Swing Oracle Stock// (\_/)
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📌 Swing Oracle Stock – Professional Cycle & Trend Detection Indicator
The Swing Oracle Stock is an advanced market analysis tool designed to highlight price cycles, trend shifts, and key trading zones with precision. It combines trendline dynamics, normalized oscillators, and multi-timeframe confirmation into a single comprehensive indicator.
🔑 Key Features
NDOS (Normalized Dynamic Oscillator System):
Measures price strength relative to recent highs and lows to detect overbought, neutral, and oversold zones.
Dynamic Trendline (EMA8 or SMA231):
Flexible source selection for adapting to different trading styles (scalping vs. swing).
Multi-Timeframe H1 Confirmation:
Adds higher-timeframe validation to improve signal reliability.
Automated Buy & Sell Signals:
Triggered only on significant crossovers above/below defined levels.
Weekly Cycles (7-day M5 projection):
Tracks recurring time-based market cycles to anticipate reversal points.
Intuitive Visualization:
Colored zones (high, low, neutral) for quick market context.
Optional background and candlestick coloring for better clarity.
Multi-Timeframe Cross Table:
Automatically compares SMA50 vs. EMA200 across multiple timeframes (1m → 4h), showing clear status:
⭐️⬆️ UP = bullish trend confirmation
💀⬇️ Drop = bearish trend confirmation
📊 Built-in Statistical Tools
Normalized difference between short and long EMA.
Projected normalized mean levels plotted directly on the main chart.
Dynamic analysis of price distance from SMA50 to capture market “waves.”
🎯 Use Cases
Spot trend reversals with multi-timeframe confirmation.
Identify powerful breakout and breakdown zones.
Time entries and exits based on trend + cycle confluence.
Enhance market timing for swing trades, scalps, or long-term positions.
⚡ Swing Oracle Stock brings together cycle detection, oscillator normalization, and multi-timeframe confirmation into one streamlined indicator for traders who want a professional edge.
Search in scripts for "Cycle"
bands ⚡ What This Script Does
This is a structured trading system specifically designed for navigating Bitcoin cycles and identifying higher-probability buy setups.
It is not a simple combination of public indicators instead, it applies a rules-based logic to adapt signals dynamically depending on the current market phase (bull/bear), while also using a triple confirmation framework (macro trend + volatility bands + buy signals).
This approach aims to reduce false signals and align trading decisions with Bitcoin’s well-known cyclical behavior.
⚡ Core Concept & Components
The system combines three complementary elements:
A macro trend filter band (red/green), shown at the bottom of the chart, representing Bitcoin’s macro trend environment.
Adaptive volatility bands using advanced smoothing techniques including HMA, KAMA, WVMA, combined with moving average (MA) and average true range (ATR) logic to capture dynamic “cheap” and “expensive” price zones. These bands adapt to Bitcoin’s volatility structure better than standard Bollinger Bands or SMA plus ATR setups.
Multi-timeframe RSI-based Buy Signals on 8h, 1D, 1W, and 1M timeframes historically calibrated for Bitcoin cycles.
These components work together through a rules-based process, dynamically adapting signal validity depending on the macro trend state.
⚡ Signal System and how to use
The red and green band at the bottom of the chart represents Bitcoin’s macro trend environment:
Light Green → Likely start of a bull market
Dark Green → Market is bullish but becoming extended; potentially nearing a local top
Red → Bear market conditions
Our trading approach uses four distinct BUY signals, depending on the market phase:
Red (8h) → weak buy signal
Yellow (1D) → medium buy signal
Green (1W) → strong buy signal
Blue (1M) → strongest buy signal
However, Day Trading and Swing Trading signals are automatically blocked during bear markets (Red Band).
Reason: low timeframe signals (1 minute to 1 day) tend to perform poorly in bear markets, as major bottoms typically form on higher timeframes (1 week or 1 month).
Therefore, during Red Band conditions, only Buy Bear Market and Buy Recession signals remain active.
to use it correctly you must go to configuration of the indicator, section input and enable 4 buy signals and check every day timefarme 8h 1d 1w and 1m.
⚡ Invalidation Conditions
To exit the bear market, the system includes an invalidation condition:
If the price closes above a specific SMA on a defined timeframe, the Red Band switches to Green → signaling a potential market recovery.
Additionally, for the Red Band to activate initially, the system requires that:
Price must break below a specific Hull Moving Average (HMA) on a defined timeframe and length.
⚡ Why These 3 Indicators Work Well Together
If the band is green (bull market conditions) and a Buy signal (any color) appears → it is generally safer to buy in a bull market than in a bear market.
(I’m trying to apply the famous phrase "the trend is your friend" in this trading indicator and trading strategy.)
If the price is also touching the lower green band, and a Buy signal appears → the buy becomes even more reliable, as you are combining big trend plus band support plus signal confirmation.
This gives you triple confirmation:
Band color plus band level touch plus Buy signal → increasing the probability that the trade is going to work.
By combining:
Blocking low timeframe signals during bear markets
Using a clear invalidation point to detect recovery
Requiring a structural break via HMA to enter a bear market phase, and requiring a break above a specific SMA (length and timeframe) to enter a bull market
Applying a triple confirmation logic when conditions are favorable
→ this framework helps you navigate Bitcoin markets more securely and profitably than using unfiltered signals alone.
⚡ Why It’s Not a Simple Mashup
The logic of the system is not just an overlay of RSI, moving averages and bands:
It applies a structured "state machine" logic:
Macro Band determines which signals are allowed.
Band-level touches condition the strength of signals.
Triple confirmation (macro trend plus band level plus signal) governs high-probability setups.
Invalidation points (SMA breakouts) dynamically switch macro state, ensuring no lagging bull signals in a bear market or vice versa.
This makes the system superior to using public domain components in isolation, as those do not provide dynamic signal filtering nor respect Bitcoin’s macro cyclicality explicitly.
⚡ Why This is Invite-Only
This script reflects deep backtesting and original integration of state logic specific to Bitcoin cycles, I also tried to choose the the correct conditions and invalidation points by using hma and smas in specific timeframes and lengths,
it has a system that also block many wrong buy signals during bearmarkets.
It encapsulates a rules-based trading process which goes beyond simply combining public indicators. The aim is to provide traders with a coherent framework that reduces false signals, adapts to bitcoin cycles, and promotes risk-aware participation in Bitcoin markets.
I also refined the line aesthetics and thicknesses to improve chart readability and help users quickly identify key levels.
⚡ Disclaimer
This is an analytical tool, not financial advice. Use with appropriate risk management and as part of a broader trading strategy.
Past positive results this indicator achieved do NOT guarantee future success !!
Per TradingView rules:
The logic is described sufficiently so that traders understand what it does and how it works.
This is not a simple mashup, but an original framework applying structured logic to Bitcoin macro trading.
This is a COMPLEMENTARY tool designed for use by my existing clients who are already familiar with my trading strategy and risk management approach. If you are not one of my clients or do not know my trading strategy, please do NOT request access or attempt to purchase it !!
⚡ Conformance
This description is written to comply with TradingView’s script publishing rules (tradingview.com/pine-script-docs/en/v5/writing/Publishing.html), as per recent moderator feedback.
If further clarification is required, I welcome additional feedback.
BTC Growth | AlchimistOfCrypto🌈 BTC Regression Bands & Halvings – Unveiling Bitcoin's Logarithmic Growth Fields 🌈
"The Bitcoin Regression Bands, engineered through advanced logarithmic mathematics, visualizes the probabilistic distribution of Bitcoin's price evolution within a multi-cycle growth paradigm. This indicator employs principles from hyperbolic regression where decay coefficients create mathematical boundaries that define Bitcoin's long-term value progression. Our implementation features algorithmically enhanced rainbow visualization derived from extensive cycle analysis, creating a dynamic representation of Bitcoin's logarithmic growth with adaptive color gradients that highlight critical halving-based phase transitions in the asset's monetary evolution."
📊 Professional Trading Application
The Bitcoin Regression Bands transcends traditional price prediction models with a sophisticated multi-band illumination system that reveals the underlying structure of Bitcoin's monetary evolution. Scientifically calibrated across multiple halving cycles and featuring seamless rainbow visualization, it enables investors to perceive Bitcoin's position within its macro growth trajectory with unprecedented clarity.
- Visual Theming 🎨
Scientifically designed rainbow gradient optimized for cycle pattern recognition:
- Violet-Blue: Lower value accumulation zones with highest mathematical growth potential
- Green: Fair value equilibrium zone representing the regression mean
- Yellow-Orange: Moderate overvaluation regions indicating potential resistance
- Red: Statistical extreme zones indicating mathematical cycle peaks
- Halving Visualization 🔍
- Precise cycle boundaries demarcating Bitcoin's fundamental supply shock events
- Adaptive band spacing based on mathematical cycle progression
- Multiple sub-cycle markers revealing the probabilistic nature of Bitcoin's trajectory
🚀 How to Use
1. Identify Macro Position ⏰: Locate Bitcoin's current price relative to the regression bands
2. Understand Cycle Context 🎚️: Note position within the current halving cycle for time-based analysis
3. Assess Mathematical Value 🌈: Determine potential over/undervaluation based on band location
4. Adjust Investment Strategy 🔎: Modulate position sizing based on mathematical value assessment
5. Identify Cycle Phases ✅: Monitor band transitions to detect accumulation and distribution zones
6. Invest with Precision 🛡️: Utilize lower bands for strategic accumulation, upper bands for strategic reduction
7. Manage Risk Dynamically 🔐: Scale investment allocations based on mathematical cycle positioning
GKD-C APA Adaptive Fisher Transform [Loxx]The Giga Kaleidoscope GKD-C APA Adaptive Fisher Transform is a confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System."
