest down-volume in lookback period + above volume MA
- **PNV Trigger**: Current down-bar volume > highest up-volume in lookback period + above volume MA
- **Accumulation**: High volume + narrow range + bullish close
- **Distribution**: Very high volume + minimal price movement
### Value Area Calculation
Starting from the POC, the algorithm expands both upward and downward, adding volume until reaching the specified percentage of total volume (default 70%).
## Configuration Parameters
### Profile Settings
- **Profile Period**: 10-500 bars (default: 50)
- **Number of Rows**: 10-50 levels (default: 24)
- **Profile Width**: 10-100% of screen (default: 30%)
- **Value Area %**: 50-90% (default: 70%)
### Institutional Analysis
- **PPV Lookback Days**: 5-20 periods (default: 10)
- **Volume MA Length**: 10-200 periods (default: 50)
- **Institutional Threshold**: 1.0-2.0x multiplier (default: 1.2)
### Visual Controls
- **Bar Style**: Solid, Gradient, Outlined, 3D Effect
- **Color Scheme**: Heat Map, Institutional, Monochrome, Rainbow
- **Profile Position**: Left or Right side
- **Opacity**: 10-100%
- **Show Labels**: Volume amount display toggle
## Interpretation Guide
### Volume Profile Elements
- **Thick Horizontal Bars**: High volume nodes (strong support/resistance)
- **Thin Horizontal Bars**: Low volume nodes (weak levels)
- **White Line (POC)**: Strongest support/resistance level
- **Blue Highlighted Area**: Value Area (fair value zone)
### Institutional Signals
- **Blue Triangles (PPV)**: Bullish institutional buying detected
- **Orange Triangles (PNV)**: Bearish institutional selling detected
- **Color-Coded Bars**: Different colors indicate institutional activity types
### Color Scheme Meanings
- **Heat Map**: Red (high volume) → Orange → Yellow → Gray (low volume)
- **Institutional**: Blue (PPV), Orange (PNV), Aqua (Accumulation), Yellow (Distribution)
- **Monochrome**: Grayscale intensity based on volume
- **Rainbow**: Color-coded by price level position
## Trading Applications
### Support and Resistance
- POC acts as dynamic support/resistance
- High volume nodes indicate strong price levels
- Low volume areas suggest potential breakout zones
### Institutional Activity
- PPV above Value Area: Strong bullish signal
- PNV below Value Area: Strong bearish signal
- Accumulation patterns: Potential upward breakouts
- Distribution patterns: Potential downward pressure
### Market Structure Analysis
- Value Area defines fair value range
- Profile shape indicates market sentiment
- Volume gaps suggest potential price targets
## Alert Conditions
- PPV Detection at current price level
- PNV Detection at current price level
- PPV above Value Area (strong bullish)
- PNV below Value Area (strong bearish)
## Best Practices
1. Use multiple timeframes for confirmation
2. Combine with price action analysis
3. Pay attention to volume context (above/below average)
4. Monitor institutional signals near key levels
5. Consider overall market conditions
## Technical Notes
- Maximum 500 boxes and 100 labels for optimal performance
- Real-time calculations update on each bar close
- Historical analysis uses complete bar data
- Compatible with all TradingView chart types and timeframes
---
*This indicator is designed for educational and informational purposes. Always combine with other analysis methods and risk management strategies.*
Smart Money Concept [TradingFinder] Major OB + FVG + Liquidity🔵 Introduction
"Smart Money" refers to funds under the control of institutional investors, central banks, funds, market makers, and other financial entities. Ordinary people recognize investments made by those who have a deep understanding of market performance and possess information typically inaccessible to regular investors as "Smart Money".
Consequently, when market movements often diverge from expectations, traders identify the footprints of smart money. For example, when a classic pattern forms in the market, traders take short positions. However, the market might move upward instead. They attribute this contradiction to smart money and seek to capitalize on such inconsistencies in their trades.
The "Smart Money Concept" (SMC) is one of the primary styles of technical analysis that falls under the subset of "Price Action". Price action encompasses various subcategories, with one of the most significant being "Supply and Demand", in which SMC is categorized.
The SMC method aims to identify trading opportunities by emphasizing the impact of large traders (Smart Money) on the market, offering specific patterns, techniques, and trading strategies.
🟣 Key Terms of Smart Money Concept (SMC)
• Market Structure (Trend)
• Change of Character (ChoCh)
• Break of Structure (BoS)
• Order Blocks (Supply and Demand)
• Imbalance (IMB)
• Inefficiency (IFC)
• Fair Value Gap (FVG)
• Liquidity
• Premium and Discount
🔵 How Does the "Smart Money Concept Indicator" Work?
🟣 Market Structure
a. Accumulation
b. Market-Up
c. Distribution
d. Market-Down
a) Accumulation Phase : During the accumulation period, typically following a downtrend, smart money enters the market without significantly affecting the pricing trend.
b) Market-Up Phase : In this phase, the price of an asset moves upward from the accumulation range and begins to rise. Usually, the buying by retail investors is the main driver of this trend, and due to positive market sentiment, it continues.
c) Distribution Phase : The distribution phase, unlike the accumulation stage, occurs after an uptrend. In this phase, smart money attempts to exit the market without causing significant price fluctuations.
d) Market-Down Phase : In this stage, the price of an asset moves downward from the distribution phase, initiating a prolonged downtrend. Smart money liquidates all its positions by creating selling pressure, trapping latecomer investors.
The result of these four phases in the market becomes the market trend.
Types of Trends in Financial Markets :
a. Up-Trend
b. Down Trend
c. Range (No Trend)
a) Up-Trend : The market breaks consecutive highs.
b) Down Trend : The market breaks consecutive lows.
c) No Trend or Range : The market oscillates within a range without breaking either highs or lows.
🟣 Change of Character (ChoCh)
The "ChoCh" or "Change of Character" pattern indicates an initial change in order flow in financial markets. This structural change occurs when a major pivot in the opposite direction of the market trend fails. It signals a potential change in the market trend and can serve as a signal for short-term or long-term trend changes in a trading symbol.
🟣 Break of Structure (BoS)
The "BoS" or "Break of Structure" pattern indicates the continuation of the trend in financial markets. This structure forms when, in an uptrend, the price breaks its ceiling or, in a downtrend, the price breaks its floor.
🟣 Order Blocks (Supply and Demand)
Order blocks consist of supply and demand areas where the likelihood of price reversal is higher. There are six order blocks in this indicator, categorized based on their origin and formation reasons.
a. Demand Main Zone, "ChoCh" Origin.
b. Demand Sub Zone, "ChoCh" Origin.
c. Demand All Zone, "BoS" Origin.
d. Supply Main Zone, "ChoCh" Origin.
e. Supply Sub Zone, "ChoCh" Origin.
f. Supply All Zone, "BoS" Origin.
