Vlad Waves█ CONCEPT
Acceleration Line (Blue)
The Acceleration Line is calculated as the difference between the 8-period SMA and the 20-period SMA.
This line helps to identify the momentum and potential turning points in the market.
Signal Line (Red)
The Signal Line is an 8-period SMA of the Acceleration Line.
This line smooths out the Acceleration Line to generate clearer signals.
Long-Term Average (Green)
The Long-Term Average is a 200-period SMA of the Acceleration Line.
This line provides a broader context of the market trend, helping to distinguish between long-term and short-term movements.
█ SIGNALS
Buy Mode
A buy signal occurs when the Acceleration Line crosses above the Signal Line while below the Long-Term Average. This indicates a potential bullish reversal in the market.
When the Signal Line crosses the Acceleration Line above the Long-Term Average, consider placing a stop rather than reversing the position to protect gains from potential pullbacks.
Sell Mode
A sell signal occurs when the Acceleration Line crosses below the Signal Line while above the Long-Term Average. This indicates a potential bearish reversal in the market.
When the Signal Line crosses the Acceleration Line below the Long-Term Average, consider placing a stop rather than reversing the position to protect gains from potential pullbacks.
█ UTILITY
This indicator is not recommended for standalone buy or sell signals. Instead, it is designed to identify market cycles and turning points, aiding in the decision-making process.
Entry signals are most effective when they occur away from the Long-Term Average, as this helps to avoid sideways movements.
Use larger timeframes, such as daily or weekly charts, for better accuracy and reliability of the signals.
█ CREDITS
The idea for this indicator came from Fabio Figueiredo (Vlad).
Cerca negli script per "Cycle"
3x MTF MACD v3.0MACD's on 3 different Time Frames
Indicator Information
- Each Time Frame shows start of Trend and end of trend of the MACD vs the Signal Cross
- They are labled 1,2,3 with respective up or down triangle for possible direction.
User Inputs
- configure the indicator by specifying various inputs. These inputs include colors for bullish
and bearish conditions, the time frame to use, whether to show a Simple Moving Average
(SMA) line, and other parameters.
- Users can choose time frames for analysis (like 30 minutes, 1 hour, etc.)
but they must be in mintues.
- The code also allows users to customize how the indicator looks on the chart by providing
options for position and color.
Main Calculations
- The script calculates the Simple Moving Average (SMA) based on the user-defined time
frame.
- It then determines the color of the plot (line) based on certain conditions, such as whether
the SMA is rising or falling. These conditions help users quickly identify market trends.
Label Creation
- The code creates labels that can be displayed on the chart.
These labels indicate whether there's a bullish or bearish signal.
Level Detection
- The script determines and labels key levels or points of interest in the chart based on
certain conditions.
- It can show labels like "①" and "▲" for bullish conditions and "▼" for bearish conditions.
Table Display
- There's an option to show a table on the chart that displays information about the MACD
indicator Chosen and the NUmber Bubble assocated with that time frame
- The table can include information like which time frame is being analyzed, whether the SMA
line is shown, and other relevant data.
Plotting on the Chart
- The script plots the Simple Moving Average (SMA) on the chart. The color of this line
changes based on the calculated trend conditions.
ATR (Average True Range)
- The script also plots the Average True Range (ATR) on the chart. ATR is used to measure
market volatility.
"In essence, this script is a highly customizable MACD and SMA indicator for traders. It assists traders in comprehending market trends, offering insights into different MACD cycles concerning various timeframes.
Users can configure it to match their trading strategies, and it presents information in a user-friendly manner with colors, labels, and tables.
This simplifies market analysis, allowing traders to make more informed decisions without the distraction of multiple indicators."
Time Cycles IndicatorThis script is used to analyze the seasonality of any asset (commodities, stocks, indices).
To use the script select a timeframe D or W and select the months you are interested in the script settings. You will see all the candles that are part of those months highlighted in the chart.
You can use this script to understand if assets have a cyclical behavior in certain months of the year.
OECD CLI Diffusion IndexWhat does the indicator measure?
This is a macro indicator. It uses OECD's composite leading indicator - see details about the CLI below.
What it does it calculate YoY changes for CLI of 38 countries that are members or are associated with the OECD. Then it measures a percent of countries which CLI is rising.
How this can be used?
The positive slope of the curve means that there probably will be an economic growth among those countries within next 6 - 9 months. The negative slope means there probably will be an economic contraction.
Forward-looking economic growth is correlated with positive S&P 500 YoY growth (equity markets are also forward looking). The chart above presents the CLI diffusion index with overlayed S&P500 YoY rate of change.
The CLI is also correlated with ISM PMI - see example below:
What is a CLI?
"The OECD system of Composite Leading Indicators (CLIs) is designed to provide early signals of turning points in business cycles - fluctuation in the output gap, i.e. fluctuation of the economic activity around its long term potential level. This approach, focusing on turning points (peaks and troughs), results in CLIs that provide qualitative rather than quantitative information on short-term economic movements."
Trend Identifier StrategyTrend Identifier Strategy for 1D BTC.USD
The indicator smoothens a closely following moving average into a polynomial like plot and assumes 4 staged cycles based on the first and the second derivatives. This is an optimized strategy for long term buying and selling with a Sortino Ratio above 3. It is designed to be a more profitable alternative to HODLing. It can be combined with 'Accumulation/Distribution Bands & Signals' and 'Exponential Top and Bottom Finder'.
Ehlers Band-Pass FilterHeyo,
This indicator is an original translation from Ehlers' book "Cycle Analytics for Traders Advanced".
First, I describe the indicator as usual and later you can find a very insightful quote of the book.
Key Features
Signal Line: Represents the output of the band-pass filter, highlighting the dominant cycle in the data.
Trigger Line: A leading indicator derived from the signal line, providing early signals for potential market reversals.
Dominant Cycle: Measures the dominant cycle period by counting the number of bars between zero crossings of the band-pass filter output.
Calculation:
The band-pass filter is implemented using a combination of high-pass and low-pass filters.
The filter's parameters, such as period and bandwidth, can be adjusted to tune the filter to specific market cycles.
The signal line is normalized using an Automatic Gain Control (AGC) to provide consistent amplitude regardless of price swings.
The trigger line is derived by applying a high-pass filter to the signal line, creating a leading
waveform.
Usage
The indicator is effective in identifying peaks and valleys in the market data.
It works best in cyclic market conditions and may produce false signals during trending periods.
The dominant cycle measurement helps traders understand the prevailing market cycle length, aiding in better decision-making.
Quoted from the Book
Band-Pass Filters
“A little of the data narrowly passed,” said Tom broadly.
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.
Measuring the Cycle Period
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.
When we measure the dominant cycle period this way, it is best to widen the pass band of the band-pass filter to avoid distorting the measurement simply due to the selectivity of the filter. Using an input bandwidth of 0.7 produces an octave-wide pass band. For example, if the center period of the filter is 20 and the relative bandwidth is 0.7, the bandwidth is 14. That means the pass band of the filter extends from 13-bar periods to 27-bar periods.
That is, roughly an octave exists because the longest period is twice the shortest period of the pass band. It is imperative that a high-pass filter is tuned one octave below the half-bandwidth edge of the band-pass filter to ensure a nominal zero mean of the filtered output. Without a zero mean, the zero crossings can have a substantial error.
Since the measurement of the dominant cycle can vary dramatically from zero crossing to zero
crossing, the code limits the change between measurements to be no more than 25 percent.
While measuring the changing dominant cycle period via zero crossings of the band-pass waveform is easy, it is not necessarily the most accurate method.
Best regards,
simwai
Good Luck with your trading! 🙌
MERV: Market Entropy & Rhythm Visualizer [BullByte]The MERV (Market Entropy & Rhythm Visualizer) indicator analyzes market conditions by measuring entropy (randomness vs. trend), tradeability (volatility/momentum), and cyclical rhythm. It provides traders with an easy-to-read dashboard and oscillator to understand when markets are structured or choppy, and when trading conditions are optimal.
Purpose of the Indicator
MERV’s goal is to help traders identify different market regimes. It quantifies how structured or random recent price action is (entropy), how strong and volatile the movement is (tradeability), and whether a repeating cycle exists. By visualizing these together, MERV highlights trending vs. choppy environments and flags when conditions are favorable for entering trades. For example, a low entropy value means prices are following a clear trend line, whereas high entropy indicates a lot of noise or sideways action. The indicator’s combination of measures is original: it fuses statistical trend-fit (entropy), volatility trends (ATR and slope), and cycle analysis to give a comprehensive view of market behavior.
Why a Trader Should Use It
Traders often need to know when a market trend is reliable vs. when it is just noise. MERV helps in several ways: it shows when the market has a strong direction (low entropy, high tradeability) and when it’s ranging (high entropy). This can prevent entering trend-following strategies during choppy periods, or help catch breakouts early. The “Optimal Regime” marker (a star) highlights moments when entropy is very low and tradeability is very high, typically the best conditions for trend trades. By using MERV, a trader gains an empirical “go/no-go” signal based on price history, rather than guessing from price alone. It’s also adaptable: you can apply it to stocks, forex, crypto, etc., on any timeframe. For example, during a bullish phase of a stock, MERV will turn green (Trending Mode) and often show a star, signaling good follow-through. If the market later grinds sideways, MERV will shift to magenta (Choppy Mode), warning you that trend-following is now risky.