█ GKD-C APA Adaptive Fisher Transform
APA Adaptive Fisher Transform is an adaptive cycle Fisher Transform using Ehlers Autocorrelation Periodogram Algorithm to calculate the dominant cycle period.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
What is Fisher Transform?
The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution.
The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, and the Average Directional Index (ADX).
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Advance Trend Pressure as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
? Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
Descending Elliot Wave Patterns [theEccentricTrader]█ OVERVIEW
This indicator automatically draws descending Elliot Wave patterns and price projections derived from the ranges that constitute the patterns.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a close price equal to or above the price it opened.
• A red candle is one that closes with a close 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.
Range
The range is simply the difference between the current peak and current trough prices, generally expressed in terms of points or pips.
Support and Resistance
• Support refers to a price level where the demand for an asset is strong enough to prevent the price from falling further.
• Resistance refers to a price level where the supply of an asset is strong enough to prevent the price from rising further.
Support and resistance levels are important because they can help traders identify where the price of an asset might pause or reverse its direction, offering potential entry and exit points. For example, a trader might look to buy an asset when it approaches a support level , with the expectation that the price will bounce back up. Alternatively, a trader might look to sell an asset when it approaches a resistance level , with the expectation that the price will drop back down.
It's important to note that support and resistance levels are not always relevant, and the price of an asset can also break through these levels and continue moving in the same direction.
Upper Trends
• A return line uptrend is formed when the current peak price is higher than the preceding peak price.
• A downtrend is formed when the current peak price is lower than the preceding peak price.
• A double-top is formed when the current peak price is equal to the preceding peak price.
Lower Trends
• An uptrend is formed when the current trough price is higher than the preceding trough price.
• A return line downtrend is formed when the current trough price is lower than the preceding trough price.
• A double-bottom is formed when the current trough price is equal to the preceding trough price.
Muti-Part Upper and Lower Trends
• A multi-part return line uptrend begins with the formation of a new return line uptrend, or higher peak, and continues until a new downtrend, or lower peak, completes the trend.
• A multi-part downtrend begins with the formation of a new downtrend, or lower peak, and continues until a new return line uptrend, or higher peak, completes the trend.
• A multi-part uptrend begins with the formation of a new uptrend, or higher trough, and continues until a new return line downtrend, or lower trough, completes the trend.
• A multi-part return line downtrend begins with the formation of a new return line downtrend, or lower trough, and continues until a new uptrend, or higher trough, completes the trend.
Double Trends
• A double uptrend is formed when the current trough price is higher than the preceding trough price and the current peak price is higher than the preceding peak price.
• A double downtrend is formed when the current peak price is lower than the preceding peak price and the current trough price is lower than the preceding trough price.
Muti-Part Double Trends
• A multi-part double uptrend begins with the formation of a new uptrend that proceeds a new return line uptrend, and continues until a new downtrend or return line downtrend ends the trend.
• A multi-part double downtrend begins with the formation of a new downtrend that proceeds a new return line downtrend, and continues until a new uptrend or return line uptrend ends the trend.
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. The first swing high or swing low will set the course for the sequence of wave cycles that follow; for example 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.
Figure 1.
Fibonacci Retracement and Extension Ratios
The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding numbers, starting with 0 and 1. For example 0 + 1 = 1, 1 + 1 = 2, 1 + 2 = 3, and so on. Ultimately, we could go on forever but the first few numbers in the sequence are as follows: 0 , 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144.
The extension ratios are calculated by dividing each number in the sequence by the number preceding it. For example 0/1 = 0, 1/1 = 1, 2/1 = 2, 3/2 = 1.5, 5/3 = 1.6666..., 8/5 = 1.6, 13/8 = 1.625, 21/13 = 1.6153..., 34/21 = 1.6190..., 55/34 = 1.6176..., 89/55 = 1.6181..., 144/89 = 1.6179..., and so on. The retracement ratios are calculated by inverting this process and dividing each number in the sequence by the number proceeding it. For example 0/1 = 0, 1/1 = 1, 1/2 = 0.5, 2/3 = 0.666..., 3/5 = 0.6, 5/8 = 0.625, 8/13 = 0.6153..., 13/21 = 0.6190..., 21/34 = 0.6176..., 34/55 = 0.6181..., 55/89 = 0.6179..., 89/144 = 0.6180..., and so on.
1.618 is considered to be the 'golden ratio', found in many natural phenomena such as the growth of seashells and the branching of trees. Some now speculate the universe oscillates at a frequency of 0,618 Hz, which could help to explain such phenomena, but this theory has yet to be proven.
Traders and analysts use Fibonacci retracement and extension indicators, consisting of horizontal lines representing different Fibonacci ratios, for identifying potential levels of support and resistance. Fibonacci ranges are typically drawn from left to right, with retracement levels representing ratios inside of the current range and extension levels representing ratios extended outside of the current range. If the current wave cycle ends on a swing low, the Fibonacci range is drawn from peak to trough. If the current wave cycle ends on a swing high the Fibonacci range is drawn from trough to peak.
Elliot Wave Patterns
Ralph Nelson Elliott, authored his book on Elliott wave theory titled "The Wave Principle" in 1938. In this book, Elliott presented his theory of market behaviour, which he believed reflected the natural laws that govern human behaviour.
The Elliott Wave Theory is based on the principle that waves have a tendency to unfold in a specific sequence of five waves in the direction of the trend, followed by three waves leading in the opposite direction. This pattern is called a 5-3 wave pattern and is the foundation of Elliott's theory.
The five waves in the direction of the trend are labelled 1, 2, 3, 4, and 5, while the three waves in the opposite direction are labelled A, B, and C. Waves 1, 3, and 5 are impulse waves, while waves 2 and 4 are corrective waves. Waves A and C are also corrective waves, while wave B is an impulse wave.
According to Elliott, the pattern of waves is fractal in nature, meaning that it occurs on all time frames, from the smallest to the largest.
In Elliott Wave Theory, the distance that waves move from each other depends on the specific market conditions and the amplitude of the waves involved. There is no fixed rule or limit for how far waves should move from each other, however, there are several guidelines to help identify and measure wave distances. One of the most common guidelines is the Fibonacci ratios, which can be used to describe the relationships between wave lengths. For example, Elliott identified that wave 3 is typically the strongest and longest wave, and it tends to be 1.618 times the length of wave 1. Meanwhile, wave 2 tends to retrace between 50% and 78.6% of wave 1, and wave 4 tends to retrace between 38.2% and 78.6% of wave 3.
In general, the patterns are quite rare and the distances that the waves move in relation to one another is subject to interpretation. For such reasons, I have simply included the ratios of the current ranges as ratios of the preceding ranges in the wave labels and it will, ultimately, be up to the user to decide whether or not the patterns qualify as valid.
█ FEATURES
Inputs
• Show Projections
• Pattern Color
• Label Color
• Extend Current Projection Lines
█ LIMITATIONS
All green and red candle calculations are based 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. This may cause some unexpected behaviour on some markets and timeframes. 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.
Ascending Elliot Wave Patterns [theEccentricTrader]█ OVERVIEW
This indicator automatically draws ascending Elliot Wave patterns and price projections derived from the ranges that constitute the patterns.
█ CONCEPTS
Green and Red Candles
• A green candle is one that closes with a close price equal to or above the price it opened.
• A red candle is one that closes with a close 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.
Range
The range is simply the difference between the current peak and current trough prices, generally expressed in terms of points or pips.
Support and Resistance
• Support refers to a price level where the demand for an asset is strong enough to prevent the price from falling further.
• Resistance refers to a price level where the supply of an asset is strong enough to prevent the price from rising further.
Support and resistance levels are important because they can help traders identify where the price of an asset might pause or reverse its direction, offering potential entry and exit points. For example, a trader might look to buy an asset when it approaches a support level , with the expectation that the price will bounce back up. Alternatively, a trader might look to sell an asset when it approaches a resistance level , with the expectation that the price will drop back down.
It's important to note that support and resistance levels are not always relevant, and the price of an asset can also break through these levels and continue moving in the same direction.
Upper Trends
• A return line uptrend is formed when the current peak price is higher than the preceding peak price.
• A downtrend is formed when the current peak price is lower than the preceding peak price.
• A double-top is formed when the current peak price is equal to the preceding peak price.
Lower Trends
• An uptrend is formed when the current trough price is higher than the preceding trough price.
• A return line downtrend is formed when the current trough price is lower than the preceding trough price.
• A double-bottom is formed when the current trough price is equal to the preceding trough price.
Muti-Part Upper and Lower Trends
• A multi-part return line uptrend begins with the formation of a new return line uptrend, or higher peak, and continues until a new downtrend, or lower peak, completes the trend.
• A multi-part downtrend begins with the formation of a new downtrend, or lower peak, and continues until a new return line uptrend, or higher peak, completes the trend.
• A multi-part uptrend begins with the formation of a new uptrend, or higher trough, and continues until a new return line downtrend, or lower trough, completes the trend.
• A multi-part return line downtrend begins with the formation of a new return line downtrend, or lower trough, and continues until a new uptrend, or higher trough, completes the trend.
Double Trends
• A double uptrend is formed when the current trough price is higher than the preceding trough price and the current peak price is higher than the preceding peak price.
• A double downtrend is formed when the current peak price is lower than the preceding peak price and the current trough price is lower than the preceding trough price.
Muti-Part Double Trends
• A multi-part double uptrend begins with the formation of a new uptrend that proceeds a new return line uptrend, and continues until a new downtrend or return line downtrend ends the trend.
• A multi-part double downtrend begins with the formation of a new downtrend that proceeds a new return line downtrend, and continues until a new uptrend or return line uptrend ends the trend.