🟣 FVG | Inefficiency | Imbalance
These three terms are almost synonymous. They describe the presence of gaps between consecutive candle shadows. This inefficiency occurs when the market moves rapidly. Primarily, imbalances and these rapid movements stem from the entry of smart money and the imbalance between buyer and seller power. Therefore, identifying these movements is crucial for traders.
These areas are significant because prices often return to fill these gaps or even before they occur to fill price gaps.
🟣 Liquidity
Liquidity zones are areas where there is a likelihood of congestion of stop-loss orders. Liquidity is considered the driving force of the entire market, and market makers may manipulate the market using these zones. However, in many cases, this does not happen because there is insufficient liquidity in some areas.
Types of Liquidity in Financial Markets :
a. Trend Lines
b. Double Tops | Double Bottoms
c. Triple Tops | Triple Bottoms
d. Support Lines | Resistance Lines
All four types of liquidity in this indicator are automatically identified.
🟣 Premium and Discount
Premium and discount zones can assist traders in making better decisions. For instance, they may sell positions in expensive ranges and buy in cheaper ranges. The closer the price is to the major resistance, the more expensive it is, and the closer it is to the major support, the cheaper it is.
🔵 How to Use
🟣 Change of Character (ChoCh) and Break of Structure (BoS)
This indicator detects "ChoCh" and "BoS" in both Minor and Major states. You can turn on the display of these lines by referring to the last part of the settings.
🟣 Order Blocks (Supply and Demand)
Order blocks are Zones where the probability of price reversal is higher. In demand Zones you can buy opportunities and in supply Zones you can check sell opportunities.
The "Refinement" feature allows you to adjust the width of the order block according to your strategy. There are two modes, "Aggressive" and "Defensive," in the "Order Block Refine". The difference between "Aggressive" and "Defensive" lies in the width of the order block.
For risk-averse traders, the "Defensive" mode is suitable as it provides a lower loss limit and a greater reward-to-risk ratio. For risk-taking traders, the "Aggressive" mode is more appropriate. These traders prefer to enter trades at higher prices, and this mode, which has a wider order block width, is more suitable for this group of individuals.
🟣 Fair Value Gap (FVG) | Imbalance (IMB) | Inefficiency (IFC)
In order to identify the "fair value gap" on the chart, it must be analyzed candle by candle. In this process, it is important to pay attention to candles with a large size, and a candle and a candle should be examined before that.
Candles before and after this central candle should have long shadows and their bodies should not overlap with the central candle body. The distance between the shadows of the first and third candles is known as the FVG range.
These areas work in two ways :
• Supply and demand area : In this case, the price reacts to these areas and the trend is reversed.
• Liquidity zone : In this scenario, the price "fills" the zone and then reaches the order block.
Important note : In most cases, the FVG zone of very small width acts as a supply and demand zone, while the zone of significant width acts as a liquidity zone and absorbs price.
When the FVG filter is activated, the FVG regions are filtered based on the specified algorithm.
FVG filter types include the following :
1. Very Aggressive Mode : In addition to the initial condition, an additional condition is considered. For bullish FVG, the maximum price of the last candle must be greater than the maximum price of the middle candle.
Similarly, for a bearish FVG, the minimum price of the last candle must be lower than the minimum price of the middle candle. This mode removes the minimum number of FVGs.
2. Aggressive : In addition to the very aggressive condition, the size of the middle candle is also considered. The size of the center candle should not be small and therefore more FVGs are removed in this case.
3. Defensive : In addition to the conditions of the very aggressive mode, this mode also considers the size of the middle pile, which should be relatively large and make up the majority of the body.
Also, to identify bullish FVGs, the second and third candles must be positive, while for bearish FVGs, the second and third candles must be negative. This mode filters out a significant number of FVGs and keeps only those of good quality.
4. Very Defensive : In addition to the conditions of the defensive mode, in this mode the first and third candles should not be very small-bodied doji candles. This mode filters out most FVGs and only the best quality ones remain.
🟣 Liquidity
These levels are where traders intend to exit their trades. "Market makers" or smart money usually accumulate or distribute their trading positions near these levels, where many retail traders have placed their "stop loss" orders. When liquidity is collected from these losses, the price often reverses.
A "Stop hunt" is a move designed to offset liquidity generated by established stop losses. Banks often use major news events to trigger stop hunts and capture liquidity released into the market. For example, if they intend to execute heavy buy orders, they encourage others to sell through stop-hots.
Consequently, if there is liquidity in the market before reaching the order block area, the validity of that order block is higher. Conversely, if the liquidity is close to the order block, that is, the price reaches the order block before reaching the liquidity limit, the validity of that order block is lower.
🟣 Alert
With the new alert functionality in this indicator, you won't miss any important trading signals. Alerts are activated when the price hits the last order block.
1. It is possible to set alerts for each "symbol" and "time frame". The system will automatically detect both and include them in the warning message.
2. Each alert provides the exact date and time it was triggered. This helps you measure the timeliness of the signal and evaluate its relevance.
3. Alerts include target order block price ranges. The "Proximal" level represents the initial price level strike, while the "Distal" level represents the maximum price gap in the block. These details are included in the warning message.
4. You can customize the alert name through the "Alert Name" entry.
5. Create custom messages for "long" and "short" alerts to be sent with notifications.
🔵 Setting
a. Pivot Period of Order Blocks Detector :
Using this parameter, you can set the zigzag period that is formed based on the pivots.
b. Order Blocks Validity Period (Bar) :
You can set the validity period of each Order Block based on the number of candles that have passed since the origin of the Order Block.
c. Demand Main Zone, "ChoCh" Origin :
You can control the display or not display as well as the color of Demand Main Zone, "ChoCh" Origin.
d. Demand Sub Zone, "ChoCh" Origin :
You can control the display or not display as well as the color of Demand Sub Zone, "ChoCh" Origin.
e. Demand All Zone, "BoS" Origin :
You can control the display or not display as well as the color of Demand All Zone, "BoS" Origin.
f. Supply Main Zone, "ChoCh" Origin :
You can control the display or not display as well as the color of Supply Main Zone, "ChoCh" Origin.
g. Supply Sub Zone, "ChoCh" Origin :
You can control the display or not display as well as the color of Supply Sub Zone, "ChoCh" Origin.
h. Supply All Zone, "BoS" Origin :
You can control the display or not display as well as the color of Supply All Zone, "BoS" Origin.
i. Refine Demand Main : You can choose to be refined or not and also the type of refining.
j. Refine Demand Sub : You can choose to be refined or not and also the type of refining.