Why These Components Were Chosen
Market Entropy (via R²) : This measures how well recent prices fit a straight line. We compute a linear regression on the last len_entropy bars and calculate R². Entropy = 1 - R², so entropy is low when prices follow a trend (R² near 1) and high when price action is erratic (R² near 0). This single number captures trend strength vs noise.
Tradeability (ATR + Slope) : We combine two familiar measures: the Average True Range (ATR) (normalized by price) and the absolute slope of the regression line (scaled by ATR). Together they reflect how active and directional the market is. A high ATR or strong slope means big moves, making a trend more “tradeable.” We take a simple average of the normalized ATR and slope to get tradeability_raw. Then we convert it to a percentile rank over the lookback window so it’s stable between 0 and 1.
Percentile Ranks : To make entropy and tradeability values easy to interpret, we convert each to a 0–100 rank based on the past len_entropy periods. This turns raw metrics into a consistent scale. (For example, an entropy rank of 90 means current entropy is higher than 90% of recent values.) We then divide by 100 to plot them on a 0–1 scale.
Market Mode (Regime) : Based on those ranks, MERV classifies the market:
Trending (Green) : Low entropy rank (<40%) and high tradeability rank (>60%). This means the market is structurally trending with high activity.
Choppy (Magenta) : High entropy rank (>60%) and low tradeability rank (<40%). This is a mostly random, low-momentum market.
Neutral (Cyan) : All other cases. This covers mixed regimes not strongly trending or choppy.
The mode is shown as a colored bar at the bottom: green for trending, magenta for choppy, cyan for neutral.
Optimal Regime Signal : Separately, we mark an “optimal” condition when entropy_norm < 0.3 and tradeability > 0.7 (both normalized 0–1). When this is true, a ★ star appears on the bottom line. This star is colored white when truly optimal, gold when only tradeability is high (but entropy not quite low enough), and black when neither condition holds. This gives a quick visual cue for very favorable conditions.
What Makes MERV Stand Out
Holistic View : Unlike a single-oscillator, MERV combines trend, volatility, and cycle analysis in one tool. This multi-faceted approach is unique.
Visual Dashboard : The fixed on-chart dashboard (shown at your chosen corner) summarizes all metrics in bar/gauge form. Even a non-technical user can glance at it: more “█” blocks = a higher value, colors match the plots. This is more intuitive than raw numbers.
Adaptive Thresholds : Using percentile ranks means MERV auto-adjusts to each market’s character, rather than requiring fixed thresholds.
Cycle Insight : The rhythm plot adds information rarely found in indicators – it shows if there’s a repeating cycle (and its period in bars) and how strong it is. This can hint at natural bounce or reversal intervals.
Modern Look : The neon color scheme and glow effects make the lines easy to distinguish (blue/pink for entropy, green/orange for tradeability, etc.) and the filled area between them highlights when one dominates the other.
Recommended Timeframes
MERV can be applied to any timeframe, but it will be more reliable on higher timeframes. The default len_entropy = 50 and len_rhythm = 30 mean we use 30–50 bars of history, so on a daily chart that’s ~2–3 months of data; on a 1-hour chart it’s about 2–3 days. In practice:
Swing/Position traders might prefer Daily or 4H charts, where the calculations smooth out small noise. Entropy and cycles are more meaningful on longer trends.
Day trader s could use 15m or 1H charts if they adjust the inputs (e.g. shorter windows). This provides more sensitivity to intraday cycles.
Scalpers might find MERV too “slow” unless input lengths are set very low.
In summary, the indicator works anywhere, but the defaults are tuned for capturing medium-term trends. Users can adjust len_entropy and len_rhythm to match their chart’s volatility. The dashboard position can also be moved (top-left, bottom-right, etc.) so it doesn’t cover important chart areas.
How the Scoring/Logic Works (Step-by-Step)
Compute Entropy : A linear regression line is fit to the last len_entropy closes. We compute R² (goodness of fit). Entropy = 1 – R². So a strong straight-line trend gives low entropy; a flat/noisy set of points gives high entropy.
Compute Tradeability : We get ATR over len_entropy bars, normalize it by price (so it’s a fraction of price). We also calculate the regression slope (difference between the predicted close and last close). We scale |slope| by ATR to get a dimensionless measure. We average these (ATR% and slope%) to get tradeability_raw. This represents how big and directional price moves are.
Convert to Percentiles : Each new entropy and tradeability value is inserted into a rolling array of the last 50 values. We then compute the percentile rank of the current value in that array (0–100%) using a simple loop. This tells us where the current bar stands relative to history. We then divide by 100 to plot on .
Determine Modes and Signal : Based on these normalized metrics: if entropy < 0.4 and tradeability > 0.6 (40% and 60% thresholds), we set mode = Trending (1). If entropy > 0.6 and tradeability < 0.4, mode = Choppy (-1). Otherwise mode = Neutral (0). Separately, if entropy_norm < 0.3 and tradeability > 0.7, we set an optimal flag. These conditions trigger the colored mode bars and the star line.
Rhythm Detection : Every bar, if we have enough data, we take the last len_rhythm closes and compute the mean and standard deviation. Then for lags from 5 up to len_rhythm, we calculate a normalized autocorrelation coefficient. We track the lag that gives the maximum correlation (best match). This “best lag” divided by len_rhythm is plotted (a value between 0 and 1). Its color changes with the correlation strength. We also smooth the best correlation value over 5 bars to plot as “Cycle Strength” (also 0 to 1). This shows if there is a consistent cycle length in recent price action.
Heatmap (Optional) : The background color behind the oscillator panel can change with entropy. If “Neon Rainbow” style is on, low entropy is blue and high entropy is pink (via a custom color function), otherwise a classic green-to-red gradient can be used. This visually reinforces the entropy value.
Volume Regime (Dashboard Only) : We compute vol_norm = volume / sma(volume, len_entropy). If this is above 1.5, it’s considered high volume (neon orange); below 0.7 is low (blue); otherwise normal (green). The dashboard shows this as a bar gauge and percentage. This is for context only.
Oscillator Plot – How to Read It
The main panel (oscillator) has multiple colored lines on a 0–1 vertical scale, with horizontal markers at 0.2 (Low), 0.5 (Mid), and 0.8 (High). Here’s each element:
Entropy Line (Blue→Pink) : This line (and its glow) shows normalized entropy (0 = very low, 1 = very high). It is blue/green when entropy is low (strong trend) and pink/purple when entropy is high (choppy). A value near 0.0 (below 0.2 line) indicates a very well-defined trend. A value near 1.0 (above 0.8 line) means the market is very random. Watch for it dipping near 0: that suggests a strong trend has formed.
Tradeability Line (Green→Yellow) : This represents normalized tradeability. It is colored bright green when tradeability is low, transitioning to yellow as tradeability increases. Higher values (approaching 1) mean big moves and strong slopes. Typically in a market rally or crash, this line will rise. A crossing above ~0.7 often coincides with good trend strength.
Filled Area (Orange Shade) : The orange-ish fill between the entropy and tradeability lines highlights when one dominates the other. If the area is large, the two metrics diverge; if small, they are similar. This is mostly aesthetic but can catch the eye when the lines cross over or remain close.
Rhythm (Cycle) Line : This is plotted as (best_lag / len_rhythm). It indicates the relative period of the strongest cycle. For example, a value of 0.5 means the strongest cycle was about half the window length. The line’s color (green, orange, or pink) reflects how strong that cycle is (green = strong). If no clear cycle is found, this line may be flat or near zero.
Cycle Strength Line : Plotted on the same scale, this shows the autocorrelation strength (0–1). A high value (e.g. above 0.7, shown in green) means the cycle is very pronounced. Low values (pink) mean any cycle is weak and unreliable.
Mode Bars (Bottom) : Below the main oscillator, thick colored bars appear: a green bar means Trending Mode, magenta means Choppy Mode, and cyan means Neutral. These bars all have a fixed height (–0.1) and make it very easy to see the current regime.
Optimal Regime Line (Bottom) : Just below the mode bars is a thick horizontal line at –0.18. Its color indicates regime quality: White (★) means “Optimal Regime” (very low entropy and high tradeability). Gold (★) means not quite optimal (high tradeability but entropy not low enough). Black means neither condition. This star line quickly tells you when conditions are ideal (white star) or simply good (gold star).
Horizontal Guides : The dotted lines at 0.2 (Low), 0.5 (Mid), and 0.8 (High) serve as reference lines. For example, an entropy or tradeability reading above 0.8 is “High,” and below 0.2 is “Low,” as labeled on the chart. These help you gauge values at a glance.