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. The first swing high or swing low will set the course for the sequence of wave cycles that follow; for example 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.
Figure 1.
Fibonacci Retracement and Extension Ratios
The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding numbers, starting with 0 and 1. For example 0 + 1 = 1, 1 + 1 = 2, 1 + 2 = 3, and so on. Ultimately, we could go on forever but the first few numbers in the sequence are as follows: 0 , 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144.
The extension ratios are calculated by dividing each number in the sequence by the number preceding it. For example 0/1 = 0, 1/1 = 1, 2/1 = 2, 3/2 = 1.5, 5/3 = 1.6666..., 8/5 = 1.6, 13/8 = 1.625, 21/13 = 1.6153..., 34/21 = 1.6190..., 55/34 = 1.6176..., 89/55 = 1.6181..., 144/89 = 1.6179..., and so on. The retracement ratios are calculated by inverting this process and dividing each number in the sequence by the number proceeding it. For example 0/1 = 0, 1/1 = 1, 1/2 = 0.5, 2/3 = 0.666..., 3/5 = 0.6, 5/8 = 0.625, 8/13 = 0.6153..., 13/21 = 0.6190..., 21/34 = 0.6176..., 34/55 = 0.6181..., 55/89 = 0.6179..., 89/144 = 0.6180..., and so on.
1.618 is considered to be the 'golden ratio', found in many natural phenomena such as the growth of seashells and the branching of trees. Some now speculate the universe oscillates at a frequency of 0,618 Hz, which could help to explain such phenomena, but this theory has yet to be proven.
Traders and analysts use Fibonacci retracement and extension indicators, consisting of horizontal lines representing different Fibonacci ratios, for identifying potential levels of support and resistance. Fibonacci ranges are typically drawn from left to right, with retracement levels representing ratios inside of the current range and extension levels representing ratios extended outside of the current range. If the current wave cycle ends on a swing low, the Fibonacci range is drawn from peak to trough. If the current wave cycle ends on a swing high the Fibonacci range is drawn from trough to peak.
Elliot Wave Patterns
Ralph Nelson Elliott, authored his book on Elliott wave theory titled "The Wave Principle" in 1938. In this book, Elliott presented his theory of market behaviour, which he believed reflected the natural laws that govern human behaviour.
The Elliott Wave Theory is based on the principle that waves have a tendency to unfold in a specific sequence of five waves in the direction of the trend, followed by three waves leading in the opposite direction. This pattern is called a 5-3 wave pattern and is the foundation of Elliott's theory.
The five waves in the direction of the trend are labelled 1, 2, 3, 4, and 5, while the three waves in the opposite direction are labelled A, B, and C. Waves 1, 3, and 5 are impulse waves, while waves 2 and 4 are corrective waves. Waves A and C are also corrective waves, while wave B is an impulse wave.
According to Elliott, the pattern of waves is fractal in nature, meaning that it occurs on all time frames, from the smallest to the largest.
In Elliott Wave Theory, the distance that waves move from each other depends on the specific market conditions and the amplitude of the waves involved. There is no fixed rule or limit for how far waves should move from each other, however, there are several guidelines to help identify and measure wave distances. One of the most common guidelines is the Fibonacci ratios, which can be used to describe the relationships between wave lengths. For example, Elliott identified that wave 3 is typically the strongest and longest wave, and it tends to be 1.618 times the length of wave 1. Meanwhile, wave 2 tends to retrace between 50% and 78.6% of wave 1, and wave 4 tends to retrace between 38.2% and 78.6% of wave 3.
In general, the patterns are quite rare and the distances that the waves move in relation to one another is subject to interpretation. For such reasons, I have simply included the ratios of the current ranges as ratios of the preceding ranges in the wave labels and it will, ultimately, be up to the user to decide whether or not the patterns qualify as valid.
█ FEATURES
Inputs
• Show Projections
• Pattern Color
• Label Color
• Extend Current Projection Lines
█ LIMITATIONS
All green and red candle calculations are based 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. This may cause some unexpected behaviour on some markets and timeframes. 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.
Elliott Wave - HAP [PRO] █ OVERVIEW
Meet HAP WAVE's real-time automated wave drawing tool on the chart. HAP WAVES Indicator is a technical analysis tool that uses Elliott Wave Theory and various technical indicators to analyze price and volume data in financial markets, such as crypto, stocks, and forex. It automatically draws Elliott Waves on the chart in real-time and provides features to identify potential buy and sell signals. The indicator can be used to identify target areas for waves 3, 5, and C, and shows both downward and upward waves. Additionally, the tool displays the real-time ratio of each wave and allows users to adjust wave rules flexibly. The tool also provides pre-sign analysis to help users predict future waves and includes alerts for various events, such as the start of a wave or the failure of a wave pattern. The indicator includes a trend channel and has several pairs of scan-wave tools. including real-time wave counting
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🎯 Feature
• Can draw wave lines in real time, indicating at that moment what wave the market is in.
• Automatically identify the target area of waves 3, 5, and C.
• Keep drawing waves automatically. All waves
• Shows both a downward wave (Bear) and an upward wave (Bull).
• Displays the real time ratio of each wave. (when hovering over the wave number)
• Each wave rule can be adjusted flexibly.
• Pre-show signs analyze waves that may occur in the future.
• Alert yourself when you encounter waves that are likely to occur in advance.
• Notify when meeting the start of a given wave.
• Alert when wave 2,4,A,B fail
• Notify when the wave is expanding again
• Multiple alert formats (save your alert, set it up just once, and receive all alerts according to 9–12)
• Hit Trend Channel from Automatic Coupled Price Swing
*There are also several pairs of scan wave tools.
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🔻Components
🎯 Unlocking the Power of the Elliott Wave Theory
feature that can set the starting point of the wave. by yourself
Trend Following Of course, adding this could be an element to help you make your decision easier.
Support and resistance
🎲 Scan version
This :
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🎯 Explanation of the wave counter setting section
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⬜ Understanding its basic principles and rules
Elliott Wave Prediction
Elliott Wave Theory is a technical analysis tool that seeks to identify patterns in financial market data and to make predictions about future price movements. The theory was developed by Ralph Nelson Elliott in the 1930s and is based on the idea that financial markets move in repetitive patterns, or waves, that can be analyzed and traded.
The Elliott Wave Theory has specific rules and guidelines for counting waves and determining their characteristics, including direction, size, and duration. The theory divides waves into two categories: motive waves (1, 2, 3, 4, and 5) and corrective waves (A, B, C). Motive waves are those that move in the direction of the overall trend and are subdivided into five smaller waves. Corrective waves are those that move against the trend and are subdivided into three smaller waves.
The theory also includes guidelines for determining the end of a wave and the start of a new wave, as well as rules for determining the characteristics of each wave, such as its size and duration. By analyzing the characteristics of waves and applying the rules and guidelines of the Elliott Wave Theory, traders and investors can make informed decisions about buying and selling in financial markets.
In summary, the Elliott Wave Theory is a technical analysis tool that seeks to identify patterns in financial market data and to make predictions about future price movements. The theory is based on the idea that financial markets move in repetitive patterns, or waves, and has specific rules and guidelines for counting and analyzing these waves.
>> The Elliott Wave Theory divides financial market movements into two categories: motive waves and corrective waves. Each wave has its own cycle and purpose, and understanding these cycles is important for making informed decisions about buying and selling in financial markets.
Motive Waves: Motive waves move in the direction of the overall trend and are subdivided into five smaller waves. The five waves of a motive wave cycle are:
•Wave 1: The first wave of a motive wave cycle is often referred to as the "impulse wave." This wave represents the beginning of a new trend and is characterized by strong price movements in the direction of the trend.
•Wave 2: The second wave is a corrective wave that retraces a portion of the gains made in Wave 1. This wave is usually smaller and less intense than Wave 1.
•Wave 3: The third wave is usually the longest and strongest wave of the motive wave cycle, and it represents the continuation of the trend. This wave is characterized by strong price movements in the direction of the trend and is usually much larger than Waves 1 and 2.
•Wave 4: The fourth wave is a corrective wave that retraces a portion of the gains made in Wave 3. This wave is usually smaller and less intense than Waves 1 and 3, and it provides a temporary pause in the trend.
•Wave 5: The fifth wave is the final wave of the motive wave cycle and is usually the shortest and weakest wave. This wave represents the end of the trend and is characterized by a final push in the direction of the trend.
Corrective Waves: Corrective waves move against the trend and are subdivided into three smaller waves. The three waves of a corrective wave cycle are:
•A. Wave A: The first wave of a corrective wave cycle is often referred to as the "A wave." This wave is characterized by a downward move against the trend and represents the start of a correction.
•B. Wave B: The second wave of a corrective wave cycle is the "B wave." This wave is characterized by an upward move against the trend, and it represents the counter-trend rally.
•C. Wave C: The final wave of a corrective wave cycle is the "C wave." This wave is characterized by a downward move in the direction of the trend, and it represents the completion of the correction.
In summary, the Elliott Wave Theory divides financial market movements into two categories: motive waves, which move in the direction of the trend, and corrective waves, which move against the trend. Understanding the cycle of each wave is important for making informed decisions about buying and selling in financial markets.
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🎯 CONCEPTS (Disclaimer)
Elliott Wave Count
The HAP WAVES indicator is designed for experienced traders who are familiar with advanced trading techniques. This tool is intended for day traders and long-term investors who have a solid understanding of the market and are looking to improve their trading strategy. The indicator was created by a well-known market analyst and is based on the principles and techniques documented in several books, including those by `Jason Perl`.
It is recommended that users of the HAP WAVES indicator have a good understanding of the concepts and techniques discussed in the books that cover this indicator. Before using the tool, it is important to have a good understanding of the underlying principles and techniques in order to make the most of the indicator and potentially improve your trading performance.