k. Refine Demand BoS : You can choose to be refined or not and also the type of refining.
l. Refine Supply Main : You can choose to be refined or not and also the type of refining.
m. Refine Supply Sub : You can choose to be refined or not and also the type of refining.
n. Refine Supply BoS : You can choose to be refined or not and also the type of refining.
o. Show Demand FVG : You can choose to show or not show Demand FVG.
p. Show Supply FVG : You can choose to show or not show Supply FVG
q. FVG Filter : You can choose whether FVG is filtered or not. Also specify the type of filter you want to use.
r. Show Statics High Liquidity Line : Show or not show Statics High Liquidity Line.
s. Show Statics Low Liquidity Line : Show or not show Statics Low Liquidity Line.
t. Show Dynamics High Liquidity Line : Show or not show Dynamics High Liquidity Line.
u. Show Dynamics Low Liquidity Line : Show or not show Dynamics Low Liquidity Line.
v. Statics Period Pivot :
Using this parameter, you can set the Swing period that is formed based on Static Liquidity Lines.
w. Dynamics Period Pivot :
Using this parameter, you can set the Swing period that is formed based Dynamics Liquidity Lines.
x. Statics Liquidity Line Sensitivity :
is a number between 0 and 0.4. Increasing this number decreases the sensitivity of the "Statics Liquidity Line Detection" function and increases the number of lines identified. The default value is 0.3.
y. Dynamics Liquidity Line Sensitivity :
is a number between 0.4 and 1.95. Increasing this number increases the sensitivity of the "Dynamics Liquidity Line Detection" function and decreases the number of lines identified. The default value is 1.
z. Alerts Name : You can customize the alert name using this input and set it to your desired name.
aa. Alert Demand Main Mitigation :
If you want to receive the alert about Demand Main 's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
bb. Alert Demand Sub Mitigation :
If you want to receive the alert about Demand Sub's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
cc. Alert Demand BoS Mitigation :
If you want to receive the alert about Demand BoS's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
dd. Alert Supply Main Mitigation :
If you want to receive the alert about Supply Main's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
ee. Alert Supply Sub Mitigation :
If you want to receive the alert about Supply Sub's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
ff. Alert Supply BoS Mitigation :
If you want to receive the alert about Supply BoS's mitigation after setting the alerts, leave this tick on. Otherwise, turn it off.
gg. Message Frequency :
This parameter, represented as a string, determines the frequency of announcements. Options include: 'All' (triggers the alert every time the function is called), 'Once Per Bar' (triggers the alert only on the first call within the bar), and 'Once Per Bar Close' (activates the alert only during the final script execution of the real-time bar upon closure). The default setting is 'Once per Bar'.
hh. Show Alert time by Time Zone :
The date, hour, and minute displayed in alert messages can be configured to reflect any chosen time zone. For instance, if you prefer London time, you should input 'UTC+1'. By default, this input is configured to the 'UTC' time zone.
ii. Display More Info : The 'Display More Info' option provides details regarding the price range of the order blocks (Zone Price), along with the date, hour, and minute. If you prefer not to include this information in the alert message, you should set it to 'Off'.
You also have access to display or not to display, choose the Style and Color of all the lines below :
a. Major Bullish "BoS" Lines
b. Major Bearish "BoS" Lines
c. Minor Bullish "BoS" Lines
d. Minor Bearish "BoS" Lines
e. Major Bullish "ChoCh" Lines
f. Major Bearish "ChoCh" Lines
g. Minor Bullish "ChoCh" Lines
h. Minor Bearish "ChoCh" Lines
i. Last Major Support Line
j. Last Major Resistance Line
k. Last Minor Support Line
l. Last Minor Resistance Line
Up Down Volume Ratio by 3iauThis script considers the total volume within a user specified time frame, and whether price closed higher or lower at the end of each period within that time frame.
EXAMPLE:
* If the time period of interest is 50-periods, the script considers the volume within each of those 50 periods beginning with the most recent closed period.
* SumUpVol = the sum of all volume occurring within only those periods where price closed higher than that of the previous period.
* SumDnVol = the sum of all volume occurring within only those periods where price closed lower than that of the previous period.
* Difference = the difference between SumUpVol and SumDnVol = SumUpVol - SumDnVol
* Total = the sum of SumUpVol and SumDnVol = SumUpVol + SumDnVol
* The plot will present the change in Difference divided by Total = Difference/Total = (SumUpVol - SumDnVol)/(SumUpVol + SumDnVol) occurring within those 50 periods. What will be plotted is the moving average of this value. The user can specify the moving average type and the number of period for which the average is calculated.
* The plot needs to be fitted into a range, for example, +/- 50 (default) or +/-100, by multiplying the result of Difference/Total by a user specified constant. The constant will contain the majority (not all) of the values within +/- the specified value.
* Range = the user specified constant. If Range = 50, the majority of values plotted will be fall within the range +/- 50.
* Therefore, what is plotted is the moving average of Range * Difference / Total.
* When the value = 0, accumulation = distribution over the user specified 50-periods time frame.
* When the value is positive, accumulation > distribution over the user specified 50-periods time frame.
* When the value is negative, distribution > accumulation over the user specified 50-periods time frame.
This plot allows one to see possible accumulation and distribution occurring within a particular stock. The slope of this plot must be considered, and not any single value. The selected constant (“Range” in the example above) does not have an effect on the slope of the plot.
Three values may be plotted at once, for comparison of accumulation or distribution occurring over different time frames. For example, compare Difference / Total calculated over a 50-periods timeframe with 10-periods timeframe, both time frames beginning with the most recent closed period.
In addition to the above, J. Welles Wilder’s Relative Strength Index (RSI) can be plotted over the Difference / Total.
NOTE: this script is not the same as the more commonly used Up/Down Volume Ratio defined as SumUpVol / SumDnVol over a 50-periods time frame, where SumUpVol = the sum of all volume occurring within only those periods where price closed higher than that of the previous period, and SumDnVol = the sum of all volume occurring within only those periods where price closed lower than that of the previous period.
Compare...
Up Down Volume Ratio = SumUpVol / SumDnVol
Up Down Volume Ratio by 3iau = the moving average of Range * (SumUpVol - SumDnVol) / (SumUpVol + SumDnVol)
TARVIS Labs - Bitcoin Macro Bottom/Top SignalsSCRIPT DESCRIPTION
This is a script specifically written to help provide indicators from a macro view. This script is best run on the 1 day interval on Bitstamp's $BTCUSD chart. It helps indicate when to accumulate bitcoin, and when its in a bull run when there are local tops, strong top warnings, and a signal to exit a bull run. This is described further below.