Dashboard (Fixed Corner Panel)
MERV also includes a compact table (dashboard) that can be positioned in any corner. It summarizes key values each bar. Here is how to read its rows:
Entropy : Shows a bar of blocks (█ and ░). More █ blocks = higher entropy. It also gives a percentage (rounded). A full bar (10 blocks) with a high % means very chaotic market. The text is colored similarly (blue-green for low, pink for high).
Rhythm : Shows the best cycle period in bars (e.g. “15 bars”). If no calculation yet, it shows “n/a.” The text color matches the rhythm line.
Cycle Strength : Gives the cycle correlation as a percentage (smoothed, as shown on chart). Higher % (green) means a strong cycle.
Tradeability : Displays a 10-block gauge for tradeability. More blocks = more tradeable market. It also shows “gauge” text colored green→yellow accordingly.
Market Mode : Simply shows “Trending”, “Choppy”, or “Neutral” (cyan text) to match the mode bar color.
Volume Regime : Similar to tradeability, shows blocks for current volume vs. average. Above-average volume gives orange blocks, below-average gives blue blocks. A % value indicates current volume relative to average. This row helps see if volume is abnormally high or low.
Optimal Status (Large Row) : In bold, either “★ Optimal Regime” (white text) if the star condition is met, “★ High Tradeability” (gold text) if tradeability alone is high, or “— Not Optimal” (gray text) otherwise. This large row catches your eye when conditions are ripe.
In short, the dashboard turns the numeric state into an easy read: filled bars, colors, and text let you see current conditions without reading the plot. For instance, five blue blocks under Entropy and “25%” tells you entropy is low (good), and a row showing “Trending” in green confirms a trend state.
Real-Life Example
Example : Consider a daily chart of a trending stock (e.g. “AAPL, 1D”). During a strong uptrend, recent prices fit a clear upward line, so Entropy would be low (blue line near bottom, perhaps below the 0.2 line). Volatility and slope are high, so Tradeability is high (green-yellow line near top). In the dashboard, Entropy might show only 1–2 blocks (e.g. 10%) and Tradeability nearly full (e.g. 90%). The Market Mode bar turns green (Trending), and you might see a white ★ on the optimal line if conditions are very good. The Volume row might light orange if volume is above average during the rally. In contrast, imagine the same stock later in a tight range: Entropy will rise (pink line up, more blocks in dashboard), Tradeability falls (fewer blocks), and the Mode bar turns magenta (Choppy). No star appears in that case.
Consolidated Use Case : Suppose on XYZ stock the dashboard reads “Entropy: █░░░░░░░░ 20%”, “Tradeability: ██████████ 80%”, Mode = Trending (green), and “★ Optimal Regime.” This tells the trader that the market is in a strong, low-noise trend, and it might be a good time to follow the trend (with appropriate risk controls). If instead it reads “Entropy: ████████░░ 80%”, “Tradeability: ███▒▒▒▒▒▒ 30%”, Mode = Choppy (magenta), the trader knows the market is random and low-momentum—likely best to sit out until conditions improve.
Example: How It Looks in Action
Screenshot 1: Trending Market with High Tradeability (SOLUSD, 30m)
What it means:
The market is in a clear, strong trend with excellent conditions for trading. Both trend-following and active strategies are favored, supported by high tradeability and strong volume.
Screenshot 2: Optimal Regime, Strong Trend (ETHUSD, 1h)
What it means:
This is an ideal environment for trend trading. The market is highly organized, tradeability is excellent, and volume supports the move. This is when the indicator signals the highest probability for success.
Screenshot 3: Choppy Market with High Volume (BTC Perpetual, 5m)
What it means:
The market is highly random and choppy, despite a surge in volume. This is a high-risk, low-reward environment, avoid trend strategies, and be cautious even with mean-reversion or scalping.
Settings and Inputs
The script is fully open-source; here are key inputs the user can adjust:
Entropy Window (len_entropy) : Number of bars used for entropy and tradeability (default 50). Larger = smoother, more lag; smaller = more sensitivity.
Rhythm Window (len_rhythm ): Bars used for cycle detection (default 30). This limits the longest cycle we detect.
Dashboard Position : Choose any corner (Top Right default) so it doesn’t cover chart action.
Show Heatmap : Toggles the entropy background coloring on/off.
Heatmap Style : “Neon Rainbow” (colorful) or “Classic” (green→red).
Show Mode Bar : Turn the bottom mode bar on/off.
Show Dashboard : Turn the fixed table panel on/off.
Each setting has a tooltip explaining its effect. In the description we will mention typical settings (e.g. default window sizes) and that the user can move the dashboard corner as desired.
Oscillator Interpretation (Recap)
Lines : Blue/Pink = Entropy (low=trend, high=chop); Green/Yellow = Tradeability (low=quiet, high=volatile).
Fill : Orange tinted area between them (for visual emphasis).
Bars : Green=Trending, Magenta=Choppy, Cyan=Neutral (at bottom).
Star Line : White star = ideal conditions, Gold = good but not ideal.
Horizontal Guides : 0.2 and 0.8 lines mark low/high thresholds for each metric.
Using the chart, a coder or trader can see exactly what each output represents and make decisions accordingly.
Disclaimer
This indicator is provided as-is for educational and analytical purposes only. It does not guarantee any particular trading outcome. Past market patterns may not repeat in the future. Users should apply their own judgment and risk management; do not rely solely on this tool for trading decisions. Remember, TradingView scripts are tools for market analysis, not personalized financial advice. We encourage users to test and combine MERV with other analysis and to trade responsibly.
-BullByte
90/30 Minute Cycle BoxesThis indicator automatically draws time-based cycle boxes to help visualize market structure and cyclical behavior.
Features:
90-Minute Primary Cycles: Highlights each 90-minute interval with a colored box, showing the high and low of that period.
30-Minute Sub-Cycles: Each 90-minute box is divided into 3 sub-boxes representing 30-minute phases.
Multi-Timeframe Compatible: Works on all timeframes, adapting dynamically to your chart.
Visual Clarity: Alternating box colors make it easy to track price action within and across cycles.
This tool is ideal for traders who use time cycles in their analysis, especially those applying ICT, Smart Money Concepts, or time-based market theories.
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.
Wavemeter [theEccentricTrader]█ OVERVIEW
This indicator is a representation of my take on price action based wave cycle theory. The indicator counts the number of confirmed wave cycles, keeps a rolling tally of the average wave length, wave height and frequency, and displays the statistics in a table. The indicator also displays the current wave measurements as an optional feature.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a high price equal to or above the price it opened.
• A red candle is one that closes with a low price that is lower than the price it opened.
Swing Highs and Swing Lows
• A swing high is a green candle or series of consecutive green candles followed by a single red candle to complete the swing and form the peak.
• A swing low is a red candle or series of consecutive red candles followed by a single green candle to complete the swing and form the trough.
Peak and Trough Prices (Basic)
• The peak price of a complete swing high is the high price of either the red candle that completes the swing high or the high price of the preceding green candle, depending on which is higher.
• The trough price of a complete swing low is the low price of either the green candle that completes the swing low or the low price of the preceding red candle, depending on which is lower.
Historic Peaks and Troughs
The current, or most recent, peak and trough occurrences are referred to as occurrence zero. Previous peak and trough occurrences are referred to as historic and ordered numerically from right to left, with the most recent historic peak and trough occurrences being occurrence one.
Wave Cycles
A wave cycle is here defined as a complete two-part move between a swing high and a swing low, or a swing low and a swing high. As can be seen in the example above, the first swing high or swing low will set the course for the sequence of wave cycles that follow; a chart that begins with a swing low will form its first complete wave cycle upon the formation of the first complete swing high and vice versa.
Wave Length
Wave length is here measured in terms of bar distance between the start and end of a wave cycle. For example, if the current wave cycle ends on a swing low the wave length will be the difference in bars between the current swing low and current swing high. In such a case, if the current swing low completes on candle 100 and the current swing high completed on candle 95, we would simply subtract 95 from 100 to give us a wave length of 5 bars.
Average wave length is here measured in terms of total bars as a proportion as total waves. The average wavelength is calculated by dividing the total candles by the total wave cycles.
Wave Height
Wave height is here measured in terms of current range. For example, if the current peak price is 100 and the current trough price is 80, the wave height will be 20.
Amplitude
Amplitude is here measured in terms of current range divided by two. For example if the current peak price is 100 and the current trough price is 80, the amplitude would be calculated by subtracting 80 from 100 and dividing the answer by 2 to give us an amplitude of 10.
Frequency
Frequency is here measured in terms of wave cycles per second (Hertz). For example, if the total wave cycle count is 10 and the amount of time it has taken to complete these 10 cycles is 1-year (31,536,000 seconds), the frequency would be calculated by dividing 10 by 31,536,000 to give us a frequency of 0.00000032 Hz.
Range
The range is simply the difference between the current peak and current trough prices, generally expressed in terms of points or pips.
█ FEATURES
Inputs
Show Sample Period
Start Date
End Date
Position
Text Size
Show Current
Show Lines
Table
The table is colour coded, consists of two columns and, as many as, nine rows. Blue cells display the total wave cycle count and average wave measurements. Green cells display the current wave measurements. And the final row in column one, coloured black, displays the sample period. Both current wave measurements and sample period cells can be hidden at the user’s discretion.