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🎯 Rules and Principles of this Indicator
* This description only addresses bullish (uptrend) markets, while bearish (downtrend) gives let you look a vice versa.
• ▶ HAP WAVES Requirements for Wave 1
The Wave 1 HAP WAVES requirements for a bullish (rising) market require that the market has closed a minimum of 21 bars below all 20 previous closes, then closes a maximum of 13 bars above all 12 previous closes, and finally reaches an 8 bar close below all 7 previous closes, to confirm that Wave 2 has formed.
• ▶ HAP WAVES Requirements for Wave 2
The first requirement for Wave 2 is the last requirement for Wave 1, which is a close of at least 8 bars with a close less than the previous 7 bars. Wave 2 continues until the market records a 21-bar close above all 20 previous closes, indicating the start of Wave 3.
• ▶ HAP WAVES Requirements for Wave 3
Wave 3 is confirmed after Wave 2 closes at 21 bars high, and it continues until there is a 13 bar close, indicating the end of Wave 3 and the start of Wave 4.
• ▶ HAP WAVES Requirements for Wave 4
The first requirement for Wave 4 is that there is a close at 13 bars after the end of Wave 3. Wave 4 is complete when the market records a 34 bar close (above all previous 33 closes), indicating the start of Wave 5.
• ▶ HAP WAVES Requirements for Wave 5
The first requirement for Wave 5 is a 34 bar close, signaling the end of Wave 4. The wave is considered complete when a 13 bar close is published, resulting in Wave A.
• ▶ HAP WAVES Requirements for Wave A
The HAP WAVES Requirements for Wave A state that the first call for Wave A is a 13 bar close (close below all previous 12 closes), signaling the end of Wave 5. Wave A is considered complete with an 8 bar high close (close above previous 7 closes), leading to the start of Wave B.
• ▶ HAP WAVES Requirements for Wave B
The first requirement for Wave B is a 8 bar high close (close above the previous 7 closes) after Wave A. Wave B is complete when there is a 21 bar low close (close below all 20 previous closes).
• ▶ HAP WAVES Requirements for Wave C
Wave C starts with a close of 21 bars low for Wave C, meaning the market close is below all 20 previous closes. Wave C is considered closed when the market closes below the close low of Wave A.
* Additional rules to apply the HAP WAVES indicator to the bullish trend
• The peak of Wave 3 must be higher than the close of Wave 1, and the peak of Wave 5 must be higher than the peak of Wave 3.
• If the pullback from Wave 1 is shallow, and the market later retraces above the high close of Wave 1, Wave 1 will slide to the right to align with the new high close.
• If the pullback from Wave 3 is shallow, and the market later retraces above the high close of Wave 3, Wave 3 will slide to the right to align with the new high close.
• Wave 5 will only be confirmed if Wave C crosses the low close of Wave A. If Wave B closes above the high close of Wave 5, Waves A, B and C will be erased and Wave 5 will slide to the right.
• If Wave 2 closes below the low close of Wave 1, the countdown will start over, and if Wave 4 closes below the low close of Wave 2, Wave 2 will move to the location of Wave 4.
• After Wave C crosses the low close of Wave A, Wave 5 will be locked in and cannot move. If the market later retraces above the high close of Wave 5, it will be marked as a new advance in Wave 1 rather than a blur of the previous Wave 5.
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APA Adaptive Fisher Transform [Loxx]APA Adaptive Fisher Transform is an adaptive cycle Fisher Transform using Ehlers Autocorrelation Periodogram Algorithm to calculate the dominant cycle period.
What is an adaptive cycle, and what is Ehlers Autocorrelation Periodogram Algorithm?
From Ehlers' book Cycle Analytics for Traders Advanced Technical Trading Concepts by John F. Ehlers , 2013, page 135:
"Adaptive filters can have several different meanings. For example, Perry Kaufman’s adaptive moving average ( KAMA ) and Tushar Chande’s variable index dynamic average ( VIDYA ) adapt to changes in volatility . By definition, these filters are reactive to price changes, and therefore they close the barn door after the horse is gone.The adaptive filters discussed in this chapter are the familiar Stochastic , relative strength index ( RSI ), commodity channel index ( CCI ), and band-pass filter.The key parameter in each case is the look-back period used to calculate the indicator. This look-back period is commonly a fixed value. However, since the measured cycle period is changing, it makes sense to adapt these indicators to the measured cycle period. When tradable market cycles are observed, they tend to persist for a short while.Therefore, by tuning the indicators to the measure cycle period they are optimized for current conditions and can even have predictive characteristics.
The dominant cycle period is measured using the Autocorrelation Periodogram Algorithm. That dominant cycle dynamically sets the look-back period for the indicators. I employ my own streamlined computation for the indicators that provide smoother and easier to interpret outputs than traditional methods. Further, the indicator codes have been modified to remove the effects of spectral dilation.This basically creates a whole new set of indicators for your trading arsenal."
What is Fisher Transform?
The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution.
The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
Included:
Zero-line and signal cross options for bar coloring
Customizable overbought/oversold thresh-holds
Alerts
Signals
Financial Astrology Uranus SpeedWhen Uranus is accelerating in speed from retrograde to direct phase, there is a quick acceleration of price change. We can observe very clearly that in BTCUSD the most relevant price growth periods happened after the retrogradation period, when Uranus was moving direct and accelerating. Additionally, is very clear that when Uranus is decelerating in speed a period of correction or price congestion occurs. Very similar speed effect pattern was observed for multiples planets speed cycles so is clear that what is good for price growth is that most of the planets are in direct motion.
Note: The Uranus speed indicator is based on an ephemeris array that covers years 2010 to 2030, prior or after this years the speed 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 timezone.
Oracle v2.0 [Market Vision]This algorithm uses Fibonacci Cycles to identify high probability reversal zones. It works on Crypto, Forex and Stocks. All timeframes are supported.
It contains several modes, such as "Equinox", "Regular", "Scalp" and "Fib". Use these modes to adjust the script to your favourite asset and timeframe.
This is a premium algorithm , that can be rented on a monthly basis.
PM if you are interested.
-Hawk
Some additional pictures:
15 minute 'Fib'
1 hour 'Equinox'
Grover Llorens Activator [alexgrover & Lucía Llorens] Trailing stops play a key role in technical analysis and are extremely popular trend following indicators. Their main strength lie in their ability to minimize whipsaws while conserving a decent reactivity, the most popular ones include the Supertrend, Parabolic SAR and Gann Hilo activator. However, and like many indicators, most trailing stops assume an infinitely long trend, which penalize their ability to provide early exit points, this isn't the case of the parabolic SAR who take this into account and thus converge toward the price at an increasing speed the longer a trend last.
Today a similar indicator is proposed. From an original idea of alexgrover & Lucía Llorens who wanted to revisit the classic parabolic SAR indicator, the Llorens activator aim to converge toward the price the longer a trend persist, thus allowing for potential early and accurate exit points. The code make use of the idea behind the price curve channel that you can find here :
I tried to make the code as concise as possible.
The Indicator
The indicator posses 2 user settings, length and mult , length control the rate of convergence of the indicator, with higher values of length making the indicator output converge more slowly toward the price. Mult is also related with the rate of convergence, basically once the price cross the trailing stop its value will become equal to the previous trailing stop value plus/minus mult*atr depending on the previous trailing stop value, therefore higher values of mult will require more time for the trailing stop to reach the closing price, use higher values of mult if you want to avoid potential whipsaws.
Above the indicator with slow convergence time (high length) and low mult.
Points with early exit points are highlighted.
Usage For Oscillators
The difference between the closing price and an overlay indicator can provide an oscillator with characteristics depending on the indicators used for differencing, Lucía Llorens stated that we should find indicators for differencing that highlight the cycles in the price, in other terms : Price - Signal , where we want to find Signal such that we maximize the visibility of the cycles, it can be demonstrated that in the case where the closing price is an additive model : Trend + Cycles + Noise , the zero lag estimation of the Trend component can allow for the conservation of the cycle and noise component, that is : Price - Estimate(Trend) , for example the difference between the price and moving average isn't optimal because of the moving average lag, instead the use of zero lag moving averages is more suitable, however the proposed indicator allow for a surprisingly good representation of the cycles when using differencing.
The normalization of this oscillator (via the RSI) allow to make the peak amplitude of the cycles more constant. Note however that such method can return an output with a sign inverse to the one of the original cycle component.
Conclusion
We proposed an indicator which share the logic of the SAR indicator, that is using convergence toward the price in order to provide early exit points detection. We have seen that this indicator can be used to highlight cycles when used for differencing and i don't exclude publishing more indicators based on this method.
Lucía Llorens has been a great person to work with, and provided enormous feedback and support while i was coding the indicator, this is why i include her in the indicator name as well as copyright notice. I hope we can make more indicators togethers in the future.
(altho i was against using buy/sells labels xD !)
Thanks for reading !
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain?
This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions.
How does a Markov Chain work, and how should I read this spheres-and-arrows diagram?
Think of three weather modes: Sunny, Rainy, Cloudy.
Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow).
The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy).
Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step.
Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny.
Now relabel the spheres to markets: Bull, Bear, Neutral.
Spheres: market regimes (uptrend, downtrend, range).
Self‑loop: tendency for the current regime to continue on the next bar.
Arrows: the most common next regime if a switch happens.
How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear).
What states and transitions are shown?
The three market states visualized are:
Bullish (Bull): Upward or strong-market regime.
Bearish (Bear): Downward or weak-market regime.
Neutral: Sideways or range-bound regime.
Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull).
How does the regime detection system work?