If you don't have interest in trading on the way to the top I suggest turning off the following indicators in the settings of the indicator:
- Opportunity To Buy Back In Indicator
- Local Top Near Bull Run Top Indicator
ACCUMULATION ZONE INDICATOR - LIGHT GREEN
Description
When we look at the history of Bitcoin every bottom has crossed below the 100 week EMA. Once it does its accompanied by hash ribbon cross with miner capitulation. After that is the prime time to accumulate as theres a clearer signal the bottom is in. Specifically, a signal to look for is the 14 day MACD/signal cross and the 14 day MACD continuing to stay above the signal until the price returns above the 100 week EMA. This is prime accumulation territory.
Strategy for Usage
A good strategy to use when accumulating the bottom is dollar-cost averaging over a 30 day period. The accumulation zone can last longer than 30 days but 30 days is a good range of time to DCA.
STRONG BUY IN ACCUMULATION ZONE INDICATOR - DARK GREEN
Description
We can add to the bottoming signal by looking for post-downtrend reversals inside the bottoming signal. We do this by using a 9/19 daily cross.
Strategy for Usage
These post-downtrend reversals can potentially provide better targeted days for accumulation than the broader bottoming signal and can be used to add more on that day than on an average day for the dollar cost average strategy. Say for example, use 1/3 of funds on these days rather than 1/30th.
OPPORTUNITY TO BUY BACK IN INDICATOR - BLUE
Description
When the 1d 18 EMA > 1d 63 EMA and the 12/52 1d crosses. These together provide good buy opportunities to buy bitcoin.
Strategy for Usage
If you happen to find yourself out of the market from your own TA or a trade, this signal can provide a buy opportunity to reenter the market if you're out of it.
BULL RUN LOCAL TOP INDICATOR - ORANGE
Description
We will similarly use the 100 week EMA to determine trend reversal into a bull run. When we see the 100 week EMA uptrending, we can begin to look for local tops using the 9/19 daily MACD/signal bearish cross along with the 12 EMA having a negative slope, which could be the beginning signal for a local top.
Strategy for Usage
This is a rather light indicator, but can be used in tandem with your own technical analysis to determine if you want to reenter after you exit from its signal.
LOCAL TOP NEAR BULL RUN TOP INDICATOR - RED
Description
When the 100 week EMA is in an uptrend we can look for significant loss of momentum in order to determine if a local top is in near a bull run top. Similar to the Bull Run Local Top Indicator, this strategy uses a MACD/signal cross but instead uses the 30/65 day EMAs.
Strategy for Usage
Ideally the right strategy to use here is to exit the market when this indicator starts. When the indicator ends if the "End of Bull Run Indicator" is not showing on the chart you can buy back into the market.
TOP IS LIKELY IN INDICATOR
Description
When the 100 week EMA is in a very strong uptrend and the 9/19 weekly MACD/signal bearish cross occurs, and the 63 EMA begins to downtrend.
Strategy for Usage
This signal typically accompanies the "Local Top Near Bull Run Top Indicator" therefore if you're following the strategy you would likely already be out of the market, but if you're not and this signal fires its a strong signal the top is in and we're likely going to start seeing a strong retrace. This is typically right before we see the "End of Bull Run Indicator". There is only one occurrence where it wasn't followed by a large drop & the "End of Bull Run Indicator" and that was in the 2017 bull run where there were many strong retracements post local top. The likelihood we see that again is low, but if it were to happen you can buy back into the market when the "Top is Likely In Indicator" and the "Local Top Near Bull Run Top Indicator" are not firing.
TOP IS LIKELY IN INDICATOR
Description
When the 100 week EMA is in a strong uptrend and the 9/19 weekly MACD/signal bearish cross occurs, and the 63 EMA begins to downtrend.
Strategy for Usage
This signal typically accompanies the "Local Top Near Bull Run Top Indicator" therefore if you're following the strategy you would likely already be out of the market, but if you're not and this signal fires its a strong signal the top is in and we're likely going to start seeing a strong retrace. This is typically right before we see the "End of Bull Run Indicator". There is only one occurrence where it wasn't followed by a large drop & the "End of Bull Run Indicator" and that was in the 2017 bull run where there were many strong retracements post local top. The likelihood we see that again is low, but if it were to happen you can buy back into the market when the "Top is Likely In Indicator" and the "Local Top Near Bull Run Top Indicator" are not firing.
END OF BULL RUN INDICATOR
Description
When the 100 week EMA is in an uptrend and the 1d 18 EMA crosses the 1d 63 EMA.
Strategy for Usage
When the 100 week EMA is a strong uptrend and the 18/63 cross occurs the top is very likely in. It has occurred in every bull run top leading to the bear market.
PA-Adaptive, Stepped-MA of Composite RSI [Loxx]PA-Adaptive, Stepped-MA of Composite RSI is an RSI indicator using a different kind of RSI called Composite RSI. This indicator is Phase Accumulation Cycle Adaptive and uses a stepped moving average.
What is Composite RSI?
The name of the composite RSI might mislead a bit.
Composite RSI is not "compositing" RSIs but is a rather new way of calculating the RSI. Unlike the RSI that is a sort of a momentum indicators, composite RSI is more a trending indicator. It tends to filter out insignificant price changes and seems to be good in identifying the underlying trends.
What is the Phase Accumulation Cycle?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Loxx's Special Phase Accumulation Cycle
PA-Adaptive MACD w/ Variety Levels [Loxx]PA-Adaptive MACD w/ Variety Levels is a Phase Accumulation Adaptive MACD with both floating and quantile levels. This is tuned for Forex. You'll have to adjust the Phase Accumulation Cycle settings to work for crypto and stock markets.
What is MACD?
Moving average convergence divergence ( MACD ) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average ( EMA ) from the 12-period EMA .
What is the Phase Accumulation Cycle?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
Included:
Zero-line and signal cross options for bar coloring, signals, and alerts
Alerts
Signals
Loxx's Expanded Source Types
4 moving average types
Adaptivity: Measures of Dominant Cycles and Price Trend [Loxx]Adaptivity: Measures of Dominant Cycles and Price Trend is an indicator that outputs adaptive lengths using various methods for dominant cycle and price trend timeframe adaptivity. While the information output from this indicator might be useful for the average trader in one off circumstances, this indicator is really meant for those need a quick comparison of dynamic length outputs who wish to fine turn algorithms and/or create adaptive indicators.
This indicator compares adaptive output lengths of all publicly known adaptive measures. Additional adaptive measures will be added as they are discovered and made public.
The first released of this indicator includes 6 measures. An additional three measures will be added with updates. Please check back regularly for new measures.