Lines
For a visual aid to the wave cycles, I have added a blue line that traces out the waves on the chart. These lines can be hidden at the user’s discretion.
█ HOW TO USE
The indicator is intended for research purposes, strategy development and strategy optimisation. I hope it will be useful in helping to gain a better understanding of the underlying dynamics at play on any given market and timeframe.
For example, the indicator can be used to compare the current range and frequency with the average range and frequency, which can be useful for gauging current market conditions versus historic and getting a feel for how different markets and timeframes behave.
█ LIMITATIONS
Some higher timeframe candles on tickers with larger lookbacks such as the DXY , do not actually contain all the open, high, low and close (OHLC) data at the beginning of the chart. Instead, they use the close price for open, high and low prices. So, while we can determine whether the close price is higher or lower than the preceding close price, there is no way of knowing what actually happened intra-bar for these candles. And by default candles that close at the same price as the open price, will be counted as green. You can avoid this problem by utilising the sample period filter.
The green and red candle calculations are based solely on differences between open and close prices, as such I have made no attempt to account for green candles that gap lower and close below the close price of the preceding candle, or red candles that gap higher and close above the close price of the preceding candle. I can only recommend using 24-hour markets, if and where possible, as there are far fewer gaps and, generally, more data to work with. Alternatively, you can replace the scenarios with your own logic to account for the gap anomalies, if you are feeling up to the challenge.
It is also worth noting that the sample size will be limited to your Trading View subscription plan. Premium users get 20,000 candles worth of data, pro+ and pro users get 10,000, and basic users get 5,000. If upgrading is currently not an option, you can always keep a rolling tally of the statistics in an excel spreadsheet or something of the like.
WD Gann: Vertical Lines for Predefined Days/Bars AgoThis Pine Script draws vertical lines on the chart at specific time intervals, inspired by WD Gann’s theories of time cycles . WD Gann, a famous trader, believed that market movements were influenced by predictable time cycles. This script enables traders to visualize these key time cycles on the chart by placing vertical lines at predefined intervals (in bars ago), helping to identify potential turning points in the market.
The time intervals used in this script are inspired by Gann’s work, as well as astrological and numerological principles , which many traders believe influence market behavior . You can customize which time intervals (such as 3, 7, 9, 21, etc.) you want to track by enabling or disabling specific vertical lines on the chart.
Key Features:
Time Cycles Based on Gann’s Theory: Draws vertical lines at significant time intervals such as 3, 7, 9, 21, 27 bars ago, which are commonly used by Gann traders.
Astrological & Numerological Significance: The predefined intervals also align with key numerological and astrological values, allowing for a broader perspective on market cycles.
Customizable Intervals: You can choose which time intervals to display by enabling or disabling checkboxes for each cycle, allowing flexibility in chart analysis.
Visual Labels: Each vertical line is labeled with its corresponding "bars ago" value, providing clear reference points for the selected time cycles.
What Users Can Do:
Track and analyze market movements based on time cycles that are significant to Gann’s theory, as well as numerological and astrological influences.
Enable or disable vertical lines for specific cycles, like the 3-bar cycle, 9-bar cycle, or 365-bar cycle, depending on the intervals that align with your trading strategy.
Combine with other technical analysis tools and Gann techniques (e.g., Gann Angles, Gann Fans, or Square of Nine) for a more comprehensive trading approach.
This tool is designed for traders who believe in the power of time cycles to influence market behavior, and is especially useful for predicting turning points or key price movements based on these cycles.
Financial Astrology Jupiter LongitudeJupiter energy influence the expansion, enthusiasm, joviality, optimism, devotion, administration and judgement. Is associated with people of nobility and good social position: ministers, bishops, religious leaders, judges, bankers, lawyers, merchants, influencers and so forth. This cycle is relevant for the industries of consumer goods, travel, publishing, higher education, banking, gambling and legal.
For most of the crypto-currencies is hard to analyse the impact of the Jupiter transit across different zodiac signs due to the emergent nature of this disrupting financial industry, many coins was launched in 2017 and have not experienced the complete Jupiter cycle. However, in BTCUSD we almost have a complete orbit and through the buy/sell frequency analysis we have observed the following patters: the bullish zodiac signs was Virgo, Libra, Capricorn and Aquarius, the bearish was Leo, and Scorpio. We was not able to obtain price data for the period when Jupiter transited Aries to Cancer so we are pending to analyze the trend direction during those zodiac positions.
This indicator provides Jupiter longitude since 2010 so will be limited to the analysis of 1 cycle, however we noted that the periods of retrogradation and stationary could give interesting trading signals. We encourage you to analyse this zodiac sign / speed phases cycles in different markets and share with us your observations, leave us a comment with your research outcomes. Happy research!
Note: The Jupiter tropical longitude indicator is based on an ephemeris array that covers years 2010 to 2030, prior or after this years the longitude is not available, this daily ephemeris are based on UTC time so in order to align properly with the price bars times you should set UTC as your chart reference timezone.
RSI cyclic smoothed v2Cyclic Smoothed Relative Strength Indicator
The cyclic smoothed RSI indicator is an enhancement of the classic RSI , adding
additional smoothing according to the market vibration,
adaptive upper and lower bands according to the cyclic memory and
using the current dominant cycle length as input for the indicator.
The cRSI is used like a standard indicator. The chart highlights trading signals where the signal line crosses above or below the adaptive lower/upper bands. It is much more responsive to market moves than the basic RSI.
You can also review this short idea where BTC went down from 4300 USD (3 Sept 17) to 3700 USD (15 Sept 17) after the idea was posted and showed the clear short exit with the next low:
The indicator uses the dominant cycle as input to optimize signal, smoothing and cyclic memory. To get more in-depth information on the cyclic-smoothed RSI indicator, please read Chapter 4 "Fine tuning technical indicators" of the book "Decoding the Hidden Market Rhythm, Part 1" available at your favorite book store.
This is the open-source code version of the requested script already published as protected indicator back in 2017 "RSI cyclic smoothed". Now made public as v2. Would love to receive feedback and see your ideas.
Financial Astrology Sun LongitudeFinancial astrology is a branch of mundane astrology that research the correlations of planet cycles with market prices, this indicator developed by the Financial Astrology Research Group provides the visualization of the Sun Tropical Zodiac Longitude to support that astrology traders can study multiple markets within the powerful Trading View UI to detect potential cyclical patterns in price action that are connected with the cosmic rhythm of the Sun.
The Sun have been very relevant cycle among all ancient civilizations such as Maya, Aztec, Inca, this cyclical move is the fundamental frequency of our life's due to the fact that our calendar year is a model from this cycle. Chinese astrologers and W.D. Gann was aware of the powerful predictive power of the solar terms which is a representation of the most relevant weather transitions within the Sun longitude path.
With this indicator we try to ease the research work of the amazing community of astro-traders that prior to this indicators needed to create hundreds of manual annotations on the markets price charts to visualize the Sun zodiac position within a long period of time in order to research potential cycles. That manual work is over. Let's move faster in our cycles research!
We encourage all traders using astrology to continue their research, please share your ideas of astro cycles trading strategies and contribute your experiments at our Github exploration projects: github.com
Note: The Sun longitude is based on an ephemeris array that covers years 2010 to 2030, prior or after this years the longitude is not available, this daily ephemeris are based on UTC time so in order to align properly with the price bars times you should set UTC as your chart reference timezone.
RS Cycles [QuantVue]The RS Cycles indicator is a technical analysis tool that expands upon traditional relative strength (RS) by incorporating Beta-based adjustments to provide deeper insights into a stock's performance relative to a benchmark index. It identifies and visualizes positive and negative performance cycles, helping traders analyze trends and make informed decisions.
Key Concepts:
Traditional Relative Strength (RS):
Definition: A popular method to compare the performance of a stock against a benchmark index (e.g., S&P 500).
Calculation: The traditional RS line is derived as the ratio of the stock's closing price to the benchmark's closing price.
RS=Stock Price/Benchmark Price
Usage: This straightforward comparison helps traders spot periods of outperformance or underperformance relative to the market or a specific sector.
Beta-Adjusted Relative Strength (Beta RS):
Concept: Traditional RS assumes equal volatility between the stock and benchmark, but Beta RS accounts for the stock's sensitivity to market movements.
Calculation:
Beta measures the stock's return relative to the benchmark's return, adjusted by their respective volatilities.
Alpha is then computed to reflect the stock's performance above or below what Beta predicts:
Alpha=Stock Return−(Benchmark Return×β)
Significance: Beta RS highlights whether a stock outperforms the benchmark beyond what its Beta would suggest, providing a more nuanced view of relative strength.
RS Cycles:
The indicator identifies positive cycles when conditions suggest sustained outperformance:
Short-term EMA (3) > Mid-term EMA (10) > Long-term EMA (50).
The EMAs are rising, indicating positive momentum.