You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.).
Values are statistically normalized (Z-scored) over a configurable lookback period.
The normalized outputs are classified into Bull, Bear, or Neutral zones.
If using three indicators, their regime signals are averaged and smoothed for robustness.
How are transition probabilities calculated?
On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions.
The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear).
Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time.
What is unique about the visualization?
3D animated spheres represent each regime and change visually when active.
Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows.
Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed.
All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format.
Can I use custom indicators for regime classification?
Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement.
What does the “Lookback Period” control?
Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt.
How is this different from a Hidden Markov Model (HMM)?
It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust.
How is this Markov Chain different from a Hidden Markov Model (HMM)?
Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable.
Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training.
A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data.
Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune.
HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data.
How does the indicator avoid “repainting” or look-ahead bias?
All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation.
Are there practical tuning tips?
Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability.
Use custom indicators if your asset has unique regime drivers.
Watch for rapid changes in transition probabilities as early warning of a possible regime shift.
Who is this indicator for?
Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models.
Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting:
The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear).
Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions.
Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies.
It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes.
The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting.
Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis.
What's the mathematical foundation behind this?
The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM).
How to use this indicator programmatically?
The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading.
Step-by-Step Integration with Pine Script (input.source)
Add the Markov Chain indicator to your chart.
This must be done first, since your custom script will "pull" the bias signal from the indicator's plot.
In your strategy, create an input using input.source()
Example:
//@version=5
strategy("MC Bias Strategy Example")
mcBias = input.source(close, "MC Bias Source")
After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot).
Use the bias in your trading logic
Example (long only on Bull, flat otherwise):
if mcBias == 1
strategy.entry("Long", strategy.long)
else
strategy.close("Long")
For more advanced workflows, combine mcBias with additional filters or trailing stops.
How does this work behind-the-scenes?
TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source).
The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.).
This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code.
Integrating 45+ Indicators with Your Markov Chain — How & Why
The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots.
// Enhanced Custom Indicators Export - 45 Technical Indicators
// Comprehensive technical analysis suite for advanced market regime detection
//@version=6
indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500)
// |----- Input Parameters -----| //
momentum_group = "Momentum Indicators"
trend_group = "Trend Indicators"
volume_group = "Volume Indicators"
volatility_group = "Volatility Indicators"
oscillator_group = "Oscillator Indicators"
display_group = "Display Settings"
// Common lengths
length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group)
length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group)
length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group)
// Display options
show_table = input.bool(true, "Show Values Table", group=display_group)
table_size = input.string("Small", "Table Size", options= , group=display_group)
// |----- MOMENTUM INDICATORS (15 indicators) -----| //
// 1. RSI (Relative Strength Index)
rsi_14 = ta.rsi(close, length_14)
rsi_centered = rsi_14 - 50
// 2. Stochastic Oscillator
stoch_k = ta.stoch(close, high, low, length_14)
stoch_d = ta.sma(stoch_k, 3)
stoch_centered = stoch_k - 50
// 3. Williams %R
williams_r = ta.stoch(close, high, low, length_14) - 100
// 4. MACD (Moving Average Convergence Divergence)
= ta.macd(close, 12, 26, 9)
// 5. Momentum (Rate of Change)
momentum = ta.mom(close, length_14)
momentum_pct = (momentum / close ) * 100
// 6. Rate of Change (ROC)
roc = ta.roc(close, length_14)
// 7. Commodity Channel Index (CCI)
cci = ta.cci(close, length_20)
// 8. Money Flow Index (MFI)
mfi = ta.mfi(close, length_14)
mfi_centered = mfi - 50
// 9. Awesome Oscillator (AO)
ao = ta.sma(hl2, 5) - ta.sma(hl2, 34)
// 10. Accelerator Oscillator (AC)
ac = ao - ta.sma(ao, 5)
// 11. Chande Momentum Oscillator (CMO)
cmo = ta.cmo(close, length_14)
// 12. Detrended Price Oscillator (DPO)
dpo = close - ta.sma(close, length_20)
// 13. Price Oscillator (PPO)
ppo = ta.sma(close, 12) - ta.sma(close, 26)
ppo_pct = (ppo / ta.sma(close, 26)) * 100
// 14. TRIX
trix_ema1 = ta.ema(close, length_14)
trix_ema2 = ta.ema(trix_ema1, length_14)
trix_ema3 = ta.ema(trix_ema2, length_14)
trix = ta.roc(trix_ema3, 1) * 10000
// 15. Klinger Oscillator
klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55)
// 16. Fisher Transform
fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25
fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2))
// 17. Stochastic RSI
stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14)
stoch_rsi_centered = stoch_rsi - 50
// 18. Relative Vigor Index (RVI)
rvi_num = ta.swma(close - open)
rvi_den = ta.swma(high - low)
rvi = rvi_den != 0 ? rvi_num / rvi_den : 0
// 19. Balance of Power (BOP)
bop = (close - open) / (high - low)
// |----- TREND INDICATORS (10 indicators) -----| //
// 20. Simple Moving Average Momentum
sma_20 = ta.sma(close, length_20)
sma_momentum = ((close - sma_20) / sma_20) * 100
// 21. Exponential Moving Average Momentum
ema_20 = ta.ema(close, length_20)
ema_momentum = ((close - ema_20) / ema_20) * 100
// 22. Parabolic SAR
sar = ta.sar(0.02, 0.02, 0.2)
sar_trend = close > sar ? 1 : -1
// 23. Linear Regression Slope
lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1)
// 24. Moving Average Convergence (MAC)
mac = ta.sma(close, 10) - ta.sma(close, 30)
// 25. Trend Intensity Index (TII)
tii_sum = 0.0
for i = 1 to length_20
tii_sum += close > close ? 1 : 0
tii = (tii_sum / length_20) * 100
// 26. Ichimoku Cloud Components
ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2
ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2
ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1
// 27. MESA Adaptive Moving Average (MAMA)
mama_alpha = 2.0 / (length_20 + 1)
mama = ta.ema(close, length_20)
mama_momentum = ((close - mama) / mama) * 100
// 28. Zero Lag Exponential Moving Average (ZLEMA)
zlema_lag = math.round((length_20 - 1) / 2)
zlema_data = close + (close - close )
zlema = ta.ema(zlema_data, length_20)
zlema_momentum = ((close - zlema) / zlema) * 100
// |----- VOLUME INDICATORS (6 indicators) -----| //
// 29. On-Balance Volume (OBV)
obv = ta.obv
// 30. Volume Rate of Change (VROC)
vroc = ta.roc(volume, length_14)
// 31. Price Volume Trend (PVT)
pvt = ta.pvt
// 32. Negative Volume Index (NVI)
nvi = 0.0
nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi
// 33. Positive Volume Index (PVI)
pvi = 0.0
pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi
// 34. Volume Oscillator
vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10)
// 35. Ease of Movement (EOM)
eom_distance = high - low
eom_box_height = volume / 1000000
eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0
eom_sma = ta.sma(eom, length_14)
// 36. Force Index
force_index = volume * (close - close )
force_index_sma = ta.sma(force_index, length_14)
// |----- VOLATILITY INDICATORS (10 indicators) -----| //
// 37. Average True Range (ATR)
atr = ta.atr(length_14)
atr_pct = (atr / close) * 100
// 38. Bollinger Bands Position
bb_basis = ta.sma(close, length_20)
bb_dev = 2.0 * ta.stdev(close, length_20)
bb_upper = bb_basis + bb_dev
bb_lower = bb_basis - bb_dev
bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0
bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0
// 39. Keltner Channels Position
kc_basis = ta.ema(close, length_20)
kc_range = ta.ema(ta.tr, length_20)
kc_upper = kc_basis + (2.0 * kc_range)
kc_lower = kc_basis - (2.0 * kc_range)
kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0
// 40. Donchian Channels Position
dc_upper = ta.highest(high, length_20)
dc_lower = ta.lowest(low, length_20)
dc_basis = (dc_upper + dc_lower) / 2
dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0
// 41. Standard Deviation
std_dev = ta.stdev(close, length_20)
std_dev_pct = (std_dev / close) * 100
// 42. Relative Volatility Index (RVI)
rvi_up = ta.stdev(close > close ? close : 0, length_14)
rvi_down = ta.stdev(close < close ? close : 0, length_14)
rvi_total = rvi_up + rvi_down
rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50
// 43. Historical Volatility
hv_returns = math.log(close / close )
hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100
// 44. Garman-Klass Volatility
gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open)
gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100
// 45. Parkinson Volatility
park_vol = math.log(high/low) * math.log(high/low)
parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100
// 46. Rogers-Satchell Volatility
rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open)
rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100
// |----- OSCILLATOR INDICATORS (5 indicators) -----| //
// 47. Elder Ray Index
elder_bull = high - ta.ema(close, 13)
elder_bear = low - ta.ema(close, 13)
elder_power = elder_bull + elder_bear
// 48. Schaff Trend Cycle (STC)
stc_macd = ta.ema(close, 23) - ta.ema(close, 50)
stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10)
stc_d = ta.ema(stc_k, 3)
stc = ta.stoch(stc_d, stc_d, stc_d, 10)
// 49. Coppock Curve
coppock_roc1 = ta.roc(close, 14)
coppock_roc2 = ta.roc(close, 11)
coppock = ta.wma(coppock_roc1 + coppock_roc2, 10)
// 50. Know Sure Thing (KST)
kst_roc1 = ta.roc(close, 10)
kst_roc2 = ta.roc(close, 15)
kst_roc3 = ta.roc(close, 20)
kst_roc4 = ta.roc(close, 30)
kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15)
// 51. Percentage Price Oscillator (PPO)
ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100
ppo_signal = ta.ema(ppo_line, 9)
ppo_histogram = ppo_line - ppo_signal
// |----- PLOT MAIN INDICATORS -----| //
// Plot key momentum indicators
plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1)
plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1)
plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1)
plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1)
plot(cci, title="05_CCI", color=color.green, linewidth=1)
// Plot trend indicators
plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1)
plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1)
plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1)
plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1)
plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1)
// Plot volatility indicators
plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1)
plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1)
plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1)
plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1)
plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1)
// Plot volume indicators
plot(vroc, title="16_VROC", color=color.blue, linewidth=1)
plot(eom_sma, title="17_EOM", color=color.red, linewidth=1)
plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1)
plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1)
plot(obv, title="20_OBV", color=color.purple, linewidth=1)
// Plot additional oscillators
plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1)
plot(cmo, title="22_CMO", color=color.maroon, linewidth=1)
plot(dpo, title="23_DPO", color=color.teal, linewidth=1)
plot(trix, title="24_TRIX", color=color.lime, linewidth=1)
plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1)
// Plot more momentum indicators
plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1)
plot(ac, title="27_AC", color=color.aqua, linewidth=1)
plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1)
plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1)
plot(klinger, title="30_Klinger", color=color.gray, linewidth=1)
// Plot trend continuation
plot(tii, title="31_TII", color=color.blue, linewidth=1)
plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1)
plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1)
plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1)
plot(bop, title="35_BOP", color=color.purple, linewidth=1)
// Plot volume continuation
plot(nvi, title="36_NVI", color=color.navy, linewidth=1)
plot(pvi, title="37_PVI", color=color.maroon, linewidth=1)
plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1)
plot(roc, title="39_ROC", color=color.lime, linewidth=1)
plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1)
// Plot volatility continuation
plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1)
plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1)
plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1)
plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1)
plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1)
// Plot final oscillators
plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1)
plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1)
plot(stc, title="48_STC", color=color.green, linewidth=1)
plot(coppock, title="49_Coppock", color=color.orange, linewidth=1)
plot(kst, title="50_KST", color=color.purple, linewidth=1)
// Plot final indicators
plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1)
plot(pvt, title="52_PVT", color=color.maroon, linewidth=1)
// |----- Reference Lines -----| //
hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1)
hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1)
// |----- Enhanced Information Table -----| //
if show_table and barstate.islast
table_position = position.top_right
table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal
var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray)
// Headers
table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70))
// Key Momentum Indicators
table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size)
table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size)
table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size)
table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size)
table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size)
// Key Trend Indicators
table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90))
table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size)
table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size)
table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size)
table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size)
table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size)
// Key Volatility Indicators
table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90))
table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size)
table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size)
table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size)
table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size)
// Key Volume Indicators
table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90))
table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size)
// Key Oscillators
table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90))
table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size)
table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size)
table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size)
// Summary Statistics
table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70))
table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size)
regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray
regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL"
table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size)
This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator.
How to use this script with the Markov Chain for research and backtesting:
Add the Enhanced Indicator Export to your chart.
Every calculated indicator is available as an individual data stream.
Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option.
In the Markov Chain indicator’s settings, turn ON the custom indicator mode.
For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name.
This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering.
Backtest regime logic directly.
Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns.
The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration.
Export the regime bias for programmatic use.
As described above, use input.source() in your Pine Script strategy or system and link the bias output.
You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes.
With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you.
Optimize your signal generation by backtesting across a universe of meaningful indicator blends.
Enhance risk management with objective, real-time regime boundaries.
Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes.
Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic.
Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem.
References & Further Reading
Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182.
Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384.
Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg.
Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544.
Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764.
Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC.
On Quantitative Finance and Markov Models:
Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press.
Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press.
TradingView Pine Script Documentation: www.tradingview.com
TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com
GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org
What makes this indicator original and unique?
- On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm.
- Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script.
- Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time.
- Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window.
- Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior.
Integrate your own signals (using input.source())
- In settings, choose the Source . This is powered by input.source() .
- Feed it price, an indicator you compute inside the script, or a custom composite series.
- The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time.
Credits:
Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community.
Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization.
Disclaimer
This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
My-Indicator - Global Liquidity & Money Supply M2 + Time OffsetThis script is designed to visualize a global liquidity and money supply index by combining data from various regions and, optionally, central bank activity. Visualizing this data on a chart allows you to see how central banks are intervening in the financial system and how the total amount of money in the economy is changing. Let’s take a look at how it works:
Central Bank Liquidity
Shows the actions of central banks (e.g. FED, ECB) providing short-term cash to commercial banks. If you see spikes or a steady increase in these indicators, it may suggest that liquidity is being increased through intervention, which often stimulates the market.
Money Supply
M2 money supply is a monetary aggregate that includes M1 (cash and current deposits) plus savings deposits, small term deposits, and other financial instruments that, while not as liquid as M1, can be quickly converted into cash. As a result, M2 provides a broader picture of the available money in the economy, which is useful for analyzing market conditions and potential economic trends.
How does it help investors?
It allows you to quickly see when central banks are injecting additional liquidity, which could signal higher prices.
It allows you to see trends in the money supply, which informs potential changes in inflation and the economic cycle.
Combining both sets of data provides a more complete picture – both in the short and long term – which makes it easier to predict upcoming price movements.
This allows investors to better respond to changes in central bank policy and broader monetary trends, increasing their chances of making better investment decisions.
Data Collection
The script retrieves money supply data for key markets such as the USA (USM2), Europe (EUM2), China (CNM2), and Japan (JPM2). It also offers additional money supply series for other markets—like Canada (CAM2), Great Britain (GBM2), Russia (RUM2), Brazil (BRM2), Mexico (MXM2), and New Zealand (NZM2)—with extra options (e.g., Australia, India, Korea, Indonesia, Malaysia, Sweden) disabled by default. Moreover, you can enable data for central bank liquidity (such as FED, RRP, TGA, ECB, PBC, BOJ, and other central banks), which are also disabled by default.
Index Calculation
The indicator calculates the index by adding together all the enabled money supply series (and the central bank data if activated) and then scales the sum by dividing it by 1,000,000,000,000 (one trillion). This scaling makes the resulting values more manageable and easier to read on the chart.
Time Offset Feature
A key feature of the script is the time offset. With the input parameter "Time Offset (days)", the user can shift the plotted index line by a specific number of days. The script converts the given offset in days into a number of bars based on the current chart's timeframe. This allows you to adjust for the delay between liquidity changes and their effect on asset prices.
Overall, the indicator plots a line on your chart representing the global liquidity and money supply index, allowing you to visually monitor trends and better understand how liquidity and central bank actions may influence market movements.
What makes this script different from others?
Every supported market—both major regions (USA, Eurozone, China, Japan, etc.) and additional ones—is available. You can toggle each series on or off, so you can view only Money Supply data, only Central Bank Liquidity, or any custom combination.
Separated Data Groups. Inputs are organized into clear groups (“Money Supply”, “Other Money Supply”, “Central Bank Liquidity”), making it easy to focus on just the data you need without clutter.
True Day‑Based Offset. This script converts your chosen “Time Offset (days)” into actual days regardless of timeframe. Whether you’re on a 5‑minute or daily chart, the index is always shifted by exactly the number of days you specify.
[iQ]PRO Ultimate Financial Analysis Tool And System SynergyUltimate Financial Analysis Tool And System Synergy (UFATASS)
Advanced Market Insights with Cycle Analysis, Trend Forecasting, and Risk Monitoring
The Ultimate Financial Analysis Tool And System Synergy (UFATASS) is a powerful indicator designed to give traders a deeper understanding of market dynamics. By blending cutting-edge techniques from signal processing, statistics, and dynamical systems theory, UFATASS provides a unique, all-in-one solution for technical analysis.
Key Features
Cycle Detection:
Pinpoints dominant market cycles using advanced spectral analysis, helping you identify potential turning points.
Trend Analysis:
Delivers multiple regression lines to capture short-term and long-term market trends, with a customizable complexity setting for precision.
Probability Forecasts:
Uses Monte Carlo simulations to estimate the likelihood of future price movements, offering a probabilistic edge for decision-making.
Risk Monitoring:
Tracks volatility and market stability, featuring an experimental chaos indicator based on Lyapunov exponents to assess price predictability.
Customization Options
Adjust the indicator to fit your trading style:
Cycle and regression lookback periods
Complexity factor for regression sensitivity
Volatility calculation window
Forecast horizon for price predictions
Visual Outputs
Price and regression lines plotted on the main chart
Cycle details and wave visuals in a separate pane
A summary label on the last bar with key metrics (e.g., cycle length, probabilities)
Background color alerts to signal risk levels
How to Use
Incorporate UFATASS into your strategy to:
Anticipate reversals with cycle analysis
Confirm trends using regression insights
Plan entries and exits with probability forecasts
Monitor market conditions and adjust risk exposure
TradingWolf Premium OscillatorsThe TradingWolf Premium Oscillators is a unique and enhanced selection of oscillators designed to help give you an edge on the markets.
Within this one indicator you will have access to RSI, Stochastic, MACD, Awesome Oscillator, Wavetrend, Zig Zag Pivots and DPO.
Including Divergence and Hidden Divergence signals for them.
Below each indicator is explained along with its enhancements to help you understand it better.
This script comes with the 'TradingWolf Premium' to get access, read the Author’s Instructions below.
There are extensive explanations on how to use these oscillators in our documentation on the website but we will give a simple overview here.
RSI
We try not to mess with these too much because if used correctly, they are very powerful tools. The main differences you will notice is that we have highlighted the areas where you should be paying attention to the oscillator with reversal/continuation zones.