Ehers:
Autocorrelation Periodogram
Band-pass
Instantaneous Cycle
Hilbert Transformer
Dual Differentiator
Phase Accumulation (future release)
Homodyne (future release)
Jurik:
Composite Fractal Behavior (CFB)
Adam White:
Veritical Horizontal Filter (VHF) (future release)
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman's adaptive moving average (KAMA) and Tushar Chande's variable index dynamic average (VIDYA) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index (RSI), commodity channel index (CCI), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
What is this Hilbert Transformer?
An analytic signal allows for time-variable parameters and is a generalization of the phasor concept, which is restricted to time-invariant amplitude, phase, and frequency. The analytic representation of a real-valued function or signal facilitates many mathematical manipulations of the signal. For example, computing the phase of a signal or the power in the wave is much simpler using analytic signals.
The Hilbert transformer is the technique to create an analytic signal from a real one. The conventional Hilbert transformer is theoretically an infinite-length FIR filter. Even when the filter length is truncated to a useful but finite length, the induced lag is far too large to make the transformer useful for trading.
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, pages 186-187:
"I want to emphasize that the only reason for including this section is for completeness. Unless you are interested in research, I suggest you skip this section entirely. To further emphasize my point, do not use the code for trading. A vastly superior approach to compute the dominant cycle in the price data is the autocorrelation periodogram. The code is included because the reader may be able to capitalize on the algorithms in a way that I do not see. All the algorithms encapsulated in the code operate reasonably well on theoretical waveforms that have no noise component. My conjecture at this time is that the sample-to-sample noise simply swamps the computation of the rate change of phase, and therefore the resulting calculations to find the dominant cycle are basically worthless.The imaginary component of the Hilbert transformer cannot be smoothed as was done in the Hilbert transformer indicator because the smoothing destroys the orthogonality of the imaginary component."
What is the Dual Differentiator, a subset of Hilbert Transformer?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 187:
"The first algorithm to compute the dominant cycle is called the dual differentiator. In this case, the phase angle is computed from the analytic signal as the arctangent of the ratio of the imaginary component to the real component. Further, the angular frequency is defined as the rate change of phase. We can use these facts to derive the cycle period."
What is the Phase Accumulation, a subset of Hilbert Transformer?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 189:
"The next algorithm to compute the dominant cycle is the phase accumulation method. The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle's worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio."
What is the Homodyne, a subset of Hilbert Transformer?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 192:
"The third algorithm for computing the dominant cycle is the homodyne approach. Homodyne means the signal is multiplied by itself. More precisely, we want to multiply the signal of the current bar with the complex value of the signal one bar ago. The complex conjugate is, by definition, a complex number whose sign of the imaginary component has been reversed."
What is the Instantaneous Cycle?
The Instantaneous Cycle Period Measurement was authored by John Ehlers; it is built upon his Hilbert Transform Indicator.
From his Ehlers' book Cybernetic Analysis for Stocks and Futures: Cutting-Edge DSP Technology to Improve Your Trading by John F. Ehlers, 2004, page 107:
"It is obvious that cycles exist in the market. They can be found on any chart by the most casual observer. What is not so clear is how to identify those cycles in real time and how to take advantage of their existence. When Welles Wilder first introduced the relative strength index (rsi), I was curious as to why he selected 14 bars as the basis of his calculations. I reasoned that if i knew the correct market conditions, then i could make indicators such as the rsi adaptive to those conditions. Cycles were the answer. I knew cycles could be measured. Once i had the cyclic measurement, a host of automatically adaptive indicators could follow.
Measurement of market cycles is not easy. The signal-to-noise ratio is often very low, making measurement difficult even using a good measurement technique. Additionally, the measurements theoretically involve simultaneously solving a triple infinity of parameter values. The parameters required for the general solutions were frequency, amplitude, and phase. Some standard engineering tools, like fast fourier transforms (ffs), are simply not appropriate for measuring market cycles because ffts cannot simultaneously meet the stationarity constraints and produce results with reasonable resolution. Therefore i introduced maximum entropy spectral analysis (mesa) for the measurement of market cycles. This approach, originally developed to interpret seismographic information for oil exploration, produces high-resolution outputs with an exceptionally short amount of information. A short data length improves the probability of having nearly stationary data. Stationary data means that frequency and amplitude are constant over the length of the data. I noticed over the years that the cycles were ephemeral. Their periods would be continuously increasing and decreasing. Their amplitudes also were changing, giving variable signal-to-noise ratio conditions. Although all this is going on with the cyclic components, the enduring characteristic is that generally only one tradable cycle at a time is present for the data set being used. I prefer the term dominant cycle to denote that one component. The assumption that there is only one cycle in the data collapses the difficulty of the measurement process dramatically."
What is the Band-pass Cycle?
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 47:
"Perhaps the least appreciated and most underutilized filter in technical analysis is the band-pass filter. The band-pass filter simultaneously diminishes the amplitude at low frequencies, qualifying it as a detrender, and diminishes the amplitude at high frequencies, qualifying it as a data smoother. It passes only those frequency components from input to output in which the trader is interested. The filtering produced by a band-pass filter is superior because the rejection in the stop bands is related to its bandwidth. The degree of rejection of undesired frequency components is called selectivity. The band-stop filter is the dual of the band-pass filter. It rejects a band of frequency components as a notch at the output and passes all other frequency components virtually unattenuated. Since the bandwidth of the deep rejection in the notch is relatively narrow and since the spectrum of market cycles is relatively broad due to systemic noise, the band-stop filter has little application in trading."
From his Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 59:
"The band-pass filter can be used as a relatively simple measurement of the dominant cycle. A cycle is complete when the waveform crosses zero two times from the last zero crossing. Therefore, each successive zero crossing of the indicator marks a half cycle period. We can establish the dominant cycle period as twice the spacing between successive zero crossings."
What is Composite Fractal Behavior (CFB)?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is VHF Adaptive Cycle?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Market PulseBINANCE:BTCUSDT
This is the "Market Pulse" indicator from TOS Indicators.
The scope of this indicator is to identify which one of the four market stages we're in
█ WHAT ARE THE 4 STAGES?
ACCELERATION (or uptrend)
DECELERATION (or downtrend)
ACCUMULATION (occurs after the market has presumably found a bottom and buyers are coming in)
DISTRIBUTION (occurs after the market has presumably found a top and sellers are coming in)
█ WHAT ARE THE TOOLS THAT IT USES TO IDENTIFY THEM?