RS line shows upward movement over a 3-period window.
EMA(21) > 0 confirms a broader uptrend.
Negative cycles are marked when the opposite conditions are met:
Short-term EMA (3) < Mid-term EMA (10) < Long-term EMA (50).
The EMAs are falling, indicating negative momentum.
RS line shows downward movement over a 3-period window.
EMA(21) < 0 confirms a broader downtrend.
This indicator combines the simplicity of traditional RS with the analytical depth of Beta RS, making highlighting true relative strength and weakness cycles.
Atlantean Bitcoin Weekly Market Condition - Top/Bottom BTC Overview:
The "Atlantean Bitcoin Weekly Market Condition Detector - Top/Bottom BTC" is a specialized TradingView indicator designed to identify significant turning points in the Bitcoin market on a weekly basis. By analyzing long-term and short-term moving averages across two distinct resolutions, this indicator provides traders with valuable insights into potential market bottoms and tops, as well as the initiation of bull markets.
Key Features:
Market Bottom Detection: The script uses a combination of a simple moving average (SMA) and an exponential moving average (EMA) calculated over long and short periods to identify potential market bottoms. When these conditions are met, the script signals a "Market Bottom" label on the chart, indicating a possible buying opportunity.
Bull Market Start Indicator: When the short-term EMA crosses above the long-term SMA, it signals the beginning of a bull market. This is marked by a "Bull Market Start" label on the chart, helping traders to prepare for potential market upswings.
Market Top Detection: The script identifies potential market tops by analyzing the crossunder of long and short-term moving averages. A "Market Top" label is plotted, suggesting a potential selling point.
Customizable Moving Averages Display: Users can choose to display the moving averages used for detecting market tops and bottoms, providing additional insights into market conditions.
How It Works: The indicator operates by monitoring the interactions between the specified moving averages:
Market Bottom: Detected when the long-term SMA (adjusted by a factor of 0.745) crosses over the short-term EMA.
Bull Market Start: Detected when the short-term EMA crosses above the long-term SMA.
Market Top: Detected when the long-term SMA (adjusted by a factor of 2) crosses under the short-term SMA.
These conditions are highlighted on the chart, allowing traders to visualize significant market events and make informed decisions.
Intended Use: This indicator is best used on weekly Bitcoin charts. It’s designed to provide long-term market insights rather than short-term trading signals. Traders can use this tool to identify strategic entry and exit points during major market cycles. The optional display of moving averages can further enhance understanding of market dynamics.
Originality and Utility: Unlike many other indicators, this script not only highlights traditional market tops and bottoms but also identifies the aggressive start of bull markets, offering a comprehensive view of market conditions. The unique combination of adjusted moving averages makes this script a valuable tool for long-term Bitcoin traders.
Disclaimer: The signals provided by this indicator are based on historical data and mathematical calculations. They do not guarantee future market performance. Traders should use this tool as part of a broader trading strategy and consider other factors before making trading decisions. Not financial advice.
Happy Trading!
By Atlantean
Elliptic bands
Why Elliptic?
Unlike traditional indicators (e.g., Bollinger Bands with constant standard deviation multiples), the elliptic model introduces a cyclical, non-linear variation in band width. This reflects the idea that price movements often follow rhythmic patterns, widening and narrowing in a predictable yet dynamic way, akin to natural market cycles.
Buy: When the price enters from below (green triangle).
Sell: When the price enters from above (red triangle).
Inputs
MA Length: 50 (This is the period for the central Simple Moving Average (SMA).)
Cycle Period: 50 (This is the elliptic cycle length.)
Volatility Multiplier: 2.0 (This value scales the band width.)
Mathematical Foundation
The indicator is based on the ellipse equation. The basic formula is:
Ellipse Equation:
(x^2) / (a^2) + (y^2) / (b^2) = 1
Solving for y:
y = b * sqrt(1 - (x^2) / (a^2))
Parameters Explained:
a: Set to 1 (normalized).
x: Varies from -1 to 1 over the period.
b: Calculated as:
ta.stdev(close, MA Length) * Volatility Multiplier
(This represents the standard deviation of the close prices over the MA period, scaled by the volatility multiplier.)
y (offset): Represents the band distance from the moving average, forming the elliptic cycle.
Behavior
Bands:
The bands are narrow at the cycle edges (when the offset is 0) and become widest at the midpoint (when the offset equals b).
Trend:
The central moving average (MA) shows the overall trend direction, while the bands adjust according to the volatility.
Signals:
Standard buy and sell signals are generated when the price interacts with the bands.
Practical Use
Trend Identification:
If the price is above the MA, it indicates an uptrend; if below, a downtrend.
Support and Resistance:
The elliptic bands act as dynamic support and resistance levels.
Narrowing bands may signal potential trend reversals.
Breakouts:
Physics CandlesPhysics Candles embed volume and motion physics directly onto price candles or market internals according to the cyclic pattern of financial securities. The indicator works on both real-time “ticks” and historical data using statistical modeling to highlight when these values, like volume or momentum, is unusual or relatively high for some periodic window in time. Each candle is made out of one or more sub-candles that each contain their own information of motion, which converts to the color and transparency, or brightness, of that particular candle segment. The segments extend throughout the entire candle, both body and wicks, and Thick Wicks can be implemented to see the color coding better. This candle segmentation allows you to see if all the volume or energy is evenly distributed throughout the candle or highly contained in one small portion of it, and how intense these values are compared to similar time periods without going to lower time frames. Candle segmentation can also change a trader’s perspective on how valuable the information is. A “low” volume candle, for instance, could signify high value short-term stopping volume if the volume is all concentrated in one segment.
The Candles are flexible. The physics information embedded on the candles need not be from the same price security or market internal as the chart when using the Physics Source option, and multiple Candles can be overlayed together. You could embed stock price Candles with market volume, market price Candles with stock momentum, market structure with internal acceleration, stock price with stock force, etc. My particular use case is scalping the SPX futures market (ES), whose price action is also dictated by the volume action in the associated cash market, or SPY, as well as a host of other securities. Physics allows you to embed the ES volume on the SPY price action, or the SPY volume on the ES price action, or you can combine them both by overlaying two Candle streams and increasing the Number of Overlays option to two. That option decreases the transparency levels of your coloring scheme so that overlaying multiple Candles converges toward the same visual color intensity as if you had one. The Candle and Physics Sources allows for both Symbols and Spreads to visualize Candle physics from a single ticker or some mathematical transformation of tickers.
Due to certain TradingView programming restrictions, each Candle can only be made out of a maximum of 8 candle segments, or an “8-bit” resolution. Since limits are just an opportunity to go beyond, the user has the option to stack multiple Candle indicators together to further increase the candle resolution. If you don’t want to see the Candles for some particular period of the day, you can hide them, or use the hiding feature to have multiple Candles calibrated to show multiple parts of the trading day. Securities tend to have low volume after hours with sharp spikes at the open or close. Multiple Candles can be used for multiple parts of the trading day to accommodate these different cycles in volume.
The Candles do not need be associated with the nominal security listed on the TV chart. The Candle Source allows the user to look at AAPL Candles, for instance, while on a TSLA or SPY chart, each with their respective volume actions integrated into the candles, for instance, to allow the user to see multiple security price and volume correlation on a single chart.
The physics information currently embeddable on Candles are volume or time, velocity, momentum, acceleration, force, and kinetic energy. In order to apply equations of motion containing a mass variable to financial securities, some analogous value for mass must be assumed. Traders often regard volume or time as inextricable variables to a securities price that can indicate the direction and strength of a move. Since mass is the inextricable variable to calculating the momentum, force, or kinetic energy of motion, the user has the option to assume either time or volume is analogous to mass. Volume may be a better option for mass as it is not strictly dependent on the speed of a security, whereas time is.
Data transformations and outlier statistics are used to color code the intensity of the physics for each candle segment relative to past periodic behavior. A million shares during pre-market or a million shares during noontime may be more intense signals than a typical million shares traded at the open, and should have more intense color signals. To account for a specific cyclic behavior in the market, the user can specify the Window and Cycle Time Frames. The Window Time Frame splits up a Cycle into windows, samples and aggregates the statistics for each window, then compares the current physics values against past values in the same window. Intraday traders may benefit from using a Daily Cycle with a 30-minute Window Time Frame and 1-minute Sample Time Frame. These settings sample and compare the physics of 1-minute candles within the current 30-minute window to the same 30-minute window statistics for all past trading days, up until the data limit imposed by TradingView, or until the Data Collection Start Date specified in the settings. Longer-term traders may benefit from using a Monthly Cycle with a Weekly Time Frame, or a Yearly Cycle with a Quarterly Time Frame.