The most popular feature from these will be the 4 divergences which can be toggled on or off in the settings.
Stochastic
Stochastic we have tried to keep as similar to the original as possible, main features are being able to select alternate timeframes for it to be calculated on as well as displaying divergences.
We have created a highlighted zone for when price enters the overbought/sold territory. A lot of traders will look for crossovers happening in these areas however from our tests we have discovered entering trades as the Stochastic comes out of these areas has hugely reduced losing trades, still not a perfect strategy but it does often show that the trend is showing weakening momentum and its commonly followed by a period of sideways action before continuing in a new direction.
MACD
We have calculated a dynamic extreme range for the MACD, you will notice the green/red bars as the bottom and top of the Oscillator. These levels help adjust with the assets volatility so they will work universally on all assets and timeframes. When these levels get more narrow, this indicates there is a potential larger move to come, similar thought process to a Bollinger band squeeze.
We like the Divergence signals you receive whilst in this OB/OS range as they give more confluence behind the divergence signal that price has over extended and is looking to retrace or consolidate.
Awesome Oscillator
The Awesome Oscillator is based on some pretty simple calculations but is hugely powerful.
The 3 main use cases are crossing the 0 value, showing weakening momentum and divergence signals.
We Particularly like the Divergence signals it gives us as they tend to be more accurate than any other oscillator.
Wavetrend
Wavetrend we try describe as a more dynamic Stochastic/MACD, it moves smoother and quicker without giving too many false signals.
Conditions we use the Wavetrend for are similar to the MACD where we are looking for crossovers or divergences in the extreme bands, these shouldn’t be used to trade alone and should be paired with other pieces of confluence for a higher probability trade however this is one of our favourites.
We also have a VWAP extreme detector which we pair with the Wavetrend, helping us identify areas where price should start cooling off.
Zig Zag
The main purpose of the standard Zig Zag is to analyse historical data to be able to observe cycle's in a market's movement, this requires a bit more explanation than we can include here so please refer to our documentation on the website for further guidance.
DPO
The detrended price oscillator is unlike other oscillators, such as the Stochastic or MACD the DPO is not a momentum indicator. It instead highlights peaks and troughs in price, which are used to estimate buy and sell points in line with the historical cycle.
We personally think this is the most under-rated oscillator out there, if you simply followed the DPO above 0 for long and below for short on higher timeframes you can outperform the buy and hold return of Bitcoin (BTCUSDT)...
This is just one simple way of using the DPO there are other more in depth methods of using it within our documentation.
Financial Astrology Mercury LongitudeMercury energy influence the mind, the intellect and mental temperament, in mundane astrology is well know that rules: news, science, debating, trading, commerce, contracts. telecommunication, short-distance travels, among others. W. D. Gann discovered that the Mercury speed phases (stationary, retrograde, direct) transitions was very relevant as trading signals, he used the Sun conjunction retrograde Mercury to confirm the formation of top and bottoms that seems to be a relevant leading indicator in multiples markets.
As part of the Financial Astrology Research Group experiments, we created hundreds of machine learning models that try to predict daily trend direction for a research portfolio of 10 crypto-currencies and is confirmed that including the Mercury speed and aspects features (variables) in the models increase the accuracy in a consistent manner. Therefore, there is enough evidence that Mercury is one of the most powerful mid term trading cycles.
This is the first open source PIneScript indicator that is able to plot the Mercury Tropical Longitude for the years 2010-2030, we publish as open source in order to support and simplify the research of the amazing astro-traders community at TradingView that have been working manually with annotations and lines to represent the Mercury longitude zodiac signs entries and the speed phases transitions. That manual work is over. Let's move faster in our cycles research!
We encourage all astro traders to continue researching and sharing your ideas of astro cycles trading strategies with us and contribute your experiments at our Github Financial Stats exploration project
so we can improve the cosmic energy models that influence traders behaviours.
Note: The Mercury 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.
Others astro trading indicators from Financial Astrology Research Group:
Bitcoin: Top & Bottom Mini-AlgoHere we have a mini-algorithm that tries to show absolute 4-year-cycle top and bottom zones for the case of the BraveNewCoin Liquid Index (BLX) for Bitcoin on the weekly (W) timeframe by using several oscillators as RSI, VPCI etc. employed with a custom logic. When the background gets red we might be near to a cycle peak, and when it gets green we might be near to the absolute bottom of the current cycle. Note that only absolute top/bottoms are indicated (at least since the end of 2013), so that the current strong drop in March 2020 was correctly not tagged, as it wasn't the lowest price of the current cycle.
It is best to combine this mini-algorithm with some of my boundary indicators for BLX, e.g. "Bitcoin: Price Action Integrals", for confluence . For the next peak one could then watch for the mini-algo to go red and for the price to hit the boundary. You can change the background transparency if you like to have this indicator be more unobstrusive on the chart.
For access please contact me via DM on TradingView or on Twitter (linked on my TradingView profile and my signature).
oscillator fast cryptosmart (Bands on Scale)The oscillator fast cryptosmart is a high-sensitivity momentum indicator designed to generate signals more rapidly than many traditional oscillators, such as the MACD. It is engineered to detect potential price breakouts by analyzing short-term market cycles.
At its core, the indicator uses a Detrended Price Oscillator (DPO) to remove the longer-term trend from price action, allowing it to focus purely on the underlying momentum cycles. It then calculates dynamic volatility bands around this oscillator line.
Signals are generated when momentum breaks out from a normal range, providing traders with an early warning of a potential acceleration in price.
How to Interpret the Signals:
Buy Signal (Green Vertical Line): A buy signal is generated when the oscillator's main line (yellow) crosses above its upper statistical band. This indicates a sharp surge in positive momentum, suggesting a potential upward move is beginning.
Sell Signal (Red Vertical Line): A sell signal is generated when the oscillator's main line crosses below its lower statistical band. This indicates a significant increase in negative momentum, suggesting a potential downward move is starting.
By focusing on momentum breakouts rather than lagging moving average crossovers, the oscillator fast cryptosmart aims to provide an edge in identifying opportunities in fast-moving markets.
Stacey Burke Signal Day LTE“Previously published as ‘Day Zero Fakeout Detector MTF’”
Stacey Burke Signal Day LTE
Automatic detection of Day Zero, Inside Days, and Outside Days for Stacey Burke’s intraday playbook
🔎 Stacey Burke’s Signal Days
This indicator highlights the key daily patterns that often lead to high-probability intraday setups in Stacey Burke’s methodology:
1️⃣ Day Zero
The reset days within a 3-day cycle (e.g. breakout → continuation → exhaustion/reversal).
Can mark the beginning of a new directional phase.
Trades back inside the prior range after a Peak Formation High (PFH) or Peak Formation Low (PFL).
Bias: Look for measured parabolic session moves. When combined with trend following indicators, these signal days can be very powerful.
2️⃣ Inside Day
A day where the entire range is contained within the prior day’s range.
Signals consolidation and energy build-up.
Often leads to explosive breakouts in the next session.
Bias: Trade breakouts of the inside day’s high/low or breakout reversal in the session at key timings in the direction of higher timeframe bias. When combined with trend following indicators, these signal days can be very powerful.
3️⃣ Outside Day (Engulfing Day)
`
A day where the range is larger than the prior day’s range, engulfing both high and low.
Marks trapped traders and fakeouts on both sides.
Often precedes strong continuations or sharp reversals from outside of the ranges.
Bias: Align trades with the true continuation move. When combined with trend following indicators, these signal days can be very powerful.
📌 How They Work Together
Day Zero → Signals the new cycle after PFH/PFL.
Inside Day → Signals compression → expect breakout setups.
Outside Day → Signals exhaustion/fakeouts → expect reversals or continuations.
Together, they give traders a clear daily roadmap for where liquidity sits and when to expect the highest-probability setups.
✅ Example in Practice
Market rallies for 3 days → PFH forms → Day Zero short bias.
Next day prints an Inside Day → watch for breakout continuation short, and breakout reversals.
Later, an Outside Day traps both longs and shorts → the following session offers a clean intraday reversal or continuation trade in line with the underlying MTF trend/bias.
⚙️ Features of This Indicator
Automatic detection of Day Zero, Inside Days, and Outside Days
Multi-Timeframe (MTF) support for cycle alignment
Visual markers for PFH/PFL and consolidation zones
Measured move projections for breakout targets
👉 Stacey Burke Signal Day LTE gives traders just a few of the most important signal days — Day Zero, Inside Day, and Outside Day — to structure their intraday trades around fake outs, breakouts, and reversals within the daily cycles of the week. (This is work in progress: Next up, FRD/FGD's, 3-day cycle detecting, 3DLs, 3DSs).
Bitcoin Pi Cycle Top Indicator - Daily Timeframe Only1 Day Timeframe Only
The Bitcoin Pi Cycle Top Indicator has garnered attention for its historical effectiveness in identifying the timing of Bitcoin's market cycle peaks with remarkable precision, typically within a margin of 3 days.
It utilizes a specific combination of moving averages—the 111-day moving average and a 2x multiple of the 350-day moving average—to signal potential tops in the Bitcoin market.
The 111-day moving average (MA): This shorter-term MA is chosen to reflect more recent price action and trends within the Bitcoin market.
The 350-day moving average (MA) multiplied by 2: This longer-term MA is adjusted to capture broader market trends and cycles over an extended period.
The key premise behind the Bitcoin Pi Cycle Top Indicator is that a potential market top for Bitcoin can be signaled when the 111-day MA crosses above the 350-day MA (which has been doubled). Historically, this crossover event has shown a remarkable correlation with the peaks of Bitcoin's price cycles, making it a tool of interest for traders and investors aiming to anticipate significant market shifts.