3 VWMA (Volume Weighted Moving Average)
1 VMA (Variable Moving Average)
VWMA = is a moving average which takes volume into account, and gives closes with higher volume an higher weight
vwma(src, len) => ta.sma(src * volume, len) / ta.sma(volume, len)
VMA = is a moving average which automatically adjusts the smoothing constant using Market Volatility
vma(src, len) =>
vi = ta.cmo(src, len) / 100
alpha = 2 / (len + 1) * math.abs(vi)
vma = 0.0
vma := alpha * src + nz(vma ) * (1 - alpha)
█ HOW CAN I INTERPRET THE INDICATOR?
1) On the top right you can see a box which tells you the Market Stage of the chart you are currently using:
If VWMA8 > VWMA21 > VWMA34 it signals ACCELERATION, color coded in green
If VWMA8 < VWMA21 < VWMA34 it signals DECELERATION, color coded in red
If neither of the previous two conditions are met it signals ACCUMULATION (yellow) if price closes above the VMA and DISTRIBUTION (orange) if price closes below the VMA
2) Next you have the actual VMA which is the line plotted on the chart and color coded in green, red or gray accordingly to the Market Stage with a filter applied:
for a bullish signal (green label) the market needs to be in ACCELERATION and price must be above the VMA
for a bearish signal (red label) the market needs to be in DECELERATION and price must be below the VMA
This characteristic makes it sometimes slower at giving direction indications, but also makes it more suitable to be considered as actual signals for buying and selling
ACCUMULATION and DISTRIBUTION are both rapresented with color gray, if you want you can consider:
the line going from green to gray as ACCUMULATION, your bias is bullish until the line turns red
the line going from red to gray as DISTRIBUTION, your bias is bearish until the line turns green
3) Then you can choose to plot the 3 VWMA to indentify pullbacks and entries for your trades
4) Finally you have the Market Screener, which you can choose to plot and gives a fast look to the markets you are interested on
It basically gives you the Market Stage for every Symbol you choose using the timeframes you input
The maximum number of Symbols you can set is 20, and for all of them you have 2 different timeframes you can choose to analyse.
By default the Symbols are set to the top 20 Cryptocurrency by Market Cap, and the timeframes to 4h and D
There is an option which is on by default and color codes ACCUMULATION and DISTRIBUTION the same as the box on the top right, you can turn it off to make them gray
As I've written in the tooltip inside the indicator you should only use the screener to analyse timeframes which are equal or higher than the one you are currently on your chart.
If you don't plan to use the screener you can delete every symbol from the input boxes to make the indicator update faster when changing timeframe or market.
Be aware that the screener is on BETA and may give repainting signals!
Investment Analysis Bar v2What It Does
A comprehensive analysis bar combining fundamental metrics with technical signals, designed for long-term investors who prioritize quality over momentum.
Core Philosophy: Quality companies trading below their 200 EMA in accumulation zones = opportunities, not warnings.
Tier 1 Bar Metrics
Margins: GM, OM, NIM, FCF Margin
Returns: ROCE, ROE
Growth: Revenue YoY, EPS YoY
Valuation: PE TTM, Forward PE, PEG
Zone: Accumulate / Hold / Trim / Exit
Signal: PRIME / BUY / TRIM / SELL / NEUTRAL
Performance: 1W to 1Y returns
Two Strategy Modes
Value Accumulator (Default) - For long-term position building. Treats below-200-EMA as an opportunity when fundamentals are intact. PRIME signals require: RSI bounce + Volume + Accumulate Zone + All Quality Gates Pass + Below 200 EMA.
Trend Follower - Traditional momentum approach. Prefers entries above 200 EMA.
Quality Gates System
Four fundamental checkpoints:
Gross Margin ≥ 40%
ROCE ≥ 15%
Debt/Equity ≤ 50%
SBC/Revenue ≤ 15%
Strong signals require quality confirmation. PRIME signals require ALL gates to pass.
Zone System
Three calculation methods:
52W Range: Accumulate in bottom 25%, Trim in top 25%
Manual Levels: Set your own price targets
ATR-Based: Dynamic zones from EMA ± ATR
Signal Hierarchy (Value Mode)
SignalMeaning
PRIME 💎Optimal entry - all conditions aligned
BUY 🔼Strong accumulation signal
BUY? ↗Decent entry, not ideal zone
ACCUM 🎯In accumulation zone, quality OK
WAIT ⏳Setup forming, no bounce yet
TRIM 📤Consider taking profits
Alerts Included
Zone transitions (Accumulate, Trim, Exit)
PRIME Entry Signal
Strong Buy / Sell signals
Quality Gate failures
Quality Accumulation Setup
Best Used On
US stocks with fundamental data available. Technical features work on all symbols.
Settings
Fully customizable:
Toggle each metric category
Adjust quality gate thresholds
Choose zone calculation method
Configure RSI/volume parameters
Position bar and panel anywhere
Clean Volume (SUV)The Problem with Raw Volume
Traditional volume bars tell you how much traded, but not whether that amount is unusual. This creates noise that misleads traders:
Stock A averages 1M shares with wild daily swings (500K-2M is normal). Today's 2M volume looks like a spike—but it's just a routine high day.
Stock B averages 1M shares with rock-steady volume (950K-1.05M typical). Today's 2M volume is genuinely extraordinary—institutions are clearly active.
Both show identical 200% relative volume. But Stock B's reading is far more significant. Raw volume and simple relative volume (RVol) can't distinguish between these situations, leading to:
- False signals on naturally volatile stocks
- Missed signals on stable stocks where smaller deviations matter
- Inconsistent comparisons across different securities
---
A Solution: Standardized Unexpected Volume (SUV)
SUV applies statistical normalization to volume, measuring how many standard deviations today's volume is from the mean. This z-score approach accounts for each stock's individual volume stability, not just its average.
SUV = (Today's Volume - Average Volume) / Standard Deviation of Volume
Using the examples above:
- Stock A (high volatility): SUV = 2.0 — elevated but not unusual for this stock
- Stock B (low volatility): SUV = 10.0 — extremely unusual, demands attention
SUV automatically calibrates to each security's behaviour, making volume readings comparable across any stock, ETF, or timeframe.
---
What SUV Is Good For
✅ Identifying genuine volume anomalies — separates signal from noise
✅ Comparing volume across different securities — apples-to-apples z-scores
✅ Spotting institutional activity — large players create statistically significant footprints
✅ Confirming breakouts — high SUV validates price moves
✅ Detecting exhaustion — extreme SUV after extended moves may signal climax
✅ Finding "dry" setups — negative SUV reveals quiet accumulation periods
---
Where SUV Has Limitations
⚠️ Earnings/news events — SUV will spike dramatically (by design), but the statistical reading may be less meaningful when fundamentals change
⚠️ Low-float stocks — extreme volume volatility can produce erratic SUV readings
⚠️ First 20 bars — needs lookback period to establish baseline; early readings are less reliable
⚠️ Doesn't predict direction — SUV measures volume intensity, not whether price will rise or fall
---
How to Read This Indicator
Bar Height
Displays actual volume (like a traditional volume chart) so you can still see absolute levels.