Multiple statistics and data transformation methods are available to convey relative intensity in different ways for different trading signals. Physics Candles allows for both Normal and Log-Normal assumptions in the physics distribution. The data can then be transformed by Linear, Logarithmic, Z-Score, or Power-Law scoring, where scoring simply assigns an intensity to the relative physics value of each candle segment based on some mathematical transformation. Z-scoring often renders adequate detection by scoring the segment value, such as volume or momentum, according to the mean and standard deviation of the data set in each window of the cycle. Logarithmic or power-law transformation with a gamma below 1 decreases the disparity between intensities so more less-important signals will show up, whereas the power-law transformation with gamma values above 1 increases the disparity between intensities, so less more-important signals will show up. These scores are then converted to color and transparency between the Min Score and the Max Score Cutoffs. The Auto-Normalization feature can automatically pick these cutoffs specific to each window based on the mean and standard deviation of the data set, or the user can manually set them. Physics was developed with novices in mind so that most users could calibrate their own settings by plotting the candle segment distributions directly on the chart and fiddling with the settings to see how different cutoffs capture different portions of the distribution and affect the relative color intensities differently. Security distributions are often skewed with fat-tails, known as kurtosis, where high-volume segments for example, have a higher-probabilities than expected for a normal distribution. These distribution are really log-normal, so that taking the logarithm leads to a standard bell-shaped distribution. Taking the Z-score of the Log-Normal distribution could make the most statistical sense, but color sensitivity is a discretionary preference.
Background Philosophy
This indicator was developed to study and trade the physics of motion in financial securities from a visually intuitive perspective. Newton’s laws of motion are loosely applied to financial motion:
“A body remains at rest, or in motion at a constant speed in a straight line, unless acted upon by a force”.
Financial securities remain at rest, or in motion at constant speed up or down, unless acted upon by the force of traders exchanging securities.
“When a body is acted upon by a force, the time rate of change of its momentum equals the force”.
Momentum is the product of mass and velocity, and force is the product of mass and acceleration. Traders render force on the security through the mass of their trading activity and the acceleration of price movement.
“If two bodies exert forces on each other, these forces have the same magnitude but opposite directions.”
Force arises from the interaction of traders, buyers and sellers. One body of motion, traders’ capitalization, exerts an equal and opposite force on another body of motion, the financial security. A securities movement arises at the expense of a buyer or seller’s capitalization.
Volume
The premise of this indicator assumes that volume, v, is an analogous means of measuring physical mass, m. This premise allows the application of the equations of motion to the movement of financial securities. We know from E=mc^2 that mass has energy. Energy can be used to create motion as kinetic energy. Taking a simple hypothetical example, the interaction of one short seller looking to cover lower and one buyer looking to sell higher exchange shares in a security at an agreed upon price to create volume or mass, and therefore, potential energy. Eventually the short seller will actively cover and buy the security from the previous buyer, moving the security higher, or the buyer will actively sell to the short seller, moving the security lower. The potential energy inherent in the initial consolidation or trading activity between buy and seller is now converted to kinetic energy on the subsequent trading activity that moves the securities price. The more potential energy that is created in the consolidation, the more kinetic energy there is to move price. This is why point and figure traders are said to give price targets based on the level of volatility or size of a consolidation range, or why Gann traders square price and time, as time is roughly proportional to mass and trading activity. The build-up of potential energy between short sellers and buyers in GME or TSLA led to their explosive moves beyond their standard fundamental valuations.
Position
Position, p, is simply the price or value of a financial security or market internal.
Time
Time, t, is another means of measuring mass to discover price behavior beyond the time snapshots that simple candle charts provide. We know from E=mc^2 that time is related to rest mass and energy given the speed of light, c, where time ≈ distance * sqrt(mass/E). This relation can also be derived from F=ma. The more mass there is, the longer it takes to compute the physics of a system. The more energy there is, the shorter it takes to compute the physics of a system. Similarly, more time is required to build a “resting” low-volatility trading consolidation with more mass. More energy added to that trading consolidation by competing buyers and sellers decreases the time it takes to build that same mass. Time is also related to price through velocity.
Velocity = (p(t1) – p(t0)) / p(t0)
Velocity, v, is the relative percent change of a securities price, p, over a period of time, t0 to t1. The period of time is between subsequent candles, and since time is constant between candles within the same timeframe, it is not used to calculate velocity or acceleration. Price moves faster with higher velocity, and slower with slower velocity, over the same fixed period of time. The product of velocity and mass gives momentum.
Momentum = mv
This indicator uses physics definition of momentum, not finance’s. In finance, momentum is defined as the amount of change in a securities price, either relative or absolute. This is definition is unfortunate, pun intended, since a one dollar move in a security from a thousand shares traded between a few traders has the exact same “momentum” as a one dollar move from millions of shares traded between hundreds of traders with everything else equal. If momentum is related to the energy of the move, momentum should consider both the level of activity in a price move, and the amount of that price move. If we equate mass to volume to account for the level of trading activity and use physics definition of momentum as the product of mass and velocity, this revised definition now gives a thousand-times more momentum to a one-dollar price move that has a thousand-times more volume behind it. If you want to use finance’s volume-less definition of momentum, use velocity in this indicator.
Acceleration = v(t1) – v(t0)
Acceleration, a, is the difference between velocities over some period of time, t0 to t1. Positive acceleration is necessary to increase a securities speed in the positive direction, while negative acceleration is necessary to decrease it. Acceleration is related to force by mass.
Force = ma
Force is required to change the speed of a securities valuation. Price movements with considerable force have considerably more impact on future direction. A change in direction requires force.
Kinetic Energy = 0.5mv^2
Kinetic energy is the energy that a financial security gains from the change in its velocity by force. The built-up of potential energy in trading consolidations can be converted to kinetic energy on a breakout from the consolidation.
Cycle Theory and Relativity
Just as the physics of motion is relative to a point of reference, so too should the physics of financial securities be relative to a point of reference. An object moving at a 100 mph towards another object moving in the same direction at 100 mph will not appear to be moving relative to each other, nor will they collide, but from an outsider observer, the objects are going 100 mph and will collide with significant impact if they run into a stationary object relative to the observer. Similarly, trading with a hundred thousand shares at the open when the average volume is a couple million may have a much smaller impact on the price compared to trading a hundred thousand shares pre-market when the average volume is ten thousand shares. The point of reference used in this indicator is the average statistics collected for a given Window Time Frame for every Cycle Time Frame. The physics values are normalized relative to these statistics.
Examples
The main chart of this publication shows the Force Candles for the SPY. An intense force candle is observed pre-market that implicates the directional overtone of the day. The assumption that direction should follow force arises from physical observation. If a large object is accelerating intensely in a particular direction, it may be fair to assume that the object continues its direction for the time being unless acted upon by another force.
The second example shows a similar Force Candle for the SPY that counters the assumption made in the first example and emphasizes the importance of both motion and context. While it’s fair to assume that a heavy highly accelerating object should continue its course, if that object runs into an obstacle, say a brick wall, it’s course may deviate. This example shows SPY running into the 50% retracement wall from the low of Mar 2020, a significant support level noted in literature. The example also conveys Gann’s idea of “lost motion”, where the SPY penetrated the 50% price but did not break through it. A brick wall is not one atom thick and price support is not one tick thick. An object can penetrate only one layer of a wall and not go through it.
The third example shows how Volume Candles can be used to identify scalping opportunities on the SPY and conveys why price behavior is as important as motion and context. It doesn’t take a brick wall to impede direction if you know that the person driving the car tends to forget to feed the cats before they leave. In the chart below, the SPY breaks down to a confluence of the 5-day SMA, 20-day SMA, and an important daily trendline (not shown) after the bullish bounce from the 50% retracement days earlier. High volume candles on the SMA signify stopping volume that reverse price direction. The character of the day changes. Bulls become more aggressive than bears with higher volume on upswings and resistance, whiles bears take on a defensive position with lower volume on downswings and support. High volume stopping candles are seen after rallies, and can tell you when to take profit, get out of a position, or go short. The character change can indicate that its relatively safe to re-enter bullish positions on many major supports, especially given the overarching bullish theme from the large reaction off the 50% retracement level.
The last example emphasizes the importance of relativity. The Volume Candles in the chart below are brightest pre-market even though the open has much higher volume since the pre-market activity is much higher compared to past pre-markets than the open is compared to past opens. Pre-market behavior is a good indicator for the character of the day. These bullish Volume Candles are some of the brightest seen since the bounce off the 50% retracement and indicates that bulls are making a relatively greater attempt to bring the SPY higher at the start of the day.
Infrequently Asked Questions
Where do I start?
The default settings are what I use to scalp the SPY throughout most of the extended trading day, on a one-minute chart using SPY volume. I also overlay another Candle set containing ES future volume on the SPY price structure by setting the Physics Source to ES1! and the Number of Overlays setting to 2 for each Candle stream in order to account for pre- and post-market trading activity better. Since the closing volume is exponential-like up until the end of the regular trading day, adding additional Candle streams with a tighter Window Time Frame (e.g., 2-5 minute) in the last 15 minutes of trading can be beneficial. The Hide feature can allow you to set certain intraday timeframes to hide one Candle set in order to show another Candle set during that time.
How crazy can you get with this indicator?