#Bitcoin
WaveTrend 3D█ OVERVIEW
WaveTrend 3D (WT3D) is a novel implementation of the famous WaveTrend (WT) indicator and has been completely redesigned from the ground up to address some of the inherent shortcomings associated with the traditional WT algorithm.
█ BACKGROUND
The WaveTrend (WT) indicator has become a widely popular tool for traders in recent years. WT was first ported to PineScript in 2014 by the user @LazyBear, and since then, it has ascended to become one of the Top 5 most popular scripts on TradingView.
The WT algorithm appears to have origins in a lesser-known proprietary algorithm called Trading Channel Index (TCI), created by AIQ Systems in 1986 as an integral part of their commercial software suite, TradingExpert Pro. The software’s reference manual states that “TCI identifies changes in price direction” and is “an adaptation of Donald R. Lambert’s Commodity Channel Index (CCI)”, which was introduced to the world six years earlier in 1980. Interestingly, a vestige of this early beginning can still be seen in the source code of LazyBear’s script, where the final EMA calculation is stored in an intermediate variable called “tci” in the code.
█ IMPLEMENTATION DETAILS
WaveTrend 3D is an alternative implementation of WaveTrend that directly addresses some of the known shortcomings of the indicator, including its unbounded extremes, susceptibility to whipsaw, and lack of insight into other timeframes.
In the canonical WT approach, an exponential moving average (EMA) for a given lookback window is used to assess the variability between price and two other EMAs relative to a second lookback window. Since the difference between the average price and its associated EMA is essentially unbounded, an arbitrary scaling factor of 0.015 is typically applied as a crude form of rescaling but still fails to capture 20-30% of values between the range of -100 to 100. Additionally, the trigger signal for the final EMA (i.e., TCI) crossover-based oscillator is a four-bar simple moving average (SMA), which further contributes to the net lag accumulated by the consecutive EMA calculations in the previous steps.
The core idea behind WT3D is to replace the EMA-based crossover system with modern Digital Signal Processing techniques. By assuming that price action adheres approximately to a Gaussian distribution, it is possible to sidestep the scaling nightmare associated with unbounded price differentials of the original WaveTrend method by focusing instead on the alteration of the underlying Probability Distribution Function (PDF) of the input series. Furthermore, using a signal processing filter such as a Butterworth Filter, we can eliminate the need for consecutive exponential moving averages along with the associated lag they bring.
Ideally, it is convenient to have the resulting probability distribution oscillate between the values of -1 and 1, with the zero line serving as a median. With this objective in mind, it is possible to borrow a common technique from the field of Machine Learning that uses a sigmoid-like activation function to transform our data set of interest. One such function is the hyperbolic tangent function (tanh), which is often used as an activation function in the hidden layers of neural networks due to its unique property of ensuring the values stay between -1 and 1. By taking the first-order derivative of our input series and normalizing it using the quadratic mean, the tanh function performs a high-quality redistribution of the input signal into the desired range of -1 to 1. Finally, using a dual-pole filter such as the Butterworth Filter popularized by John Ehlers, excessive market noise can be filtered out, leaving behind a crisp moving average with minimal lag.
Furthermore, WT3D expands upon the original functionality of WT by providing:
First-class support for multi-timeframe (MTF) analysis
Kernel-based regression for trend reversal confirmation
Various options for signal smoothing and transformation
A unique mode for visualizing an input series as a symmetrical, three-dimensional waveform useful for pattern identification and cycle-related analysis
█ SETTINGS
This is a summary of the settings used in the script listed in roughly the order in which they appear. By default, all default colors are from Google's TensorFlow framework and are considered to be colorblind safe.
Source: The input series. Usually, it is the close or average price, but it can be any series.
Use Mirror: Whether to display a mirror image of the source series; for visualizing the series as a 3D waveform similar to a soundwave.
Use EMA: Whether to use an exponential moving average of the input series.
EMA Length: The length of the exponential moving average.
Use COG: Whether to use the center of gravity of the input series.
COG Length: The length of the center of gravity.
Speed to Emphasize: The target speed to emphasize.
Width: The width of the emphasized line.
Display Kernel Moving Average: Whether to display the kernel moving average of the signal. Like PCA, an unsupervised Machine Learning technique whereby neighboring vectors are projected onto the Principal Component.
Display Kernel Signal: Whether to display the kernel estimator for the emphasized line. Like the Kernel MA, it can show underlying shifts in bias within a more significant trend by the colors reflected on the ribbon itself.
Show Oscillator Lines: Whether to show the oscillator lines.
Offset: The offset of the emphasized oscillator plots.
Fast Length: The length scale factor for the fast oscillator.
Fast Smoothing: The smoothing scale factor for the fast oscillator.
Normal Length: The length scale factor for the normal oscillator.
Normal Smoothing: The smoothing scale factor for the normal frequency.
Slow Length: The length scale factor for the slow oscillator.
Slow Smoothing: The smoothing scale factor for the slow frequency.
Divergence Threshold: The number of bars for the divergence to be considered significant.
Trigger Wave Percent Size: How big the current wave should be relative to the previous wave.
Background Area Transparency Factor: Transparency factor for the background area.
Foreground Area Transparency Factor: Transparency factor for the foreground area.
Background Line Transparency Factor: Transparency factor for the background line.
Foreground Line Transparency Factor: Transparency factor for the foreground line.
Custom Transparency: Transparency of the custom colors.
Total Gradient Steps: The maximum amount of steps supported for a gradient calculation is 256.
Fast Bullish Color: The color of the fast bullish line.
Normal Bullish Color: The color of the normal bullish line.
Slow Bullish Color: The color of the slow bullish line.
Fast Bearish Color: The color of the fast bearish line.
Normal Bearish Color: The color of the normal bearish line.
Slow Bearish Color: The color of the slow bearish line.
Bullish Divergence Signals: The color of the bullish divergence signals.
Bearish Divergence Signals: The color of the bearish divergence signals.
█ ACKNOWLEDGEMENTS
@LazyBear - For authoring the original WaveTrend port on TradingView
@PineCoders - For the beautiful color gradient framework used in this indicator
@veryfid - For the inspiration of using mirrored signals for cycle analysis and using multiple lookback windows as proxies for other timeframes
[blackcat] L2 Ehlers Cyber Cycle Trading StrategyLevel: 2
Background
John F. Ehlers introuced Cyber Cycle Trading Strategy in his "Cybernetic Analysis for Stocks and Futures" chapter 4 on 2004.
Function
With cyber cycle alone, the Trigger lags the Cycle by one bar, so that their crossing introduces at least another bar of lag. Finally, Dr Ehler concluded that we can’t execute the trade until the bar after the signal is observed. In total, that means our trade execution will be at least four bars late. If we are working with an eight-bar cycle, that means the signal will be exactly wrong. We could do better to buy when the signal says sell, and vice versa.
The difficulties arising from the lag suggest a way to build an automatic trading strategy. Suppose we choose to use the trading signal in the opposite direction of the signal. That will work if we can introduce lag so the correct signal will be given in the more general case, not just the case of an eight-bar cycle. Therefore, the Cyber Cycle trading strategy was introduced by Dr. Ehlers. It starts exactly the same as the Cyber Cycle Indicator. Dr. Ehlers then introduce the variable Signal, which is an exponential moving average of the Cycle variable. The exponential moving average generates the desired lag in the trading signal. The relationship between the alpha of an exponential moving average and lag is alpha2 = 1/ (Lag+1). This relationship is used to create the variable alpha2 in the code and the variable Signal using the exponential moving average. The trading signals using the variable Signal crossing itself delayed by one bar are exactly the opposite of the trading signals I would have used if there were no delay. But, since the variable Signal is delayed such that the net delay is less than half a cycle, the trading signals are correct to catch the next cyclic reversal. The idea of betting against the correct direction by waiting for the next cycle reversal can be pretty scary because that reversal may “never” happen because the market takes off in a trend. For this reason Dr. Ehlers included two lines of code that are escape mechanisms if we were wrong in our entry signal. These last two Signal lines of code reverse the trading position if we have been in the trade for more than eight bars and the trade has an open position loss.
Key Signal
Cycle ---> Cyber Cycle fast line
Cycle (2) ---> Cyber Cycle slow line
Signal ---> Trading signal fast line
Signal(1) ---> Trading signal slow line
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 25th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
Ehlers Stable Dominant Cycle Length [graylange]Stable Dominant Cycle Length – Adaptive Cycle Detection for Market Timing
This script calculates the dominant cycle length of the market using an improved version of John Ehlers' Hilbert Transform approach. Unlike traditional implementations, this version includes advanced smoothing techniques to reduce noise and prevent erratic spikes, making it more reliable for adaptive cycle-based strategies.
🔥 Key Features:
✅ Noise-Reduced Cycle Detection – Uses a Weighted Moving Average (WMA) detrending method instead of raw Hilbert Transform values to enhance stability.
✅ Adaptive Smoothing – Applies an Exponential Moving Average (EMA) to the instantaneous period, reducing excessive volatility in cycle length calculations.
✅ Phase Wrapping & Constraints – Clamps phase changes to prevent unrealistic cycle swings and division errors.
✅ Dynamic Cycle Adjustment – The dominant cycle length updates in real time, constrained within a reasonable range (6 to 50 bars) to avoid extreme peaks.
📌 How to Use It:
Identify Market Cycles – Use the dominant cycle length to determine optimal trend-following vs. mean-reversion strategies.
Enhance MESA Filters – Apply the detected cycle length to adjust Ehlers’ MESA Adaptive Moving Average (MAMA) dynamically.
Fine-Tune Alpha Settings – Reduce overfitting in cycle-based indicators by basing parameters on a stable dominant cycle estimate.