Bar Color (SUV Intensity)
Color intensity reflects the SUV z-score. Brighter = more unusual.
Up Days (Green Gradient):
| Color | SUV Range | Meaning |
|--------------|-----------|------------------------------------------|
| Bright Green | ≥ 3.0 | EXTREME — Highly unusual buying activity |
| Green | ≥ 2.0 | VERY HIGH — Significant accumulation |
| Light Green | ≥ 1.5 | HIGH — Above-average interest |
| Pale Green | ≥ 1.0 | ELEVATED — Moderately active |
| Muted Green | 0 to 1.0 | NORMAL — Typical volume |
| Dark Grey | < 0 | DRY — Below-average, quiet |
Down Days (Red Gradient):
| Color | SUV Range | Meaning |
|------------|-----------|-----------------------------------------|
| Bright Red | ≥ 3.0 | EXTREME — Panic selling or capitulation |
| Red | ≥ 2.0 | VERY HIGH — Heavy distribution |
| Light Red | ≥ 1.5 | HIGH — Active selling |
| Pale Red | ≥ 1.0 | ELEVATED — Moderate selling |
| Muted Red | 0 to 1.0 | NORMAL — Routine down day |
| Dark Grey | < 0 | DRY — Light profit-taking |
Coiled State (Tan/Beige):
When detected, bars turn muted tan regardless of direction. This indicates:
- Volume compression (SUV below threshold for consecutive days)
- Volatility contraction (ATR below average)
- Price tightness (small recent moves)
Coiled states may precede significant breakouts.
Special Markers
"P" Label (Blue) — Pocket Pivot detected. Morales & Kacher's signal fires when:
- Price closes higher than previous close
- Price closes above the open (green candle)
- Volume exceeds the highest down-day volume of the last 10 bars
Pocket Pivots may indicate institutional buying before a traditional breakout.
"C" Label (Orange) — Coiled state confirmed. The stock is consolidating with compressed volume and tight price action. Watch for expansion.
Dashboard
The configurable dashboard displays real-time metrics. Default items:
- Vol — Current bar volume
- SUV — Z-score value
- Class — Classification (EXTREME/VERY HIGH/HIGH/ELEVATED/NORMAL/DRY/COILED)
- Proj RVol — Projected end-of-day relative volume (intraday only)
Additional optional items: Direction, Coil Status, Relative ATR, Pocket Pivot, Average Volume.
---
Practical Usage Tips
1. SUV ≥ 2 on breakouts — Validates the move has institutional participation
2. Watch for SUV < 0 bases — Quiet accumulation zones where smart money builds positions
3. Coil → Expansion — After consecutive coiled days, the first SUV ≥ 1.5 bar often signals direction
4. Pocket Pivots in bases — Early accumulation signals before price breaks out
5. Extreme SUV (≥3) after extended moves — May indicate climax/exhaustion rather than continuation
---
Settings Overview
| Group | Key Settings |
|-----------------|-----------------------------------------------------|
| SUV Settings | Lookback period (default 20) |
| Coil Detection | Enable/disable, sensitivity thresholds |
| Pocket Pivot | Enable/disable, lookback period |
| Display | Dashboard style (Ribbon/Table), position, text size |
| Dashboard Items | Toggle which metrics appear |
| Colors | Fully customizable gradient colors |
---
Credits
SUV concept adapted from academic literature on standardized unexpected volume in market microstructure research. Pocket Pivot methodology based on Gil Morales and Chris Kacher's work. Coil detection inspired by volatility contraction patterns.
---
This indicator does not provide financial advice. Always combine volume analysis with price action, market context, and proper risk management. No animals were harmed during the coding and testing of this indicator.
Smart Money Alpha Signals (Performance Dashboard) Smart Money Alpha Signals: Identifying Market Leaders & Generating Alpha
GMP Alpha Signals (Global Market Performance Alpha) is a specialized analysis tool designed not merely to find stocks that are rising, but to identify "Alpha" assets—Market Leaders that defend their price or rise even under adverse conditions where the market index falls or consolidates.
This indicator visualizes the concept of Comparative Relative Strength (RS) and Smart Money accumulation patterns, helping traders capture profit opportunities even during bearish market phases.
Key Objectives (Purpose)
Alpha Capture: Identifying assets generating 'excess returns' that outperform the market Beta.
Smart Money Tracking: Detecting traces of 'institutional buying' and 'accumulation' that defend prices during index plunges.
Decoupling Identification: Spotting assets moving on independent catalysts or momentum, regardless of the broader market direction.
Stop Hunt Filtering: Distinguishing 'fake drops' where price dips temporarily, but Relative Strength remains intact.
Dashboard Guide
Interpretation of the information panel (Table) displayed on the chart.
Rel. Performance: Shows the excess return compared to the index over the set period. (Positive/Green = Stronger than the market).
Decoupling Strength: The correlation coefficient with the index. Lower values (0 or negative) indicate movement independent of market risk.
Bullish: The count/rate of rising or limiting losses when the index drops sharply (e.g., < -0.5%). (Gold = Market Crash Leader).
Defended: The count/rate of holding support levels when the index shows mild weakness (e.g., < -0.05%). (Gold = Strong Accumulation).
Bench. Defense: The defense rate of the comparison benchmark (e.g., TSLA, ETH). Your target asset must be higher to be considered the sector leader.
Input Options & Settings Guide
You can optimize settings according to your trading style and asset class (Stocks/Crypto).
(1) Main Settings
Major Index: The baseline market index for comparison.
(US Stocks: NASDAQ:NDX or TVC:SPX / Crypto: BINANCE:BTCUSDT)
Benchmark Symbol: A competitor within the sector.
(e.g., Set NVDA when analyzing Semiconductor stocks).
Correlation Lookback: The lookback period for judging decoupling. (Default: 30)
Performance Lookback: The number of bars to calculate cumulative returns and defense rates. (Default: 60)
(2) Dashboard Thresholds
These settings define the criteria for what qualifies as "Defended" or "Bullish".
Performance (Max %): Used to find assets that haven't pumped yet. Signals trigger only when Alpha is below this value.
Defended Logic:
Index Drop Condition: The index must drop by at least this amount to start checking. (e.g., -0.05%)
Asset Buffer: How much the asset must outperform the index drop.
(Example: If Index drops -1.0% and Buffer is 0.2%, the asset must be at least -0.8% to count as 'Defended').
Bullish Logic: Measures resilience during steeper market dumps (e.g., -0.5% drop) compared to the Defended Logic.