I hope you can answer this question better. One interesting use case is embedding the velocity of market volume onto an internal market structure. The PCTABOVEVWAP.US is a market statistic that indicates the percent of securities above their VWAP among US stocks and is helpful for determining short term trends in the US market. When securities are rising above their VWAP, the average long is up on the day and a rising PCTABOVEVWAP.US can be viewed as more bullish. When securities are falling below their VWAP, the average short is up on the day and a falling PCTABOVEVWAP.US can be viewed as more bearish. (UPVOL.US - DNVOL.US) / TVOL.US is a “spread” symbol, in TV parlance, that indicates the decimal percent difference between advancing volume and declining volume in the US market, showing the relative flow of volume between stocks that are up on the day, and stocks that are down on the day. Setting PCTABOVEVWAP.US in the Candle Source, (UPVOL.US - DNVOL.US) / TVOL.US in the Physics Source, and selecting the Physics to Velocity will embed the relative velocity of the spread symbol onto the PCTABOVEVWAP.US candles. This can be helpful in seeing short term trends in the US market that have an increasing amount of volume behind them compared to other trends. The chart below shows Volume Candles (top) and these Spread Candles (bottom). The first top at 9:30 and second top at 10:30, the high of the day, break down when the spread candles light up, showing a high velocity volume transfer from up stocks to down stocks.
How do I plot the indicator distribution and why should I even care?
The distribution is visually helpful in seeing how different normalization settings effect the distribution of candle segments. It is also helpful in seeing what physics intensities you want to ignore or show by segmenting part of the distribution within the Min and Max Cutoff values. The intensity of color is proportional to the physics value between the Min and Max Cutoff values, which correspond to the Min and Max Colors in your color scheme. Any physics value outside these Min and Max Cutoffs will be the same as the Min and Max Colors.
Select the Print Windows feature to show the window numbers according to the Cycle Time Frame and Window Time Frame settings. The window numbers are labeled at the start of each window and are candle width in size, so you may need to zoom into to see them. Selecting the Plot Window feature and input the window number of interest to shows the distribution of physics values for that particular window along with some statistics.
A log-normal volume distribution of segmented z-scores is shown below for 30-minute opening of the SPY. The Min and Max Cutoff at the top of the graph contain the part of the distribution whose intensities will be linearly color-coded between the Min and Max Colors of the color scheme. The part of the distribution below the Min Cutoff will be treated as lowest quality signals and set to the Min Color, while the few segments above the Max Cutoff will be treated as the highest quality signals and set to the Max Color.
What do I do if I don’t see anything?
Troubleshooting issues with this indicator can involve checking for error messages shown near the indicator name on the chart or using the Data Validation section to evaluate the statistics and normalization cutoffs. For example, if the Plot Window number is set to a window number that doesn’t exist, an error message will tell you and you won’t see any candles. You can use the Print Windows option to show windows that do exist for you current settings. The auto-normalization cutoff values may be inappropriate for your particular use case and literally cut the candles out of the chart. Try changing the chart time frame to see if they are appropriate for your cycle, sample and window time frames. If you get a “Timeframe passed to the request.security_lower_tf() function must be lower than the timeframe of the main chart” error, this means that the chart timeframe should be increased above the sample time frame. If you get a “Symbol resolve error”, ensure that you have correct symbol or spread in the Candle or Physics Source.
How do I see a relative physics values without cycles?
Set the Window Time Frame to be equal to the Cycle Time Frame. This will aggregate all the statistics into one bucket and show the physics values, such as volume, relative to all the past volumes that TV will allow.
How do I see candles without segmentation?
Segmentation can be very helpful in one context or annoying in another. Segmentation can be removed by setting the candle resolution value to 1.
Notes
I have yet to find a trading platform that consistently provides accurate real-time volume and pricing information, lacking adequate end-user data validation or quality control. I can provide plenty of examples of real-time volume counts or prices provided by TradingView and other platforms that were significantly off from what they should have been when comparing against the exchanges own data, and later retroactively corrected or not corrected at all. Since no indicator can work accurately with inaccurate data, please use at your own discretion.
The first version is a beta version. Debugging and validating code in Pine script is difficult without proper unit testing. Please report any bugs with enough information to reproduce them and indicate why they are important. I also encourage you to export the data from TradingView and verify the calculations for your particular use case.
The indicator works on real-time updates that occur at a higher frequency than the candle time frame, which TV incorrectly refers to as ticks. They use this terminology inaccurately as updates are really aggregated tick data that can take place at different prices and may not accurately reflect the real tick price action. Consequently, this inaccuracy also impacts the real-time segmentation accuracy to some degree. TV does not provide a means of retaining “tick” information, so the higher granularity of information seen real-time will be lost on a disconnect.
TV does not provide time and sales information. The volume and price information collected using the Sample Time Frame is intraday, which provides only part of the picture. Intraday volume is generally 50 to 80% of the end of day volume. Consequently, the daily+ OHLC prices are intraday, and may differ significantly from exchanged settled OHLC prices.
The Cycle and Window Time Frames refer to calendar days and time, not trading days or time. For example, the first window week of a monthly cycle is the first seven days of the month, not the first Monday through Friday of trading for the month.
Chart Time Frames that are higher than the Window Time Frames average the normalized physics for price action that occurred within a given Candle segment. It does not average price action that did not occur.
One of the main performance bottleneck in TradingView’s Pine Script is client-side drawing and plotting. The performance of this indicator can be increased by lowering the resolution (the number of sub-candles this indicator plots), getting a faster computer, or increasing the performance of your computer like plugging your laptop in and eliminating unnecessary processes.
The statistical integrity of this indicator relies on the number of samples collected per sample window in a given cycle. Higher sample counts can be obtained by increasing the chart time frame or upgrading the TradingView plan for a higher bar count. While increasing the chart time frame doesn’t increase the visual number of bars plotted on the chart, it does increase the number of bars that can be pulled at a lower time frame, up to 100,000.
Due to a limitation in Pine Scripts request_lower_tf() function, using a spread symbol will only work for regular trading hours, not extended trading hours.
Ideally, velocity or momentum should be calculated between candle closes. To eliminate the need to deal with price gaps that would lead to an incorrect statistical distributions, momentum is calculated between candle open and closes as a percent change of the price or value, which should not be an issue for most liquid securities.
Fast Fourier Transform (FFT) FilterDear friends!
I'm happy to present an implementation of the Fast Fourier Transform (FFT) algorithm. The script uses the FFT procedure to decompose the input time series into its cyclical constituents, in other words, its frequency components , and convert it back to the time domain with modified frequency content, that is, to filter it.
Input Description and Usage
Source and Length :
Indicates where the data comes from and the size of the lookback window used to build the dataset.
Standardize Input Dataset :
If enabled, the dataset is preprocessed by subtracting its mean and normalizing the result by the standard deviation, which is sometimes useful when analyzing seasonalities. This procedure is not recommended when using the FFT filter for smoothing (see below), as it will not preserve the average of the dataset.
Show Frequency-Domain Power Spectrum :
When enabled, the results of Fourier analysis (for the last price bar!) are plotted as a frequency-domain power spectrum , where “power” is a measure of the significance of the component in the dataset. In the spectrum, lower frequencies (longer cycles) are on the right, higher frequencies are on the left. The graph does not display the 0th component, which contains only information about the mean value. Frequency components that are allowed to pass through the filter (see below) are highlighted in magenta .
Dominant Cycles, Rows :
If this option is activated, the periods and relative powers of several dominant cyclical components that is, those that have a higher power, are listed in the table. The number of the component in the power spectrum (N) is shown in the first column. The number of rows in the table is defined by the user.
Show Inverse Fourier Transform (Filtered) :
When enabled, the reconstructed and filtered time-domain dataset (for the last price bar!) is displayed.
Apply FFT Filter in a Moving Window :
When enabled, the FFT filter with the same parameters is applied to each bar. The last data point of the reconstructed and filtered dataset is used to build a new time series. For example, by getting rid of high-frequency noise, the FFT filter can make the data smoother. By removing slowly evolving low-frequency components (including non-periodic constituents), one can reveal and analyze shorter cycles. Since filtering is done in real-time in a moving window (similar to the moving average), the modified data can potentially be used as part of a strategy and be subjected to other technical indicators.
Lowest Allowed N :
Indicates the number of the lowest frequency component used in the reconstructed time series.
Highest Allowed N :
Indicates the number of the highest frequency component used in the reconstructed time series.
Filtering Time Range block:
Specifies the time range over which real-time FFT filtering is applied. The reason for the presence of this block is that the FFT procedure is relatively computationally intensive. Therefore, the script execution may encounter the time limit imposed by TradingView when all historical bars are processed.
As always, I look forward to your feedback!
Also, leave a comment if you'd be interested in the tutorial on how to use this tool and/or in seeing the FFT filter in a strategy.
MVRV Ratio [Alpha Extract]The MVRV Ratio Indicator provides valuable insights into Bitcoin market cycles by tracking the relationship between market value and realized value. This powerful on-chain metric helps traders identify potential market tops and bottoms, offering clear buy and sell signals based on historical patterns of Bitcoin valuation.