Volume Settings: Decides whether to count Defended/Bullish instances only when accompanied by volume above the SMA.
(3) Signal Logic Settings (Crucial)
Customize conditions to trigger alerts. The choice between AND / OR is crucial.
AND: Condition must be met SIMULTANEOUSLY with other active conditions (Conservative/High Certainty).
OR: Condition triggers the signal INDEPENDENTLY (Aggressive/Opportunity Capture).
Performance: Is the relative performance within the threshold? (Basic Filter).
Decoupling: Has the correlation dropped? (Start of independent move).
Bullish Rate: Is the Bullish rate high during market dumps?
Defended Rate (High): (Recommended) Is there continuous price defense occurring? (Accumulation detection).
Defended Rate (Low): (Warning) Has the defense rate broken down? (For Stop Loss).
Defended > Benchmark: Is it stronger than the Benchmark (2nd tier)?
Volume Spike: Has volume surged compared to the average? (Institutional involvement).
RSI Oversold: Is it in oversold territory? (Counter-trend trading).
Decoupling Move: Does the current bar show the "Index Down / Asset Up" pattern?
Min USD Volume: Transaction value filter (To exclude low liquidity assets).
BTC Fear & Greed Incremental StrategyIMPORTANT: READ SETUP GUIDE BELOW OR IT WON'T WORK
# BTC Fear & Greed Incremental Strategy — TradeMaster AI (Pure BTC Stack)
## Strategy Overview
This advanced Bitcoin accumulation strategy is designed for long-term hodlers who want to systematically take profits during greed cycles and accumulate during fear periods, while preserving their core BTC position. Unlike traditional strategies that start with cash, this approach begins with a specified BTC allocation, making it perfect for existing Bitcoin holders who want to optimize their stack management.
## Key Features
### 🎯 **Pure BTC Stack Mode**
- Start with any amount of BTC (configurable)
- Strategy manages your existing stack, not new purchases
- Perfect for hodlers who want to optimize without timing markets
### 📊 **Fear & Greed Integration**
- Uses market sentiment data to drive buy/sell decisions
- Configurable thresholds for greed (selling) and fear (buying) triggers
- Automatic validation to ensure proper 0-100 scale data source
### 🐂 **Bull Year Optimization**
- Smart quarterly selling during bull market years (2017, 2021, 2025)
- Q1: 1% sells, Q2: 2% sells, Q3/Q4: 5% sells (configurable)
- **NO SELLING** during non-bull years - pure accumulation mode
- Preserves BTC during early bull phases, maximizes profits at peaks
### 🐻 **Bear Market Intelligence**
- Multi-regime detection: Bull, Early Bear, Deep Bear, Early Bull
- Different buying strategies based on market conditions
- Enhanced buying during deep bear markets with configurable multipliers
- Visual regime backgrounds for easy market condition identification
### 🛡️ **Risk Management**
- Minimum BTC allocation floor (prevents selling entire stack)
- Configurable position sizing for all trades
- Multiple safety checks and validation
### 📈 **Advanced Visualization**
- Clean 0-100 scale with 2 decimal precision
- Three main indicators: BTC Allocation %, Fear & Greed Index, BTC Holdings
- Real-time portfolio tracking with cash position display
- Enhanced info table showing all key metrics
## How to Use
### **Step 1: Setup**
1. Add the strategy to your BTC/USD chart (daily timeframe recommended)
2. **CRITICAL**: In settings, change the "Fear & Greed Source" from "close" to a proper 0-100 Fear & Greed indicator
---------------
I recommend Crypto Fear & Greed Index by TIA_Technology indicator
When selecting source with this indicator, look for "Crypto Fear and Greed Index:Index"
---------------
3. Set your "Starting BTC Quantity" to match your actual holdings
4. Configure your preferred "Start Date" (when you want the strategy to begin)
### **Step 2: Configure Bull Year Logic**
- Enable "Bull Year Logic" (default: enabled)
- Adjust quarterly sell percentages:
- Q1 (Jan-Mar): 1% (conservative early bull)
- Q2 (Apr-Jun): 2% (moderate mid bull)
- Q3/Q4 (Jul-Dec): 5% (aggressive peak targeting)
- Add future bull years to the list as needed
### **Step 3: Fine-tune Thresholds**
- **Greed Threshold**: 80 (sell when F&G > 80)
- **Fear Threshold**: 20 (buy when F&G < 20 in bull markets)
- **Deep Bear Fear Threshold**: 25 (enhanced buying in bear markets)
- Adjust based on your risk tolerance
### **Step 4: Risk Management**
- Set "Minimum BTC Allocation %" (default 20%) - prevents selling entire stack
- Configure sell/buy percentages based on your position size
- Enable bear market filters for enhanced timing
### **Step 5: Monitor Performance**
- **Orange Line**: Your BTC allocation percentage (target: fluctuate between 20-100%)
- **Blue Line**: Actual BTC holdings (should preserve core position)
- **Pink Line**: Fear & Greed Index (drives all decisions)
- **Table**: Real-time portfolio metrics including cash position
## Reading the Indicators
### **BTC Allocation Percentage (Orange Line)**
- **100%**: All portfolio in BTC, no cash available for buying
- **80%**: 80% BTC, 20% cash ready for fear buying
- **20%**: Minimum allocation, maximum cash position
### **Trading Signals**
- **Green Buy Signals**: Appear during fear periods with available cash
- **Red Sell Signals**: Appear during greed periods in bull years only
- **No Signals**: Either allocation limits reached or non-bull year
## Strategy Logic
### **Bull Years (2017, 2021, 2025)**
- Q1: Conservative 1% sells (preserve stack for later)
- Q2: Moderate 2% sells (gradual profit taking)
- Q3/Q4: Aggressive 5% sells (peak targeting)
- Fear buying active (accumulate on dips)
### **Non-Bull Years**
- **Zero selling** - pure accumulation mode
- Enhanced fear buying during bear markets
- Focus on rebuilding stack for next bull cycle
## Important Notes
- **This is not financial advice** - backtest thoroughly before use
- Designed for **long-term holders** (4+ year cycles)
- **Requires proper Fear & Greed data source** - validate in settings
- Best used on **daily timeframe** for major trend following
- **Cash calculations**: Use allocation % and BTC holdings to calculate available cash: `Cash = (Total Portfolio × (1 - Allocation%/100))`
## Risk Disclaimer
This strategy involves active trading and position management. Past performance does not guarantee future results. Always do your own research and never invest more than you can afford to lose. The strategy is designed for educational purposes and long-term Bitcoin accumulation thesis.
---
*Developed by Sol_Crypto for the Bitcoin community. Happy stacking! 🚀*