🔶 CALCULATION The indicator processes MVRV ratio data through several analytical methods:
Raw MVRV Data: Collects MVRV data directly from INTOTHEBLOCK for Bitcoin
Optional Smoothing: Applies simple moving average (SMA) to reduce noise
Status Classification: Categorizes market conditions into four distinct states
Signal Generation: Produces trading signals based on MVRV thresholds
Price Estimation: Calculates estimated realized price (Current price / MVRV ratio)
Historical Context: Compares current values to historical extremes
Formula:
MVRV Ratio = Market Value / Realized Value
Smoothed MVRV = SMA(MVRV Ratio, Smoothing Length)
Estimated Realized Price = Current Price / MVRV Ratio
Distance to Top = ((3.5 / MVRV Ratio) - 1) * 100
Distance to Bottom = ((MVRV Ratio / 0.8) - 1) * 100
🔶 DETAILS Visual Features:
MVRV Plot: Color-coded line showing current MVRV value (red for overvalued, orange for moderately overvalued, blue for fair value, teal for undervalued)
Reference Levels: Horizontal lines indicating key MVRV thresholds (3.5, 2.5, 1.0, 0.8)
Zone Highlighting: Background color changes to highlight extreme market conditions (red for potentially overvalued, blue for potentially undervalued)
Information Table: Comprehensive dashboard showing current MVRV value, market status, trading signal, price information, and historical context
Interpretation:
MVRV ≥ 3.5: Potential market top, strong sell signal
MVRV ≥ 2.5: Overvalued market, consider selling
MVRV 1.5-2.5: Neutral market conditions
MVRV 1.0-1.5: Fair value, consider buying
MVRV < 1.0: Potential market bottom, strong buy signal
🔶 EXAMPLES
Market Top Identification: When MVRV ratio exceeds 3.5, the indicator signals potential market tops, highlighting periods where Bitcoin may be significantly overvalued.
Example: During bull market peaks, MVRV exceeding 3.5 has historically preceded major corrections, helping traders time their exits.
Bottom Detection: MVRV values below 1.0, especially approaching 0.8, have historically marked excellent buying opportunities.
Example: During bear market bottoms, MVRV falling below 1.0 has identified the most profitable entry points for long-term Bitcoin accumulation.
Tracking Market Cycles: The indicator provides a clear visualization of Bitcoin's market cycles from undervalued to overvalued states.
Example: Following the progression of MVRV from below 1.0 through fair value and eventually to overvalued territory helps traders position themselves appropriately throughout Bitcoin's market cycle.
Realized Price Support: The estimated realized price often acts as a significant
support/resistance level during market transitions.
Example: During corrections, price often finds support near the realized price level calculated by the indicator, providing potential entry points.
🔶 SETTINGS
Customization Options:
Smoothing: Toggle smoothing option and adjust smoothing length (1-50)
Table Display: Show/hide the information table
Table Position: Choose between top right, top left, bottom right, or bottom left positions
Visual Elements: All plots, lines, and background highlights can be customized for color and style
The MVRV Ratio Indicator provides traders with a powerful on-chain metric to identify potential market tops and bottoms in Bitcoin. By tracking the relationship between market value and realized value, this indicator helps identify periods of overvaluation and undervaluation, offering clear buy and sell signals based on historical patterns. The comprehensive information table delivers valuable context about current market conditions, helping traders make more informed decisions about market positioning throughout Bitcoin's cyclical patterns.
VIDYA with Dynamic Length Based on ICPThis script is a Pine Script-based indicator that combines two key concepts: the Instantaneous Cycle Period (ICP) from Dr. John Ehlers and the Variable Index Dynamic Average (VIDYA). Here's an overview of how the script works:
Components:
Instantaneous Cycle Period (ICP):
This part of the indicator uses Dr. John Ehlers' approach to detect the market cycle length dynamically. It calculates the phase of price movement by computing the in-phase and quadrature components of the price detrended over a specific period.
The ICP helps adjust the smoothing length dynamically, giving a real-time estimate of the dominant cycle in price action. The script uses a phase calculation, adjusts it for cycle dynamics, and smoothes it for more reliable readings.
VIDYA (Variable Index Dynamic Average):
VIDYA is a moving average that dynamically adjusts its smoothing length based on the market conditions, in this case, using the RSI (Relative Strength Index) as a weight.
The length of VIDYA is determined by the dynamically calculated ICP, allowing it to adapt to changing market cycles.
This indicator performs several recursive layers of VIDYA smoothing (applying VIDYA multiple times) to provide a more refined result.
Key Features:
Dynamic Length: The length for the VIDYA calculation is derived from the smoothed ICP value, meaning that the smoothing adapts to the detected cycle length in real-time, making the indicator more responsive to market conditions.
Multiple VIDYA Layers: The script applies multiple layers of VIDYA smoothing (up to 5 iterations), further refining the output to smooth out market noise while maintaining responsiveness.
Plotting: The final smoothed VIDYA value and the smoothed ICP length are plotted. Additionally, overbought (70) and oversold (30) horizontal lines are provided for visual reference.
Application:
This indicator helps identify trends, smooths out price data, and adapts dynamically to market cycles. It's useful for detecting shifts in momentum and trends, and traders can use it to identify overbought or oversold conditions based on dynamically calculated thresholds.
Intellect_city - World Cycle - Ath - Timeframe 1D and 1WIndicator Overview
The Pi Cycle Top Indicator has historically been effective in picking out the timing of market cycle highs within 3 days.
It uses the 111 day moving average (111DMA) and a newly created multiple of the 350 day moving average, the 350DMA x 2.
Note: The multiple is of the price values of the 350DMA, not the number of days.
For the past three market cycles, when the 111DMA moves up and crosses the 350DMA x 2 we see that it coincides with the price of Bitcoin peaking.
It is also interesting to note that 350 / 111 is 3.153, which is very close to Pi = 3.142. In fact, it is the closest we can get to Pi when dividing 350 by another whole number.
It once again demonstrates the cyclical nature of Bitcoin price action over long time frames. However, in this instance, it does so with a high degree of accuracy over Bitcoin's adoption phase of growth.
Bitcoin Price Prediction Using This Tool
The Pi Cycle Top Indicator forecasts the cycle top of Bitcoin’s market cycles. It attempts to predict the point where Bitcoin price will peak before pulling back. It does this on major high time frames and has picked the absolute tops of Bitcoin’s major price moves throughout most of its history.
How It Can Be Used
Pi Cycle Top is useful to indicate when the market is very overheated. So overheated that the shorter-term moving average, which is the 111-day moving average, has reached an x2 multiple of the 350-day moving average. Historically, it has proved advantageous to sell Bitcoin around this time in Bitcoin's price cycles.
It is also worth noting that this indicator has worked during Bitcoin's adoption growth phase, the first 15 years or so of Bitcoin's life. With the launch of Bitcoin ETF's and Bitcoin's increased integration into the global financial system, this indicator may cease to be relevant at some point in this new market structure.
Gherkinit Futures Cycle█ OVERVIEW
Presented here is code for the " NYSE:GME Futures cycle theory" originally conceived by Gherkinit (Pi-Fi) and his quantitative analysts which is still under peer review.
This theory was built upon the knowledge that many intelligent investors on Reddit accrued over the past year in regards to the Mother Of All Short Squeezes this stock has to offer.
Up until now, what happened in January 2021 was considered an anomaly brought on by FOMO and retail interest but it's starting to look like unfair market makers and similar went to cover and ran head on into retail FOMO which is why they cut off the buying at that time. In order to understand what happened and what's to come, visualizing the theory with ease is essential.
█ WHAT THE SETTINGS MEAN
- Enable Draw | Visual Clean up
(True/False) Quarterly dates : Enables or disables the quarterly dates that repeat every "cycle".
(True/False) Roll dates : Enables or disables the roll dates that repeat every "cycle".
(True/False) Expiration dates : Enables or disables the expiration dates that repeat every "cycle".
(True/False) Run dates : Enables or disables the run dates that repeat every "cycle".
- Date Colors | Making things look good
(Color) Quarterly : Color for the respective date.
(Color) Roll : Color for the respective date.
(Color) Expiration : Color for the respective date.
(Color) Run : Color for the respective date.
- Extended Cycle | Look into the future
(Integer) Extended line height multiplier : A multiplier value for the height of the lines representing the selected "future" cycle.
(Dollar Amount) Extended line height : The height value in dollars of the lines representing the selected "future" cycle.
(Integer) Extended line width : The width of the lines representing the selected "future" cycle.
(Integer) Extended cycle ID : The cycle you want to see "ahead" or in the "future". For example if you set the value to "0" you'll only see cycles from the past up until the present (already occurred). If you set the value to "1" you will see the estimated dates for the specific cycle in the future i.e. 1 cycle ahead of the last completed/visible cycle on the chart.
█ EXTRA INFO
This indicator was simply made by a bored CS student who didn't want to endlessly mark dates on a graph after learning more about the theory.
Hope this help whoever uses this. To the moon fellow apes!
- Winter ;)
P.s. Pickle 4 Life
